# CUFinder API Documentation
Source: https://apidoc.cufinder.io/apis
Data enrichment APIs transform incomplete records into actionable intelligence that sales and marketing teams convert into revenue. CUFinder's RESTful API suite provides 40 specialized endpoints that access 1B+ person profiles and 85M+ company records, all refreshed daily for maximum accuracy. Whether you're building CRM automation, lead generation platforms, or recruiting tools, our APIs deliver verified business data with 93-99% confidence scores that power your entire sales stack. Enterprise developers choose CUFinder because our endpoints handle everything from basic domain lookups to complex multi-filter searches, all through simple HTTP requests. Each API returns structured JSON responses with confidence levels, remaining credits, and comprehensive data fields including emails, phone numbers, LinkedIn profiles, tech stacks, funding details, and organizational hierarchies. Integration takes minutes, not weeks, and our credit-based pricing ensures you only pay for successful data returns, making CUFinder the most cost-effective enrichment solution for businesses scaling their outreach operations. Explore our complete API collection below to discover endpoints that solve your specific data challenges, from finding company websites to mapping entire subsidiary networks.
## Company APIs
Resolve a company name to its verified website domain.
Find a company's LinkedIn page URL.
Get the company name behind a domain.
Return verified role-based company emails.
Find a company's phone numbers.
Discover companies similar to a target.
Retrieve funding rounds and investors.
Estimate a company's annual revenue.
Map a company's subsidiaries.
List the technologies a company uses.
Enrich a company with full firmographics.
Get a company's current employee count.
Find employees working at a company.
List a company's office locations.
Search companies with multiple filters.
Find local businesses via Google Maps.
Identify a company's likely B2B customers.
Locate a company's careers page.
Check whether a company is a SaaS business.
Classify a company as B2B or B2C.
Get a company's mission statement.
Get a quick company overview.
Check whether a company offers a product demo.
Check whether a company offers a free trial.
## Contact APIs
Identify the person behind an email address.
Enrich a person from their LinkedIn profile.
Find an email from a LinkedIn profile.
Enrich a person with full contact data.
Search 1B+ profiles with combined filters.
Find 25 people similar to a given contact.
## General APIs
Standardize phone numbers to a clean format.
Standardize and validate postal addresses.
Clean and standardize company names.
Clean and standardize person names.
Normalize URLs to a canonical form.
## Jobs API
Find companies that match your hiring and firmographic filters.
# Accepted Values
Source: https://apidoc.cufinder.io/apis/accepted-values
The reference lists of accepted values for CUFinder API filters. Countries, states, cities, industries, and every enum the APIs accept. Use these exact values in your requests for reliable matches.
Several endpoints filter by location, industry, or other constrained fields. This page is the single reference for the exact values those fields accept. All location and industry values are **lowercase full names**, for example `united states`, not `US` or `USA`.
Send values exactly as listed here. Filters match on the full value, so `germany` works while `Germany ` (trailing space) or `DE` may not.
## Countries
Used by `country` (Company Search, Person Search, Local Business Search, Jobs Search) and `company_country`-style filters. **251 values.**
All 251 accepted country values as CSV.
| Country |
| ---------------------------------------------- |
| `afghanistan` |
| `albania` |
| `algeria` |
| `american samoa` |
| `andorra` |
| `angola` |
| `anguilla` |
| `antarctica` |
| `antigua and barbuda` |
| `argentina` |
| `armenia` |
| `aruba` |
| `australia` |
| `austria` |
| `azerbaijan` |
| `bahamas` |
| `bahrain` |
| `bangladesh` |
| `barbados` |
| `belarus` |
| `belgium` |
| `belize` |
| `benin` |
| `bermuda` |
| `bhutan` |
| `bolivia` |
| `bosnia and herzegovina` |
| `botswana` |
| `bouvet island` |
| `brazil` |
| `british indian ocean territory` |
| `british virgin islands` |
| `brunei` |
| `bulgaria` |
| `burkina faso` |
| `burundi` |
| `cambodia` |
| `cameroon` |
| `canada` |
| `cape verde` |
| `caribbean netherlands` |
| `cayman islands` |
| `central african republic` |
| `chad` |
| `chile` |
| `china` |
| `christmas island` |
| `cocos (keeling) islands` |
| `colombia` |
| `comoros` |
| `cook islands` |
| `costa rica` |
| `croatia` |
| `cuba` |
| `curaçao` |
| `cyprus` |
| `czechia` |
| `côte d’ivoire` |
| `democratic republic of the congo` |
| `denmark` |
| `djibouti` |
| `dominica` |
| `dominican republic` |
| `ecuador` |
| `egypt` |
| `el salvador` |
| `equatorial guinea` |
| `eritrea` |
| `estonia` |
| `ethiopia` |
| `falkland islands` |
| `faroe islands` |
| `fiji` |
| `finland` |
| `france` |
| `french guiana` |
| `french polynesia` |
| `french southern territories` |
| `gabon` |
| `gambia` |
| `georgia` |
| `germany` |
| `ghana` |
| `gibraltar` |
| `greece` |
| `greenland` |
| `grenada` |
| `guadeloupe` |
| `guam` |
| `guatemala` |
| `guernsey` |
| `guinea` |
| `guinea-bissau` |
| `guyana` |
| `haiti` |
| `heard island and mcdonald islands` |
| `honduras` |
| `hong kong` |
| `hungary` |
| `iceland` |
| `india` |
| `indonesia` |
| `iran` |
| `iraq` |
| `ireland` |
| `isle of man` |
| `israel` |
| `italy` |
| `jamaica` |
| `japan` |
| `jersey` |
| `jordan` |
| `kazakhstan` |
| `kenya` |
| `kiribati` |
| `kosovo` |
| `kuwait` |
| `kyrgyzstan` |
| `laos` |
| `latvia` |
| `lebanon` |
| `lesotho` |
| `liberia` |
| `libya` |
| `liechtenstein` |
| `lithuania` |
| `luxembourg` |
| `macau` |
| `macedonia` |
| `madagascar` |
| `malawi` |
| `malaysia` |
| `maldives` |
| `mali` |
| `malta` |
| `marshall islands` |
| `martinique` |
| `mauritania` |
| `mauritius` |
| `mayotte` |
| `mexico` |
| `micronesia` |
| `moldova` |
| `monaco` |
| `mongolia` |
| `montenegro` |
| `montserrat` |
| `morocco` |
| `mozambique` |
| `myanmar` |
| `namibia` |
| `nauru` |
| `nepal` |
| `netherlands` |
| `netherlands antilles` |
| `new caledonia` |
| `new zealand` |
| `nicaragua` |
| `niger` |
| `nigeria` |
| `niue` |
| `norfolk island` |
| `north korea` |
| `northern mariana islands` |
| `norway` |
| `oman` |
| `pakistan` |
| `palau` |
| `palestine` |
| `panama` |
| `papua new guinea` |
| `paraguay` |
| `peru` |
| `philippines` |
| `pitcairn` |
| `poland` |
| `portugal` |
| `puerto rico` |
| `qatar` |
| `republic of the congo` |
| `romania` |
| `russia` |
| `rwanda` |
| `réunion` |
| `saint barthélemy` |
| `saint helena` |
| `saint kitts and nevis` |
| `saint lucia` |
| `saint martin` |
| `saint pierre and miquelon` |
| `saint vincent and the grenadines` |
| `samoa` |
| `san marino` |
| `saudi arabia` |
| `senegal` |
| `serbia` |
| `seychelles` |
| `sierra leone` |
| `singapore` |
| `sint maarten` |
| `slovakia` |
| `slovenia` |
| `solomon islands` |
| `somalia` |
| `south africa` |
| `south georgia and the south sandwich islands` |
| `south korea` |
| `south sudan` |
| `spain` |
| `sri lanka` |
| `sudan` |
| `suriname` |
| `svalbard and jan mayen` |
| `swaziland` |
| `sweden` |
| `switzerland` |
| `syria` |
| `são tomé and príncipe` |
| `taiwan` |
| `tajikistan` |
| `tanzania` |
| `thailand` |
| `timor-leste` |
| `togo` |
| `tokelau` |
| `tonga` |
| `trinidad and tobago` |
| `tunisia` |
| `turkey` |
| `turkmenistan` |
| `turks and caicos islands` |
| `tuvalu` |
| `u.s. virgin islands` |
| `uganda` |
| `ukraine` |
| `united arab emirates` |
| `united kingdom` |
| `united states` |
| `united states minor outlying islands` |
| `uruguay` |
| `uzbekistan` |
| `vanuatu` |
| `vatican city` |
| `venezuela` |
| `vietnam` |
| `wallis and futuna` |
| `western sahara` |
| `yemen` |
| `zambia` |
| `zimbabwe` |
| `Åland islands` |
## States
Used by `state` filters. Values are listed with the country they belong to. **5,084 values.**
All 5,084 accepted state values with their countries as CSV.
| Country | State |
| ------------------------------------ | ---------------------------------------------------------- |
| afghanistan | `badakhshan` |
| afghanistan | `badghis` |
| afghanistan | `baghlan` |
| afghanistan | `balkh` |
| afghanistan | `bamyan` |
| afghanistan | `daykundi` |
| afghanistan | `farah` |
| afghanistan | `faryab` |
| afghanistan | `ghazni` |
| afghanistan | `ghor` |
| afghanistan | `helmand` |
| afghanistan | `herat` |
| afghanistan | `jowzjan` |
| afghanistan | `kabul` |
| afghanistan | `kandahar` |
| afghanistan | `kapisa` |
| afghanistan | `khost` |
| afghanistan | `kunar` |
| afghanistan | `kunduz province` |
| afghanistan | `laghman` |
| afghanistan | `logar` |
| afghanistan | `nangarhar` |
| afghanistan | `nimruz` |
| afghanistan | `nuristan` |
| afghanistan | `paktia` |
| afghanistan | `paktika` |
| afghanistan | `panjshir` |
| afghanistan | `parwan` |
| afghanistan | `samangan` |
| afghanistan | `sar-e pol` |
| afghanistan | `takhar` |
| afghanistan | `urozgan` |
| afghanistan | `zabul` |
| albania | `berat county` |
| albania | `berat district` |
| albania | `bulqizė district` |
| albania | `delvinė district` |
| albania | `devoll district` |
| albania | `dibėr county` |
| albania | `dibėr district` |
| albania | `durrės county` |
| albania | `durrės district` |
| albania | `elbasan county` |
| albania | `fier county` |
| albania | `fier district` |
| albania | `gjirokastėr county` |
| albania | `gjirokastėr district` |
| albania | `gramsh district` |
| albania | `has district` |
| albania | `kavajė district` |
| albania | `kolonjė district` |
| albania | `korēė county` |
| albania | `korēė district` |
| albania | `krujė district` |
| albania | `kuēovė district` |
| albania | `kukės county` |
| albania | `kukės district` |
| albania | `kurbin district` |
| albania | `lezhė county` |
| albania | `lezhė district` |
| albania | `librazhd district` |
| albania | `lushnjė district` |
| albania | `malėsi e madhe district` |
| albania | `mallakastėr district` |
| albania | `mat district` |
| albania | `mirditė district` |
| albania | `peqin district` |
| albania | `pėrmet district` |
| albania | `pogradec district` |
| albania | `pukė district` |
| albania | `sarandė district` |
| albania | `shkodėr county` |
| albania | `shkodėr district` |
| albania | `skrapar district` |
| albania | `tepelenė district` |
| albania | `tirana county` |
| albania | `tirana district` |
| albania | `tropojė district` |
| albania | `vlorė county` |
| albania | `vlorė district` |
| algeria | `adrar` |
| algeria | `aļn defla` |
| algeria | `aļn témouchent` |
| algeria | `algiers` |
| algeria | `annaba` |
| algeria | `batna` |
| algeria | `béchar` |
| algeria | `béjaļa` |
| algeria | `béni abbčs` |
| algeria | `biskra` |
| algeria | `blida` |
| algeria | `bordj baji mokhtar` |
| algeria | `bordj bou arréridj` |
| algeria | `bouļra` |
| algeria | `boumerdčs` |
| algeria | `chlef` |
| algeria | `constantine` |
| algeria | `djanet` |
| algeria | `djelfa` |
| algeria | `el bayadh` |
| algeria | `el m'ghair` |
| algeria | `el menia` |
| algeria | `el oued` |
| algeria | `el tarf` |
| algeria | `ghardaļa` |
| algeria | `guelma` |
| algeria | `illizi` |
| algeria | `in guezzam` |
| algeria | `in salah` |
| algeria | `jijel` |
| algeria | `khenchela` |
| algeria | `laghouat` |
| algeria | `m'sila` |
| algeria | `mascara` |
| algeria | `médéa` |
| algeria | `mila` |
| algeria | `mostaganem` |
| algeria | `naama` |
| algeria | `oran` |
| algeria | `ouargla` |
| algeria | `ouled djellal` |
| algeria | `oum el bouaghi` |
| algeria | `relizane` |
| algeria | `saļda` |
| algeria | `sétif` |
| algeria | `sidi bel abbčs` |
| algeria | `skikda` |
| algeria | `souk ahras` |
| algeria | `tamanghasset` |
| algeria | `tébessa` |
| algeria | `tiaret` |
| algeria | `timimoun` |
| algeria | `tindouf` |
| algeria | `tipasa` |
| algeria | `tissemsilt` |
| algeria | `tizi ouzou` |
| algeria | `tlemcen` |
| algeria | `touggourt` |
| andorra | `andorra la vella` |
| andorra | `canillo` |
| andorra | `encamp` |
| andorra | `escaldes-engordany` |
| andorra | `la massana` |
| andorra | `ordino` |
| andorra | `sant julią de lņria` |
| angola | `bengo province` |
| angola | `benguela province` |
| angola | `bié province` |
| angola | `cabinda province` |
| angola | `cuando cubango province` |
| angola | `cuanza norte province` |
| angola | `cuanza sul` |
| angola | `cunene province` |
| angola | `huambo province` |
| angola | `huķla province` |
| angola | `luanda province` |
| angola | `lunda norte province` |
| angola | `lunda sul province` |
| angola | `malanje province` |
| angola | `moxico province` |
| angola | `uķge province` |
| angola | `zaire province` |
| antigua and barbuda | `barbuda` |
| antigua and barbuda | `redonda` |
| antigua and barbuda | `saint george parish` |
| antigua and barbuda | `saint john parish` |
| antigua and barbuda | `saint mary parish` |
| antigua and barbuda | `saint paul parish` |
| antigua and barbuda | `saint peter parish` |
| antigua and barbuda | `saint philip parish` |
| argentina | `buenos aires` |
| argentina | `catamarca` |
| argentina | `chaco` |
| argentina | `chubut` |
| argentina | `ciudad autónoma de buenos aires` |
| argentina | `córdoba` |
| argentina | `corrientes` |
| argentina | `entre rķos` |
| argentina | `formosa` |
| argentina | `jujuy` |
| argentina | `la pampa` |
| argentina | `la rioja` |
| argentina | `mendoza` |
| argentina | `misiones` |
| argentina | `neuquén` |
| argentina | `rķo negro` |
| argentina | `salta` |
| argentina | `san juan` |
| argentina | `san luis` |
| argentina | `santa cruz` |
| argentina | `santa fe` |
| argentina | `santiago del estero` |
| argentina | `tierra del fuego` |
| argentina | `tucumįn` |
| armenia | `aragatsotn region` |
| armenia | `ararat province` |
| armenia | `armavir region` |
| armenia | `gegharkunik province` |
| armenia | `kotayk region` |
| armenia | `lori region` |
| armenia | `shirak region` |
| armenia | `syunik province` |
| armenia | `tavush region` |
| armenia | `vayots dzor region` |
| armenia | `yerevan` |
| australia | `australian capital territory` |
| australia | `new south wales` |
| australia | `northern territory` |
| australia | `queensland` |
| australia | `south australia` |
| australia | `tasmania` |
| australia | `victoria` |
| australia | `western australia` |
| austria | `burgenland` |
| austria | `carinthia` |
| austria | `lower austria` |
| austria | `salzburg` |
| austria | `styria` |
| austria | `tyrol` |
| austria | `upper austria` |
| austria | `vienna` |
| austria | `vorarlberg` |
| azerbaijan | `absheron district` |
| azerbaijan | `agdam district` |
| azerbaijan | `agdash district` |
| azerbaijan | `aghjabadi district` |
| azerbaijan | `agstafa district` |
| azerbaijan | `agsu district` |
| azerbaijan | `astara district` |
| azerbaijan | `babek district` |
| azerbaijan | `baku` |
| azerbaijan | `balakan district` |
| azerbaijan | `barda district` |
| azerbaijan | `beylagan district` |
| azerbaijan | `bilasuvar district` |
| azerbaijan | `dashkasan district` |
| azerbaijan | `fizuli district` |
| azerbaijan | `ganja` |
| azerbaijan | `g?d?b?y` |
| azerbaijan | `gobustan district` |
| azerbaijan | `goranboy district` |
| azerbaijan | `goychay` |
| azerbaijan | `goygol district` |
| azerbaijan | `hajigabul district` |
| azerbaijan | `imishli district` |
| azerbaijan | `ismailli district` |
| azerbaijan | `jabrayil district` |
| azerbaijan | `jalilabad district` |
| azerbaijan | `julfa district` |
| azerbaijan | `kalbajar district` |
| azerbaijan | `kangarli district` |
| azerbaijan | `khachmaz district` |
| azerbaijan | `khizi district` |
| azerbaijan | `khojali district` |
| azerbaijan | `kurdamir district` |
| azerbaijan | `lachin district` |
| azerbaijan | `lankaran` |
| azerbaijan | `lankaran district` |
| azerbaijan | `lerik district` |
| azerbaijan | `martuni` |
| azerbaijan | `masally district` |
| azerbaijan | `mingachevir` |
| azerbaijan | `nakhchivan autonomous republic` |
| azerbaijan | `neftchala district` |
| azerbaijan | `oghuz district` |
| azerbaijan | `ordubad district` |
| azerbaijan | `qabala district` |
| azerbaijan | `qakh district` |
| azerbaijan | `qazakh district` |
| azerbaijan | `quba district` |
| azerbaijan | `qubadli district` |
| azerbaijan | `qusar district` |
| azerbaijan | `saatly district` |
| azerbaijan | `sabirabad district` |
| azerbaijan | `sadarak district` |
| azerbaijan | `salyan district` |
| azerbaijan | `samukh district` |
| azerbaijan | `shabran district` |
| azerbaijan | `shahbuz district` |
| azerbaijan | `shaki` |
| azerbaijan | `shaki district` |
| azerbaijan | `shamakhi district` |
| azerbaijan | `shamkir district` |
| azerbaijan | `sharur district` |
| azerbaijan | `shirvan` |
| azerbaijan | `shusha district` |
| azerbaijan | `siazan district` |
| azerbaijan | `sumqayit` |
| azerbaijan | `tartar district` |
| azerbaijan | `tovuz district` |
| azerbaijan | `ujar district` |
| azerbaijan | `yardymli district` |
| azerbaijan | `yevlakh` |
| azerbaijan | `yevlakh district` |
| azerbaijan | `zangilan district` |
| azerbaijan | `zaqatala district` |
| azerbaijan | `zardab district` |
| bahrain | `capital` |
| bahrain | `central` |
| bahrain | `muharraq` |
| bahrain | `northern` |
| bahrain | `southern` |
| bangladesh | `bagerhat district` |
| bangladesh | `bahadia` |
| bangladesh | `bandarban district` |
| bangladesh | `barguna district` |
| bangladesh | `barisal district` |
| bangladesh | `barisal division` |
| bangladesh | `bhola district` |
| bangladesh | `bogra district` |
| bangladesh | `brahmanbaria district` |
| bangladesh | `chandpur district` |
| bangladesh | `chapai nawabganj district` |
| bangladesh | `chittagong district` |
| bangladesh | `chittagong division` |
| bangladesh | `chuadanga district` |
| bangladesh | `comilla district` |
| bangladesh | `cox's bazar district` |
| bangladesh | `dhaka district` |
| bangladesh | `dhaka division` |
| bangladesh | `dinajpur district` |
| bangladesh | `faridpur district` |
| bangladesh | `feni district` |
| bangladesh | `gaibandha district` |
| bangladesh | `gazipur district` |
| bangladesh | `gopalganj district` |
| bangladesh | `habiganj district` |
| bangladesh | `jamalpur district` |
| bangladesh | `jessore district` |
| bangladesh | `jhalokati district` |
| bangladesh | `jhenaidah district` |
| bangladesh | `joypurhat district` |
| bangladesh | `khagrachari district` |
| bangladesh | `khulna district` |
| bangladesh | `khulna division` |
| bangladesh | `kishoreganj district` |
| bangladesh | `kurigram district` |
| bangladesh | `kushtia district` |
| bangladesh | `lakshmipur district` |
| bangladesh | `lalmonirhat district` |
| bangladesh | `madaripur district` |
| bangladesh | `meherpur district` |
| bangladesh | `moulvibazar district` |
| bangladesh | `munshiganj district` |
| bangladesh | `mymensingh district` |
| bangladesh | `mymensingh division` |
| bangladesh | `naogaon district` |
| bangladesh | `narail district` |
| bangladesh | `narayanganj district` |
| bangladesh | `natore district` |
| bangladesh | `netrokona district` |
| bangladesh | `nilphamari district` |
| bangladesh | `noakhali district` |
| bangladesh | `pabna district` |
| bangladesh | `panchagarh district` |
| bangladesh | `patuakhali district` |
| bangladesh | `pirojpur district` |
| bangladesh | `rajbari district` |
| bangladesh | `rajshahi district` |
| bangladesh | `rajshahi division` |
| bangladesh | `rangamati hill district` |
| bangladesh | `rangpur district` |
| bangladesh | `rangpur division` |
| bangladesh | `satkhira district` |
| bangladesh | `shariatpur district` |
| bangladesh | `sherpur district` |
| bangladesh | `sirajganj district` |
| bangladesh | `sunamganj district` |
| bangladesh | `sylhet district` |
| bangladesh | `sylhet division` |
| bangladesh | `tangail district` |
| bangladesh | `thakurgaon district` |
| barbados | `christ church` |
| barbados | `saint andrew` |
| barbados | `saint george` |
| barbados | `saint james` |
| barbados | `saint john` |
| barbados | `saint joseph` |
| barbados | `saint lucy` |
| barbados | `saint michael` |
| barbados | `saint peter` |
| barbados | `saint philip` |
| barbados | `saint thomas` |
| belarus | `brest region` |
| belarus | `gomel region` |
| belarus | `grodno region` |
| belarus | `minsk` |
| belarus | `minsk region` |
| belarus | `mogilev region` |
| belarus | `vitebsk region` |
| belgium | `antwerp` |
| belgium | `brussels-capital region` |
| belgium | `east flanders` |
| belgium | `flanders` |
| belgium | `flemish brabant` |
| belgium | `hainaut` |
| belgium | `ličge` |
| belgium | `limburg` |
| belgium | `luxembourg` |
| belgium | `namur` |
| belgium | `wallonia` |
| belgium | `walloon brabant` |
| belgium | `west flanders` |
| belize | `belize district` |
| belize | `cayo district` |
| belize | `corozal district` |
| belize | `orange walk district` |
| belize | `stann creek district` |
| belize | `toledo district` |
| benin | `alibori department` |
| benin | `atakora department` |
| benin | `atlantique department` |
| benin | `borgou department` |
| benin | `collines department` |
| benin | `donga department` |
| benin | `kouffo department` |
| benin | `littoral department` |
| benin | `mono department` |
| benin | `ouémé department` |
| benin | `plateau department` |
| benin | `zou department` |
| bermuda | `devonshire` |
| bermuda | `hamilton` |
| bermuda | `paget` |
| bermuda | `pembroke` |
| bermuda | `saint george's` |
| bermuda | `sandys` |
| bermuda | `smith's` |
| bermuda | `southampton` |
| bermuda | `warwick` |
| bhutan | `bumthang district` |
| bhutan | `chukha district` |
| bhutan | `dagana district` |
| bhutan | `gasa district` |
| bhutan | `haa district` |
| bhutan | `lhuntse district` |
| bhutan | `mongar district` |
| bhutan | `paro district` |
| bhutan | `pemagatshel district` |
| bhutan | `punakha district` |
| bhutan | `samdrup jongkhar district` |
| bhutan | `samtse district` |
| bhutan | `sarpang district` |
| bhutan | `thimphu district` |
| bhutan | `trashigang district` |
| bhutan | `trongsa district` |
| bhutan | `tsirang district` |
| bhutan | `wangdue phodrang district` |
| bhutan | `zhemgang district` |
| bolivia | `beni department` |
| bolivia | `chuquisaca department` |
| bolivia | `cochabamba department` |
| bolivia | `la paz department` |
| bolivia | `oruro department` |
| bolivia | `pando department` |
| bolivia | `potosķ department` |
| bolivia | `santa cruz department` |
| bolivia | `tarija department` |
| bonaire, sint eustatius and saba | `bonaire` |
| bonaire, sint eustatius and saba | `saba` |
| bonaire, sint eustatius and saba | `sint eustatius` |
| bosnia and herzegovina | `bosnian podrinje canton` |
| bosnia and herzegovina | `brcko district` |
| bosnia and herzegovina | `canton 10` |
| bosnia and herzegovina | `central bosnia canton` |
| bosnia and herzegovina | `federation of bosnia and herzegovina` |
| bosnia and herzegovina | `herzegovina-neretva canton` |
| bosnia and herzegovina | `posavina canton` |
| bosnia and herzegovina | `republika srpska` |
| bosnia and herzegovina | `sarajevo canton` |
| bosnia and herzegovina | `tuzla canton` |
| bosnia and herzegovina | `una-sana canton` |
| bosnia and herzegovina | `west herzegovina canton` |
| bosnia and herzegovina | `zenica-doboj canton` |
| botswana | `central district` |
| botswana | `ghanzi district` |
| botswana | `kgalagadi district` |
| botswana | `kgatleng district` |
| botswana | `kweneng district` |
| botswana | `ngamiland` |
| botswana | `north-east district` |
| botswana | `north-west district` |
| botswana | `south-east district` |
| botswana | `southern district` |
| brazil | `acre` |
| brazil | `alagoas` |
| brazil | `amapį` |
| brazil | `amazonas` |
| brazil | `bahia` |
| brazil | `cearį` |
| brazil | `distrito federal` |
| brazil | `espķrito santo` |
| brazil | `goiįs` |
| brazil | `maranhćo` |
| brazil | `mato grosso` |
| brazil | `mato grosso do sul` |
| brazil | `minas gerais` |
| brazil | `parį` |
| brazil | `paraķba` |
| brazil | `paranį` |
| brazil | `pernambuco` |
| brazil | `piauķ` |
| brazil | `rio de janeiro` |
| brazil | `rio grande do norte` |
| brazil | `rio grande do sul` |
| brazil | `rondōnia` |
| brazil | `roraima` |
| brazil | `santa catarina` |
| brazil | `sćo paulo` |
| brazil | `sergipe` |
| brazil | `tocantins` |
| brunei | `belait district` |
| brunei | `brunei-muara district` |
| brunei | `temburong district` |
| brunei | `tutong district` |
| bulgaria | `blagoevgrad province` |
| bulgaria | `burgas province` |
| bulgaria | `dobrich province` |
| bulgaria | `gabrovo province` |
| bulgaria | `haskovo province` |
| bulgaria | `kardzhali province` |
| bulgaria | `kyustendil province` |
| bulgaria | `lovech province` |
| bulgaria | `montana province` |
| bulgaria | `pazardzhik province` |
| bulgaria | `pernik province` |
| bulgaria | `pleven province` |
| bulgaria | `plovdiv province` |
| bulgaria | `razgrad province` |
| bulgaria | `ruse province` |
| bulgaria | `shumen` |
| bulgaria | `silistra province` |
| bulgaria | `sliven province` |
| bulgaria | `smolyan province` |
| bulgaria | `sofia city province` |
| bulgaria | `sofia province` |
| bulgaria | `stara zagora province` |
| bulgaria | `targovishte province` |
| bulgaria | `varna province` |
| bulgaria | `veliko tarnovo province` |
| bulgaria | `vidin province` |
| bulgaria | `vratsa province` |
| bulgaria | `yambol province` |
| burkina faso | `balé province` |
| burkina faso | `bam province` |
| burkina faso | `banwa province` |
| burkina faso | `bazčga province` |
| burkina faso | `boucle du mouhoun region` |
| burkina faso | `bougouriba province` |
| burkina faso | `boulgou` |
| burkina faso | `cascades region` |
| burkina faso | `centre` |
| burkina faso | `centre-est region` |
| burkina faso | `centre-nord region` |
| burkina faso | `centre-ouest region` |
| burkina faso | `centre-sud region` |
| burkina faso | `comoé province` |
| burkina faso | `est region` |
| burkina faso | `ganzourgou province` |
| burkina faso | `gnagna province` |
| burkina faso | `gourma province` |
| burkina faso | `hauts-bassins region` |
| burkina faso | `houet province` |
| burkina faso | `ioba province` |
| burkina faso | `kadiogo province` |
| burkina faso | `kénédougou province` |
| burkina faso | `komondjari province` |
| burkina faso | `kompienga province` |
| burkina faso | `kossi province` |
| burkina faso | `koulpélogo province` |
| burkina faso | `kouritenga province` |
| burkina faso | `kourwéogo province` |
| burkina faso | `léraba province` |
| burkina faso | `loroum province` |
| burkina faso | `mouhoun` |
| burkina faso | `nahouri province` |
| burkina faso | `namentenga province` |
| burkina faso | `nayala province` |
| burkina faso | `nord region, burkina faso` |
| burkina faso | `noumbiel province` |
| burkina faso | `oubritenga province` |
| burkina faso | `oudalan province` |
| burkina faso | `passoré province` |
| burkina faso | `plateau-central region` |
| burkina faso | `poni province` |
| burkina faso | `sahel region` |
| burkina faso | `sanguié province` |
| burkina faso | `sanmatenga province` |
| burkina faso | `séno province` |
| burkina faso | `sissili province` |
| burkina faso | `soum province` |
| burkina faso | `sourou province` |
| burkina faso | `sud-ouest region` |
| burkina faso | `tapoa province` |
| burkina faso | `tuy province` |
| burkina faso | `yagha province` |
| burkina faso | `yatenga province` |
| burkina faso | `ziro province` |
| burkina faso | `zondoma province` |
| burkina faso | `zoundwéogo province` |
| burundi | `bubanza province` |
| burundi | `bujumbura mairie province` |
| burundi | `bujumbura rural province` |
| burundi | `bururi province` |
| burundi | `cankuzo province` |
| burundi | `cibitoke province` |
| burundi | `gitega province` |
| burundi | `karuzi province` |
| burundi | `kayanza province` |
| burundi | `kirundo province` |
| burundi | `makamba province` |
| burundi | `muramvya province` |
| burundi | `muyinga province` |
| burundi | `mwaro province` |
| burundi | `ngozi province` |
| burundi | `rumonge province` |
| burundi | `rutana province` |
| burundi | `ruyigi province` |
| cambodia | `banteay meanchey` |
| cambodia | `battambang` |
| cambodia | `kampong cham` |
| cambodia | `kampong chhnang` |
| cambodia | `kampong speu` |
| cambodia | `kampong thom` |
| cambodia | `kampot` |
| cambodia | `kandal` |
| cambodia | `kep` |
| cambodia | `koh kong` |
| cambodia | `kratie` |
| cambodia | `mondulkiri` |
| cambodia | `oddar meanchey` |
| cambodia | `pailin` |
| cambodia | `phnom penh` |
| cambodia | `preah vihear` |
| cambodia | `prey veng` |
| cambodia | `pursat` |
| cambodia | `ratanakiri` |
| cambodia | `siem reap` |
| cambodia | `sihanoukville` |
| cambodia | `stung treng` |
| cambodia | `svay rieng` |
| cambodia | `takeo` |
| cameroon | `adamawa` |
| cameroon | `centre` |
| cameroon | `east` |
| cameroon | `far north` |
| cameroon | `littoral` |
| cameroon | `north` |
| cameroon | `northwest` |
| cameroon | `south` |
| cameroon | `southwest` |
| cameroon | `west` |
| canada | `alberta` |
| canada | `british columbia` |
| canada | `manitoba` |
| canada | `new brunswick` |
| canada | `newfoundland and labrador` |
| canada | `northwest territories` |
| canada | `nova scotia` |
| canada | `nunavut` |
| canada | `ontario` |
| canada | `prince edward island` |
| canada | `quebec` |
| canada | `saskatchewan` |
| canada | `yukon` |
| cape verde | `barlavento islands` |
| cape verde | `boa vista` |
| cape verde | `brava` |
| cape verde | `maio municipality` |
| cape verde | `mosteiros` |
| cape verde | `paul` |
| cape verde | `porto novo` |
| cape verde | `praia` |
| cape verde | `ribeira brava municipality` |
| cape verde | `ribeira grande` |
| cape verde | `ribeira grande de santiago` |
| cape verde | `sal` |
| cape verde | `santa catarina` |
| cape verde | `santa catarina do fogo` |
| cape verde | `santa cruz` |
| cape verde | `sćo domingos` |
| cape verde | `sćo filipe` |
| cape verde | `sćo lourenēo dos órgćos` |
| cape verde | `sćo miguel` |
| cape verde | `sćo vicente` |
| cape verde | `sotavento islands` |
| cape verde | `tarrafal` |
| cape verde | `tarrafal de sćo nicolau` |
| central african republic | `bamingui-bangoran prefecture` |
| central african republic | `bangui` |
| central african republic | `basse-kotto prefecture` |
| central african republic | `haut-mbomou prefecture` |
| central african republic | `haute-kotto prefecture` |
| central african republic | `kémo prefecture` |
| central african republic | `lobaye prefecture` |
| central african republic | `mambéré-kadéļ` |
| central african republic | `mbomou prefecture` |
| central african republic | `nana-grébizi economic prefecture` |
| central african republic | `nana-mambéré prefecture` |
| central african republic | `ombella-m'poko prefecture` |
| central african republic | `ouaka prefecture` |
| central african republic | `ouham prefecture` |
| central african republic | `ouham-pendé prefecture` |
| central african republic | `sangha-mbaéré` |
| central african republic | `vakaga prefecture` |
| chad | `bahr el gazel` |
| chad | `batha` |
| chad | `borkou` |
| chad | `chari-baguirmi` |
| chad | `ennedi-est` |
| chad | `ennedi-ouest` |
| chad | `guéra` |
| chad | `hadjer-lamis` |
| chad | `kanem` |
| chad | `lac` |
| chad | `logone occidental` |
| chad | `logone oriental` |
| chad | `mandoul` |
| chad | `mayo-kebbi est` |
| chad | `mayo-kebbi ouest` |
| chad | `moyen-chari` |
| chad | `n'djamena` |
| chad | `ouaddaļ` |
| chad | `salamat` |
| chad | `sila` |
| chad | `tandjilé` |
| chad | `tibesti` |
| chad | `wadi fira` |
| chile | `aisén del general carlos ibańez del campo` |
| chile | `antofagasta` |
| chile | `arica y parinacota` |
| chile | `atacama` |
| chile | `biobķo` |
| chile | `coquimbo` |
| chile | `la araucanķa` |
| chile | `libertador general bernardo o'higgins` |
| chile | `los lagos` |
| chile | `los rķos` |
| chile | `magallanes y de la antįrtica chilena` |
| chile | `maule` |
| chile | `ńuble` |
| chile | `región metropolitana de santiago` |
| chile | `tarapacį` |
| chile | `valparaķso` |
| china | `anhui` |
| china | `beijing` |
| china | `chongqing` |
| china | `fujian` |
| china | `gansu` |
| china | `guangdong` |
| china | `guangxi zhuang` |
| china | `guizhou` |
| china | `hainan` |
| china | `hebei` |
| china | `heilongjiang` |
| china | `henan` |
| china | `hong kong sar` |
| china | `hubei` |
| china | `hunan` |
| china | `inner mongolia` |
| china | `jiangsu` |
| china | `jiangxi` |
| china | `jilin` |
| china | `liaoning` |
| china | `macau sar` |
| china | `ningxia huizu` |
| china | `qinghai` |
| china | `shaanxi` |
| china | `shandong` |
| china | `shanghai` |
| china | `shanxi` |
| china | `sichuan` |
| china | `taiwan` |
| china | `tianjin` |
| china | `xinjiang` |
| china | `xizang` |
| china | `yunnan` |
| china | `zhejiang` |
| colombia | `amazonas` |
| colombia | `antioquia` |
| colombia | `arauca` |
| colombia | `archipiélago de san andrés, providencia y santa catalina` |
| colombia | `atlįntico` |
| colombia | `bogotį d.c.` |
| colombia | `bolķvar` |
| colombia | `boyacį` |
| colombia | `caldas` |
| colombia | `caquetį` |
| colombia | `casanare` |
| colombia | `cauca` |
| colombia | `cesar` |
| colombia | `chocó` |
| colombia | `córdoba` |
| colombia | `cundinamarca` |
| colombia | `guainķa` |
| colombia | `guaviare` |
| colombia | `huila` |
| colombia | `la guajira` |
| colombia | `magdalena` |
| colombia | `meta` |
| colombia | `narińo` |
| colombia | `norte de santander` |
| colombia | `putumayo` |
| colombia | `quindķo` |
| colombia | `risaralda` |
| colombia | `santander` |
| colombia | `sucre` |
| colombia | `tolima` |
| colombia | `valle del cauca` |
| colombia | `vaupés` |
| colombia | `vichada` |
| comoros | `anjouan` |
| comoros | `grande comore` |
| comoros | `mohéli` |
| congo | `bouenza department` |
| congo | `brazzaville` |
| congo | `cuvette department` |
| congo | `cuvette-ouest department` |
| congo | `kouilou department` |
| congo | `lékoumou department` |
| congo | `likouala department` |
| congo | `niari department` |
| congo | `plateaux department` |
| congo | `pointe-noire` |
| congo | `pool department` |
| congo | `sangha department` |
| costa rica | `alajuela province` |
| costa rica | `guanacaste province` |
| costa rica | `heredia province` |
| costa rica | `limón province` |
| costa rica | `provincia de cartago` |
| costa rica | `puntarenas province` |
| costa rica | `san josé province` |
| cote d'ivoire (ivory coast) | `abidjan` |
| cote d'ivoire (ivory coast) | `agnéby` |
| cote d'ivoire (ivory coast) | `bafing region` |
| cote d'ivoire (ivory coast) | `bas-sassandra district` |
| cote d'ivoire (ivory coast) | `bas-sassandra region` |
| cote d'ivoire (ivory coast) | `comoé district` |
| cote d'ivoire (ivory coast) | `denguélé district` |
| cote d'ivoire (ivory coast) | `denguélé region` |
| cote d'ivoire (ivory coast) | `dix-huit montagnes` |
| cote d'ivoire (ivory coast) | `fromager` |
| cote d'ivoire (ivory coast) | `gōh-djiboua district` |
| cote d'ivoire (ivory coast) | `haut-sassandra` |
| cote d'ivoire (ivory coast) | `lacs district` |
| cote d'ivoire (ivory coast) | `lacs region` |
| cote d'ivoire (ivory coast) | `lagunes district` |
| cote d'ivoire (ivory coast) | `lagunes region` |
| cote d'ivoire (ivory coast) | `marahoué region` |
| cote d'ivoire (ivory coast) | `montagnes district` |
| cote d'ivoire (ivory coast) | `moyen-cavally` |
| cote d'ivoire (ivory coast) | `moyen-comoé` |
| cote d'ivoire (ivory coast) | `n'zi-comoé` |
| cote d'ivoire (ivory coast) | `sassandra-marahoué district` |
| cote d'ivoire (ivory coast) | `savanes region` |
| cote d'ivoire (ivory coast) | `sud-bandama` |
| cote d'ivoire (ivory coast) | `sud-comoé` |
| cote d'ivoire (ivory coast) | `vallée du bandama district` |
| cote d'ivoire (ivory coast) | `vallée du bandama region` |
| cote d'ivoire (ivory coast) | `woroba district` |
| cote d'ivoire (ivory coast) | `worodougou` |
| cote d'ivoire (ivory coast) | `yamoussoukro` |
| cote d'ivoire (ivory coast) | `zanzan region` |
| croatia | `bjelovar-bilogora` |
| croatia | `brod-posavina` |
| croatia | `dubrovnik-neretva` |
| croatia | `istria` |
| croatia | `karlovac` |
| croatia | `koprivnica-kri˛evci` |
| croatia | `krapina-zagorje` |
| croatia | `lika-senj` |
| croatia | `medimurje` |
| croatia | `osijek-baranja` |
| croatia | `po˛ega-slavonia` |
| croatia | `primorje-gorski kotar` |
| croatia | `�ibenik-knin` |
| croatia | `sisak-moslavina` |
| croatia | `split-dalmatia` |
| croatia | `vara˛din` |
| croatia | `virovitica-podravina` |
| croatia | `vukovar-syrmia` |
| croatia | `zadar` |
| croatia | `zagreb` |
| croatia | `zagreb` |
| cuba | `artemisa province` |
| cuba | `camagüey province` |
| cuba | `ciego de įvila province` |
| cuba | `cienfuegos province` |
| cuba | `granma province` |
| cuba | `guantįnamo province` |
| cuba | `havana province` |
| cuba | `holguķn province` |
| cuba | `isla de la juventud` |
| cuba | `las tunas province` |
| cuba | `matanzas province` |
| cuba | `mayabeque province` |
| cuba | `pinar del rķo province` |
| cuba | `sancti spķritus province` |
| cuba | `santiago de cuba province` |
| cuba | `villa clara province` |
| cyprus | `famagusta district (magusa)` |
| cyprus | `kyrenia district (keryneia)` |
| cyprus | `larnaca district (larnaka)` |
| cyprus | `limassol district (leymasun)` |
| cyprus | `nicosia district (lefkosa)` |
| cyprus | `paphos district (pafos)` |
| czech republic | `bene�ov` |
| czech republic | `beroun` |
| czech republic | `blansko` |
| czech republic | `breclav` |
| czech republic | `brno-mesto` |
| czech republic | `brno-venkov` |
| czech republic | `bruntįl` |
| czech republic | `ceskį lķpa` |
| czech republic | `ceské budejovice` |
| czech republic | `ceskż krumlov` |
| czech republic | `cheb` |
| czech republic | `chomutov` |
| czech republic | `chrudim` |
| czech republic | `decķn` |
| czech republic | `doma˛lice` |
| czech republic | `frżdek-mķstek` |
| czech republic | `havlķckuv brod` |
| czech republic | `hodonķn` |
| czech republic | `hradec krįlové` |
| czech republic | `jablonec nad nisou` |
| czech republic | `jesenķk` |
| czech republic | `jicķn` |
| czech republic | `jihlava` |
| czech republic | `jihoceskż kraj` |
| czech republic | `jihomoravskż kraj` |
| czech republic | `jindrichuv hradec` |
| czech republic | `karlovarskż kraj` |
| czech republic | `karlovy vary` |
| czech republic | `karvinį` |
| czech republic | `kladno` |
| czech republic | `klatovy` |
| czech republic | `kolķn` |
| czech republic | `kraj vysocina` |
| czech republic | `krįlovéhradeckż kraj` |
| czech republic | `kromerķ˛` |
| czech republic | `kutnį hora` |
| czech republic | `liberec` |
| czech republic | `libereckż kraj` |
| czech republic | `litomerice` |
| czech republic | `louny` |
| czech republic | `melnķk` |
| czech republic | `mladį boleslav` |
| czech republic | `moravskoslezskż kraj` |
| czech republic | `most` |
| czech republic | `nįchod` |
| czech republic | `novż jicķn` |
| czech republic | `nymburk` |
| czech republic | `olomouc` |
| czech republic | `olomouckż kraj` |
| czech republic | `opava` |
| czech republic | `ostrava-mesto` |
| czech republic | `pardubice` |
| czech republic | `pardubickż kraj` |
| czech republic | `pelhrimov` |
| czech republic | `pķsek` |
| czech republic | `plzen-jih` |
| czech republic | `plzen-mesto` |
| czech republic | `plzen-sever` |
| czech republic | `plzenskż kraj` |
| czech republic | `prachatice` |
| czech republic | `praha-vżchod` |
| czech republic | `praha-zįpad` |
| czech republic | `praha, hlavnķ mesto` |
| czech republic | `prerov` |
| czech republic | `prķbram` |
| czech republic | `prostejov` |
| czech republic | `rakovnķk` |
| czech republic | `rokycany` |
| czech republic | `rychnov nad kne˛nou` |
| czech republic | `semily` |
| czech republic | `sokolov` |
| czech republic | `strakonice` |
| czech republic | `stredoceskż kraj` |
| czech republic | `�umperk` |
| czech republic | `svitavy` |
| czech republic | `tįbor` |
| czech republic | `tachov` |
| czech republic | `teplice` |
| czech republic | `trebķc` |
| czech republic | `trutnov` |
| czech republic | `uherské hradi�te` |
| czech republic | `śsteckż kraj` |
| czech republic | `śstķ nad labem` |
| czech republic | `śstķ nad orlicķ` |
| czech republic | `vsetķn` |
| czech republic | `vy�kov` |
| czech republic | `ˇdįr nad sįzavou` |
| czech republic | `zlķn` |
| czech republic | `zlķnskż kraj` |
| czech republic | `znojmo` |
| democratic republic of the congo | `bas-uélé` |
| democratic republic of the congo | `équateur` |
| democratic republic of the congo | `haut-katanga` |
| democratic republic of the congo | `haut-lomami` |
| democratic republic of the congo | `haut-uélé` |
| democratic republic of the congo | `ituri` |
| democratic republic of the congo | `kasaļ` |
| democratic republic of the congo | `kasaļ central` |
| democratic republic of the congo | `kasaļ oriental` |
| democratic republic of the congo | `kinshasa` |
| democratic republic of the congo | `kongo central` |
| democratic republic of the congo | `kwango` |
| democratic republic of the congo | `kwilu` |
| democratic republic of the congo | `lomami` |
| democratic republic of the congo | `lualaba` |
| democratic republic of the congo | `mai-ndombe` |
| democratic republic of the congo | `maniema` |
| democratic republic of the congo | `mongala` |
| democratic republic of the congo | `nord-kivu` |
| democratic republic of the congo | `nord-ubangi` |
| democratic republic of the congo | `sankuru` |
| democratic republic of the congo | `sud-kivu` |
| democratic republic of the congo | `sud-ubangi` |
| democratic republic of the congo | `tanganyika` |
| democratic republic of the congo | `tshopo` |
| democratic republic of the congo | `tshuapa` |
| denmark | `capital region of denmark` |
| denmark | `central denmark region` |
| denmark | `north denmark region` |
| denmark | `region of southern denmark` |
| denmark | `region zealand` |
| djibouti | `ali sabieh region` |
| djibouti | `arta region` |
| djibouti | `dikhil region` |
| djibouti | `djibouti` |
| djibouti | `obock region` |
| djibouti | `tadjourah region` |
| dominica | `saint andrew parish` |
| dominica | `saint david parish` |
| dominica | `saint george parish` |
| dominica | `saint john parish` |
| dominica | `saint joseph parish` |
| dominica | `saint luke parish` |
| dominica | `saint mark parish` |
| dominica | `saint patrick parish` |
| dominica | `saint paul parish` |
| dominica | `saint peter parish` |
| dominican republic | `azua province` |
| dominican republic | `baoruco province` |
| dominican republic | `barahona province` |
| dominican republic | `dajabón province` |
| dominican republic | `distrito nacional` |
| dominican republic | `duarte province` |
| dominican republic | `el seibo province` |
| dominican republic | `espaillat province` |
| dominican republic | `hato mayor province` |
| dominican republic | `hermanas mirabal province` |
| dominican republic | `independencia` |
| dominican republic | `la altagracia province` |
| dominican republic | `la romana province` |
| dominican republic | `la vega province` |
| dominican republic | `marķa trinidad sįnchez province` |
| dominican republic | `monseńor nouel province` |
| dominican republic | `monte cristi province` |
| dominican republic | `monte plata province` |
| dominican republic | `pedernales province` |
| dominican republic | `peravia province` |
| dominican republic | `puerto plata province` |
| dominican republic | `samanį province` |
| dominican republic | `san cristóbal province` |
| dominican republic | `san josé de ocoa province` |
| dominican republic | `san juan province` |
| dominican republic | `san pedro de macorķs` |
| dominican republic | `sįnchez ramķrez province` |
| dominican republic | `santiago province` |
| dominican republic | `santiago rodrķguez province` |
| dominican republic | `santo domingo province` |
| dominican republic | `valverde province` |
| east timor | `aileu municipality` |
| east timor | `ainaro municipality` |
| east timor | `baucau municipality` |
| east timor | `bobonaro municipality` |
| east timor | `cova lima municipality` |
| east timor | `dili municipality` |
| east timor | `ermera district` |
| east timor | `lautém municipality` |
| east timor | `liquiēį municipality` |
| east timor | `manatuto district` |
| east timor | `manufahi municipality` |
| east timor | `viqueque municipality` |
| ecuador | `azuay` |
| ecuador | `bolķvar` |
| ecuador | `cańar` |
| ecuador | `carchi` |
| ecuador | `chimborazo` |
| ecuador | `cotopaxi` |
| ecuador | `el oro` |
| ecuador | `esmeraldas` |
| ecuador | `galįpagos` |
| ecuador | `guayas` |
| ecuador | `imbabura` |
| ecuador | `loja` |
| ecuador | `los rķos` |
| ecuador | `manabķ` |
| ecuador | `morona-santiago` |
| ecuador | `napo` |
| ecuador | `orellana` |
| ecuador | `pastaza` |
| ecuador | `pichincha` |
| ecuador | `santa elena` |
| ecuador | `santo domingo de los tsįchilas` |
| ecuador | `sucumbķos` |
| ecuador | `tungurahua` |
| ecuador | `zamora chinchipe` |
| egypt | `alexandria` |
| egypt | `aswan` |
| egypt | `asyut` |
| egypt | `beheira` |
| egypt | `beni suef` |
| egypt | `cairo` |
| egypt | `dakahlia` |
| egypt | `damietta` |
| egypt | `faiyum` |
| egypt | `gharbia` |
| egypt | `giza` |
| egypt | `ismailia` |
| egypt | `kafr el-sheikh` |
| egypt | `luxor` |
| egypt | `matrouh` |
| egypt | `minya` |
| egypt | `monufia` |
| egypt | `new valley` |
| egypt | `north sinai` |
| egypt | `port said` |
| egypt | `qalyubia` |
| egypt | `qena` |
| egypt | `red sea` |
| egypt | `sharqia` |
| egypt | `sohag` |
| egypt | `south sinai` |
| egypt | `suez` |
| el salvador | `ahuachapįn department` |
| el salvador | `cabańas department` |
| el salvador | `chalatenango department` |
| el salvador | `cuscatlįn department` |
| el salvador | `la libertad department` |
| el salvador | `la paz department` |
| el salvador | `la unión department` |
| el salvador | `morazįn department` |
| el salvador | `san miguel department` |
| el salvador | `san salvador department` |
| el salvador | `san vicente department` |
| el salvador | `santa ana department` |
| el salvador | `sonsonate department` |
| el salvador | `usulutįn department` |
| equatorial guinea | `annobón province` |
| equatorial guinea | `bioko norte province` |
| equatorial guinea | `bioko sur province` |
| equatorial guinea | `centro sur province` |
| equatorial guinea | `insular region` |
| equatorial guinea | `kié-ntem province` |
| equatorial guinea | `litoral province` |
| equatorial guinea | `rķo muni` |
| equatorial guinea | `wele-nzas province` |
| eritrea | `anseba region` |
| eritrea | `debub region` |
| eritrea | `gash-barka region` |
| eritrea | `maekel region` |
| eritrea | `northern red sea region` |
| eritrea | `southern red sea region` |
| estonia | `harju county` |
| estonia | `hiiu county` |
| estonia | `ida-viru county` |
| estonia | `järva county` |
| estonia | `jõgeva county` |
| estonia | `lääne county` |
| estonia | `lääne-viru county` |
| estonia | `pärnu county` |
| estonia | `põlva county` |
| estonia | `rapla county` |
| estonia | `saare county` |
| estonia | `tartu county` |
| estonia | `valga county` |
| estonia | `viljandi county` |
| estonia | `võru county` |
| ethiopia | `addis ababa` |
| ethiopia | `afar region` |
| ethiopia | `amhara region` |
| ethiopia | `benishangul-gumuz region` |
| ethiopia | `dire dawa` |
| ethiopia | `gambela region` |
| ethiopia | `harari region` |
| ethiopia | `oromia region` |
| ethiopia | `somali region` |
| ethiopia | `southern nations, nationalities, and peoples' region` |
| ethiopia | `tigray region` |
| fiji islands | `ba` |
| fiji islands | `bua` |
| fiji islands | `cakaudrove` |
| fiji islands | `central division` |
| fiji islands | `eastern division` |
| fiji islands | `kadavu` |
| fiji islands | `lau` |
| fiji islands | `lomaiviti` |
| fiji islands | `macuata` |
| fiji islands | `nadroga-navosa` |
| fiji islands | `naitasiri` |
| fiji islands | `namosi` |
| fiji islands | `northern division` |
| fiji islands | `ra` |
| fiji islands | `rewa` |
| fiji islands | `rotuma` |
| fiji islands | `serua` |
| fiji islands | `tailevu` |
| fiji islands | `western division` |
| finland | `åland islands` |
| finland | `central finland` |
| finland | `central ostrobothnia` |
| finland | `finland proper` |
| finland | `kainuu` |
| finland | `kymenlaakso` |
| finland | `lapland` |
| finland | `north karelia` |
| finland | `northern ostrobothnia` |
| finland | `northern savonia` |
| finland | `ostrobothnia` |
| finland | `päijänne tavastia` |
| finland | `pirkanmaa` |
| finland | `satakunta` |
| finland | `south karelia` |
| finland | `southern ostrobothnia` |
| finland | `southern savonia` |
| finland | `tavastia proper` |
| finland | `uusimaa` |
| france | `ain` |
| france | `aisne` |
| france | `allier` |
| france | `alpes-de-haute-provence` |
| france | `alpes-maritimes` |
| france | `alsace` |
| france | `ardčche` |
| france | `ardennes` |
| france | `aričge` |
| france | `aube` |
| france | `aude` |
| france | `auvergne-rhōne-alpes` |
| france | `aveyron` |
| france | `bas-rhin` |
| france | `bouches-du-rhōne` |
| france | `bourgogne-franche-comté` |
| france | `bretagne` |
| france | `calvados` |
| france | `cantal` |
| france | `centre-val de loire` |
| france | `charente` |
| france | `charente-maritime` |
| france | `cher` |
| france | `clipperton` |
| france | `corrčze` |
| france | `corse` |
| france | `corse-du-sud` |
| france | `cōte-d'or` |
| france | `cōtes-d'armor` |
| france | `creuse` |
| france | `deux-sčvres` |
| france | `dordogne` |
| france | `doubs` |
| france | `drōme` |
| france | `essonne` |
| france | `eure` |
| france | `eure-et-loir` |
| france | `finistčre` |
| france | `french guiana` |
| france | `french polynesia` |
| france | `french southern and antarctic lands` |
| france | `gard` |
| france | `gers` |
| france | `gironde` |
| france | `grand-est` |
| france | `guadeloupe` |
| france | `haut-rhin` |
| france | `haute-corse` |
| france | `haute-garonne` |
| france | `haute-loire` |
| france | `haute-marne` |
| france | `haute-saōne` |
| france | `haute-savoie` |
| france | `haute-vienne` |
| france | `hautes-alpes` |
| france | `hautes-pyrénées` |
| france | `hauts-de-france` |
| france | `hauts-de-seine` |
| france | `hérault` |
| france | `īle-de-france` |
| france | `ille-et-vilaine` |
| france | `indre` |
| france | `indre-et-loire` |
| france | `isčre` |
| france | `jura` |
| france | `la réunion` |
| france | `landes` |
| france | `loir-et-cher` |
| france | `loire` |
| france | `loire-atlantique` |
| france | `loiret` |
| france | `lot` |
| france | `lot-et-garonne` |
| france | `lozčre` |
| france | `maine-et-loire` |
| france | `manche` |
| france | `marne` |
| france | `martinique` |
| france | `mayenne` |
| france | `mayotte` |
| france | `métropole de lyon` |
| france | `meurthe-et-moselle` |
| france | `meuse` |
| france | `morbihan` |
| france | `moselle` |
| france | `ničvre` |
| france | `nord` |
| france | `normandie` |
| france | `nouvelle-aquitaine` |
| france | `occitanie` |
| france | `oise` |
| france | `orne` |
| france | `paris` |
| france | `pas-de-calais` |
| france | `pays-de-la-loire` |
| france | `provence-alpes-cōte-d’azur` |
| france | `puy-de-dōme` |
| france | `pyrénées-atlantiques` |
| france | `pyrénées-orientales` |
| france | `rhōne` |
| france | `saint pierre and miquelon` |
| france | `saint-barthélemy` |
| france | `saint-martin` |
| france | `saōne-et-loire` |
| france | `sarthe` |
| france | `savoie` |
| france | `seine-et-marne` |
| france | `seine-maritime` |
| france | `seine-saint-denis` |
| france | `somme` |
| france | `tarn` |
| france | `tarn-et-garonne` |
| france | `territoire de belfort` |
| france | `val-d'oise` |
| france | `val-de-marne` |
| france | `var` |
| france | `vaucluse` |
| france | `vendée` |
| france | `vienne` |
| france | `vosges` |
| france | `wallis and futuna` |
| france | `yonne` |
| france | `yvelines` |
| gabon | `estuaire province` |
| gabon | `haut-ogooué province` |
| gabon | `moyen-ogooué province` |
| gabon | `ngounié province` |
| gabon | `nyanga province` |
| gabon | `ogooué-ivindo province` |
| gabon | `ogooué-lolo province` |
| gabon | `ogooué-maritime province` |
| gabon | `woleu-ntem province` |
| gambia the | `banjul` |
| gambia the | `central river division` |
| gambia the | `lower river division` |
| gambia the | `north bank division` |
| gambia the | `upper river division` |
| gambia the | `west coast division` |
| georgia | `adjara` |
| georgia | `autonomous republic of abkhazia` |
| georgia | `guria` |
| georgia | `imereti` |
| georgia | `kakheti` |
| georgia | `khelvachauri municipality` |
| georgia | `kvemo kartli` |
| georgia | `mtskheta-mtianeti` |
| georgia | `racha-lechkhumi and kvemo svaneti` |
| georgia | `samegrelo-zemo svaneti` |
| georgia | `samtskhe-javakheti` |
| georgia | `senaki municipality` |
| georgia | `shida kartli` |
| georgia | `tbilisi` |
| germany | `baden-württemberg` |
| germany | `bavaria` |
| germany | `berlin` |
| germany | `brandenburg` |
| germany | `bremen` |
| germany | `hamburg` |
| germany | `hesse` |
| germany | `lower saxony` |
| germany | `mecklenburg-vorpommern` |
| germany | `north rhine-westphalia` |
| germany | `rhineland-palatinate` |
| germany | `saarland` |
| germany | `saxony` |
| germany | `saxony-anhalt` |
| germany | `schleswig-holstein` |
| germany | `thuringia` |
| ghana | `ahafo` |
| ghana | `ashanti` |
| ghana | `bono` |
| ghana | `bono east` |
| ghana | `central` |
| ghana | `eastern` |
| ghana | `greater accra` |
| ghana | `north east` |
| ghana | `northern` |
| ghana | `oti` |
| ghana | `savannah` |
| ghana | `upper east` |
| ghana | `upper west` |
| ghana | `volta` |
| ghana | `western` |
| ghana | `western north` |
| greece | `achaea regional unit` |
| greece | `aetolia-acarnania regional unit` |
| greece | `arcadia prefecture` |
| greece | `argolis regional unit` |
| greece | `attica region` |
| greece | `boeotia regional unit` |
| greece | `central greece region` |
| greece | `central macedonia` |
| greece | `chania regional unit` |
| greece | `corfu prefecture` |
| greece | `corinthia regional unit` |
| greece | `crete region` |
| greece | `drama regional unit` |
| greece | `east attica regional unit` |
| greece | `east macedonia and thrace` |
| greece | `epirus region` |
| greece | `euboea` |
| greece | `grevena prefecture` |
| greece | `imathia regional unit` |
| greece | `ioannina regional unit` |
| greece | `ionian islands region` |
| greece | `karditsa regional unit` |
| greece | `kastoria regional unit` |
| greece | `kefalonia prefecture` |
| greece | `kilkis regional unit` |
| greece | `kozani prefecture` |
| greece | `laconia` |
| greece | `larissa prefecture` |
| greece | `lefkada regional unit` |
| greece | `pella regional unit` |
| greece | `peloponnese region` |
| greece | `phthiotis prefecture` |
| greece | `preveza prefecture` |
| greece | `serres prefecture` |
| greece | `south aegean` |
| greece | `thessaloniki regional unit` |
| greece | `west greece region` |
| greece | `west macedonia region` |
| grenada | `carriacou and petite martinique` |
| grenada | `saint andrew parish` |
| grenada | `saint david parish` |
| grenada | `saint george parish` |
| grenada | `saint john parish` |
| grenada | `saint mark parish` |
| grenada | `saint patrick parish` |
| guatemala | `alta verapaz department` |
| guatemala | `baja verapaz department` |
| guatemala | `chimaltenango department` |
| guatemala | `chiquimula department` |
| guatemala | `el progreso department` |
| guatemala | `escuintla department` |
| guatemala | `guatemala department` |
| guatemala | `huehuetenango department` |
| guatemala | `izabal department` |
| guatemala | `jalapa department` |
| guatemala | `jutiapa department` |
| guatemala | `petén department` |
| guatemala | `quetzaltenango department` |
| guatemala | `quiché department` |
| guatemala | `retalhuleu department` |
| guatemala | `sacatepéquez department` |
| guatemala | `san marcos department` |
| guatemala | `santa rosa department` |
| guatemala | `sololį department` |
| guatemala | `suchitepéquez department` |
| guatemala | `totonicapįn department` |
| guinea | `beyla prefecture` |
| guinea | `boffa prefecture` |
| guinea | `boké prefecture` |
| guinea | `boké region` |
| guinea | `conakry` |
| guinea | `coyah prefecture` |
| guinea | `dabola prefecture` |
| guinea | `dalaba prefecture` |
| guinea | `dinguiraye prefecture` |
| guinea | `dubréka prefecture` |
| guinea | `faranah prefecture` |
| guinea | `forécariah prefecture` |
| guinea | `fria prefecture` |
| guinea | `gaoual prefecture` |
| guinea | `guéckédou prefecture` |
| guinea | `kankan prefecture` |
| guinea | `kankan region` |
| guinea | `kérouané prefecture` |
| guinea | `kindia prefecture` |
| guinea | `kindia region` |
| guinea | `kissidougou prefecture` |
| guinea | `koubia prefecture` |
| guinea | `koundara prefecture` |
| guinea | `kouroussa prefecture` |
| guinea | `labé prefecture` |
| guinea | `labé region` |
| guinea | `lélouma prefecture` |
| guinea | `lola prefecture` |
| guinea | `macenta prefecture` |
| guinea | `mali prefecture` |
| guinea | `mamou prefecture` |
| guinea | `mamou region` |
| guinea | `mandiana prefecture` |
| guinea | `nzérékoré prefecture` |
| guinea | `nzérékoré region` |
| guinea | `pita prefecture` |
| guinea | `siguiri prefecture` |
| guinea | `télimélé prefecture` |
| guinea | `tougué prefecture` |
| guinea | `yomou prefecture` |
| guinea-bissau | `bafatį` |
| guinea-bissau | `biombo region` |
| guinea-bissau | `bolama region` |
| guinea-bissau | `cacheu region` |
| guinea-bissau | `gabś region` |
| guinea-bissau | `leste province` |
| guinea-bissau | `norte province` |
| guinea-bissau | `oio region` |
| guinea-bissau | `quinara region` |
| guinea-bissau | `sul province` |
| guinea-bissau | `tombali region` |
| guyana | `barima-waini` |
| guyana | `cuyuni-mazaruni` |
| guyana | `demerara-mahaica` |
| guyana | `east berbice-corentyne` |
| guyana | `essequibo islands-west demerara` |
| guyana | `mahaica-berbice` |
| guyana | `pomeroon-supenaam` |
| guyana | `potaro-siparuni` |
| guyana | `upper demerara-berbice` |
| guyana | `upper takutu-upper essequibo` |
| haiti | `artibonite` |
| haiti | `centre` |
| haiti | `grand'anse` |
| haiti | `nippes` |
| haiti | `nord` |
| haiti | `nord-est` |
| haiti | `nord-ouest` |
| haiti | `ouest` |
| haiti | `sud` |
| haiti | `sud-est` |
| honduras | `atlįntida department` |
| honduras | `bay islands department` |
| honduras | `choluteca department` |
| honduras | `colón department` |
| honduras | `comayagua department` |
| honduras | `copįn department` |
| honduras | `cortés department` |
| honduras | `el paraķso department` |
| honduras | `francisco morazįn department` |
| honduras | `gracias a dios department` |
| honduras | `intibucį department` |
| honduras | `la paz department` |
| honduras | `lempira department` |
| honduras | `ocotepeque department` |
| honduras | `olancho department` |
| honduras | `santa bįrbara department` |
| honduras | `valle department` |
| honduras | `yoro department` |
| hong kong s.a.r. | `central and western` |
| hong kong s.a.r. | `eastern` |
| hong kong s.a.r. | `islands` |
| hong kong s.a.r. | `kowloon city` |
| hong kong s.a.r. | `kwai tsing` |
| hong kong s.a.r. | `kwun tong` |
| hong kong s.a.r. | `north` |
| hong kong s.a.r. | `sai kung` |
| hong kong s.a.r. | `sha tin` |
| hong kong s.a.r. | `sham shui po` |
| hong kong s.a.r. | `southern` |
| hong kong s.a.r. | `tai po` |
| hong kong s.a.r. | `tsuen wan` |
| hong kong s.a.r. | `tuen mun` |
| hong kong s.a.r. | `wan chai` |
| hong kong s.a.r. | `wong tai sin` |
| hong kong s.a.r. | `yau tsim mong` |
| hong kong s.a.r. | `yuen long` |
| hungary | `bįcs-kiskun` |
| hungary | `baranya` |
| hungary | `békés` |
| hungary | `békéscsaba` |
| hungary | `borsod-abaśj-zemplén` |
| hungary | `budapest` |
| hungary | `csongrįd county` |
| hungary | `debrecen` |
| hungary | `dunaśjvįros` |
| hungary | `eger` |
| hungary | `érd` |
| hungary | `fejér county` |
| hungary | `gyor` |
| hungary | `gyor-moson-sopron county` |
| hungary | `hajdś-bihar county` |
| hungary | `heves county` |
| hungary | `hódmezovįsįrhely` |
| hungary | `jįsz-nagykun-szolnok county` |
| hungary | `kaposvįr` |
| hungary | `kecskemét` |
| hungary | `komįrom-esztergom` |
| hungary | `miskolc` |
| hungary | `nagykanizsa` |
| hungary | `nógrįd county` |
| hungary | `nyķregyhįza` |
| hungary | `pécs` |
| hungary | `pest county` |
| hungary | `salgótarjįn` |
| hungary | `somogy county` |
| hungary | `sopron` |
| hungary | `szabolcs-szatmįr-bereg county` |
| hungary | `szeged` |
| hungary | `székesfehérvįr` |
| hungary | `szekszįrd` |
| hungary | `szolnok` |
| hungary | `szombathely` |
| hungary | `tatabįnya` |
| hungary | `tolna county` |
| hungary | `vas county` |
| hungary | `veszprém` |
| hungary | `veszprém county` |
| hungary | `zala county` |
| hungary | `zalaegerszeg` |
| iceland | `capital region` |
| iceland | `eastern region` |
| iceland | `northeastern region` |
| iceland | `northwestern region` |
| iceland | `southern peninsula region` |
| iceland | `southern region` |
| iceland | `western region` |
| iceland | `westfjords` |
| india | `andaman and nicobar islands` |
| india | `andhra pradesh` |
| india | `arunachal pradesh` |
| india | `assam` |
| india | `bihar` |
| india | `chandigarh` |
| india | `chhattisgarh` |
| india | `dadra and nagar haveli and daman and diu` |
| india | `delhi` |
| india | `goa` |
| india | `gujarat` |
| india | `haryana` |
| india | `himachal pradesh` |
| india | `jammu and kashmir` |
| india | `jharkhand` |
| india | `karnataka` |
| india | `kerala` |
| india | `ladakh` |
| india | `lakshadweep` |
| india | `madhya pradesh` |
| india | `maharashtra` |
| india | `manipur` |
| india | `meghalaya` |
| india | `mizoram` |
| india | `nagaland` |
| india | `odisha` |
| india | `puducherry` |
| india | `punjab` |
| india | `rajasthan` |
| india | `sikkim` |
| india | `tamil nadu` |
| india | `telangana` |
| india | `tripura` |
| india | `uttar pradesh` |
| india | `uttarakhand` |
| india | `west bengal` |
| indonesia | `aceh` |
| indonesia | `bali` |
| indonesia | `banten` |
| indonesia | `bengkulu` |
| indonesia | `di yogyakarta` |
| indonesia | `dki jakarta` |
| indonesia | `gorontalo` |
| indonesia | `jambi` |
| indonesia | `jawa barat` |
| indonesia | `jawa tengah` |
| indonesia | `jawa timur` |
| indonesia | `kalimantan barat` |
| indonesia | `kalimantan selatan` |
| indonesia | `kalimantan tengah` |
| indonesia | `kalimantan timur` |
| indonesia | `kalimantan utara` |
| indonesia | `kepulauan bangka belitung` |
| indonesia | `kepulauan riau` |
| indonesia | `lampung` |
| indonesia | `maluku` |
| indonesia | `maluku utara` |
| indonesia | `nusa tenggara barat` |
| indonesia | `nusa tenggara timur` |
| indonesia | `papua` |
| indonesia | `papua barat` |
| indonesia | `riau` |
| indonesia | `sulawesi barat` |
| indonesia | `sulawesi selatan` |
| indonesia | `sulawesi tengah` |
| indonesia | `sulawesi tenggara` |
| indonesia | `sulawesi utara` |
| indonesia | `sumatera barat` |
| indonesia | `sumatera selatan` |
| indonesia | `sumatera utara` |
| iran | `alborz` |
| iran | `ardabil` |
| iran | `bushehr` |
| iran | `chaharmahal and bakhtiari` |
| iran | `east azerbaijan` |
| iran | `fars` |
| iran | `gilan` |
| iran | `golestan` |
| iran | `hamadan` |
| iran | `hormozgan` |
| iran | `ilam` |
| iran | `isfahan` |
| iran | `kerman` |
| iran | `kermanshah` |
| iran | `khuzestan` |
| iran | `kohgiluyeh and boyer-ahmad` |
| iran | `kurdistan` |
| iran | `lorestan` |
| iran | `markazi` |
| iran | `mazandaran` |
| iran | `north khorasan` |
| iran | `qazvin` |
| iran | `qom` |
| iran | `razavi khorasan` |
| iran | `semnan` |
| iran | `sistan and baluchestan` |
| iran | `south khorasan` |
| iran | `tehran` |
| iran | `west azarbaijan` |
| iran | `yazd` |
| iran | `zanjan` |
| iraq | `al anbar` |
| iraq | `al muthanna` |
| iraq | `al-qadisiyyah` |
| iraq | `babylon` |
| iraq | `baghdad` |
| iraq | `basra` |
| iraq | `dhi qar` |
| iraq | `diyala` |
| iraq | `dohuk` |
| iraq | `erbil` |
| iraq | `karbala` |
| iraq | `kirkuk` |
| iraq | `maysan` |
| iraq | `najaf` |
| iraq | `nineveh` |
| iraq | `saladin` |
| iraq | `sulaymaniyah` |
| iraq | `wasit` |
| ireland | `carlow` |
| ireland | `cavan` |
| ireland | `clare` |
| ireland | `connacht` |
| ireland | `cork` |
| ireland | `donegal` |
| ireland | `dublin` |
| ireland | `galway` |
| ireland | `kerry` |
| ireland | `kildare` |
| ireland | `kilkenny` |
| ireland | `laois` |
| ireland | `leinster` |
| ireland | `limerick` |
| ireland | `longford` |
| ireland | `louth` |
| ireland | `mayo` |
| ireland | `meath` |
| ireland | `monaghan` |
| ireland | `munster` |
| ireland | `offaly` |
| ireland | `roscommon` |
| ireland | `sligo` |
| ireland | `tipperary` |
| ireland | `ulster` |
| ireland | `waterford` |
| ireland | `westmeath` |
| ireland | `wexford` |
| ireland | `wicklow` |
| israel | `central district` |
| israel | `haifa district` |
| israel | `jerusalem district` |
| israel | `northern district` |
| israel | `southern district` |
| israel | `tel aviv district` |
| italy | `abruzzo` |
| italy | `agrigento` |
| italy | `alessandria` |
| italy | `ancona` |
| italy | `aosta valley` |
| italy | `apulia` |
| italy | `ascoli piceno` |
| italy | `asti` |
| italy | `avellino` |
| italy | `barletta-andria-trani` |
| italy | `basilicata` |
| italy | `belluno` |
| italy | `benevento` |
| italy | `bergamo` |
| italy | `biella` |
| italy | `brescia` |
| italy | `brindisi` |
| italy | `calabria` |
| italy | `caltanissetta` |
| italy | `campania` |
| italy | `campobasso` |
| italy | `caserta` |
| italy | `catanzaro` |
| italy | `chieti` |
| italy | `como` |
| italy | `cosenza` |
| italy | `cremona` |
| italy | `crotone` |
| italy | `cuneo` |
| italy | `emilia-romagna` |
| italy | `enna` |
| italy | `fermo` |
| italy | `ferrara` |
| italy | `foggia` |
| italy | `forlģ-cesena` |
| italy | `friuli-venezia giulia` |
| italy | `frosinone` |
| italy | `gorizia` |
| italy | `grosseto` |
| italy | `imperia` |
| italy | `isernia` |
| italy | `l'aquila` |
| italy | `la spezia` |
| italy | `latina` |
| italy | `lazio` |
| italy | `lecce` |
| italy | `lecco` |
| italy | `liguria` |
| italy | `livorno` |
| italy | `lodi` |
| italy | `lombardy` |
| italy | `lucca` |
| italy | `macerata` |
| italy | `mantua` |
| italy | `marche` |
| italy | `massa and carrara` |
| italy | `matera` |
| italy | `medio campidano` |
| italy | `modena` |
| italy | `molise` |
| italy | `monza and brianza` |
| italy | `novara` |
| italy | `nuoro` |
| italy | `oristano` |
| italy | `padua` |
| italy | `palermo` |
| italy | `parma` |
| italy | `pavia` |
| italy | `perugia` |
| italy | `pesaro and urbino` |
| italy | `pescara` |
| italy | `piacenza` |
| italy | `piedmont` |
| italy | `pisa` |
| italy | `pistoia` |
| italy | `pordenone` |
| italy | `potenza` |
| italy | `prato` |
| italy | `ragusa` |
| italy | `ravenna` |
| italy | `reggio emilia` |
| italy | `rieti` |
| italy | `rimini` |
| italy | `rovigo` |
| italy | `salerno` |
| italy | `sardinia` |
| italy | `sassari` |
| italy | `savona` |
| italy | `sicily` |
| italy | `siena` |
| italy | `siracusa` |
| italy | `sondrio` |
| italy | `south sardinia` |
| italy | `taranto` |
| italy | `teramo` |
| italy | `terni` |
| italy | `trapani` |
| italy | `trentino-south tyrol` |
| italy | `treviso` |
| italy | `trieste` |
| italy | `tuscany` |
| italy | `udine` |
| italy | `umbria` |
| italy | `varese` |
| italy | `veneto` |
| italy | `verbano-cusio-ossola` |
| italy | `vercelli` |
| italy | `verona` |
| italy | `vibo valentia` |
| italy | `vicenza` |
| italy | `viterbo` |
| jamaica | `clarendon parish` |
| jamaica | `hanover parish` |
| jamaica | `kingston parish` |
| jamaica | `manchester parish` |
| jamaica | `portland parish` |
| jamaica | `saint andrew` |
| jamaica | `saint ann parish` |
| jamaica | `saint catherine parish` |
| jamaica | `saint elizabeth parish` |
| jamaica | `saint james parish` |
| jamaica | `saint mary parish` |
| jamaica | `saint thomas parish` |
| jamaica | `trelawny parish` |
| jamaica | `westmoreland parish` |
| japan | `aichi prefecture` |
| japan | `akita prefecture` |
| japan | `aomori prefecture` |
| japan | `chiba prefecture` |
| japan | `ehime prefecture` |
| japan | `fukui prefecture` |
| japan | `fukuoka prefecture` |
| japan | `fukushima prefecture` |
| japan | `gifu prefecture` |
| japan | `gunma prefecture` |
| japan | `hiroshima prefecture` |
| japan | `hokkaido prefecture` |
| japan | `hyogo prefecture` |
| japan | `ibaraki prefecture` |
| japan | `ishikawa prefecture` |
| japan | `iwate prefecture` |
| japan | `kagawa prefecture` |
| japan | `kagoshima prefecture` |
| japan | `kanagawa prefecture` |
| japan | `kochi prefecture` |
| japan | `kumamoto prefecture` |
| japan | `kyoto prefecture` |
| japan | `mie prefecture` |
| japan | `miyagi prefecture` |
| japan | `miyazaki prefecture` |
| japan | `nagano prefecture` |
| japan | `nagasaki prefecture` |
| japan | `nara prefecture` |
| japan | `niigata prefecture` |
| japan | `oita prefecture` |
| japan | `okayama prefecture` |
| japan | `okinawa prefecture` |
| japan | `osaka prefecture` |
| japan | `saga prefecture` |
| japan | `saitama prefecture` |
| japan | `shiga prefecture` |
| japan | `shimane prefecture` |
| japan | `shizuoka prefecture` |
| japan | `tochigi prefecture` |
| japan | `tokushima prefecture` |
| japan | `tokyo` |
| japan | `tottori prefecture` |
| japan | `toyama prefecture` |
| japan | `wakayama prefecture` |
| japan | `yamagata prefecture` |
| japan | `yamaguchi prefecture` |
| japan | `yamanashi prefecture` |
| jordan | `ajloun` |
| jordan | `amman` |
| jordan | `aqaba` |
| jordan | `balqa` |
| jordan | `irbid` |
| jordan | `jerash` |
| jordan | `karak` |
| jordan | `ma'an` |
| jordan | `madaba` |
| jordan | `mafraq` |
| jordan | `tafilah` |
| jordan | `zarqa` |
| kazakhstan | `akmola region` |
| kazakhstan | `aktobe region` |
| kazakhstan | `almaty` |
| kazakhstan | `almaty region` |
| kazakhstan | `atyrau region` |
| kazakhstan | `baikonur` |
| kazakhstan | `east kazakhstan region` |
| kazakhstan | `jambyl region` |
| kazakhstan | `karaganda region` |
| kazakhstan | `kostanay region` |
| kazakhstan | `kyzylorda region` |
| kazakhstan | `mangystau region` |
| kazakhstan | `north kazakhstan region` |
| kazakhstan | `nur-sultan` |
| kazakhstan | `pavlodar region` |
| kazakhstan | `turkestan region` |
| kazakhstan | `west kazakhstan province` |
| kenya | `baringo` |
| kenya | `bomet` |
| kenya | `bungoma` |
| kenya | `busia` |
| kenya | `elgeyo-marakwet` |
| kenya | `embu` |
| kenya | `garissa` |
| kenya | `homa bay` |
| kenya | `isiolo` |
| kenya | `kajiado` |
| kenya | `kakamega` |
| kenya | `kericho` |
| kenya | `kiambu` |
| kenya | `kilifi` |
| kenya | `kirinyaga` |
| kenya | `kisii` |
| kenya | `kisumu` |
| kenya | `kitui` |
| kenya | `kwale` |
| kenya | `laikipia` |
| kenya | `lamu` |
| kenya | `machakos` |
| kenya | `makueni` |
| kenya | `mandera` |
| kenya | `marsabit` |
| kenya | `meru` |
| kenya | `migori` |
| kenya | `mombasa` |
| kenya | `murang'a` |
| kenya | `nairobi city` |
| kenya | `nakuru` |
| kenya | `nandi` |
| kenya | `narok` |
| kenya | `nyamira` |
| kenya | `nyandarua` |
| kenya | `nyeri` |
| kenya | `samburu` |
| kenya | `siaya` |
| kenya | `taita-taveta` |
| kenya | `tana river` |
| kenya | `tharaka-nithi` |
| kenya | `trans nzoia` |
| kenya | `turkana` |
| kenya | `uasin gishu` |
| kenya | `vihiga` |
| kenya | `wajir` |
| kenya | `west pokot` |
| kiribati | `gilbert islands` |
| kiribati | `line islands` |
| kiribati | `phoenix islands` |
| kosovo | `dakovica district (gjakove)` |
| kosovo | `gjilan district` |
| kosovo | `kosovska mitrovica district` |
| kosovo | `pec district` |
| kosovo | `pristina (pristine)` |
| kosovo | `prizren district` |
| kosovo | `uro�evac district (ferizaj)` |
| kuwait | `al ahmadi` |
| kuwait | `al farwaniyah` |
| kuwait | `al jahra` |
| kuwait | `capital` |
| kuwait | `hawalli` |
| kuwait | `mubarak al-kabeer` |
| kyrgyzstan | `batken region` |
| kyrgyzstan | `bishkek` |
| kyrgyzstan | `chuy region` |
| kyrgyzstan | `issyk-kul region` |
| kyrgyzstan | `jalal-abad region` |
| kyrgyzstan | `naryn region` |
| kyrgyzstan | `osh` |
| kyrgyzstan | `osh region` |
| kyrgyzstan | `talas region` |
| laos | `attapeu province` |
| laos | `bokeo province` |
| laos | `bolikhamsai province` |
| laos | `champasak province` |
| laos | `houaphanh province` |
| laos | `khammouane province` |
| laos | `luang namtha province` |
| laos | `luang prabang province` |
| laos | `oudomxay province` |
| laos | `phongsaly province` |
| laos | `sainyabuli province` |
| laos | `salavan province` |
| laos | `savannakhet province` |
| laos | `sekong province` |
| laos | `vientiane prefecture` |
| laos | `vientiane province` |
| laos | `xaisomboun` |
| laos | `xaisomboun province` |
| laos | `xiangkhouang province` |
| latvia | `aglona municipality` |
| latvia | `aizkraukle municipality` |
| latvia | `aizpute municipality` |
| latvia | `akniste municipality` |
| latvia | `aloja municipality` |
| latvia | `alsunga municipality` |
| latvia | `aluksne municipality` |
| latvia | `amata municipality` |
| latvia | `ape municipality` |
| latvia | `auce municipality` |
| latvia | `babite municipality` |
| latvia | `baldone municipality` |
| latvia | `baltinava municipality` |
| latvia | `balvi municipality` |
| latvia | `bauska municipality` |
| latvia | `beverina municipality` |
| latvia | `broceni municipality` |
| latvia | `burtnieki municipality` |
| latvia | `carnikava municipality` |
| latvia | `cesis municipality` |
| latvia | `cesvaine municipality` |
| latvia | `cibla municipality` |
| latvia | `dagda municipality` |
| latvia | `daugavpils` |
| latvia | `daugavpils municipality` |
| latvia | `dobele municipality` |
| latvia | `dundaga municipality` |
| latvia | `durbe municipality` |
| latvia | `engure municipality` |
| latvia | `ergli municipality` |
| latvia | `garkalne municipality` |
| latvia | `grobina municipality` |
| latvia | `gulbene municipality` |
| latvia | `iecava municipality` |
| latvia | `ik�kile municipality` |
| latvia | `ilukste municipality` |
| latvia | `incukalns municipality` |
| latvia | `jaunjelgava municipality` |
| latvia | `jaunpiebalga municipality` |
| latvia | `jaunpils municipality` |
| latvia | `jekabpils` |
| latvia | `jekabpils municipality` |
| latvia | `jelgava` |
| latvia | `jelgava municipality` |
| latvia | `jurmala` |
| latvia | `kandava municipality` |
| latvia | `karsava municipality` |
| latvia | `kegums municipality` |
| latvia | `kekava municipality` |
| latvia | `koceni municipality` |
| latvia | `koknese municipality` |
| latvia | `kraslava municipality` |
| latvia | `krimulda municipality` |
| latvia | `krustpils municipality` |
| latvia | `kuldiga municipality` |
| latvia | `lielvarde municipality` |
| latvia | `liepaja` |
| latvia | `ligatne municipality` |
| latvia | `limba˛i municipality` |
| latvia | `livani municipality` |
| latvia | `lubana municipality` |
| latvia | `ludza municipality` |
| latvia | `madona municipality` |
| latvia | `malpils municipality` |
| latvia | `marupe municipality` |
| latvia | `mazsalaca municipality` |
| latvia | `mersrags municipality` |
| latvia | `nauk�eni municipality` |
| latvia | `nereta municipality` |
| latvia | `nica municipality` |
| latvia | `ogre municipality` |
| latvia | `olaine municipality` |
| latvia | `ozolnieki municipality` |
| latvia | `pargauja municipality` |
| latvia | `pavilosta municipality` |
| latvia | `plavinas municipality` |
| latvia | `preili municipality` |
| latvia | `priekule municipality` |
| latvia | `priekuli municipality` |
| latvia | `rauna municipality` |
| latvia | `rezekne` |
| latvia | `rezekne municipality` |
| latvia | `riebini municipality` |
| latvia | `riga` |
| latvia | `roja municipality` |
| latvia | `ropa˛i municipality` |
| latvia | `rucava municipality` |
| latvia | `rugaji municipality` |
| latvia | `rujiena municipality` |
| latvia | `rundale municipality` |
| latvia | `sala municipality` |
| latvia | `salacgriva municipality` |
| latvia | `salaspils municipality` |
| latvia | `saldus municipality` |
| latvia | `saulkrasti municipality` |
| latvia | `seja municipality` |
| latvia | `sigulda municipality` |
| latvia | `skriveri municipality` |
| latvia | `skrunda municipality` |
| latvia | `smiltene municipality` |
| latvia | `stopini municipality` |
| latvia | `strenci municipality` |
| latvia | `talsi municipality` |
| latvia | `tervete municipality` |
| latvia | `tukums municipality` |
| latvia | `vainode municipality` |
| latvia | `valka municipality` |
| latvia | `valmiera` |
| latvia | `varaklani municipality` |
| latvia | `varkava municipality` |
| latvia | `vecpiebalga municipality` |
| latvia | `vecumnieki municipality` |
| latvia | `ventspils` |
| latvia | `ventspils municipality` |
| latvia | `viesite municipality` |
| latvia | `vilaka municipality` |
| latvia | `vilani municipality` |
| latvia | `zilupe municipality` |
| lebanon | `akkar` |
| lebanon | `baalbek-hermel` |
| lebanon | `beirut` |
| lebanon | `beqaa` |
| lebanon | `mount lebanon` |
| lebanon | `nabatieh` |
| lebanon | `north` |
| lebanon | `south` |
| lesotho | `berea district` |
| lesotho | `butha-buthe district` |
| lesotho | `leribe district` |
| lesotho | `mafeteng district` |
| lesotho | `maseru district` |
| lesotho | `mohale's hoek district` |
| lesotho | `mokhotlong district` |
| lesotho | `qacha's nek district` |
| lesotho | `quthing district` |
| lesotho | `thaba-tseka district` |
| liberia | `bomi county` |
| liberia | `bong county` |
| liberia | `gbarpolu county` |
| liberia | `grand bassa county` |
| liberia | `grand cape mount county` |
| liberia | `grand gedeh county` |
| liberia | `grand kru county` |
| liberia | `lofa county` |
| liberia | `margibi county` |
| liberia | `maryland county` |
| liberia | `montserrado county` |
| liberia | `nimba` |
| liberia | `river cess county` |
| liberia | `river gee county` |
| liberia | `sinoe county` |
| libya | `al wahat district` |
| libya | `benghazi` |
| libya | `derna district` |
| libya | `ghat district` |
| libya | `jabal al akhdar` |
| libya | `jabal al gharbi district` |
| libya | `jafara` |
| libya | `jufra` |
| libya | `kufra district` |
| libya | `marj district` |
| libya | `misrata district` |
| libya | `murqub` |
| libya | `murzuq district` |
| libya | `nalut district` |
| libya | `nuqat al khams` |
| libya | `sabha district` |
| libya | `sirte district` |
| libya | `tripoli district` |
| libya | `wadi al hayaa district` |
| libya | `wadi al shatii district` |
| libya | `zawiya district` |
| liechtenstein | `balzers` |
| liechtenstein | `eschen` |
| liechtenstein | `gamprin` |
| liechtenstein | `mauren` |
| liechtenstein | `planken` |
| liechtenstein | `ruggell` |
| liechtenstein | `schaan` |
| liechtenstein | `schellenberg` |
| liechtenstein | `triesen` |
| liechtenstein | `triesenberg` |
| liechtenstein | `vaduz` |
| lithuania | `akmene district municipality` |
| lithuania | `alytus city municipality` |
| lithuania | `alytus county` |
| lithuania | `alytus district municipality` |
| lithuania | `bir�tonas municipality` |
| lithuania | `bir˛ai district municipality` |
| lithuania | `druskininkai municipality` |
| lithuania | `elektrenai municipality` |
| lithuania | `ignalina district municipality` |
| lithuania | `jonava district municipality` |
| lithuania | `joni�kis district municipality` |
| lithuania | `jurbarkas district municipality` |
| lithuania | `kai�iadorys district municipality` |
| lithuania | `kalvarija municipality` |
| lithuania | `kaunas city municipality` |
| lithuania | `kaunas county` |
| lithuania | `kaunas district municipality` |
| lithuania | `kazlu ruda municipality` |
| lithuania | `kedainiai district municipality` |
| lithuania | `kelme district municipality` |
| lithuania | `klaipeda city municipality` |
| lithuania | `klaipeda county` |
| lithuania | `klaipeda district municipality` |
| lithuania | `kretinga district municipality` |
| lithuania | `kupi�kis district municipality` |
| lithuania | `lazdijai district municipality` |
| lithuania | `marijampole county` |
| lithuania | `marijampole municipality` |
| lithuania | `ma˛eikiai district municipality` |
| lithuania | `moletai district municipality` |
| lithuania | `neringa municipality` |
| lithuania | `pagegiai municipality` |
| lithuania | `pakruojis district municipality` |
| lithuania | `palanga city municipality` |
| lithuania | `paneve˛ys city municipality` |
| lithuania | `paneve˛ys county` |
| lithuania | `paneve˛ys district municipality` |
| lithuania | `pasvalys district municipality` |
| lithuania | `plunge district municipality` |
| lithuania | `prienai district municipality` |
| lithuania | `radvili�kis district municipality` |
| lithuania | `raseiniai district municipality` |
| lithuania | `rietavas municipality` |
| lithuania | `roki�kis district municipality` |
| lithuania | `�akiai district municipality` |
| lithuania | `�alcininkai district municipality` |
| lithuania | `�iauliai city municipality` |
| lithuania | `�iauliai county` |
| lithuania | `�iauliai district municipality` |
| lithuania | `�ilale district municipality` |
| lithuania | `�ilute district municipality` |
| lithuania | `�irvintos district municipality` |
| lithuania | `skuodas district municipality` |
| lithuania | `�vencionys district municipality` |
| lithuania | `taurage county` |
| lithuania | `taurage district municipality` |
| lithuania | `tel�iai county` |
| lithuania | `tel�iai district municipality` |
| lithuania | `trakai district municipality` |
| lithuania | `ukmerge district municipality` |
| lithuania | `utena county` |
| lithuania | `utena district municipality` |
| lithuania | `varena district municipality` |
| lithuania | `vilkavi�kis district municipality` |
| lithuania | `vilnius city municipality` |
| lithuania | `vilnius county` |
| lithuania | `vilnius district municipality` |
| lithuania | `visaginas municipality` |
| lithuania | `zarasai district municipality` |
| luxembourg | `canton of capellen` |
| luxembourg | `canton of clervaux` |
| luxembourg | `canton of diekirch` |
| luxembourg | `canton of echternach` |
| luxembourg | `canton of esch-sur-alzette` |
| luxembourg | `canton of grevenmacher` |
| luxembourg | `canton of luxembourg` |
| luxembourg | `canton of mersch` |
| luxembourg | `canton of redange` |
| luxembourg | `canton of remich` |
| luxembourg | `canton of vianden` |
| luxembourg | `canton of wiltz` |
| luxembourg | `diekirch district` |
| luxembourg | `grevenmacher district` |
| luxembourg | `luxembourg district` |
| madagascar | `antananarivo province` |
| madagascar | `antsiranana province` |
| madagascar | `fianarantsoa province` |
| madagascar | `mahajanga province` |
| madagascar | `toamasina province` |
| madagascar | `toliara province` |
| malawi | `balaka district` |
| malawi | `blantyre district` |
| malawi | `central region` |
| malawi | `chikwawa district` |
| malawi | `chiradzulu district` |
| malawi | `chitipa district` |
| malawi | `dedza district` |
| malawi | `dowa district` |
| malawi | `karonga district` |
| malawi | `kasungu district` |
| malawi | `likoma district` |
| malawi | `lilongwe district` |
| malawi | `machinga district` |
| malawi | `mangochi district` |
| malawi | `mchinji district` |
| malawi | `mulanje district` |
| malawi | `mwanza district` |
| malawi | `mzimba district` |
| malawi | `nkhata bay district` |
| malawi | `nkhotakota district` |
| malawi | `northern region` |
| malawi | `nsanje district` |
| malawi | `ntcheu district` |
| malawi | `ntchisi district` |
| malawi | `phalombe district` |
| malawi | `rumphi district` |
| malawi | `salima district` |
| malawi | `southern region` |
| malawi | `thyolo district` |
| malawi | `zomba district` |
| malaysia | `johor` |
| malaysia | `kedah` |
| malaysia | `kelantan` |
| malaysia | `kuala lumpur` |
| malaysia | `labuan` |
| malaysia | `malacca` |
| malaysia | `negeri sembilan` |
| malaysia | `pahang` |
| malaysia | `penang` |
| malaysia | `perak` |
| malaysia | `perlis` |
| malaysia | `putrajaya` |
| malaysia | `sabah` |
| malaysia | `sarawak` |
| malaysia | `selangor` |
| malaysia | `terengganu` |
| maldives | `addu atoll` |
| maldives | `alif alif atoll` |
| maldives | `alif dhaal atoll` |
| maldives | `central province` |
| maldives | `dhaalu atoll` |
| maldives | `faafu atoll` |
| maldives | `gaafu alif atoll` |
| maldives | `gaafu dhaalu atoll` |
| maldives | `gnaviyani atoll` |
| maldives | `haa alif atoll` |
| maldives | `haa dhaalu atoll` |
| maldives | `kaafu atoll` |
| maldives | `laamu atoll` |
| maldives | `lhaviyani atoll` |
| maldives | `malé` |
| maldives | `meemu atoll` |
| maldives | `noonu atoll` |
| maldives | `north central province` |
| maldives | `north province` |
| maldives | `raa atoll` |
| maldives | `shaviyani atoll` |
| maldives | `south central province` |
| maldives | `south province` |
| maldives | `thaa atoll` |
| maldives | `upper south province` |
| maldives | `vaavu atoll` |
| mali | `bamako` |
| mali | `gao region` |
| mali | `kayes region` |
| mali | `kidal region` |
| mali | `koulikoro region` |
| mali | `ménaka region` |
| mali | `mopti region` |
| mali | `ségou region` |
| mali | `sikasso region` |
| mali | `taoudénit region` |
| mali | `tombouctou region` |
| malta | `attard` |
| malta | `balzan` |
| malta | `birgu` |
| malta | `birkirkara` |
| malta | `birzebbuga` |
| malta | `cospicua` |
| malta | `dingli` |
| malta | `fgura` |
| malta | `floriana` |
| malta | `fontana` |
| malta | `ghajnsielem` |
| malta | `gharb` |
| malta | `gharghur` |
| malta | `ghasri` |
| malta | `ghaxaq` |
| malta | `gudja` |
| malta | `gzira` |
| malta | `hamrun` |
| malta | `iklin` |
| malta | `kalkara` |
| malta | `kercem` |
| malta | `kirkop` |
| malta | `lija` |
| malta | `luqa` |
| malta | `marsa` |
| malta | `marsaskala` |
| malta | `marsaxlokk` |
| malta | `mdina` |
| malta | `mellieha` |
| malta | `mgarr` |
| malta | `mosta` |
| malta | `mqabba` |
| malta | `msida` |
| malta | `mtarfa` |
| malta | `munxar` |
| malta | `nadur` |
| malta | `naxxar` |
| malta | `paola` |
| malta | `pembroke` |
| malta | `pietą` |
| malta | `qala` |
| malta | `qormi` |
| malta | `qrendi` |
| malta | `rabat` |
| malta | `saint lawrence` |
| malta | `san gwann` |
| malta | `sannat` |
| malta | `santa lucija` |
| malta | `santa venera` |
| malta | `senglea` |
| malta | `siggiewi` |
| malta | `sliema` |
| malta | `st. julian's` |
| malta | `st. paul's bay` |
| malta | `swieqi` |
| malta | `ta' xbiex` |
| malta | `tarxien` |
| malta | `valletta` |
| malta | `victoria` |
| malta | `xaghra` |
| malta | `xewkija` |
| malta | `xghajra` |
| malta | `zabbar` |
| malta | `zebbug gozo` |
| malta | `zebbug malta` |
| malta | `zejtun` |
| malta | `zurrieq` |
| marshall islands | `ralik chain` |
| marshall islands | `ratak chain` |
| mauritania | `adrar` |
| mauritania | `assaba` |
| mauritania | `brakna` |
| mauritania | `dakhlet nouadhibou` |
| mauritania | `gorgol` |
| mauritania | `guidimaka` |
| mauritania | `hodh ech chargui` |
| mauritania | `hodh el gharbi` |
| mauritania | `inchiri` |
| mauritania | `nouakchott-nord` |
| mauritania | `nouakchott-ouest` |
| mauritania | `nouakchott-sud` |
| mauritania | `tagant` |
| mauritania | `tiris zemmour` |
| mauritania | `trarza` |
| mauritius | `agalega islands` |
| mauritius | `black river` |
| mauritius | `flacq` |
| mauritius | `grand port` |
| mauritius | `moka` |
| mauritius | `pamplemousses` |
| mauritius | `plaines wilhems` |
| mauritius | `port louis` |
| mauritius | `rivičre du rempart` |
| mauritius | `rodrigues island` |
| mauritius | `saint brandon islands` |
| mauritius | `savanne` |
| mexico | `aguascalientes` |
| mexico | `baja california` |
| mexico | `baja california sur` |
| mexico | `campeche` |
| mexico | `chiapas` |
| mexico | `chihuahua` |
| mexico | `ciudad de méxico` |
| mexico | `coahuila de zaragoza` |
| mexico | `colima` |
| mexico | `durango` |
| mexico | `estado de méxico` |
| mexico | `guanajuato` |
| mexico | `guerrero` |
| mexico | `hidalgo` |
| mexico | `jalisco` |
| mexico | `michoacįn de ocampo` |
| mexico | `morelos` |
| mexico | `nayarit` |
| mexico | `nuevo león` |
| mexico | `oaxaca` |
| mexico | `puebla` |
| mexico | `querétaro` |
| mexico | `quintana roo` |
| mexico | `san luis potosķ` |
| mexico | `sinaloa` |
| mexico | `sonora` |
| mexico | `tabasco` |
| mexico | `tamaulipas` |
| mexico | `tlaxcala` |
| mexico | `veracruz de ignacio de la llave` |
| mexico | `yucatįn` |
| mexico | `zacatecas` |
| micronesia | `chuuk state` |
| micronesia | `kosrae state` |
| micronesia | `pohnpei state` |
| micronesia | `yap state` |
| moldova | `anenii noi district` |
| moldova | `balti municipality` |
| moldova | `basarabeasca district` |
| moldova | `bender municipality` |
| moldova | `briceni district` |
| moldova | `cahul district` |
| moldova | `calarasi district` |
| moldova | `cantemir district` |
| moldova | `causeni district` |
| moldova | `chisinau municipality` |
| moldova | `cimislia district` |
| moldova | `criuleni district` |
| moldova | `donduseni district` |
| moldova | `drochia district` |
| moldova | `dubasari district` |
| moldova | `edinet district` |
| moldova | `falesti district` |
| moldova | `floresti district` |
| moldova | `gagauzia` |
| moldova | `glodeni district` |
| moldova | `hīncesti district` |
| moldova | `ialoveni district` |
| moldova | `nisporeni district` |
| moldova | `ocnita district` |
| moldova | `orhei district` |
| moldova | `rezina district` |
| moldova | `rīscani district` |
| moldova | `sīngerei district` |
| moldova | `soldanesti district` |
| moldova | `soroca district` |
| moldova | `stefan voda district` |
| moldova | `straseni district` |
| moldova | `taraclia district` |
| moldova | `telenesti district` |
| moldova | `transnistria autonomous territorial unit` |
| moldova | `ungheni district` |
| monaco | `la colle` |
| monaco | `la condamine` |
| monaco | `moneghetti` |
| mongolia | `arkhangai province` |
| mongolia | `bayan-ölgii province` |
| mongolia | `bayankhongor province` |
| mongolia | `bulgan province` |
| mongolia | `darkhan-uul province` |
| mongolia | `dornod province` |
| mongolia | `dornogovi province` |
| mongolia | `dundgovi province` |
| mongolia | `govi-altai province` |
| mongolia | `govisümber province` |
| mongolia | `khentii province` |
| mongolia | `khovd province` |
| mongolia | `khövsgöl province` |
| mongolia | `ömnögovi province` |
| mongolia | `orkhon province` |
| mongolia | `övörkhangai province` |
| mongolia | `selenge province` |
| mongolia | `sükhbaatar province` |
| mongolia | `töv province` |
| mongolia | `uvs province` |
| mongolia | `zavkhan province` |
| montenegro | `andrijevica municipality` |
| montenegro | `bar municipality` |
| montenegro | `berane municipality` |
| montenegro | `bijelo polje municipality` |
| montenegro | `budva municipality` |
| montenegro | `danilovgrad municipality` |
| montenegro | `gusinje municipality` |
| montenegro | `kola�in municipality` |
| montenegro | `kotor municipality` |
| montenegro | `mojkovac municipality` |
| montenegro | `nik�ic municipality` |
| montenegro | `old royal capital cetinje` |
| montenegro | `petnjica municipality` |
| montenegro | `plav municipality` |
| montenegro | `pljevlja municipality` |
| montenegro | `plu˛ine municipality` |
| montenegro | `podgorica municipality` |
| montenegro | `ro˛aje municipality` |
| montenegro | `�avnik municipality` |
| montenegro | `tivat municipality` |
| montenegro | `ulcinj municipality` |
| montenegro | `ˇabljak municipality` |
| morocco | `agadir-ida-ou-tanane` |
| morocco | `al haouz` |
| morocco | `al hoceļma` |
| morocco | `aousserd (eh)` |
| morocco | `assa-zag (eh-partial)` |
| morocco | `azilal` |
| morocco | `béni mellal` |
| morocco | `béni mellal-khénifra` |
| morocco | `benslimane` |
| morocco | `berkane` |
| morocco | `berrechid` |
| morocco | `boujdour (eh)` |
| morocco | `boulemane` |
| morocco | `casablanca` |
| morocco | `casablanca-settat` |
| morocco | `chefchaouen` |
| morocco | `chichaoua` |
| morocco | `chtouka-ait baha` |
| morocco | `dakhla-oued ed-dahab (eh)` |
| morocco | `drāa-tafilalet` |
| morocco | `driouch` |
| morocco | `el hajeb` |
| morocco | `el jadida` |
| morocco | `el kelāa des sraghna` |
| morocco | `errachidia` |
| morocco | `es-semara (eh-partial)` |
| morocco | `essaouira` |
| morocco | `fahs-anjra` |
| morocco | `fčs` |
| morocco | `fčs-meknčs` |
| morocco | `figuig` |
| morocco | `fquih ben salah` |
| morocco | `guelmim` |
| morocco | `guelmim-oued noun (eh-partial)` |
| morocco | `guercif` |
| morocco | `ifrane` |
| morocco | `inezgane-ait melloul` |
| morocco | `jerada` |
| morocco | `kénitra` |
| morocco | `khémisset` |
| morocco | `khénifra` |
| morocco | `khouribga` |
| morocco | `l'oriental` |
| morocco | `laāyoune (eh)` |
| morocco | `laāyoune-sakia el hamra (eh-partial)` |
| morocco | `larache` |
| morocco | `m’diq-fnideq` |
| morocco | `marrakech` |
| morocco | `marrakesh-safi` |
| morocco | `médiouna` |
| morocco | `meknčs` |
| morocco | `midelt` |
| morocco | `mohammadia` |
| morocco | `moulay yacoub` |
| morocco | `nador` |
| morocco | `nouaceur` |
| morocco | `ouarzazate` |
| morocco | `oued ed-dahab (eh)` |
| morocco | `ouezzane` |
| morocco | `oujda-angad` |
| morocco | `rabat` |
| morocco | `rabat-salé-kénitra` |
| morocco | `rehamna` |
| morocco | `safi` |
| morocco | `salé` |
| morocco | `sefrou` |
| morocco | `settat` |
| morocco | `sidi bennour` |
| morocco | `sidi ifni` |
| morocco | `sidi kacem` |
| morocco | `sidi slimane` |
| morocco | `skhirate-témara` |
| morocco | `souss-massa` |
| morocco | `tan-tan (eh-partial)` |
| morocco | `tanger-assilah` |
| morocco | `tanger-tétouan-al hoceļma` |
| morocco | `taounate` |
| morocco | `taourirt` |
| morocco | `tarfaya (eh-partial)` |
| morocco | `taroudannt` |
| morocco | `tata` |
| morocco | `taza` |
| morocco | `tétouan` |
| morocco | `tinghir` |
| morocco | `tiznit` |
| morocco | `youssoufia` |
| morocco | `zagora` |
| mozambique | `cabo delgado province` |
| mozambique | `gaza province` |
| mozambique | `inhambane province` |
| mozambique | `manica province` |
| mozambique | `maputo` |
| mozambique | `maputo province` |
| mozambique | `nampula province` |
| mozambique | `niassa province` |
| mozambique | `sofala province` |
| mozambique | `tete province` |
| mozambique | `zambezia province` |
| myanmar | `ayeyarwady region` |
| myanmar | `bago` |
| myanmar | `chin state` |
| myanmar | `kachin state` |
| myanmar | `kayah state` |
| myanmar | `kayin state` |
| myanmar | `magway region` |
| myanmar | `mandalay region` |
| myanmar | `mon state` |
| myanmar | `naypyidaw union territory` |
| myanmar | `rakhine state` |
| myanmar | `sagaing region` |
| myanmar | `shan state` |
| myanmar | `tanintharyi region` |
| myanmar | `yangon region` |
| namibia | `erongo region` |
| namibia | `hardap region` |
| namibia | `karas region` |
| namibia | `kavango east region` |
| namibia | `kavango west region` |
| namibia | `khomas region` |
| namibia | `kunene region` |
| namibia | `ohangwena region` |
| namibia | `omaheke region` |
| namibia | `omusati region` |
| namibia | `oshana region` |
| namibia | `oshikoto region` |
| namibia | `otjozondjupa region` |
| namibia | `zambezi region` |
| nauru | `aiwo district` |
| nauru | `anabar district` |
| nauru | `anetan district` |
| nauru | `anibare district` |
| nauru | `baiti district` |
| nauru | `boe district` |
| nauru | `buada district` |
| nauru | `denigomodu district` |
| nauru | `ewa district` |
| nauru | `ijuw district` |
| nauru | `meneng district` |
| nauru | `nibok district` |
| nauru | `uaboe district` |
| nauru | `yaren district` |
| nepal | `bagmati zone` |
| nepal | `bheri zone` |
| nepal | `central region` |
| nepal | `dhaulagiri zone` |
| nepal | `eastern development region` |
| nepal | `far-western development region` |
| nepal | `gandaki zone` |
| nepal | `janakpur zone` |
| nepal | `karnali zone` |
| nepal | `kosi zone` |
| nepal | `lumbini zone` |
| nepal | `mahakali zone` |
| nepal | `mechi zone` |
| nepal | `mid-western region` |
| nepal | `narayani zone` |
| nepal | `rapti zone` |
| nepal | `sagarmatha zone` |
| nepal | `seti zone` |
| nepal | `western region` |
| netherlands | `bonaire` |
| netherlands | `drenthe` |
| netherlands | `flevoland` |
| netherlands | `friesland` |
| netherlands | `gelderland` |
| netherlands | `groningen` |
| netherlands | `limburg` |
| netherlands | `north brabant` |
| netherlands | `north holland` |
| netherlands | `overijssel` |
| netherlands | `saba` |
| netherlands | `sint eustatius` |
| netherlands | `south holland` |
| netherlands | `utrecht` |
| netherlands | `zeeland` |
| new caledonia | `loyalty islands province` |
| new caledonia | `north province` |
| new caledonia | `south province` |
| new zealand | `auckland region` |
| new zealand | `bay of plenty region` |
| new zealand | `canterbury region` |
| new zealand | `chatham islands` |
| new zealand | `gisborne district` |
| new zealand | `hawke's bay region` |
| new zealand | `manawatu-wanganui region` |
| new zealand | `marlborough region` |
| new zealand | `nelson region` |
| new zealand | `northland region` |
| new zealand | `otago region` |
| new zealand | `southland region` |
| new zealand | `taranaki region` |
| new zealand | `tasman district` |
| new zealand | `waikato region` |
| new zealand | `wellington region` |
| new zealand | `west coast region` |
| nicaragua | `boaco` |
| nicaragua | `carazo` |
| nicaragua | `chinandega` |
| nicaragua | `chontales` |
| nicaragua | `estelķ` |
| nicaragua | `granada` |
| nicaragua | `jinotega` |
| nicaragua | `león` |
| nicaragua | `madriz` |
| nicaragua | `managua` |
| nicaragua | `masaya` |
| nicaragua | `matagalpa` |
| nicaragua | `north caribbean coast` |
| nicaragua | `nueva segovia` |
| nicaragua | `rķo san juan` |
| nicaragua | `rivas` |
| nicaragua | `south caribbean coast` |
| niger | `agadez region` |
| niger | `diffa region` |
| niger | `dosso region` |
| niger | `maradi region` |
| niger | `tahoua region` |
| niger | `tillabéri region` |
| niger | `zinder region` |
| nigeria | `abia` |
| nigeria | `abuja federal capital territory` |
| nigeria | `adamawa` |
| nigeria | `akwa ibom` |
| nigeria | `anambra` |
| nigeria | `bauchi` |
| nigeria | `bayelsa` |
| nigeria | `benue` |
| nigeria | `borno` |
| nigeria | `cross river` |
| nigeria | `delta` |
| nigeria | `ebonyi` |
| nigeria | `edo` |
| nigeria | `ekiti` |
| nigeria | `enugu` |
| nigeria | `gombe` |
| nigeria | `imo` |
| nigeria | `jigawa` |
| nigeria | `kaduna` |
| nigeria | `kano` |
| nigeria | `katsina` |
| nigeria | `kebbi` |
| nigeria | `kogi` |
| nigeria | `kwara` |
| nigeria | `lagos` |
| nigeria | `nasarawa` |
| nigeria | `niger` |
| nigeria | `ogun` |
| nigeria | `ondo` |
| nigeria | `osun` |
| nigeria | `oyo` |
| nigeria | `plateau` |
| nigeria | `rivers` |
| nigeria | `sokoto` |
| nigeria | `taraba` |
| nigeria | `yobe` |
| nigeria | `zamfara` |
| north korea | `chagang province` |
| north korea | `kangwon province` |
| north korea | `north hamgyong province` |
| north korea | `north hwanghae province` |
| north korea | `north pyongan province` |
| north korea | `pyongyang` |
| north korea | `rason` |
| north korea | `ryanggang province` |
| north korea | `south hamgyong province` |
| north korea | `south hwanghae province` |
| north korea | `south pyongan province` |
| north macedonia | `aerodrom municipality` |
| north macedonia | `aracinovo municipality` |
| north macedonia | `berovo municipality` |
| north macedonia | `bitola municipality` |
| north macedonia | `bogdanci municipality` |
| north macedonia | `bogovinje municipality` |
| north macedonia | `bosilovo municipality` |
| north macedonia | `brvenica municipality` |
| north macedonia | `butel municipality` |
| north macedonia | `cair municipality` |
| north macedonia | `ca�ka municipality` |
| north macedonia | `centar municipality` |
| north macedonia | `centar ˇupa municipality` |
| north macedonia | `ce�inovo-oble�evo municipality` |
| north macedonia | `cucer-sandevo municipality` |
| north macedonia | `debarca municipality` |
| north macedonia | `delcevo municipality` |
| north macedonia | `demir hisar municipality` |
| north macedonia | `demir kapija municipality` |
| north macedonia | `dojran municipality` |
| north macedonia | `dolneni municipality` |
| north macedonia | `drugovo municipality` |
| north macedonia | `gazi baba municipality` |
| north macedonia | `gevgelija municipality` |
| north macedonia | `gjorce petrov municipality` |
| north macedonia | `gostivar municipality` |
| north macedonia | `gradsko municipality` |
| north macedonia | `greater skopje` |
| north macedonia | `ilinden municipality` |
| north macedonia | `jegunovce municipality` |
| north macedonia | `karbinci` |
| north macedonia | `karpo� municipality` |
| north macedonia | `kavadarci municipality` |
| north macedonia | `kicevo municipality` |
| north macedonia | `kisela voda municipality` |
| north macedonia | `kocani municipality` |
| north macedonia | `konce municipality` |
| north macedonia | `kratovo municipality` |
| north macedonia | `kriva palanka municipality` |
| north macedonia | `krivoga�tani municipality` |
| north macedonia | `kru�evo municipality` |
| north macedonia | `kumanovo municipality` |
| north macedonia | `lipkovo municipality` |
| north macedonia | `lozovo municipality` |
| north macedonia | `makedonska kamenica municipality` |
| north macedonia | `makedonski brod municipality` |
| north macedonia | `mavrovo and rostu�a municipality` |
| north macedonia | `mogila municipality` |
| north macedonia | `negotino municipality` |
| north macedonia | `novaci municipality` |
| north macedonia | `novo selo municipality` |
| north macedonia | `ohrid municipality` |
| north macedonia | `oslomej municipality` |
| north macedonia | `pehcevo municipality` |
| north macedonia | `petrovec municipality` |
| north macedonia | `plasnica municipality` |
| north macedonia | `prilep municipality` |
| north macedonia | `probi�tip municipality` |
| north macedonia | `radovi� municipality` |
| north macedonia | `rankovce municipality` |
| north macedonia | `resen municipality` |
| north macedonia | `rosoman municipality` |
| north macedonia | `saraj municipality` |
| north macedonia | `sopi�te municipality` |
| north macedonia | `staro nagoricane municipality` |
| north macedonia | `�tip municipality` |
| north macedonia | `struga municipality` |
| north macedonia | `strumica municipality` |
| north macedonia | `studenicani municipality` |
| north macedonia | `�uto orizari municipality` |
| north macedonia | `sveti nikole municipality` |
| north macedonia | `tearce municipality` |
| north macedonia | `tetovo municipality` |
| north macedonia | `valandovo municipality` |
| north macedonia | `vasilevo municipality` |
| north macedonia | `veles municipality` |
| north macedonia | `vevcani municipality` |
| north macedonia | `vinica municipality` |
| north macedonia | `vrane�tica municipality` |
| north macedonia | `vrapci�te municipality` |
| north macedonia | `zajas municipality` |
| north macedonia | `zelenikovo municipality` |
| north macedonia | `ˇelino municipality` |
| north macedonia | `zrnovci municipality` |
| norway | `agder` |
| norway | `innlandet` |
| norway | `jan mayen` |
| norway | `mųre og romsdal` |
| norway | `nordland` |
| norway | `oslo` |
| norway | `rogaland` |
| norway | `svalbard` |
| norway | `troms og finnmark` |
| norway | `trųndelag` |
| norway | `vestfold og telemark` |
| norway | `vestland` |
| norway | `viken` |
| oman | `ad dakhiliyah` |
| oman | `ad dhahirah` |
| oman | `al batinah north` |
| oman | `al batinah region` |
| oman | `al batinah south` |
| oman | `al buraimi` |
| oman | `al wusta` |
| oman | `ash sharqiyah north` |
| oman | `ash sharqiyah region` |
| oman | `ash sharqiyah south` |
| oman | `dhofar` |
| oman | `musandam` |
| oman | `muscat` |
| pakistan | `azad kashmir` |
| pakistan | `balochistan` |
| pakistan | `federally administered tribal areas` |
| pakistan | `gilgit-baltistan` |
| pakistan | `islamabad capital territory` |
| pakistan | `khyber pakhtunkhwa` |
| pakistan | `punjab` |
| pakistan | `sindh` |
| palau | `aimeliik` |
| palau | `airai` |
| palau | `angaur` |
| palau | `hatohobei` |
| palau | `kayangel` |
| palau | `koror` |
| palau | `melekeok` |
| palau | `ngaraard` |
| palau | `ngarchelong` |
| palau | `ngardmau` |
| palau | `ngatpang` |
| palau | `ngchesar` |
| palau | `ngeremlengui` |
| palau | `ngiwal` |
| palau | `peleliu` |
| palau | `sonsorol` |
| palestinian territory occupied | `bethlehem` |
| palestinian territory occupied | `deir el balah` |
| palestinian territory occupied | `gaza` |
| palestinian territory occupied | `hebron` |
| palestinian territory occupied | `jenin` |
| palestinian territory occupied | `jericho and al aghwar` |
| palestinian territory occupied | `jerusalem` |
| palestinian territory occupied | `khan yunis` |
| palestinian territory occupied | `nablus` |
| palestinian territory occupied | `north gaza` |
| palestinian territory occupied | `qalqilya` |
| palestinian territory occupied | `rafah` |
| palestinian territory occupied | `ramallah` |
| palestinian territory occupied | `salfit` |
| palestinian territory occupied | `tubas` |
| palestinian territory occupied | `tulkarm` |
| panama | `bocas del toro province` |
| panama | `chiriquķ province` |
| panama | `coclé province` |
| panama | `colón province` |
| panama | `darién province` |
| panama | `emberį-wounaan comarca` |
| panama | `guna yala` |
| panama | `herrera province` |
| panama | `los santos province` |
| panama | `ngöbe-buglé comarca` |
| panama | `panamį oeste province` |
| panama | `panamį province` |
| panama | `veraguas province` |
| papua new guinea | `bougainville` |
| papua new guinea | `central province` |
| papua new guinea | `chimbu province` |
| papua new guinea | `east new britain` |
| papua new guinea | `eastern highlands province` |
| papua new guinea | `enga province` |
| papua new guinea | `gulf` |
| papua new guinea | `hela` |
| papua new guinea | `jiwaka province` |
| papua new guinea | `madang province` |
| papua new guinea | `manus province` |
| papua new guinea | `milne bay province` |
| papua new guinea | `morobe province` |
| papua new guinea | `new ireland province` |
| papua new guinea | `oro province` |
| papua new guinea | `port moresby` |
| papua new guinea | `sandaun province` |
| papua new guinea | `southern highlands province` |
| papua new guinea | `west new britain province` |
| papua new guinea | `western highlands province` |
| papua new guinea | `western province` |
| paraguay | `alto paraguay department` |
| paraguay | `alto paranį department` |
| paraguay | `amambay department` |
| paraguay | `asuncion` |
| paraguay | `boquerón department` |
| paraguay | `caaguazś` |
| paraguay | `caazapį` |
| paraguay | `canindeyś` |
| paraguay | `central department` |
| paraguay | `concepción department` |
| paraguay | `cordillera department` |
| paraguay | `guairį department` |
| paraguay | `itapśa` |
| paraguay | `misiones department` |
| paraguay | `ńeembucś department` |
| paraguay | `paraguarķ department` |
| paraguay | `presidente hayes department` |
| paraguay | `san pedro department` |
| peru | `amazonas` |
| peru | `įncash` |
| peru | `apurķmac` |
| peru | `arequipa` |
| peru | `ayacucho` |
| peru | `cajamarca` |
| peru | `callao` |
| peru | `cusco` |
| peru | `huancavelica` |
| peru | `huanuco` |
| peru | `ica` |
| peru | `junķn` |
| peru | `la libertad` |
| peru | `lambayeque` |
| peru | `lima` |
| peru | `loreto` |
| peru | `madre de dios` |
| peru | `moquegua` |
| peru | `pasco` |
| peru | `piura` |
| peru | `puno` |
| peru | `san martķn` |
| peru | `tacna` |
| peru | `tumbes` |
| peru | `ucayali` |
| philippines | `abra` |
| philippines | `agusan del norte` |
| philippines | `agusan del sur` |
| philippines | `aklan` |
| philippines | `albay` |
| philippines | `antique` |
| philippines | `apayao` |
| philippines | `aurora` |
| philippines | `autonomous region in muslim mindanao` |
| philippines | `basilan` |
| philippines | `bataan` |
| philippines | `batanes` |
| philippines | `batangas` |
| philippines | `benguet` |
| philippines | `bicol` |
| philippines | `biliran` |
| philippines | `bohol` |
| philippines | `bukidnon` |
| philippines | `bulacan` |
| philippines | `cagayan` |
| philippines | `cagayan valley` |
| philippines | `calabarzon` |
| philippines | `camarines norte` |
| philippines | `camarines sur` |
| philippines | `camiguin` |
| philippines | `capiz` |
| philippines | `caraga` |
| philippines | `catanduanes` |
| philippines | `cavite` |
| philippines | `cebu` |
| philippines | `central luzon` |
| philippines | `central visayas` |
| philippines | `compostela valley` |
| philippines | `cordillera administrative` |
| philippines | `cotabato` |
| philippines | `davao` |
| philippines | `davao del norte` |
| philippines | `davao del sur` |
| philippines | `davao occidental` |
| philippines | `davao oriental` |
| philippines | `dinagat islands` |
| philippines | `eastern samar` |
| philippines | `eastern visayas` |
| philippines | `guimaras` |
| philippines | `ifugao` |
| philippines | `ilocos` |
| philippines | `ilocos norte` |
| philippines | `ilocos sur` |
| philippines | `iloilo` |
| philippines | `isabela` |
| philippines | `kalinga` |
| philippines | `la union` |
| philippines | `laguna` |
| philippines | `lanao del norte` |
| philippines | `lanao del sur` |
| philippines | `leyte` |
| philippines | `maguindanao` |
| philippines | `marinduque` |
| philippines | `masbate` |
| philippines | `metro manila` |
| philippines | `mimaropa` |
| philippines | `misamis occidental` |
| philippines | `misamis oriental` |
| philippines | `mountain province` |
| philippines | `negros occidental` |
| philippines | `negros oriental` |
| philippines | `northern mindanao` |
| philippines | `northern samar` |
| philippines | `nueva ecija` |
| philippines | `nueva vizcaya` |
| philippines | `occidental mindoro` |
| philippines | `oriental mindoro` |
| philippines | `palawan` |
| philippines | `pampanga` |
| philippines | `pangasinan` |
| philippines | `quezon` |
| philippines | `quirino` |
| philippines | `rizal` |
| philippines | `romblon` |
| philippines | `sarangani` |
| philippines | `siquijor` |
| philippines | `soccsksargen` |
| philippines | `sorsogon` |
| philippines | `south cotabato` |
| philippines | `southern leyte` |
| philippines | `sultan kudarat` |
| philippines | `sulu` |
| philippines | `surigao del norte` |
| philippines | `surigao del sur` |
| philippines | `tarlac` |
| philippines | `tawi-tawi` |
| philippines | `western samar` |
| philippines | `western visayas` |
| philippines | `zambales` |
| philippines | `zamboanga del norte` |
| philippines | `zamboanga del sur` |
| philippines | `zamboanga peninsula` |
| philippines | `zamboanga sibugay` |
| poland | `greater poland voivodeship` |
| poland | `kuyavian-pomeranian voivodeship` |
| poland | `lesser poland voivodeship` |
| poland | `lower silesian voivodeship` |
| poland | `lublin voivodeship` |
| poland | `lubusz voivodeship` |
| poland | `lódz voivodeship` |
| poland | `masovian voivodeship` |
| poland | `opole voivodeship` |
| poland | `podkarpackie voivodeship` |
| poland | `podlaskie voivodeship` |
| poland | `pomeranian voivodeship` |
| poland | `silesian voivodeship` |
| poland | `swietokrzyskie voivodeship` |
| poland | `warmian-masurian voivodeship` |
| poland | `west pomeranian voivodeship` |
| portugal | `aēores` |
| portugal | `aveiro` |
| portugal | `beja` |
| portugal | `braga` |
| portugal | `braganēa` |
| portugal | `castelo branco` |
| portugal | `coimbra` |
| portugal | `évora` |
| portugal | `faro` |
| portugal | `guarda` |
| portugal | `leiria` |
| portugal | `lisbon` |
| portugal | `madeira` |
| portugal | `portalegre` |
| portugal | `porto` |
| portugal | `santarém` |
| portugal | `setśbal` |
| portugal | `viana do castelo` |
| portugal | `vila real` |
| portugal | `viseu` |
| puerto rico | `adjuntas` |
| puerto rico | `aguada` |
| puerto rico | `aguadilla` |
| puerto rico | `aguas buenas` |
| puerto rico | `aibonito` |
| puerto rico | `ańasco` |
| puerto rico | `arecibo` |
| puerto rico | `arecibo` |
| puerto rico | `arroyo` |
| puerto rico | `barceloneta` |
| puerto rico | `barranquitas` |
| puerto rico | `bayamon` |
| puerto rico | `bayamón` |
| puerto rico | `cabo rojo` |
| puerto rico | `caguas` |
| puerto rico | `caguas` |
| puerto rico | `camuy` |
| puerto rico | `canóvanas` |
| puerto rico | `carolina` |
| puerto rico | `carolina` |
| puerto rico | `catańo` |
| puerto rico | `cayey` |
| puerto rico | `ceiba` |
| puerto rico | `ciales` |
| puerto rico | `cidra` |
| puerto rico | `coamo` |
| puerto rico | `comerķo` |
| puerto rico | `corozal` |
| puerto rico | `culebra` |
| puerto rico | `dorado` |
| puerto rico | `fajardo` |
| puerto rico | `florida` |
| puerto rico | `guįnica` |
| puerto rico | `guayama` |
| puerto rico | `guayanilla` |
| puerto rico | `guaynabo` |
| puerto rico | `guaynabo` |
| puerto rico | `gurabo` |
| puerto rico | `hatillo` |
| puerto rico | `hormigueros` |
| puerto rico | `humacao` |
| puerto rico | `isabela` |
| puerto rico | `jayuya` |
| puerto rico | `juana dķaz` |
| puerto rico | `juncos` |
| puerto rico | `lajas` |
| puerto rico | `lares` |
| puerto rico | `las marķas` |
| puerto rico | `las piedras` |
| puerto rico | `loķza` |
| puerto rico | `luquillo` |
| puerto rico | `manatķ` |
| puerto rico | `maricao` |
| puerto rico | `maunabo` |
| puerto rico | `mayagüez` |
| puerto rico | `mayagüez` |
| puerto rico | `moca` |
| puerto rico | `morovis` |
| puerto rico | `naguabo` |
| puerto rico | `naranjito` |
| puerto rico | `orocovis` |
| puerto rico | `patillas` |
| puerto rico | `peńuelas` |
| puerto rico | `ponce` |
| puerto rico | `ponce` |
| puerto rico | `quebradillas` |
| puerto rico | `rincón` |
| puerto rico | `rķo grande` |
| puerto rico | `sabana grande` |
| puerto rico | `salinas` |
| puerto rico | `san germįn` |
| puerto rico | `san juan` |
| puerto rico | `san juan` |
| puerto rico | `san lorenzo` |
| puerto rico | `san sebastiįn` |
| puerto rico | `santa isabel` |
| puerto rico | `toa alta` |
| puerto rico | `toa baja` |
| puerto rico | `toa baja` |
| puerto rico | `trujillo alto` |
| puerto rico | `trujillo alto` |
| puerto rico | `utuado` |
| puerto rico | `vega alta` |
| puerto rico | `vega baja` |
| puerto rico | `vieques` |
| puerto rico | `villalba` |
| puerto rico | `yabucoa` |
| puerto rico | `yauco` |
| qatar | `al daayen` |
| qatar | `al khor` |
| qatar | `al rayyan municipality` |
| qatar | `al wakrah` |
| qatar | `al-shahaniya` |
| qatar | `doha` |
| qatar | `madinat ash shamal` |
| qatar | `umm salal municipality` |
| romania | `alba` |
| romania | `arad county` |
| romania | `arges` |
| romania | `bacau county` |
| romania | `bihor county` |
| romania | `bistrita-nasaud county` |
| romania | `botosani county` |
| romania | `braila` |
| romania | `brasov county` |
| romania | `bucharest` |
| romania | `buzau county` |
| romania | `calarasi county` |
| romania | `caras-severin county` |
| romania | `cluj county` |
| romania | `constanta county` |
| romania | `covasna county` |
| romania | `dāmbovita county` |
| romania | `dolj county` |
| romania | `galati county` |
| romania | `giurgiu county` |
| romania | `gorj county` |
| romania | `harghita county` |
| romania | `hunedoara county` |
| romania | `ialomita county` |
| romania | `iasi county` |
| romania | `ilfov county` |
| romania | `maramures county` |
| romania | `mehedinti county` |
| romania | `mures county` |
| romania | `neamt county` |
| romania | `olt county` |
| romania | `prahova county` |
| romania | `salaj county` |
| romania | `satu mare county` |
| romania | `sibiu county` |
| romania | `suceava county` |
| romania | `teleorman county` |
| romania | `timis county` |
| romania | `tulcea county` |
| romania | `vālcea county` |
| romania | `vaslui county` |
| romania | `vrancea county` |
| russia | `altai krai` |
| russia | `altai republic` |
| russia | `amur oblast` |
| russia | `arkhangelsk` |
| russia | `astrakhan oblast` |
| russia | `belgorod oblast` |
| russia | `bryansk oblast` |
| russia | `chechen republic` |
| russia | `chelyabinsk oblast` |
| russia | `chukotka autonomous okrug` |
| russia | `chuvash republic` |
| russia | `irkutsk` |
| russia | `ivanovo oblast` |
| russia | `jewish autonomous oblast` |
| russia | `kabardino-balkar republic` |
| russia | `kaliningrad` |
| russia | `kaluga oblast` |
| russia | `kamchatka krai` |
| russia | `karachay-cherkess republic` |
| russia | `kemerovo oblast` |
| russia | `khabarovsk krai` |
| russia | `khanty-mansi autonomous okrug` |
| russia | `kirov oblast` |
| russia | `komi republic` |
| russia | `kostroma oblast` |
| russia | `krasnodar krai` |
| russia | `krasnoyarsk krai` |
| russia | `kurgan oblast` |
| russia | `kursk oblast` |
| russia | `leningrad oblast` |
| russia | `lipetsk oblast` |
| russia | `magadan oblast` |
| russia | `mari el republic` |
| russia | `moscow` |
| russia | `moscow oblast` |
| russia | `murmansk oblast` |
| russia | `nenets autonomous okrug` |
| russia | `nizhny novgorod oblast` |
| russia | `novgorod oblast` |
| russia | `novosibirsk` |
| russia | `omsk oblast` |
| russia | `orenburg oblast` |
| russia | `oryol oblast` |
| russia | `penza oblast` |
| russia | `perm krai` |
| russia | `primorsky krai` |
| russia | `pskov oblast` |
| russia | `republic of adygea` |
| russia | `republic of bashkortostan` |
| russia | `republic of buryatia` |
| russia | `republic of dagestan` |
| russia | `republic of ingushetia` |
| russia | `republic of kalmykia` |
| russia | `republic of karelia` |
| russia | `republic of khakassia` |
| russia | `republic of mordovia` |
| russia | `republic of north ossetia-alania` |
| russia | `republic of tatarstan` |
| russia | `rostov oblast` |
| russia | `ryazan oblast` |
| russia | `saint petersburg` |
| russia | `sakha republic` |
| russia | `sakhalin` |
| russia | `samara oblast` |
| russia | `saratov oblast` |
| russia | `smolensk oblast` |
| russia | `stavropol krai` |
| russia | `sverdlovsk` |
| russia | `tambov oblast` |
| russia | `tomsk oblast` |
| russia | `tula oblast` |
| russia | `tuva republic` |
| russia | `tver oblast` |
| russia | `tyumen oblast` |
| russia | `udmurt republic` |
| russia | `ulyanovsk oblast` |
| russia | `vladimir oblast` |
| russia | `volgograd oblast` |
| russia | `vologda oblast` |
| russia | `voronezh oblast` |
| russia | `yamalo-nenets autonomous okrug` |
| russia | `yaroslavl oblast` |
| russia | `zabaykalsky krai` |
| rwanda | `eastern province` |
| rwanda | `kigali district` |
| rwanda | `northern province` |
| rwanda | `southern province` |
| rwanda | `western province` |
| saint kitts and nevis | `christ church nichola town parish` |
| saint kitts and nevis | `nevis` |
| saint kitts and nevis | `saint anne sandy point parish` |
| saint kitts and nevis | `saint george gingerland parish` |
| saint kitts and nevis | `saint james windward parish` |
| saint kitts and nevis | `saint john capisterre parish` |
| saint kitts and nevis | `saint john figtree parish` |
| saint kitts and nevis | `saint kitts` |
| saint kitts and nevis | `saint mary cayon parish` |
| saint kitts and nevis | `saint paul capisterre parish` |
| saint kitts and nevis | `saint paul charlestown parish` |
| saint kitts and nevis | `saint peter basseterre parish` |
| saint kitts and nevis | `saint thomas lowland parish` |
| saint kitts and nevis | `saint thomas middle island parish` |
| saint kitts and nevis | `trinity palmetto point parish` |
| saint lucia | `anse la raye quarter` |
| saint lucia | `canaries` |
| saint lucia | `castries quarter` |
| saint lucia | `choiseul quarter` |
| saint lucia | `dauphin quarter` |
| saint lucia | `dennery quarter` |
| saint lucia | `gros islet quarter` |
| saint lucia | `laborie quarter` |
| saint lucia | `micoud quarter` |
| saint lucia | `praslin quarter` |
| saint lucia | `soufričre quarter` |
| saint lucia | `vieux fort quarter` |
| saint vincent and the grenadines | `charlotte parish` |
| saint vincent and the grenadines | `grenadines parish` |
| saint vincent and the grenadines | `saint andrew parish` |
| saint vincent and the grenadines | `saint david parish` |
| saint vincent and the grenadines | `saint george parish` |
| saint vincent and the grenadines | `saint patrick parish` |
| samoa | `a'ana` |
| samoa | `aiga-i-le-tai` |
| samoa | `atua` |
| samoa | `fa'asaleleaga` |
| samoa | `gaga'emauga` |
| samoa | `gaga'ifomauga` |
| samoa | `palauli` |
| samoa | `satupa'itea` |
| samoa | `tuamasaga` |
| samoa | `va'a-o-fonoti` |
| samoa | `vaisigano` |
| san marino | `acquaviva` |
| san marino | `borgo maggiore` |
| san marino | `chiesanuova` |
| san marino | `domagnano` |
| san marino | `faetano` |
| san marino | `fiorentino` |
| san marino | `montegiardino` |
| san marino | `san marino` |
| san marino | `serravalle` |
| sao tome and principe | `prķncipe province` |
| sao tome and principe | `sćo tomé province` |
| saudi arabia | `asir` |
| saudi arabia | `al bahah` |
| saudi arabia | `al jawf` |
| saudi arabia | `al madinah` |
| saudi arabia | `al-qassim` |
| saudi arabia | `eastern province` |
| saudi arabia | `ha'il` |
| saudi arabia | `jizan` |
| saudi arabia | `makkah` |
| saudi arabia | `najran` |
| saudi arabia | `northern borders` |
| saudi arabia | `riyadh` |
| saudi arabia | `tabuk` |
| senegal | `dakar` |
| senegal | `diourbel region` |
| senegal | `fatick` |
| senegal | `kaffrine` |
| senegal | `kaolack` |
| senegal | `kédougou` |
| senegal | `kolda` |
| senegal | `louga` |
| senegal | `matam` |
| senegal | `saint-louis` |
| senegal | `sédhiou` |
| senegal | `tambacounda region` |
| senegal | `thičs region` |
| senegal | `ziguinchor` |
| serbia | `belgrade` |
| serbia | `bor district` |
| serbia | `branicevo district` |
| serbia | `central banat district` |
| serbia | `jablanica district` |
| serbia | `kolubara district` |
| serbia | `macva district` |
| serbia | `moravica district` |
| serbia | `ni�ava district` |
| serbia | `north backa district` |
| serbia | `north banat district` |
| serbia | `pcinja district` |
| serbia | `pirot district` |
| serbia | `podunavlje district` |
| serbia | `pomoravlje district` |
| serbia | `rasina district` |
| serbia | `ra�ka district` |
| serbia | `south backa district` |
| serbia | `south banat district` |
| serbia | `srem district` |
| serbia | `�umadija district` |
| serbia | `toplica district` |
| serbia | `vojvodina` |
| serbia | `west backa district` |
| serbia | `zajecar district` |
| serbia | `zlatibor district` |
| seychelles | `anse boileau` |
| seychelles | `anse royale` |
| seychelles | `anse-aux-pins` |
| seychelles | `au cap` |
| seychelles | `baie lazare` |
| seychelles | `baie sainte anne` |
| seychelles | `beau vallon` |
| seychelles | `bel air` |
| seychelles | `bel ombre` |
| seychelles | `cascade` |
| seychelles | `glacis` |
| seychelles | `grand'anse mahé` |
| seychelles | `grand'anse praslin` |
| seychelles | `la digue` |
| seychelles | `la rivičre anglaise` |
| seychelles | `les mamelles` |
| seychelles | `mont buxton` |
| seychelles | `mont fleuri` |
| seychelles | `plaisance` |
| seychelles | `pointe la rue` |
| seychelles | `port glaud` |
| seychelles | `roche caiman` |
| seychelles | `saint louis` |
| seychelles | `takamaka` |
| sierra leone | `eastern province` |
| sierra leone | `northern province` |
| sierra leone | `southern province` |
| sierra leone | `western area` |
| singapore | `central singapore` |
| singapore | `north east` |
| singapore | `north west` |
| singapore | `south east` |
| singapore | `south west` |
| slovakia | `banskį bystrica region` |
| slovakia | `bratislava region` |
| slovakia | `ko�ice region` |
| slovakia | `nitra region` |
| slovakia | `pre�ov region` |
| slovakia | `trencķn region` |
| slovakia | `trnava region` |
| slovakia | `ˇilina region` |
| slovenia | `ajdov�cina municipality` |
| slovenia | `ankaran municipality` |
| slovenia | `beltinci municipality` |
| slovenia | `benedikt municipality` |
| slovenia | `bistrica ob sotli municipality` |
| slovenia | `bled municipality` |
| slovenia | `bloke municipality` |
| slovenia | `bohinj municipality` |
| slovenia | `borovnica municipality` |
| slovenia | `bovec municipality` |
| slovenia | `braslovce municipality` |
| slovenia | `brda municipality` |
| slovenia | `bre˛ice municipality` |
| slovenia | `brezovica municipality` |
| slovenia | `cankova municipality` |
| slovenia | `cerklje na gorenjskem municipality` |
| slovenia | `cerknica municipality` |
| slovenia | `cerkno municipality` |
| slovenia | `cerkvenjak municipality` |
| slovenia | `city municipality of celje` |
| slovenia | `city municipality of novo mesto` |
| slovenia | `cren�ovci municipality` |
| slovenia | `crna na koro�kem municipality` |
| slovenia | `crnomelj municipality` |
| slovenia | `destrnik municipality` |
| slovenia | `divaca municipality` |
| slovenia | `dobje municipality` |
| slovenia | `dobrepolje municipality` |
| slovenia | `dobrna municipality` |
| slovenia | `dobrova-polhov gradec municipality` |
| slovenia | `dobrovnik municipality` |
| slovenia | `dol pri ljubljani municipality` |
| slovenia | `dolenjske toplice municipality` |
| slovenia | `dom˛ale municipality` |
| slovenia | `dornava municipality` |
| slovenia | `dravograd municipality` |
| slovenia | `duplek municipality` |
| slovenia | `gorenja vas-poljane municipality` |
| slovenia | `gori�nica municipality` |
| slovenia | `gorje municipality` |
| slovenia | `gornja radgona municipality` |
| slovenia | `gornji grad municipality` |
| slovenia | `gornji petrovci municipality` |
| slovenia | `grad municipality` |
| slovenia | `grosuplje municipality` |
| slovenia | `hajdina municipality` |
| slovenia | `hoce-slivnica municipality` |
| slovenia | `hodo� municipality` |
| slovenia | `horjul municipality` |
| slovenia | `hrastnik municipality` |
| slovenia | `hrpelje-kozina municipality` |
| slovenia | `idrija municipality` |
| slovenia | `ig municipality` |
| slovenia | `ivancna gorica municipality` |
| slovenia | `izola municipality` |
| slovenia | `jesenice municipality` |
| slovenia | `jezersko municipality` |
| slovenia | `jur�inci municipality` |
| slovenia | `kamnik municipality` |
| slovenia | `kanal ob soci municipality` |
| slovenia | `kidricevo municipality` |
| slovenia | `kobarid municipality` |
| slovenia | `kobilje municipality` |
| slovenia | `kocevje municipality` |
| slovenia | `komen municipality` |
| slovenia | `komenda municipality` |
| slovenia | `koper city municipality` |
| slovenia | `kostanjevica na krki municipality` |
| slovenia | `kostel municipality` |
| slovenia | `kozje municipality` |
| slovenia | `kranj city municipality` |
| slovenia | `kranjska gora municipality` |
| slovenia | `kri˛evci municipality` |
| slovenia | `kungota` |
| slovenia | `kuzma municipality` |
| slovenia | `la�ko municipality` |
| slovenia | `lenart municipality` |
| slovenia | `lendava municipality` |
| slovenia | `litija municipality` |
| slovenia | `ljubljana city municipality` |
| slovenia | `ljubno municipality` |
| slovenia | `ljutomer municipality` |
| slovenia | `log-dragomer municipality` |
| slovenia | `logatec municipality` |
| slovenia | `lo�ka dolina municipality` |
| slovenia | `lo�ki potok municipality` |
| slovenia | `lovrenc na pohorju municipality` |
| slovenia | `luce municipality` |
| slovenia | `lukovica municipality` |
| slovenia | `maj�perk municipality` |
| slovenia | `makole municipality` |
| slovenia | `maribor city municipality` |
| slovenia | `markovci municipality` |
| slovenia | `medvode municipality` |
| slovenia | `menge� municipality` |
| slovenia | `metlika municipality` |
| slovenia | `me˛ica municipality` |
| slovenia | `miklav˛ na dravskem polju municipality` |
| slovenia | `miren-kostanjevica municipality` |
| slovenia | `mirna municipality` |
| slovenia | `mirna pec municipality` |
| slovenia | `mislinja municipality` |
| slovenia | `mokronog-trebelno municipality` |
| slovenia | `moravce municipality` |
| slovenia | `moravske toplice municipality` |
| slovenia | `mozirje municipality` |
| slovenia | `municipality of apace` |
| slovenia | `municipality of cirkulane` |
| slovenia | `municipality of ilirska bistrica` |
| slovenia | `municipality of kr�ko` |
| slovenia | `municipality of �kofljica` |
| slovenia | `murska sobota city municipality` |
| slovenia | `muta municipality` |
| slovenia | `naklo municipality` |
| slovenia | `nazarje municipality` |
| slovenia | `nova gorica city municipality` |
| slovenia | `odranci municipality` |
| slovenia | `oplotnica` |
| slovenia | `ormo˛ municipality` |
| slovenia | `osilnica municipality` |
| slovenia | `pesnica municipality` |
| slovenia | `piran municipality` |
| slovenia | `pivka municipality` |
| slovenia | `podcetrtek municipality` |
| slovenia | `podlehnik municipality` |
| slovenia | `podvelka municipality` |
| slovenia | `poljcane municipality` |
| slovenia | `polzela municipality` |
| slovenia | `postojna municipality` |
| slovenia | `prebold municipality` |
| slovenia | `preddvor municipality` |
| slovenia | `prevalje municipality` |
| slovenia | `ptuj city municipality` |
| slovenia | `puconci municipality` |
| slovenia | `race-fram municipality` |
| slovenia | `radece municipality` |
| slovenia | `radenci municipality` |
| slovenia | `radlje ob dravi municipality` |
| slovenia | `radovljica municipality` |
| slovenia | `ravne na koro�kem municipality` |
| slovenia | `razkri˛je municipality` |
| slovenia | `recica ob savinji municipality` |
| slovenia | `rence-vogrsko municipality` |
| slovenia | `ribnica municipality` |
| slovenia | `ribnica na pohorju municipality` |
| slovenia | `roga�ka slatina municipality` |
| slovenia | `roga�ovci municipality` |
| slovenia | `rogatec municipality` |
| slovenia | `ru�e municipality` |
| slovenia | `�alovci municipality` |
| slovenia | `selnica ob dravi municipality` |
| slovenia | `semic municipality` |
| slovenia | `�empeter-vrtojba municipality` |
| slovenia | `�encur municipality` |
| slovenia | `�entilj municipality` |
| slovenia | `�entjernej municipality` |
| slovenia | `�entjur municipality` |
| slovenia | `�entrupert municipality` |
| slovenia | `sevnica municipality` |
| slovenia | `se˛ana municipality` |
| slovenia | `�kocjan municipality` |
| slovenia | `�kofja loka municipality` |
| slovenia | `slovenj gradec city municipality` |
| slovenia | `slovenska bistrica municipality` |
| slovenia | `slovenske konjice municipality` |
| slovenia | `�marje pri jel�ah municipality` |
| slovenia | `�marje�ke toplice municipality` |
| slovenia | `�martno ob paki municipality` |
| slovenia | `�martno pri litiji municipality` |
| slovenia | `sodra˛ica municipality` |
| slovenia | `solcava municipality` |
| slovenia | `�o�tanj municipality` |
| slovenia | `sredi�ce ob dravi` |
| slovenia | `star�e municipality` |
| slovenia | `�tore municipality` |
| slovenia | `stra˛a municipality` |
| slovenia | `sveta ana municipality` |
| slovenia | `sveta trojica v slovenskih goricah municipality` |
| slovenia | `sveti andra˛ v slovenskih goricah municipality` |
| slovenia | `sveti jurij ob �cavnici municipality` |
| slovenia | `sveti jurij v slovenskih goricah municipality` |
| slovenia | `sveti toma˛ municipality` |
| slovenia | `tabor municipality` |
| slovenia | `ti�ina municipality` |
| slovenia | `tolmin municipality` |
| slovenia | `trbovlje municipality` |
| slovenia | `trebnje municipality` |
| slovenia | `trnovska vas municipality` |
| slovenia | `tr˛ic municipality` |
| slovenia | `trzin municipality` |
| slovenia | `turni�ce municipality` |
| slovenia | `velika polana municipality` |
| slovenia | `velike la�ce municipality` |
| slovenia | `ver˛ej municipality` |
| slovenia | `videm municipality` |
| slovenia | `vipava municipality` |
| slovenia | `vitanje municipality` |
| slovenia | `vodice municipality` |
| slovenia | `vojnik municipality` |
| slovenia | `vransko municipality` |
| slovenia | `vrhnika municipality` |
| slovenia | `vuzenica municipality` |
| slovenia | `zagorje ob savi municipality` |
| slovenia | `ˇalec municipality` |
| slovenia | `zavrc municipality` |
| slovenia | `ˇelezniki municipality` |
| slovenia | `ˇetale municipality` |
| slovenia | `ˇiri municipality` |
| slovenia | `ˇirovnica municipality` |
| slovenia | `zrece municipality` |
| slovenia | `ˇu˛emberk municipality` |
| solomon islands | `central province` |
| solomon islands | `choiseul province` |
| solomon islands | `guadalcanal province` |
| solomon islands | `honiara` |
| solomon islands | `isabel province` |
| solomon islands | `makira-ulawa province` |
| solomon islands | `malaita province` |
| solomon islands | `rennell and bellona province` |
| solomon islands | `temotu province` |
| solomon islands | `western province` |
| somalia | `awdal region` |
| somalia | `bakool` |
| somalia | `banaadir` |
| somalia | `bari` |
| somalia | `bay` |
| somalia | `galguduud` |
| somalia | `gedo` |
| somalia | `hiran` |
| somalia | `lower juba` |
| somalia | `lower shebelle` |
| somalia | `middle juba` |
| somalia | `middle shebelle` |
| somalia | `mudug` |
| somalia | `nugal` |
| somalia | `sanaag region` |
| somalia | `togdheer region` |
| south africa | `eastern cape` |
| south africa | `free state` |
| south africa | `gauteng` |
| south africa | `kwazulu-natal` |
| south africa | `limpopo` |
| south africa | `mpumalanga` |
| south africa | `north west` |
| south africa | `northern cape` |
| south africa | `western cape` |
| south korea | `busan` |
| south korea | `daegu` |
| south korea | `daejeon` |
| south korea | `gangwon province` |
| south korea | `gwangju` |
| south korea | `gyeonggi province` |
| south korea | `incheon` |
| south korea | `jeju` |
| south korea | `north chungcheong province` |
| south korea | `north gyeongsang province` |
| south korea | `north jeolla province` |
| south korea | `sejong city` |
| south korea | `seoul` |
| south korea | `south chungcheong province` |
| south korea | `south gyeongsang province` |
| south korea | `south jeolla province` |
| south korea | `ulsan` |
| south sudan | `central equatoria` |
| south sudan | `eastern equatoria` |
| south sudan | `jonglei state` |
| south sudan | `lakes` |
| south sudan | `northern bahr el ghazal` |
| south sudan | `unity` |
| south sudan | `upper nile` |
| south sudan | `warrap` |
| south sudan | `western bahr el ghazal` |
| south sudan | `western equatoria` |
| spain | `a coruńa` |
| spain | `albacete` |
| spain | `alicante` |
| spain | `almeria` |
| spain | `araba` |
| spain | `asturias` |
| spain | `įvila` |
| spain | `badajoz` |
| spain | `barcelona` |
| spain | `bizkaia` |
| spain | `burgos` |
| spain | `caceres` |
| spain | `cįdiz` |
| spain | `canarias` |
| spain | `cantabria` |
| spain | `castellón` |
| spain | `ceuta` |
| spain | `ciudad real` |
| spain | `córdoba` |
| spain | `cuenca` |
| spain | `gipuzkoa` |
| spain | `girona` |
| spain | `granada` |
| spain | `guadalajara` |
| spain | `huelva` |
| spain | `huesca` |
| spain | `islas baleares` |
| spain | `jaén` |
| spain | `la rioja` |
| spain | `las palmas` |
| spain | `léon` |
| spain | `lleida` |
| spain | `lugo` |
| spain | `madrid` |
| spain | `mįlaga` |
| spain | `melilla` |
| spain | `murcia` |
| spain | `navarra` |
| spain | `ourense` |
| spain | `palencia` |
| spain | `pontevedra` |
| spain | `salamanca` |
| spain | `santa cruz de tenerife` |
| spain | `segovia` |
| spain | `sevilla` |
| spain | `soria` |
| spain | `tarragona` |
| spain | `teruel` |
| spain | `toledo` |
| spain | `valencia` |
| spain | `valladolid` |
| spain | `zamora` |
| spain | `zaragoza` |
| sri lanka | `ampara district` |
| sri lanka | `anuradhapura district` |
| sri lanka | `badulla district` |
| sri lanka | `batticaloa district` |
| sri lanka | `central province` |
| sri lanka | `colombo district` |
| sri lanka | `eastern province` |
| sri lanka | `galle district` |
| sri lanka | `gampaha district` |
| sri lanka | `hambantota district` |
| sri lanka | `jaffna district` |
| sri lanka | `kalutara district` |
| sri lanka | `kandy district` |
| sri lanka | `kegalle district` |
| sri lanka | `kilinochchi district` |
| sri lanka | `mannar district` |
| sri lanka | `matale district` |
| sri lanka | `matara district` |
| sri lanka | `monaragala district` |
| sri lanka | `mullaitivu district` |
| sri lanka | `north central province` |
| sri lanka | `north western province` |
| sri lanka | `northern province` |
| sri lanka | `nuwara eliya district` |
| sri lanka | `polonnaruwa district` |
| sri lanka | `puttalam district` |
| sri lanka | `ratnapura district` |
| sri lanka | `sabaragamuwa province` |
| sri lanka | `southern province` |
| sri lanka | `trincomalee district` |
| sri lanka | `uva province` |
| sri lanka | `vavuniya district` |
| sri lanka | `western province` |
| sudan | `al jazirah` |
| sudan | `al qadarif` |
| sudan | `blue nile` |
| sudan | `central darfur` |
| sudan | `east darfur` |
| sudan | `kassala` |
| sudan | `khartoum` |
| sudan | `north darfur` |
| sudan | `north kordofan` |
| sudan | `northern` |
| sudan | `red sea` |
| sudan | `river nile` |
| sudan | `sennar` |
| sudan | `south darfur` |
| sudan | `south kordofan` |
| sudan | `west darfur` |
| sudan | `west kordofan` |
| sudan | `white nile` |
| suriname | `brokopondo district` |
| suriname | `commewijne district` |
| suriname | `coronie district` |
| suriname | `marowijne district` |
| suriname | `nickerie district` |
| suriname | `para district` |
| suriname | `paramaribo district` |
| suriname | `saramacca district` |
| suriname | `sipaliwini district` |
| suriname | `wanica district` |
| swaziland | `hhohho district` |
| swaziland | `lubombo district` |
| swaziland | `manzini district` |
| swaziland | `shiselweni district` |
| sweden | `blekinge` |
| sweden | `dalarna county` |
| sweden | `gävleborg county` |
| sweden | `gotland county` |
| sweden | `halland county` |
| sweden | `jämtland county` |
| sweden | `jönköping county` |
| sweden | `kalmar county` |
| sweden | `kronoberg county` |
| sweden | `norrbotten county` |
| sweden | `örebro county` |
| sweden | `östergötland county` |
| sweden | `skåne county` |
| sweden | `södermanland county` |
| sweden | `stockholm county` |
| sweden | `uppsala county` |
| sweden | `värmland county` |
| sweden | `västerbotten county` |
| sweden | `västernorrland county` |
| sweden | `västmanland county` |
| sweden | `västra götaland county` |
| switzerland | `aargau` |
| switzerland | `appenzell ausserrhoden` |
| switzerland | `appenzell innerrhoden` |
| switzerland | `basel-land` |
| switzerland | `basel-stadt` |
| switzerland | `bern` |
| switzerland | `fribourg` |
| switzerland | `geneva` |
| switzerland | `glarus` |
| switzerland | `graubünden` |
| switzerland | `jura` |
| switzerland | `lucerne` |
| switzerland | `neuchātel` |
| switzerland | `nidwalden` |
| switzerland | `obwalden` |
| switzerland | `schaffhausen` |
| switzerland | `schwyz` |
| switzerland | `solothurn` |
| switzerland | `st. gallen` |
| switzerland | `thurgau` |
| switzerland | `ticino` |
| switzerland | `uri` |
| switzerland | `valais` |
| switzerland | `vaud` |
| switzerland | `zug` |
| switzerland | `zürich` |
| syria | `al-hasakah` |
| syria | `al-raqqah` |
| syria | `aleppo` |
| syria | `as-suwayda` |
| syria | `damascus` |
| syria | `daraa` |
| syria | `deir ez-zor` |
| syria | `hama` |
| syria | `homs` |
| syria | `idlib` |
| syria | `latakia` |
| syria | `quneitra` |
| syria | `rif dimashq` |
| syria | `tartus` |
| taiwan | `changhua` |
| taiwan | `chiayi` |
| taiwan | `chiayi` |
| taiwan | `hsinchu` |
| taiwan | `hsinchu` |
| taiwan | `hualien` |
| taiwan | `kaohsiung` |
| taiwan | `keelung` |
| taiwan | `kinmen` |
| taiwan | `lienchiang` |
| taiwan | `miaoli` |
| taiwan | `nantou` |
| taiwan | `new taipei` |
| taiwan | `penghu` |
| taiwan | `pingtung` |
| taiwan | `taichung` |
| taiwan | `tainan` |
| taiwan | `taipei` |
| taiwan | `taitung` |
| taiwan | `taoyuan` |
| taiwan | `yilan` |
| taiwan | `yunlin` |
| tajikistan | `districts of republican subordination` |
| tajikistan | `gorno-badakhshan autonomous province` |
| tajikistan | `khatlon province` |
| tajikistan | `sughd province` |
| tanzania | `arusha` |
| tanzania | `dar es salaam` |
| tanzania | `dodoma` |
| tanzania | `geita` |
| tanzania | `iringa` |
| tanzania | `kagera` |
| tanzania | `katavi` |
| tanzania | `kigoma` |
| tanzania | `kilimanjaro` |
| tanzania | `lindi` |
| tanzania | `manyara` |
| tanzania | `mara` |
| tanzania | `mbeya` |
| tanzania | `morogoro` |
| tanzania | `mtwara` |
| tanzania | `mwanza` |
| tanzania | `njombe` |
| tanzania | `pemba north` |
| tanzania | `pemba south` |
| tanzania | `pwani` |
| tanzania | `rukwa` |
| tanzania | `ruvuma` |
| tanzania | `shinyanga` |
| tanzania | `simiyu` |
| tanzania | `singida` |
| tanzania | `songwe` |
| tanzania | `tabora` |
| tanzania | `tanga` |
| tanzania | `zanzibar north` |
| tanzania | `zanzibar south` |
| tanzania | `zanzibar west` |
| thailand | `amnat charoen` |
| thailand | `ang thong` |
| thailand | `bangkok` |
| thailand | `bueng kan` |
| thailand | `buri ram` |
| thailand | `chachoengsao` |
| thailand | `chai nat` |
| thailand | `chaiyaphum` |
| thailand | `chanthaburi` |
| thailand | `chiang mai` |
| thailand | `chiang rai` |
| thailand | `chon buri` |
| thailand | `chumphon` |
| thailand | `kalasin` |
| thailand | `kamphaeng phet` |
| thailand | `kanchanaburi` |
| thailand | `khon kaen` |
| thailand | `krabi` |
| thailand | `lampang` |
| thailand | `lamphun` |
| thailand | `loei` |
| thailand | `lop buri` |
| thailand | `mae hong son` |
| thailand | `maha sarakham` |
| thailand | `mukdahan` |
| thailand | `nakhon nayok` |
| thailand | `nakhon pathom` |
| thailand | `nakhon phanom` |
| thailand | `nakhon ratchasima` |
| thailand | `nakhon sawan` |
| thailand | `nakhon si thammarat` |
| thailand | `nan` |
| thailand | `narathiwat` |
| thailand | `nong bua lam phu` |
| thailand | `nong khai` |
| thailand | `nonthaburi` |
| thailand | `pathum thani` |
| thailand | `pattani` |
| thailand | `pattaya` |
| thailand | `phangnga` |
| thailand | `phatthalung` |
| thailand | `phayao` |
| thailand | `phetchabun` |
| thailand | `phetchaburi` |
| thailand | `phichit` |
| thailand | `phitsanulok` |
| thailand | `phra nakhon si ayutthaya` |
| thailand | `phrae` |
| thailand | `phuket` |
| thailand | `prachin buri` |
| thailand | `prachuap khiri khan` |
| thailand | `ranong` |
| thailand | `ratchaburi` |
| thailand | `rayong` |
| thailand | `roi et` |
| thailand | `sa kaeo` |
| thailand | `sakon nakhon` |
| thailand | `samut prakan` |
| thailand | `samut sakhon` |
| thailand | `samut songkhram` |
| thailand | `saraburi` |
| thailand | `satun` |
| thailand | `si sa ket` |
| thailand | `sing buri` |
| thailand | `songkhla` |
| thailand | `sukhothai` |
| thailand | `suphan buri` |
| thailand | `surat thani` |
| thailand | `surin` |
| thailand | `tak` |
| thailand | `trang` |
| thailand | `trat` |
| thailand | `ubon ratchathani` |
| thailand | `udon thani` |
| thailand | `uthai thani` |
| thailand | `uttaradit` |
| thailand | `yala` |
| thailand | `yasothon` |
| the bahamas | `acklins` |
| the bahamas | `acklins and crooked islands` |
| the bahamas | `berry islands` |
| the bahamas | `bimini` |
| the bahamas | `black point` |
| the bahamas | `cat island` |
| the bahamas | `central abaco` |
| the bahamas | `central andros` |
| the bahamas | `central eleuthera` |
| the bahamas | `crooked island` |
| the bahamas | `east grand bahama` |
| the bahamas | `exuma` |
| the bahamas | `freeport` |
| the bahamas | `fresh creek` |
| the bahamas | `governor's harbour` |
| the bahamas | `grand cay` |
| the bahamas | `green turtle cay` |
| the bahamas | `harbour island` |
| the bahamas | `high rock` |
| the bahamas | `hope town` |
| the bahamas | `inagua` |
| the bahamas | `kemps bay` |
| the bahamas | `long island` |
| the bahamas | `mangrove cay` |
| the bahamas | `marsh harbour` |
| the bahamas | `mayaguana district` |
| the bahamas | `new providence` |
| the bahamas | `nichollstown and berry islands` |
| the bahamas | `north abaco` |
| the bahamas | `north andros` |
| the bahamas | `north eleuthera` |
| the bahamas | `ragged island` |
| the bahamas | `rock sound` |
| the bahamas | `rum cay district` |
| the bahamas | `san salvador and rum cay` |
| the bahamas | `san salvador island` |
| the bahamas | `sandy point` |
| the bahamas | `south abaco` |
| the bahamas | `south andros` |
| the bahamas | `south eleuthera` |
| the bahamas | `spanish wells` |
| the bahamas | `west grand bahama` |
| togo | `centrale region` |
| togo | `kara region` |
| togo | `maritime` |
| togo | `plateaux region` |
| togo | `savanes region` |
| tonga | `ha‘apai` |
| tonga | `‘eua` |
| tonga | `niuas` |
| tonga | `tongatapu` |
| tonga | `vava‘u` |
| trinidad and tobago | `arima` |
| trinidad and tobago | `chaguanas` |
| trinidad and tobago | `couva-tabaquite-talparo regional corporation` |
| trinidad and tobago | `diego martin regional corporation` |
| trinidad and tobago | `eastern tobago` |
| trinidad and tobago | `penal-debe regional corporation` |
| trinidad and tobago | `point fortin` |
| trinidad and tobago | `port of spain` |
| trinidad and tobago | `princes town regional corporation` |
| trinidad and tobago | `rio claro-mayaro regional corporation` |
| trinidad and tobago | `san fernando` |
| trinidad and tobago | `san juan-laventille regional corporation` |
| trinidad and tobago | `sangre grande regional corporation` |
| trinidad and tobago | `siparia regional corporation` |
| trinidad and tobago | `tunapuna-piarco regional corporation` |
| trinidad and tobago | `western tobago` |
| tunisia | `ariana` |
| tunisia | `béja` |
| tunisia | `ben arous` |
| tunisia | `bizerte` |
| tunisia | `gabčs` |
| tunisia | `gafsa` |
| tunisia | `jendouba` |
| tunisia | `kairouan` |
| tunisia | `kasserine` |
| tunisia | `kebili` |
| tunisia | `kef` |
| tunisia | `mahdia` |
| tunisia | `manouba` |
| tunisia | `medenine` |
| tunisia | `monastir` |
| tunisia | `nabeul` |
| tunisia | `sfax` |
| tunisia | `sidi bouzid` |
| tunisia | `siliana` |
| tunisia | `sousse` |
| tunisia | `tataouine` |
| tunisia | `tozeur` |
| tunisia | `tunis` |
| tunisia | `zaghouan` |
| turkey | `adana` |
| turkey | `adiyaman` |
| turkey | `afyonkarahisar` |
| turkey | `agri` |
| turkey | `aksaray` |
| turkey | `amasya` |
| turkey | `ankara` |
| turkey | `antalya` |
| turkey | `ardahan` |
| turkey | `artvin` |
| turkey | `aydin` |
| turkey | `balikesir` |
| turkey | `bartin` |
| turkey | `batman` |
| turkey | `bayburt` |
| turkey | `bilecik` |
| turkey | `bingöl` |
| turkey | `bitlis` |
| turkey | `bolu` |
| turkey | `burdur` |
| turkey | `bursa` |
| turkey | `ēanakkale` |
| turkey | `ēankiri` |
| turkey | `ēorum` |
| turkey | `denizli` |
| turkey | `diyarbakir` |
| turkey | `düzce` |
| turkey | `edirne` |
| turkey | `elazig` |
| turkey | `erzincan` |
| turkey | `erzurum` |
| turkey | `eskisehir` |
| turkey | `gaziantep` |
| turkey | `giresun` |
| turkey | `gümüshane` |
| turkey | `hakkāri` |
| turkey | `hatay` |
| turkey | `igdir` |
| turkey | `isparta` |
| turkey | `istanbul` |
| turkey | `izmir` |
| turkey | `kahramanmaras` |
| turkey | `karabük` |
| turkey | `karaman` |
| turkey | `kars` |
| turkey | `kastamonu` |
| turkey | `kayseri` |
| turkey | `kilis` |
| turkey | `kirikkale` |
| turkey | `kirklareli` |
| turkey | `kirsehir` |
| turkey | `kocaeli` |
| turkey | `konya` |
| turkey | `kütahya` |
| turkey | `malatya` |
| turkey | `manisa` |
| turkey | `mardin` |
| turkey | `mersin` |
| turkey | `mugla` |
| turkey | `mus` |
| turkey | `nevsehir` |
| turkey | `nigde` |
| turkey | `ordu` |
| turkey | `osmaniye` |
| turkey | `rize` |
| turkey | `sakarya` |
| turkey | `samsun` |
| turkey | `sanliurfa` |
| turkey | `siirt` |
| turkey | `sinop` |
| turkey | `sivas` |
| turkey | `sirnak` |
| turkey | `tekirdag` |
| turkey | `tokat` |
| turkey | `trabzon` |
| turkey | `tunceli` |
| turkey | `usak` |
| turkey | `van` |
| turkey | `yalova` |
| turkey | `yozgat` |
| turkey | `zonguldak` |
| turkmenistan | `ahal region` |
| turkmenistan | `ashgabat` |
| turkmenistan | `balkan region` |
| turkmenistan | `dasoguz region` |
| turkmenistan | `lebap region` |
| turkmenistan | `mary region` |
| tuvalu | `funafuti` |
| tuvalu | `nanumanga` |
| tuvalu | `nanumea` |
| tuvalu | `niutao island council` |
| tuvalu | `nui` |
| tuvalu | `nukufetau` |
| tuvalu | `nukulaelae` |
| tuvalu | `vaitupu` |
| uganda | `abim district` |
| uganda | `adjumani district` |
| uganda | `agago district` |
| uganda | `alebtong district` |
| uganda | `amolatar district` |
| uganda | `amudat district` |
| uganda | `amuria district` |
| uganda | `amuru district` |
| uganda | `apac district` |
| uganda | `arua district` |
| uganda | `budaka district` |
| uganda | `bududa district` |
| uganda | `bugiri district` |
| uganda | `buhweju district` |
| uganda | `buikwe district` |
| uganda | `bukedea district` |
| uganda | `bukomansimbi district` |
| uganda | `bukwo district` |
| uganda | `bulambuli district` |
| uganda | `buliisa district` |
| uganda | `bundibugyo district` |
| uganda | `bunyangabu district` |
| uganda | `bushenyi district` |
| uganda | `busia district` |
| uganda | `butaleja district` |
| uganda | `butambala district` |
| uganda | `butebo district` |
| uganda | `buvuma district` |
| uganda | `buyende district` |
| uganda | `central region` |
| uganda | `dokolo district` |
| uganda | `eastern region` |
| uganda | `gomba district` |
| uganda | `gulu district` |
| uganda | `ibanda district` |
| uganda | `iganga district` |
| uganda | `isingiro district` |
| uganda | `jinja district` |
| uganda | `kaabong district` |
| uganda | `kabale district` |
| uganda | `kabarole district` |
| uganda | `kaberamaido district` |
| uganda | `kagadi district` |
| uganda | `kakumiro district` |
| uganda | `kalangala district` |
| uganda | `kaliro district` |
| uganda | `kalungu district` |
| uganda | `kampala district` |
| uganda | `kamuli district` |
| uganda | `kamwenge district` |
| uganda | `kanungu district` |
| uganda | `kapchorwa district` |
| uganda | `kasese district` |
| uganda | `katakwi district` |
| uganda | `kayunga district` |
| uganda | `kibaale district` |
| uganda | `kiboga district` |
| uganda | `kibuku district` |
| uganda | `kiruhura district` |
| uganda | `kiryandongo district` |
| uganda | `kisoro district` |
| uganda | `kitgum district` |
| uganda | `koboko district` |
| uganda | `kole district` |
| uganda | `kotido district` |
| uganda | `kumi district` |
| uganda | `kween district` |
| uganda | `kyankwanzi district` |
| uganda | `kyegegwa district` |
| uganda | `kyenjojo district` |
| uganda | `kyotera district` |
| uganda | `lamwo district` |
| uganda | `lira district` |
| uganda | `luuka district` |
| uganda | `luwero district` |
| uganda | `lwengo district` |
| uganda | `lyantonde district` |
| uganda | `manafwa district` |
| uganda | `maracha district` |
| uganda | `masaka district` |
| uganda | `masindi district` |
| uganda | `mayuge district` |
| uganda | `mbale district` |
| uganda | `mbarara district` |
| uganda | `mitooma district` |
| uganda | `mityana district` |
| uganda | `moroto district` |
| uganda | `moyo district` |
| uganda | `mpigi district` |
| uganda | `mubende district` |
| uganda | `mukono district` |
| uganda | `nakapiripirit district` |
| uganda | `nakaseke district` |
| uganda | `nakasongola district` |
| uganda | `namayingo district` |
| uganda | `namisindwa district` |
| uganda | `namutumba district` |
| uganda | `napak district` |
| uganda | `nebbi district` |
| uganda | `ngora district` |
| uganda | `northern region` |
| uganda | `ntoroko district` |
| uganda | `ntungamo district` |
| uganda | `nwoya district` |
| uganda | `omoro district` |
| uganda | `otuke district` |
| uganda | `oyam district` |
| uganda | `pader district` |
| uganda | `pakwach district` |
| uganda | `pallisa district` |
| uganda | `rakai district` |
| uganda | `rubanda district` |
| uganda | `rubirizi district` |
| uganda | `rukiga district` |
| uganda | `rukungiri district` |
| uganda | `sembabule district` |
| uganda | `serere district` |
| uganda | `sheema district` |
| uganda | `sironko district` |
| uganda | `soroti district` |
| uganda | `tororo district` |
| uganda | `wakiso district` |
| uganda | `western region` |
| uganda | `yumbe district` |
| uganda | `zombo district` |
| ukraine | `autonomous republic of crimea` |
| ukraine | `cherkaska oblast` |
| ukraine | `chernihivska oblast` |
| ukraine | `chernivetska oblast` |
| ukraine | `dnipropetrovska oblast` |
| ukraine | `donetska oblast` |
| ukraine | `ivano-frankivska oblast` |
| ukraine | `kharkivska oblast` |
| ukraine | `khersonska oblast` |
| ukraine | `khmelnytska oblast` |
| ukraine | `kirovohradska oblast` |
| ukraine | `kyiv` |
| ukraine | `kyivska oblast` |
| ukraine | `luhanska oblast` |
| ukraine | `lvivska oblast` |
| ukraine | `mykolaivska oblast` |
| ukraine | `odeska oblast` |
| ukraine | `poltavska oblast` |
| ukraine | `rivnenska oblast` |
| ukraine | `sevastopol` |
| ukraine | `sumska oblast` |
| ukraine | `ternopilska oblast` |
| ukraine | `vinnytska oblast` |
| ukraine | `volynska oblast` |
| ukraine | `zakarpatska oblast` |
| ukraine | `zaporizka oblast` |
| ukraine | `zhytomyrska oblast` |
| united arab emirates | `abu dhabi emirate` |
| united arab emirates | `ajman emirate` |
| united arab emirates | `dubai` |
| united arab emirates | `fujairah` |
| united arab emirates | `ras al-khaimah` |
| united arab emirates | `sharjah emirate` |
| united arab emirates | `umm al-quwain` |
| united kingdom | `aberdeen` |
| united kingdom | `aberdeenshire` |
| united kingdom | `angus` |
| united kingdom | `antrim` |
| united kingdom | `antrim and newtownabbey` |
| united kingdom | `ards` |
| united kingdom | `ards and north down` |
| united kingdom | `argyll and bute` |
| united kingdom | `armagh city and district council` |
| united kingdom | `armagh, banbridge and craigavon` |
| united kingdom | `ascension island` |
| united kingdom | `ballymena borough` |
| united kingdom | `ballymoney` |
| united kingdom | `banbridge` |
| united kingdom | `barnsley` |
| united kingdom | `bath and north east somerset` |
| united kingdom | `bedford` |
| united kingdom | `belfast district` |
| united kingdom | `birmingham` |
| united kingdom | `blackburn with darwen` |
| united kingdom | `blackpool` |
| united kingdom | `blaenau gwent county borough` |
| united kingdom | `bolton` |
| united kingdom | `bournemouth` |
| united kingdom | `bracknell forest` |
| united kingdom | `bradford` |
| united kingdom | `bridgend county borough` |
| united kingdom | `brighton and hove` |
| united kingdom | `buckinghamshire` |
| united kingdom | `bury` |
| united kingdom | `caerphilly county borough` |
| united kingdom | `calderdale` |
| united kingdom | `cambridgeshire` |
| united kingdom | `carmarthenshire` |
| united kingdom | `carrickfergus borough council` |
| united kingdom | `castlereagh` |
| united kingdom | `causeway coast and glens` |
| united kingdom | `central bedfordshire` |
| united kingdom | `ceredigion` |
| united kingdom | `cheshire east` |
| united kingdom | `cheshire west and chester` |
| united kingdom | `city and county of cardiff` |
| united kingdom | `city and county of swansea` |
| united kingdom | `city of bristol` |
| united kingdom | `city of derby` |
| united kingdom | `city of kingston upon hull` |
| united kingdom | `city of leicester` |
| united kingdom | `city of london` |
| united kingdom | `city of nottingham` |
| united kingdom | `city of peterborough` |
| united kingdom | `city of plymouth` |
| united kingdom | `city of portsmouth` |
| united kingdom | `city of southampton` |
| united kingdom | `city of stoke-on-trent` |
| united kingdom | `city of sunderland` |
| united kingdom | `city of westminster` |
| united kingdom | `city of wolverhampton` |
| united kingdom | `city of york` |
| united kingdom | `clackmannanshire` |
| united kingdom | `coleraine borough council` |
| united kingdom | `conwy county borough` |
| united kingdom | `cookstown district council` |
| united kingdom | `cornwall` |
| united kingdom | `county durham` |
| united kingdom | `coventry` |
| united kingdom | `craigavon borough council` |
| united kingdom | `cumbria` |
| united kingdom | `darlington` |
| united kingdom | `denbighshire` |
| united kingdom | `derbyshire` |
| united kingdom | `derry city and strabane` |
| united kingdom | `derry city council` |
| united kingdom | `devon` |
| united kingdom | `doncaster` |
| united kingdom | `dorset` |
| united kingdom | `down district council` |
| united kingdom | `dudley` |
| united kingdom | `dumfries and galloway` |
| united kingdom | `dundee` |
| united kingdom | `dungannon and south tyrone borough council` |
| united kingdom | `east ayrshire` |
| united kingdom | `east dunbartonshire` |
| united kingdom | `east lothian` |
| united kingdom | `east renfrewshire` |
| united kingdom | `east riding of yorkshire` |
| united kingdom | `east sussex` |
| united kingdom | `edinburgh` |
| united kingdom | `england` |
| united kingdom | `essex` |
| united kingdom | `falkirk` |
| united kingdom | `fermanagh and omagh` |
| united kingdom | `fermanagh district council` |
| united kingdom | `fife` |
| united kingdom | `flintshire` |
| united kingdom | `gateshead` |
| united kingdom | `glasgow` |
| united kingdom | `gloucestershire` |
| united kingdom | `gwynedd` |
| united kingdom | `halton` |
| united kingdom | `hampshire` |
| united kingdom | `hartlepool` |
| united kingdom | `herefordshire` |
| united kingdom | `hertfordshire` |
| united kingdom | `highland` |
| united kingdom | `inverclyde` |
| united kingdom | `isle of wight` |
| united kingdom | `isles of scilly` |
| united kingdom | `kent` |
| united kingdom | `kirklees` |
| united kingdom | `knowsley` |
| united kingdom | `lancashire` |
| united kingdom | `larne borough council` |
| united kingdom | `leeds` |
| united kingdom | `leicestershire` |
| united kingdom | `limavady borough council` |
| united kingdom | `lincolnshire` |
| united kingdom | `lisburn and castlereagh` |
| united kingdom | `lisburn city council` |
| united kingdom | `liverpool` |
| united kingdom | `london borough of barking and dagenham` |
| united kingdom | `london borough of barnet` |
| united kingdom | `london borough of bexley` |
| united kingdom | `london borough of brent` |
| united kingdom | `london borough of bromley` |
| united kingdom | `london borough of camden` |
| united kingdom | `london borough of croydon` |
| united kingdom | `london borough of ealing` |
| united kingdom | `london borough of enfield` |
| united kingdom | `london borough of hackney` |
| united kingdom | `london borough of hammersmith and fulham` |
| united kingdom | `london borough of haringey` |
| united kingdom | `london borough of harrow` |
| united kingdom | `london borough of havering` |
| united kingdom | `london borough of hillingdon` |
| united kingdom | `london borough of hounslow` |
| united kingdom | `london borough of islington` |
| united kingdom | `london borough of lambeth` |
| united kingdom | `london borough of lewisham` |
| united kingdom | `london borough of merton` |
| united kingdom | `london borough of newham` |
| united kingdom | `london borough of redbridge` |
| united kingdom | `london borough of richmond upon thames` |
| united kingdom | `london borough of southwark` |
| united kingdom | `london borough of sutton` |
| united kingdom | `london borough of tower hamlets` |
| united kingdom | `london borough of waltham forest` |
| united kingdom | `london borough of wandsworth` |
| united kingdom | `magherafelt district council` |
| united kingdom | `manchester` |
| united kingdom | `medway` |
| united kingdom | `merthyr tydfil county borough` |
| united kingdom | `metropolitan borough of wigan` |
| united kingdom | `mid and east antrim` |
| united kingdom | `mid ulster` |
| united kingdom | `middlesbrough` |
| united kingdom | `midlothian` |
| united kingdom | `milton keynes` |
| united kingdom | `monmouthshire` |
| united kingdom | `moray` |
| united kingdom | `moyle district council` |
| united kingdom | `neath port talbot county borough` |
| united kingdom | `newcastle upon tyne` |
| united kingdom | `newport` |
| united kingdom | `newry and mourne district council` |
| united kingdom | `newry, mourne and down` |
| united kingdom | `newtownabbey borough council` |
| united kingdom | `norfolk` |
| united kingdom | `north ayrshire` |
| united kingdom | `north down borough council` |
| united kingdom | `north east lincolnshire` |
| united kingdom | `north lanarkshire` |
| united kingdom | `north lincolnshire` |
| united kingdom | `north somerset` |
| united kingdom | `north tyneside` |
| united kingdom | `north yorkshire` |
| united kingdom | `northamptonshire` |
| united kingdom | `northern ireland` |
| united kingdom | `northumberland` |
| united kingdom | `nottinghamshire` |
| united kingdom | `oldham` |
| united kingdom | `omagh district council` |
| united kingdom | `orkney islands` |
| united kingdom | `outer hebrides` |
| united kingdom | `oxfordshire` |
| united kingdom | `pembrokeshire` |
| united kingdom | `perth and kinross` |
| united kingdom | `poole` |
| united kingdom | `powys` |
| united kingdom | `reading` |
| united kingdom | `redcar and cleveland` |
| united kingdom | `renfrewshire` |
| united kingdom | `rhondda cynon taf` |
| united kingdom | `rochdale` |
| united kingdom | `rotherham` |
| united kingdom | `royal borough of greenwich` |
| united kingdom | `royal borough of kensington and chelsea` |
| united kingdom | `royal borough of kingston upon thames` |
| united kingdom | `rutland` |
| united kingdom | `saint helena` |
| united kingdom | `salford` |
| united kingdom | `sandwell` |
| united kingdom | `scotland` |
| united kingdom | `scottish borders` |
| united kingdom | `sefton` |
| united kingdom | `sheffield` |
| united kingdom | `shetland islands` |
| united kingdom | `shropshire` |
| united kingdom | `slough` |
| united kingdom | `solihull` |
| united kingdom | `somerset` |
| united kingdom | `south ayrshire` |
| united kingdom | `south gloucestershire` |
| united kingdom | `south lanarkshire` |
| united kingdom | `south tyneside` |
| united kingdom | `southend-on-sea` |
| united kingdom | `st helens` |
| united kingdom | `staffordshire` |
| united kingdom | `stirling` |
| united kingdom | `stockport` |
| united kingdom | `stockton-on-tees` |
| united kingdom | `strabane district council` |
| united kingdom | `suffolk` |
| united kingdom | `surrey` |
| united kingdom | `swindon` |
| united kingdom | `tameside` |
| united kingdom | `telford and wrekin` |
| united kingdom | `thurrock` |
| united kingdom | `torbay` |
| united kingdom | `torfaen` |
| united kingdom | `trafford` |
| united kingdom | `united kingdom` |
| united kingdom | `vale of glamorgan` |
| united kingdom | `wakefield` |
| united kingdom | `wales` |
| united kingdom | `walsall` |
| united kingdom | `warrington` |
| united kingdom | `warwickshire` |
| united kingdom | `west berkshire` |
| united kingdom | `west dunbartonshire` |
| united kingdom | `west lothian` |
| united kingdom | `west sussex` |
| united kingdom | `wiltshire` |
| united kingdom | `windsor and maidenhead` |
| united kingdom | `wirral` |
| united kingdom | `wokingham` |
| united kingdom | `worcestershire` |
| united kingdom | `wrexham county borough` |
| united states | `alabama` |
| united states | `alaska` |
| united states | `american samoa` |
| united states | `arizona` |
| united states | `arkansas` |
| united states | `baker island` |
| united states | `california` |
| united states | `colorado` |
| united states | `connecticut` |
| united states | `delaware` |
| united states | `district of columbia` |
| united states | `florida` |
| united states | `georgia` |
| united states | `guam` |
| united states | `hawaii` |
| united states | `howland island` |
| united states | `idaho` |
| united states | `illinois` |
| united states | `indiana` |
| united states | `iowa` |
| united states | `jarvis island` |
| united states | `johnston atoll` |
| united states | `kansas` |
| united states | `kentucky` |
| united states | `kingman reef` |
| united states | `louisiana` |
| united states | `maine` |
| united states | `maryland` |
| united states | `massachusetts` |
| united states | `michigan` |
| united states | `midway atoll` |
| united states | `minnesota` |
| united states | `mississippi` |
| united states | `missouri` |
| united states | `montana` |
| united states | `navassa island` |
| united states | `nebraska` |
| united states | `nevada` |
| united states | `new hampshire` |
| united states | `new jersey` |
| united states | `new mexico` |
| united states | `new york` |
| united states | `north carolina` |
| united states | `north dakota` |
| united states | `northern mariana islands` |
| united states | `ohio` |
| united states | `oklahoma` |
| united states | `oregon` |
| united states | `palmyra atoll` |
| united states | `pennsylvania` |
| united states | `puerto rico` |
| united states | `rhode island` |
| united states | `south carolina` |
| united states | `south dakota` |
| united states | `tennessee` |
| united states | `texas` |
| united states | `united states minor outlying islands` |
| united states | `united states virgin islands` |
| united states | `utah` |
| united states | `vermont` |
| united states | `virginia` |
| united states | `wake island` |
| united states | `washington` |
| united states | `west virginia` |
| united states | `wisconsin` |
| united states | `wyoming` |
| united states minor outlying islands | `baker island` |
| united states minor outlying islands | `howland island` |
| united states minor outlying islands | `jarvis island` |
| united states minor outlying islands | `johnston atoll` |
| united states minor outlying islands | `kingman reef` |
| united states minor outlying islands | `midway islands` |
| united states minor outlying islands | `navassa island` |
| united states minor outlying islands | `palmyra atoll` |
| united states minor outlying islands | `wake island` |
| uruguay | `artigas` |
| uruguay | `canelones` |
| uruguay | `cerro largo` |
| uruguay | `colonia` |
| uruguay | `durazno` |
| uruguay | `flores` |
| uruguay | `florida` |
| uruguay | `lavalleja` |
| uruguay | `maldonado` |
| uruguay | `montevideo` |
| uruguay | `paysandś` |
| uruguay | `rķo negro` |
| uruguay | `rivera` |
| uruguay | `rocha` |
| uruguay | `salto` |
| uruguay | `san josé` |
| uruguay | `soriano` |
| uruguay | `tacuarembó` |
| uruguay | `treinta y tres` |
| uzbekistan | `andijan region` |
| uzbekistan | `bukhara region` |
| uzbekistan | `fergana region` |
| uzbekistan | `jizzakh region` |
| uzbekistan | `karakalpakstan` |
| uzbekistan | `namangan region` |
| uzbekistan | `navoiy region` |
| uzbekistan | `qashqadaryo region` |
| uzbekistan | `samarqand region` |
| uzbekistan | `sirdaryo region` |
| uzbekistan | `surxondaryo region` |
| uzbekistan | `tashkent` |
| uzbekistan | `tashkent region` |
| uzbekistan | `xorazm region` |
| vanuatu | `malampa` |
| vanuatu | `penama` |
| vanuatu | `sanma` |
| vanuatu | `shefa` |
| vanuatu | `tafea` |
| vanuatu | `torba` |
| venezuela | `amazonas` |
| venezuela | `anzoįtegui` |
| venezuela | `apure` |
| venezuela | `aragua` |
| venezuela | `barinas` |
| venezuela | `bolķvar` |
| venezuela | `carabobo` |
| venezuela | `cojedes` |
| venezuela | `delta amacuro` |
| venezuela | `distrito capital` |
| venezuela | `falcón` |
| venezuela | `federal dependencies of venezuela` |
| venezuela | `guįrico` |
| venezuela | `la guaira` |
| venezuela | `lara` |
| venezuela | `mérida` |
| venezuela | `miranda` |
| venezuela | `monagas` |
| venezuela | `nueva esparta` |
| venezuela | `portuguesa` |
| venezuela | `sucre` |
| venezuela | `tįchira` |
| venezuela | `trujillo` |
| venezuela | `yaracuy` |
| venezuela | `zulia` |
| vietnam | `an giang` |
| vietnam | `bą ria-vung tąu` |
| vietnam | `bac giang` |
| vietnam | `bac kan` |
| vietnam | `bac liźu` |
| vietnam | `bac ninh` |
| vietnam | `ben tre` |
| vietnam | `bģnh duong` |
| vietnam | `bģnh dinh` |
| vietnam | `bģnh phuoc` |
| vietnam | `bģnh thuan` |
| vietnam | `cą mau` |
| vietnam | `can tho` |
| vietnam | `cao bang` |
| vietnam | `dą nang` |
| vietnam | `dak lak` |
| vietnam | `dak nōng` |
| vietnam | `dien biźn` |
| vietnam | `dong nai` |
| vietnam | `dong thįp` |
| vietnam | `gia lai` |
| vietnam | `hą giang` |
| vietnam | `hą nam` |
| vietnam | `hą noi` |
| vietnam | `hą tinh` |
| vietnam | `hai duong` |
| vietnam | `hai phņng` |
| vietnam | `hau giang` |
| vietnam | `ho chķ minh` |
| vietnam | `hņa bģnh` |
| vietnam | `hung yźn` |
| vietnam | `khįnh hņa` |
| vietnam | `kiźn giang` |
| vietnam | `kon tum` |
| vietnam | `lai chāu` |
| vietnam | `lām dong` |
| vietnam | `lang son` |
| vietnam | `ląo cai` |
| vietnam | `long an` |
| vietnam | `nam dinh` |
| vietnam | `nghe an` |
| vietnam | `ninh bģnh` |
| vietnam | `ninh thuan` |
| vietnam | `phś tho` |
| vietnam | `phś yźn` |
| vietnam | `quang bģnh` |
| vietnam | `quang nam` |
| vietnam | `quang ngći` |
| vietnam | `quang ninh` |
| vietnam | `quang tri` |
| vietnam | `sóc trang` |
| vietnam | `son la` |
| vietnam | `tāy ninh` |
| vietnam | `thįi bģnh` |
| vietnam | `thįi nguyźn` |
| vietnam | `thanh hóa` |
| vietnam | `thua thiźn-hue` |
| vietnam | `tien giang` |
| vietnam | `trą vinh` |
| vietnam | `tuyźn quang` |
| vietnam | `vinh long` |
| vietnam | `vinh phśc` |
| vietnam | `yźn bįi` |
| virgin islands (us) | `saint croix` |
| virgin islands (us) | `saint john` |
| virgin islands (us) | `saint thomas` |
| yemen | `adan` |
| yemen | `amran` |
| yemen | `abyan` |
| yemen | `al bayda'` |
| yemen | `al hudaydah` |
| yemen | `al jawf` |
| yemen | `al mahrah` |
| yemen | `al mahwit` |
| yemen | `amanat al asimah` |
| yemen | `dhamar` |
| yemen | `hadhramaut` |
| yemen | `hajjah` |
| yemen | `ibb` |
| yemen | `lahij` |
| yemen | `ma'rib` |
| yemen | `raymah` |
| yemen | `saada` |
| yemen | `sana'a` |
| yemen | `shabwah` |
| yemen | `socotra` |
| yemen | `ta'izz` |
| zambia | `central province` |
| zambia | `copperbelt province` |
| zambia | `eastern province` |
| zambia | `luapula province` |
| zambia | `lusaka province` |
| zambia | `muchinga province` |
| zambia | `northern province` |
| zambia | `northwestern province` |
| zambia | `southern province` |
| zambia | `western province` |
| zimbabwe | `bulawayo province` |
| zimbabwe | `harare province` |
| zimbabwe | `manicaland` |
| zimbabwe | `mashonaland central province` |
| zimbabwe | `mashonaland east province` |
| zimbabwe | `mashonaland west province` |
| zimbabwe | `masvingo province` |
| zimbabwe | `matabeleland north province` |
| zimbabwe | `matabeleland south province` |
| zimbabwe | `midlands province` |
## Cities
Used by `city` filters. The full list is **150,553 values**, too large to display inline, so download it as CSV. Each row is `country, state, city`.
All 150,553 accepted city values with their country and state as CSV (about 5 MB).
For example, the first rows look like:
| Country | State | City |
| ----------- | ---------- | ----------- |
| afghanistan | badakhshan | `ashkasham` |
| afghanistan | badakhshan | `fayzabad` |
| afghanistan | badakhshan | `jurm` |
## Industries
Used by the `industry` filter in Company Search and Jobs Search, and by `company_industry` in Person Search. **487 values.**
Local Business Search uses its own separate list. See [Local business industries](/apis/accepted-values#local-business-industries) below.
All 487 accepted industry values as CSV.
| Industry |
| ----------------------------------------------------------- |
| `abrasives and nonmetallic minerals manufacturing` |
| `accessible architecture and design` |
| `accessible hardware manufacturing` |
| `accommodation and food services` |
| `accounting` |
| `administration of justice` |
| `administrative and support services` |
| `advertising services` |
| `agricultural chemical manufacturing` |
| `agriculture, construction, mining machinery manufacturing` |
| `air, water, and waste program management` |
| `airlines and aviation` |
| `alternative dispute resolution` |
| `alternative fuel vehicle manufacturing` |
| `alternative medicine` |
| `ambulance services` |
| `amusement parks and arcades` |
| `animal feed manufacturing` |
| `animation` |
| `animation and post-production` |
| `apparel & fashion` |
| `apparel manufacturing` |
| `appliances, electrical, and electronics manufacturing` |
| `architectural and structural metal manufacturing` |
| `architecture and planning` |
| `armed forces` |
| `artificial rubber and synthetic fiber manufacturing` |
| `artists and writers` |
| `arts & crafts` |
| `audio and video equipment manufacturing` |
| `automation machinery manufacturing` |
| `automotive` |
| `aviation & aerospace` |
| `aviation and aerospace component manufacturing` |
| `baked goods manufacturing` |
| `banking` |
| `bars, taverns, and nightclubs` |
| `bed-and-breakfasts, hostels, homestays` |
| `beverage manufacturing` |
| `biomass electric power generation` |
| `biotechnology` |
| `biotechnology research` |
| `blockchain services` |
| `blogs` |
| `boilers, tanks, and shipping container manufacturing` |
| `book and periodical publishing` |
| `book publishing` |
| `breweries` |
| `broadcast media production and distribution` |
| `building construction` |
| `building equipment contractors` |
| `building finishing contractors` |
| `building materials` |
| `building structure and exterior contractors` |
| `business consulting and services` |
| `business content` |
| `business intelligence platforms` |
| `business supplies & equipment` |
| `cable and satellite programming` |
| `capital markets` |
| `caterers` |
| `chemical manufacturing` |
| `chemical raw materials manufacturing` |
| `child day care services` |
| `chiropractors` |
| `circuses and magic shows` |
| `civic and social organizations` |
| `civil engineering` |
| `claims adjusting, actuarial services` |
| `clay and refractory products manufacturing` |
| `climate data and analytics` |
| `climate technology product manufacturing` |
| `coal mining` |
| `collection agencies` |
| `commercial and industrial equipment rental` |
| `commercial and industrial machinery maintenance` |
| `commercial and service industry machinery manufacturing` |
| `commercial real estate` |
| `communications equipment manufacturing` |
| `community development and urban planning` |
| `community services` |
| `computer and network security` |
| `computer games` |
| `computer hardware` |
| `computer hardware manufacturing` |
| `computer networking` |
| `computer networking products` |
| `computers and electronics manufacturing` |
| `conservation programs` |
| `construction` |
| `construction hardware manufacturing` |
| `consumer electronics` |
| `consumer goods` |
| `consumer goods rental` |
| `consumer services` |
| `correctional institutions` |
| `cosmetics` |
| `cosmetology and barber schools` |
| `courts of law` |
| `credit intermediation` |
| `cutlery and handtool manufacturing` |
| `dairy` |
| `dairy product manufacturing` |
| `dance companies` |
| `data infrastructure and analytics` |
| `data security software products` |
| `defense & space` |
| `defense and space manufacturing` |
| `dentists` |
| `design` |
| `design services` |
| `desktop computing software products` |
| `digital accessibility services` |
| `distilleries` |
| `e-learning` |
| `e-learning providers` |
| `economic programs` |
| `education` |
| `education administration programs` |
| `education management` |
| `electric lighting equipment manufacturing` |
| `electric power generation` |
| `electric power transmission, control, and distribution` |
| `electrical equipment manufacturing` |
| `electronic and precision equipment maintenance` |
| `embedded software products` |
| `emergency and relief services` |
| `engineering services` |
| `engines and power transmission equipment manufacturing` |
| `entertainment` |
| `entertainment providers` |
| `environmental quality programs` |
| `environmental services` |
| `equipment rental services` |
| `events services` |
| `executive offices` |
| `executive search services` |
| `fabricated metal products` |
| `facilities services` |
| `family planning centers` |
| `farming` |
| `farming, ranching, forestry` |
| `fashion accessories manufacturing` |
| `financial services` |
| `fine art` |
| `fine arts schools` |
| `fire protection` |
| `fisheries` |
| `flight training` |
| `food & beverages` |
| `food and beverage manufacturing` |
| `food and beverage retail` |
| `food and beverage services` |
| `food production` |
| `footwear and leather goods repair` |
| `footwear manufacturing` |
| `forestry and logging` |
| `fossil fuel electric power generation` |
| `freight and package transportation` |
| `fruit and vegetable preserves manufacturing` |
| `fuel cell manufacturing` |
| `fundraising` |
| `funds and trusts` |
| `furniture` |
| `furniture and home furnishings manufacturing` |
| `gambling facilities and casinos` |
| `geothermal electric power generation` |
| `glass product manufacturing` |
| `glass, ceramics and concrete manufacturing` |
| `golf courses and country clubs` |
| `government administration` |
| `government relations` |
| `government relations services` |
| `graphic design` |
| `ground passenger transportation` |
| `health and human services` |
| `health, wellness & fitness` |
| `higher education` |
| `highway, street, and bridge construction` |
| `historical sites` |
| `holding companies` |
| `home health care services` |
| `horticulture` |
| `hospitality` |
| `hospitals` |
| `hospitals and health care` |
| `hotels and motels` |
| `household and institutional furniture manufacturing` |
| `household appliance manufacturing` |
| `household services` |
| `housing and community development` |
| `housing programs` |
| `human resources` |
| `human resources services` |
| `hvac and refrigeration equipment manufacturing` |
| `hydroelectric power generation` |
| `import & export` |
| `individual and family services` |
| `industrial automation` |
| `industrial machinery manufacturing` |
| `industry associations` |
| `information services` |
| `information technology & services` |
| `insurance` |
| `insurance agencies and brokerages` |
| `insurance and employee benefit funds` |
| `insurance carriers` |
| `interior design` |
| `international affairs` |
| `international trade and development` |
| `internet marketplace platforms` |
| `internet news` |
| `internet publishing` |
| `interurban and rural bus services` |
| `investment advice` |
| `investment banking` |
| `investment management` |
| `it services and it consulting` |
| `it system custom software development` |
| `it system data services` |
| `it system design services` |
| `it system installation and disposal` |
| `it system operations and maintenance` |
| `it system testing and evaluation` |
| `it system training and support` |
| `janitorial services` |
| `landscaping services` |
| `language schools` |
| `laundry and drycleaning services` |
| `law enforcement` |
| `law practice` |
| `leasing non-residential real estate` |
| `leasing residential real estate` |
| `leather product manufacturing` |
| `legal services` |
| `legislative offices` |
| `leisure, travel & tourism` |
| `libraries` |
| `lime and gypsum products manufacturing` |
| `loan brokers` |
| `luxury goods & jewelry` |
| `machinery manufacturing` |
| `magnetic and optical media manufacturing` |
| `manufacturing` |
| `maritime` |
| `maritime transportation` |
| `market research` |
| `marketing services` |
| `mattress and blinds manufacturing` |
| `measuring and control instrument manufacturing` |
| `meat products manufacturing` |
| `mechanical or industrial engineering` |
| `media and telecommunications` |
| `media production` |
| `medical and diagnostic laboratories` |
| `medical device` |
| `medical equipment manufacturing` |
| `medical practices` |
| `mental health care` |
| `metal ore mining` |
| `metal treatments` |
| `metal valve, ball, and roller manufacturing` |
| `metalworking machinery manufacturing` |
| `military and international affairs` |
| `mining` |
| `mobile computing software products` |
| `mobile food services` |
| `mobile gaming apps` |
| `motor vehicle manufacturing` |
| `motor vehicle parts manufacturing` |
| `movies and sound recording` |
| `movies, videos, and sound` |
| `museums` |
| `museums, historical sites, and zoos` |
| `music` |
| `musicians` |
| `nanotechnology research` |
| `natural gas distribution` |
| `natural gas extraction` |
| `newspaper publishing` |
| `non-profit organization management` |
| `non-profit organizations` |
| `nonmetallic mineral mining` |
| `nonresidential building construction` |
| `nuclear electric power generation` |
| `nursing homes and residential care facilities` |
| `office administration` |
| `office furniture and fixtures manufacturing` |
| `oil and coal product manufacturing` |
| `oil and gas` |
| `oil extraction` |
| `oil, gas, and mining` |
| `online and mail order retail` |
| `online audio and video media` |
| `online media` |
| `operations consulting` |
| `optometrists` |
| `outpatient care centers` |
| `outsourcing and offshoring consulting` |
| `outsourcing/offshoring` |
| `packaging & containers` |
| `packaging and containers manufacturing` |
| `paint, coating, and adhesive manufacturing` |
| `paper & forest products` |
| `paper and forest product manufacturing` |
| `pension funds` |
| `performing arts` |
| `performing arts and spectator sports` |
| `periodical publishing` |
| `personal and laundry services` |
| `personal care product manufacturing` |
| `personal care services` |
| `pet services` |
| `pharmaceutical manufacturing` |
| `philanthropic fundraising services` |
| `philanthropy` |
| `photography` |
| `physical, occupational and speech therapists` |
| `physicians` |
| `pipeline transportation` |
| `plastics and rubber product manufacturing` |
| `plastics manufacturing` |
| `political organizations` |
| `postal services` |
| `primary and secondary education` |
| `primary metal manufacturing` |
| `printing services` |
| `professional organizations` |
| `professional services` |
| `professional training and coaching` |
| `program development` |
| `public assistance programs` |
| `public health` |
| `public policy` |
| `public policy offices` |
| `public relations and communications services` |
| `public safety` |
| `racetracks` |
| `radio and television broadcasting` |
| `rail transportation` |
| `railroad equipment manufacturing` |
| `ranching` |
| `ranching and fisheries` |
| `real estate` |
| `real estate agents and brokers` |
| `real estate and equipment rental services` |
| `recreational facilities` |
| `regenerative design` |
| `religious institutions` |
| `renewable energy equipment manufacturing` |
| `renewable energy power generation` |
| `renewable energy semiconductor manufacturing` |
| `renewables & environment` |
| `repair and maintenance` |
| `research` |
| `research services` |
| `residential building construction` |
| `restaurants` |
| `retail` |
| `retail apparel and fashion` |
| `retail appliances, electrical, and electronic equipment` |
| `retail art dealers` |
| `retail art supplies` |
| `retail books and printed news` |
| `retail building materials and garden equipment` |
| `retail florists` |
| `retail furniture and home furnishings` |
| `retail gasoline` |
| `retail groceries` |
| `retail health and personal care products` |
| `retail luxury goods and jewelry` |
| `retail motor vehicles` |
| `retail musical instruments` |
| `retail office equipment` |
| `retail office supplies and gifts` |
| `retail pharmacies` |
| `retail recyclable materials & used merchandise` |
| `reupholstery and furniture repair` |
| `robot manufacturing` |
| `robotics engineering` |
| `rubber products manufacturing` |
| `satellite telecommunications` |
| `savings institutions` |
| `school and employee bus services` |
| `seafood product manufacturing` |
| `secretarial schools` |
| `securities and commodity exchanges` |
| `security and investigations` |
| `security guards and patrol services` |
| `security systems services` |
| `semiconductor manufacturing` |
| `semiconductors` |
| `services for renewable energy` |
| `services for the elderly and disabled` |
| `sheet music publishing` |
| `shipbuilding` |
| `shuttles and special needs transportation services` |
| `sightseeing transportation` |
| `skiing facilities` |
| `smart meter manufacturing` |
| `soap and cleaning product manufacturing` |
| `social networking platforms` |
| `software development` |
| `solar electric power generation` |
| `sound recording` |
| `space research and technology` |
| `specialty trade contractors` |
| `spectator sports` |
| `sporting goods` |
| `sporting goods manufacturing` |
| `sports and recreation instruction` |
| `sports teams and clubs` |
| `spring and wire product manufacturing` |
| `staffing and recruiting` |
| `steam and air-conditioning supply` |
| `strategic management services` |
| `subdivision of land` |
| `sugar and confectionery product manufacturing` |
| `surveying and mapping services` |
| `taxi and limousine services` |
| `technical and vocational training` |
| `technology, information and internet` |
| `technology, information and media` |
| `telecommunications` |
| `telecommunications carriers` |
| `telephone call centers` |
| `temporary help services` |
| `textile manufacturing` |
| `theater companies` |
| `think tanks` |
| `tobacco` |
| `tobacco manufacturing` |
| `translation and localization` |
| `transportation equipment manufacturing` |
| `transportation programs` |
| `transportation, logistics, supply chain and storage` |
| `transportation/trucking/railroad` |
| `travel arrangements` |
| `truck transportation` |
| `trusts and estates` |
| `turned products and fastener manufacturing` |
| `urban transit services` |
| `utilities` |
| `utilities administration` |
| `utility system construction` |
| `vehicle repair and maintenance` |
| `venture capital and private equity principals` |
| `veterinary` |
| `veterinary services` |
| `vocational rehabilitation services` |
| `warehousing` |
| `warehousing and storage` |
| `waste collection` |
| `waste treatment and disposal` |
| `water supply and irrigation systems` |
| `water, waste, steam, and air conditioning services` |
| `wellness and fitness services` |
| `wholesale` |
| `wholesale alcoholic beverages` |
| `wholesale apparel and sewing supplies` |
| `wholesale appliances, electrical, and electronics` |
| `wholesale building materials` |
| `wholesale chemical and allied products` |
| `wholesale computer equipment` |
| `wholesale drugs and sundries` |
| `wholesale food and beverage` |
| `wholesale footwear` |
| `wholesale furniture and home furnishings` |
| `wholesale hardware, plumbing, heating equipment` |
| `wholesale import and export` |
| `wholesale luxury goods and jewelry` |
| `wholesale machinery` |
| `wholesale metals and minerals` |
| `wholesale motor vehicles and parts` |
| `wholesale paper products` |
| `wholesale petroleum and petroleum products` |
| `wholesale photography equipment and supplies` |
| `wholesale raw farm products` |
| `wholesale recyclable materials` |
| `wind electric power generation` |
| `wine & spirits` |
| `wineries` |
| `wireless services` |
| `women's handbag manufacturing` |
| `wood product manufacturing` |
| `writing and editing` |
| `zoos and botanical gardens` |
## Local business industries
Used by the `industry` filter in [Local Business Search](/apis/local-business-search-google-maps-search-api). This is a separate list from the company industries above: it contains local business categories such as `restaurant`, `dentist`, or `clothing store`. The full list is **35,429 values**, too large to display inline, so download it as CSV.
All 35,429 accepted local business industry values as CSV.
For example, some of the most common values are:
| Industry |
| ------------------ |
| `restaurant` |
| `store` |
| `corporate office` |
| `clothing store` |
| `dentist` |
## Employee ranges
Used by `employee_range` and `company_employee_range`.
| Value |
| ------------ |
| `1-10` |
| `11-50` |
| `51-200` |
| `201-500` |
| `501-1000` |
| `1001-5000` |
| `5001-10000` |
| `10001+` |
## Job title levels
Used by `job_title_level` in Person Search.
| Value |
| ---------- |
| `cxo` |
| `vp` |
| `director` |
| `manager` |
| `senior` |
| `entry` |
| `owner` |
| `partner` |
| `training` |
## Signal parameters
Used by the [Signals APIs](/buying-signals/signals-apis/introduction).
| Field | Accepted values |
| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `time_frame` | `7`, `30`, `90`, `180` |
| `bucket` | `low`, `moderate`, `high`, `hyper` |
| `type` (job changes) | `company_change`, `promotion`, `lateral_title_change`, `no_company_to_company` |
| `signal_name` | Signal keys from the [signal index](/buying-signals/concepts/signal-index). The People Signals API uses the 17 People-category keys; the Company Signals API uses the 82 company-related keys |
# Address Normalizer API
Source: https://apidoc.cufinder.io/apis/address-normalizer
POST https://api.cufinder.io/v3/normalize/address
Shipping errors and duplicate records multiply when addresses exist in inconsistent formats across your customer database. The Address Normalizer API standardizes physical addresses into USPS-compliant and international postal formats, returning cleaned addresses with validation status and component parsing that ensure delivery accuracy and database consistency. Built for logistics platforms, CRM systems, and data quality tools, this RESTful endpoint delivers normalized address data, including street corrections, postal code validation, and administrative region standardization, that power shipping automation, territory assignment accuracy, and duplicate contact consolidation workflows across your operations stack.
## Common Use Cases
Clean, consistent addresses keep deliveries, billing, and location reporting from breaking down.
* **Data Hygiene:** Standardizing messy address records before they enter your CRM or database.
* **Shipping and Logistics:** Validating delivery addresses to cut failed deliveries and returns.
* **Deduplication:** Matching records that describe the same location written different ways.
* **Billing and Compliance:** Keeping customer addresses consistent for invoicing and tax.
* **Analytics:** Cleaning location data so regional reports and maps stay accurate.
Credit usage is 1 credit per request.
## Attributes
The postal address to normalize.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"normalized": "5340 ALLA RD LOS ANGELES CA 90066 UNITED STATES"
},
"meta": {
"confidence": 95,
"query": {
"value": "5340 alla rd, los angeles, ca 90066, united states"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Standardize phone numbers to a clean format.
Clean and standardize company names.
List a company's office locations.
Enrich a company with full firmographics.
# Authentication
Source: https://apidoc.cufinder.io/apis/authentication
You'll need to authenticate your requests to access any of the endpoints in the CUFinder API. In this guide, we'll look at how authentication works.
## API Key authentication
To access the APIs provided by CUFinder, you will need to obtain an API key, which serves as the authentication mechanism for secure and authorized access to the platform's services.
This API key acts as a unique identifier that establishes the identity and permissions of your application, ensuring that only authorized users can interact with the CUFinder APIs. By requiring an API key for authentication, CUFinder maintains a secure and controlled environment, protecting the privacy and integrity of the data exchanged between your application and the platform.
## Get your API key
To obtain your API key, you must first log in to your CUFinder dashboard, which serves as a centralized hub for managing your account and accessing the various features and services provided by CUFinder. Once you have successfully logged in, navigate to the designated section or settings page within the dashboard where API key management is available. Within this section, you will find the option to generate or retrieve your unique API key. By clicking on the corresponding button or link, CUFinder will generate a secure and individualized API key specifically tied to your account and application.
Get your API key
## Use your API key
Send your key in the `x-api-key` header on every request:
```bash theme={null}
curl -X POST 'https://api.cufinder.io/v3/companies/email-finder' \
--header 'Content-Type: application/json' \
--header 'x-api-key: YOUR_API_KEY' \
--data '{"query": "cufinder.io"}'
```
A missing or invalid key returns `401` with the standard error envelope:
```json theme={null}
{
"success": false,
"error": {
"code": "unauthorized",
"message": "API key is missing or invalid."
}
}
```
Keep your API key secret. Do not embed it in client-side code or public repositories. Call the API from your backend, and rotate the key from your dashboard if it is ever exposed.
## Explore APIs
This next section provides a comprehensive list of all the available APIs, along with detailed instructions on how to use them. Additionally, you will find pseudocode examples in various programming languages for your reference.
# B2B Customers Finder API
Source: https://apidoc.cufinder.io/apis/b2b-customers-finder
POST https://api.cufinder.io/v3/companies/customers
Knowing who buys from your competitors reveals targeting opportunities that firmographic filters alone cannot surface. The B2B Customers Finder API analyzes domain relationships across CUFinder's 85M+ company database to identify verified business customers of any target company, returning customer lists with confidence scores, company profiles, and relationship indicators that expose competitive whitespace. Built for market intelligence platforms, competitive analysis tools, and partnership mapping systems, this RESTful endpoint delivers customer relationship data, including company size, industry, location, and technology stack, that power competitor displacement strategies, market expansion planning, and strategic partnership identification workflows across your revenue operations.
## Common Use Cases
Knowing who a company already sells to opens the door to lookalike targeting and competitive plays.
* **Account-Based Marketing:** Building target lists of companies that already buy from a similar vendor.
* **Competitive Intelligence:** Mapping who a competitor sells to and where to win share.
* **Partner Sourcing:** Finding companies that fit an ideal customer profile for partnerships.
* **Sales Prospecting:** Feeding likely buyers straight into outbound campaigns.
* **Market Research:** Sizing a segment by the customers already active in it.
Credit usage is 3 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"companies": [
{
"name": "Hertz",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "URBN",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Instacart",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Le Monde",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Gamma",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Runway",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Supabase",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Linear",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Decagon",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "ElevenLabs",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Browserbase",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Crypto.com",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Substack",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Jobber",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "Lightspeed",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 97,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Discover companies similar to a target.
Search companies with multiple filters.
Enrich a company with full firmographics.
Search 1B+ profiles with combined filters.
# Company B2B or B2C Checker API
Source: https://apidoc.cufinder.io/apis/company-b2b-or-b2c-checker
POST https://api.cufinder.io/v3/companies/business-model
Targeting precision depends on knowing whether companies sell to businesses or consumers before you allocate outreach resources. The Company B2B or B2C Checker API analyzes domain signals across CUFinder's 85M+ company database to classify whether a company operates a B2B or B2C business model, returning categorization labels with confidence scores and supporting indicators like customer type mentions, transaction patterns, and product positioning language. Built for sales automation platforms, market segmentation tools, and lead scoring systems, this RESTful endpoint delivers business model classification data, including mixed-model indicators, vertical market focus, and customer acquisition signals, that power qualified lead routing, personalized messaging strategies, and TAM calculation workflows across your revenue intelligence stack.
## Common Use Cases
A company's business model decides how you should sell to it, so it pays to sort accounts early.
* **Lead Routing:** Sending B2B and B2C leads down the right sales playbook automatically.
* **List Segmentation:** Splitting prospect lists by business model before outreach.
* **Ad Targeting:** Tailoring messaging and channels to the audience type.
* **Data Enrichment:** Tagging CRM records with a business-model field for filtering.
* **Market Research:** Profiling a market by its mix of B2B and B2C players.
Credit usage is 3 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"business_model": "b2b"
},
"meta": {
"confidence": 98,
"query": {
"query": "cufinder.io"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Check whether a company is a SaaS business.
List the technologies a company uses.
Enrich a company with full firmographics.
Search companies with multiple filters.
# Company Career Page Finder API
Source: https://apidoc.cufinder.io/apis/company-career-page-finder
POST https://api.cufinder.io/v3/companies/careers-page
Recruiting intelligence starts with knowing which companies are actively hiring and where they post open roles. The Company Career Page Finder API extracts verified career page URLs from any company domain across CUFinder's 85M+ company database, returning direct links to job boards, application portals, and recruitment landing pages with 97% accuracy. Built for recruiting platforms, talent sourcing tools, and HR technology systems, this RESTful endpoint delivers career page URLs, including subdomain variations, ATS-hosted pages, and third-party job board integrations, that power candidate outreach automation, competitive hiring intelligence, and employer brand monitoring workflows across your recruitment stack.
## Common Use Cases
Open roles work as both a growth signal and a buying signal, which is why several teams keep an eye on them.
* **Recruiting and Sourcing:** Linking straight to a company's open roles for candidate research.
* **Hiring Signals:** Tracking who is hiring as a buying signal for sales.
* **Competitive Intelligence:** Watching a competitor's roles to read their roadmap and growth.
* **Lead Enrichment:** Adding a careers URL to company records for recruiters.
* **Growth Research:** Gauging a company's stage before outreach.
Credit usage is 3 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"careers_page_url": "https://stripe.com/careers"
},
"meta": {
"confidence": 97,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Find employees working at a company.
Enrich a company with full firmographics.
Get a company's current employee count.
Search companies with multiple filters.
# Company Demo Checker API
Source: https://apidoc.cufinder.io/apis/company-demo-check
POST https://api.cufinder.io/v3/companies/demo-check
The Company Demo Checker API tells you whether a company offers a product demo. Pass a domain and get back a simple boolean, a fast qualifier for sales-motion research, competitive analysis, and product-led-growth segmentation.
## Common Use Cases
How a company sells is as revealing as what it sells.
* **Sales-Motion Research:** Classifying prospects by whether they run a demo-led sales process.
* **Competitive Analysis:** Mapping how companies in a category let buyers evaluate their product.
* **Lead Qualification:** Segmenting targets by sales model before outreach.
* **Market Research:** Measuring how common demo-led selling is in a segment.
Credit usage is 1 credit per request.
## Attributes
Company domain or website URL, for example `cufinder.io`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"offers_demo": true
},
"meta": {
"confidence": 97,
"query": {
"query": "cufinder.io"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Check whether a company offers a free trial.
Check whether a company is a SaaS business.
Classify a company as B2B or B2C.
Enrich a company with full firmographics.
# Company Email Finder API
Source: https://apidoc.cufinder.io/apis/company-email-finder
POST https://api.cufinder.io/v3/companies/email-finder
Business email addresses unlock direct communication channels that drive B2B outreach success. The [Company Email Finder](https://cufinder.io/enrichment-engine/company-name-to-company-email) API returns up to five verified company emails (info@, contact@, support@) from company names or domains with 97% confidence scores. Built for developers automating lead generation and contact enrichment, this RESTful endpoint delivers role-based email addresses that power CRM completion, outreach automation, and customer support workflows across your sales stack.
## Common Use Cases
Sales, marketing, and support teams reach for this endpoint whenever they have a company but no way to email it.
* **CRM Enrichment:** Automatically filling empty email fields in HubSpot or Salesforce.
* **Sales Prospecting:** Adding verified role-based addresses to cold email and SDR sequences.
* **Lead List Building:** Turning a batch of company names or domains into a contactable prospect list.
* **Support Routing:** Directing inbound questions to the right support@ or contact@ inbox.
* **Data Hygiene:** Replacing bounced or outdated company emails to protect deliverability.
* **Customer Onboarding:** Attaching a company email to new signups so sales can follow up.
Credit usage is 1 credit per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"emails": [
"info@cufinder.io",
"affiliate@cufinder.io",
"marketing@cufinder.io"
]
},
"meta": {
"confidence": 93,
"query": {
"query": "cufinder.io"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Find a company's phone numbers.
Find a company's LinkedIn page URL.
Enrich a person with full contact data.
Enrich a company with full firmographics.
# Company Employee Count API
Source: https://apidoc.cufinder.io/apis/company-employee-count
POST https://api.cufinder.io/v3/companies/employee-count
Employee distribution data reveals organizational scale and geographic presence that sales teams need for territory planning. The Company Employee Count API returns detailed headcount breakdowns by country from company names or domains with 98% confidence scores. Built for developers creating workforce intelligence and market sizing tools, this RESTful endpoint delivers granular employee location data that power territory assignment, market entry strategies, and account prioritization workflows across your sales stack.
## Common Use Cases
Headcount is a fast proxy for company size, and it quietly drives scoring, segmentation, and planning.
* **Lead Scoring:** Prioritizing accounts by company size before reps spend time on them.
* **Segmentation:** Splitting lists into SMB, mid-market, and enterprise tiers.
* **Territory Planning:** Balancing sales territories by headcount.
* **Data Enrichment:** Filling the employee-count field across your CRM.
* **Market Sizing:** Estimating the reach of a segment by total headcount.
Credit usage is 1 credit per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"employee_count": 12918,
"countries": [
{
"country": "united states",
"employee_count": 7758
},
{
"country": "india",
"employee_count": 1134
},
{
"country": "ireland",
"employee_count": 647
},
{
"country": "null",
"employee_count": 640
},
{
"country": "canada",
"employee_count": 593
},
{
"country": "united kingdom",
"employee_count": 381
},
{
"country": "singapore",
"employee_count": 378
},
{
"country": "mexico",
"employee_count": 270
},
{
"country": "australia",
"employee_count": 134
},
{
"country": "germany",
"employee_count": 105
},
{
"country": "spain",
"employee_count": 101
},
{
"country": "france",
"employee_count": 97
},
{
"country": "romania",
"employee_count": 83
},
{
"country": "japan",
"employee_count": 48
},
{
"country": "brazil",
"employee_count": 42
},
{
"country": "netherlands",
"employee_count": 39
},
{
"country": "philippines",
"employee_count": 37
},
{
"country": "sweden",
"employee_count": 28
},
{
"country": "united arab emirates",
"employee_count": 26
},
{
"country": "poland",
"employee_count": 22
},
{
"country": "indonesia",
"employee_count": 18
},
{
"country": "italy",
"employee_count": 16
},
{
"country": "nigeria",
"employee_count": 15
},
{
"country": "south africa",
"employee_count": 15
},
{
"country": "pakistan",
"employee_count": 14
},
{
"country": "taiwan",
"employee_count": 14
},
{
"country": "thailand",
"employee_count": 14
},
{
"country": "malaysia",
"employee_count": 14
},
{
"country": "bangladesh",
"employee_count": 13
},
{
"country": "new zealand",
"employee_count": 11
},
{
"country": "kenya",
"employee_count": 11
},
{
"country": "turkey",
"employee_count": 10
},
{
"country": "belgium",
"employee_count": 10
},
{
"country": "hong kong s.a.r.",
"employee_count": 8
},
{
"country": "switzerland",
"employee_count": 8
},
{
"country": "israel",
"employee_count": 8
},
{
"country": "costa rica",
"employee_count": 7
},
{
"country": "morocco",
"employee_count": 7
},
{
"country": "egypt",
"employee_count": 7
},
{
"country": "ghana",
"employee_count": 6
},
{
"country": "china",
"employee_count": 6
},
{
"country": "ukraine",
"employee_count": 6
},
{
"country": "panama",
"employee_count": 6
},
{
"country": "colombia",
"employee_count": 5
},
{
"country": "austria",
"employee_count": 5
},
{
"country": "greece",
"employee_count": 5
},
{
"country": "serbia",
"employee_count": 4
},
{
"country": "nepal",
"employee_count": 4
},
{
"country": "peru",
"employee_count": 4
},
{
"country": "uzbekistan",
"employee_count": 4
},
{
"country": "cambodia",
"employee_count": 4
},
{
"country": "vietnam",
"employee_count": 4
},
{
"country": "algeria",
"employee_count": 3
},
{
"country": "denmark",
"employee_count": 3
},
{
"country": "south korea",
"employee_count": 3
},
{
"country": "tunisia",
"employee_count": 3
},
{
"country": "norway",
"employee_count": 3
},
{
"country": "bosnia and herzegovina",
"employee_count": 3
},
{
"country": "estonia",
"employee_count": 3
},
{
"country": "uruguay",
"employee_count": 3
},
{
"country": "hungary",
"employee_count": 2
},
{
"country": "azerbaijan",
"employee_count": 2
},
{
"country": "argentina",
"employee_count": 2
},
{
"country": "iraq",
"employee_count": 2
},
{
"country": "czech republic",
"employee_count": 2
},
{
"country": "uganda",
"employee_count": 2
},
{
"country": "bulgaria",
"employee_count": 2
},
{
"country": "iran",
"employee_count": 2
},
{
"country": "luxembourg",
"employee_count": 2
},
{
"country": "saudi arabia",
"employee_count": 2
},
{
"country": "jamaica",
"employee_count": 2
},
{
"country": "portugal",
"employee_count": 2
},
{
"country": "sri lanka",
"employee_count": 2
},
{
"country": "gambia the",
"employee_count": 1
},
{
"country": "senegal",
"employee_count": 1
},
{
"country": "cote d'ivoire (ivory coast)",
"employee_count": 1
},
{
"country": "suriname",
"employee_count": 1
},
{
"country": "the bahamas",
"employee_count": 1
},
{
"country": "finland",
"employee_count": 1
},
{
"country": "lithuania",
"employee_count": 1
},
{
"country": "cuba",
"employee_count": 1
},
{
"country": "bolivia",
"employee_count": 1
},
{
"country": "montenegro",
"employee_count": 1
},
{
"country": "lebanon",
"employee_count": 1
},
{
"country": "new caledonia",
"employee_count": 1
},
{
"country": "gabon",
"employee_count": 1
},
{
"country": "albania",
"employee_count": 1
},
{
"country": "belarus",
"employee_count": 1
},
{
"country": "belize",
"employee_count": 1
},
{
"country": "saint lucia",
"employee_count": 1
},
{
"country": "kazakhstan",
"employee_count": 1
},
{
"country": "ecuador",
"employee_count": 1
},
{
"country": "slovenia",
"employee_count": 1
},
{
"country": "jordan",
"employee_count": 1
},
{
"country": "malawi",
"employee_count": 1
}
]
},
"meta": {
"confidence": 97,
"query": {
"query": "stripe"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Find employees working at a company.
Estimate a company's annual revenue.
Enrich a company with full firmographics.
Search companies with multiple filters.
# Company Employee Finder API
Source: https://apidoc.cufinder.io/apis/company-employee-finder
POST https://api.cufinder.io/v3/companies/employees
Account-based selling requires knowing exactly who works at target companies before crafting personalized outreach sequences. The Company Employee Finder API retrieves verified employee lists from any company name across CUFinder's 1B+ professional profiles, returning contact records with job titles, departments, seniority levels, and professional details that enable multi-threaded engagement strategies. Built for sales intelligence platforms, recruiting tools, and ABM systems, this RESTful endpoint delivers employee data, including decision-maker identification, reporting structures, and LinkedIn profile URLs, that power org chart mapping, buying committee targeting, and talent pipeline building workflows across your revenue and HR operations.
## Common Use Cases
Reaching the right people inside an account starts with knowing who actually works there.
* **Sales Prospecting:** Surfacing the people at a target account worth reaching.
* **Recruiting:** Sourcing candidates who work at a specific company.
* **Org Mapping:** Building a picture of teams and reporting lines inside an account.
* **Account-Based Marketing:** Expanding contacts within a target account.
* **Competitive Intelligence:** Seeing who works on a competitor's key teams.
Credit usage is 2 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
Page number for paginated results. Each page returns up to 10 records.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"people": [
{
"first_name": "morteza",
"last_name": "heydari",
"full_name": "morteza heydari",
"avatar_url": "https://media.cufinder.io/person_profile/mortezaheydari1997",
"job": {
"title": "backend developer",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "turkey",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/mortezaheydari1997",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "iman",
"last_name": "moem",
"full_name": "iman moem",
"avatar_url": "https://media.cufinder.io/person_profile/iman-moem-619083281",
"job": {
"title": "customer marketing support specialist | driving customer success and engagement",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "germany",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/iman-moem-619083281",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "mary",
"last_name": "jalilibaleh",
"full_name": "mary jalilibaleh",
"avatar_url": "https://media.cufinder.io/person_profile/mary-jalilibaleh",
"job": {
"title": "marketing manager",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "germany",
"state": "bavaria",
"city": "gunzenhausen",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/mary-jalilibaleh",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "ruaa hussein naji al",
"last_name": "wawi",
"full_name": "ruaa hussein naji al wawi",
"avatar_url": "https://media.cufinder.io/person_profile/ruaa-hussein-naji-al-wawi-92398a280",
"job": {
"title": "technical support",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "germany",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/ruaa-hussein-naji-al-wawi-92398a280",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "seyyed mohammad",
"last_name": "razavi",
"full_name": "seyyed mohammad razavi",
"avatar_url": "https://media.cufinder.io/person_profile/seyyed-mohammad-razavi-b201384b",
"job": {
"title": "run gtms on signals | the real-time signal data for ai agents | ceo @cufinder",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "united states",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/seyyed-mohammad-razavi-b201384b",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "cufinder",
"last_name": "meet",
"full_name": "cufinder meet",
"avatar_url": "https://media.cufinder.io/person_profile/cufinder-meet-04536931b",
"job": {
"title": "salesperson",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "germany",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/cufinder-meet-04536931b",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "elnaz",
"last_name": "reihani",
"full_name": "elnaz reihani",
"avatar_url": "https://media.cufinder.io/person_profile/elnaz-reihani-2623011b6",
"job": {
"title": "master candidate of cardiovascular science | institute of medicine",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "germany",
"state": "lower saxony",
"city": "göttingen",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/elnaz-reihani-2623011b6",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "mujtaba",
"last_name": "hashemi",
"full_name": "mujtaba hashemi",
"avatar_url": "https://media.cufinder.io/person_profile/mujtaba-hashemi-725077282",
"job": {
"title": "data analyst",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "turkey",
"state": "istanbul",
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/mujtaba-hashemi-725077282",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "url",
"last_name": "finder",
"full_name": "url finder",
"avatar_url": "https://media.cufinder.io/person_profile/url-finder-1842a81a9",
"job": {
"title": "software engineer",
"level": null,
"categories": []
},
"company": {
"name": "company url finder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "iran",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/url-finder-1842a81a9",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": "shaghayegh",
"last_name": "ensafi",
"full_name": "shaghayegh ensafi",
"avatar_url": "https://media.cufinder.io/person_profile/shaghayegh-ensafi",
"job": {
"title": "b.sc in computer science | forex technical analyst | web app developer | fx trader",
"level": null,
"categories": []
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "hamburg",
"city": "hamburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "united arab emirates",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/shaghayegh-ensafi",
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 97,
"query": {
"query": "linkedin.com/company/cufinder",
"page": 1
},
"credits": {
"charged": 2,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Get a company's current employee count.
Search 1B+ profiles with combined filters.
Enrich a person with full contact data.
Enrich a company with full firmographics.
# Company Enrichment API
Source: https://apidoc.cufinder.io/apis/company-enrichment
POST https://api.cufinder.io/v3/companies/enrich
Complete company profiles eliminate manual research that slows down sales cycles and qualification processes. The [Company Enrichment](https://cufinder.io/enrichment-engine/company-enrichment) API returns comprehensive business data, including industry, revenue, headcount, contact info, and tech stack, from a single company name, domain, or LinkedIn URL with 95% confidence scores. Built for developers creating full-scale B2B enrichment and CRM automation tools, this RESTful endpoint delivers 15+ verified data points that power lead qualification, account intelligence, and data completion workflows across your sales stack.
## Common Use Cases
A complete company record feeds almost everything downstream, from lead scoring to routing.
* **CRM Enrichment:** Filling in firmographics like size, industry, and location on every record.
* **Lead Scoring:** Qualifying inbound leads with complete company data.
* **Segmentation:** Grouping accounts by industry, size, or revenue for targeting.
* **Data Hygiene:** Refreshing stale company records on a schedule.
* **Routing and Assignment:** Sending leads to the right rep based on firmographics.
Credit usage is 4 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"company": {
"name": "stripe",
"domain": "stripe.com",
"website_url": "https://stripe.com",
"industry": "technology, information and internet",
"type": "privately held",
"employee_range": "5001-10000",
"followers_count": 1649844,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/stripe.com",
"location": {
"country": "united states",
"state": "california",
"city": "south san francisco",
"address": "354 oyster point blvd,south san francisco, california 94080, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/stripe",
"facebook_url": null,
"twitter_url": null
},
"description": "stripe builds programmable financial services. millions of companies—from the world’s largest enterprises to the most ambitious startups—use stripe to accept payments, grow their revenue, and accelerate new business oppo …",
"tagline": null,
"founded_year": 2010,
"industries": [
"technology, information and internet"
],
"specialties": [],
"employee_count": 17142,
"locations": [
{
"country": "united states",
"state": "california",
"city": "south san francisco",
"address": "354 oyster point blvd,south san francisco, california 94080, us",
"postal_code": null
}
],
"emails": [],
"phones": [
"+14585552863"
],
"revenue": null,
"funding": null,
"attributes": {
"is_saas": null,
"business_model": null,
"is_school": null,
"offers_demo": null,
"offers_free_trial": null
},
"tech_stack": [],
"careers_page_url": null,
"mission_statement": null,
"snapshot": null
}
},
"meta": {
"confidence": 99,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 4,
"remaining": 9862
}
}
}
```
## Related APIs
Get a company's current employee count.
Estimate a company's annual revenue.
List a company's office locations.
List the technologies a company uses.
# Company Free Trial Checker API
Source: https://apidoc.cufinder.io/apis/company-free-trial-check
POST https://api.cufinder.io/v3/companies/free-trial-check
The Company Free Trial Checker API tells you whether a company offers a free trial. Pass a domain and get back a simple boolean, useful for product-led-growth research, competitive teardowns, and segmenting markets by go-to-market model.
## Common Use Cases
A free trial is the clearest public marker of a product-led go-to-market.
* **PLG Research:** Identifying which companies in a market sell product-led.
* **Competitive Teardowns:** Tracking which rivals let buyers self-serve an evaluation.
* **Segmentation:** Splitting target lists by sales model for tailored messaging.
* **Partner Sourcing:** Finding self-serve products that fit marketplace or integration plays.
Credit usage is 1 credit per request.
## Attributes
Company domain or website URL, for example `cufinder.io`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"offers_free_trial": true
},
"meta": {
"confidence": 93,
"query": {
"query": "cufinder.io"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Check whether a company offers a product demo.
Check whether a company is a SaaS business.
List the technologies a company uses.
Enrich a company with full firmographics.
# Company Fundraising API
Source: https://apidoc.cufinder.io/apis/company-fundraising
POST https://api.cufinder.io/v3/companies/fundraising
Funding data signals company growth momentum that sales and investment teams track religiously. The [Company Fundraising](https://cufinder.io/enrichment-engine/enrich-company-with-latest-fundraising-data) API returns detailed investment information including funding rounds, raised amounts, and investor names from company queries with 97% confidence scores. Built for developers creating VC tools and sales intelligence platforms, this RESTful endpoint delivers verified fundraising details that power lead scoring systems, market research dashboards, and account prioritization workflows across your sales stack.
## Common Use Cases
Fresh funding usually means new budget and urgency, which makes it worth watching across sales and research.
* **Buying Signals:** Reaching out to companies right after they raise new capital.
* **Investor Research:** Tracking who funded a company and at what stage.
* **Lead Prioritization:** Ranking accounts by funding stage and total raised.
* **Market Research:** Following funding trends across a sector.
* **Partnership Timing:** Approaching well-funded companies with budget to spend.
Credit usage is 4 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"funding": {
"total_raised_usd": 6500000000,
"last_round_type": "series i",
"last_round_date": null,
"rounds": []
}
},
"meta": {
"confidence": 96,
"query": {
"query": "stripe"
},
"credits": {
"charged": 4,
"remaining": 9862
}
}
}
```
## Related APIs
Estimate a company's annual revenue.
Enrich a company with full firmographics.
Get a company's current employee count.
Get a quick company overview.
# LinkedIn Company URL Finder API
Source: https://apidoc.cufinder.io/apis/company-linkedin-url-finder
POST https://api.cufinder.io/v3/companies/linkedin-finder
LinkedIn company profiles contain valuable business intelligence that sales teams need daily. The [LinkedIn Company URL Finder](https://cufinder.io/enrichment-engine/company-name-to-company-linkedin) API solves this by converting company names or domains into verified LinkedIn URLs with 96% confidence scores. Built for developers creating sales intelligence platforms, this RESTful endpoint returns accurate LinkedIn profile links that power prospect research automation, CRM data enrichment, and competitive analysis workflows across your tech stack.
## Common Use Cases
A company's LinkedIn page is a hub for research and social selling, so teams like to keep it on the record.
* **CRM Enrichment:** Adding a company's LinkedIn page to its record for the whole team.
* **Social Selling:** Giving reps a direct link to engage an account on LinkedIn.
* **Data Enrichment:** Connecting a name or domain to richer social data.
* **Lead Research:** Jumping from a domain to a company's LinkedIn for quick context.
* **Recruiting:** Finding the official company page to source from.
Credit usage is 3 credits per request.
## Attributes
Company name, for example `apple`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"linkedin_url": "linkedin.com/company/apple"
},
"meta": {
"confidence": 95,
"query": {
"name": "apple"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Resolve a company name to its verified website domain.
Enrich a company with full firmographics.
Find employees working at a company.
Get the company name behind a domain.
# Company Locations API
Source: https://apidoc.cufinder.io/apis/company-locations
POST https://api.cufinder.io/v3/companies/locations
Office addresses provide geographic context that field sales teams need for territory planning and in-person meetings. The Company Locations API returns verified business addresses including country, state, city, postal code, and street details from company queries with 95% confidence scores. Built for developers creating field sales and logistics tools, this RESTful endpoint delivers complete location data that power route planning, regional targeting, and local market analysis workflows across your sales stack.
## Common Use Cases
Where a company operates shapes territory assignments, field visits, and compliance checks.
* **Territory Planning:** Assigning accounts to reps by office location.
* **Field Sales:** Routing on-site visits and events around company sites.
* **Market Research:** Mapping a company's geographic footprint.
* **Data Enrichment:** Adding office and HQ locations to CRM records.
* **Compliance:** Checking where a company operates for regional rules.
Credit usage is 2 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"locations": [
{
"country": "united states",
"state": "washington",
"city": "redmond",
"address": "1 Microsoft Way",
"postal_code": "98052"
},
{
"country": "australia",
"state": "nsw",
"city": "north sydney",
"address": "1 Denison Street",
"postal_code": "2060"
},
{
"country": "canada",
"state": "ontario",
"city": "mississauga",
"address": "1950 Meadowvale Blvd",
"postal_code": "L5N 8L9"
},
{
"country": "france",
"state": "île-de-france",
"city": "issy-les-moulineaux",
"address": "39, Quai du Président Roosevelt",
"postal_code": "92130"
},
{
"country": "germany",
"state": null,
"city": "münchen",
"address": "Walter-Gropius-Straße 5",
"postal_code": "80807"
},
{
"country": "japan",
"state": "tokyo",
"city": "minato-ku",
"address": "2-16-3 Konan",
"postal_code": "108-0075"
},
{
"country": "united kingdom",
"state": "berkshire",
"city": "reading",
"address": "Thames Valley Park Drive",
"postal_code": "RG6 1WG"
},
{
"country": "denmark",
"state": null,
"city": null,
"address": "Kanalvej 7",
"postal_code": "2800 Kongens Lyngby"
},
{
"country": "belgium",
"state": "flemish region",
"city": "zaventem",
"address": "Luchthavenlaan 1k",
"postal_code": "1930"
},
{
"country": "finland",
"state": null,
"city": "espoo",
"address": "Keilalahdentie 2-4",
"postal_code": "02150"
},
{
"country": "italy",
"state": "lombardy",
"city": "milan",
"address": "Viale Pasubio, 21",
"postal_code": "20154"
},
{
"country": "south korea",
"state": "seoul",
"city": "jongno-gu",
"address": "50, Jongro 1-gil",
"postal_code": null
},
{
"country": "netherlands",
"state": "north holland",
"city": "schiphol",
"address": "Evert van de Beekstraat 354",
"postal_code": "1118 CZ"
},
{
"country": "norway",
"state": null,
"city": "oslo",
"address": "Dronning Eufemia gate 71",
"postal_code": "0194"
},
{
"country": "spain",
"state": null,
"city": "pozuelo de alarcón",
"address": "Paseo del Club Deportivo, 1",
"postal_code": "28223"
},
{
"country": "sweden",
"state": null,
"city": "stockholm",
"address": "Regeringsgatan 25",
"postal_code": "111 53"
},
{
"country": "switzerland",
"state": null,
"city": "zürich",
"address": "8058 Zürich-Flughafen",
"postal_code": null
},
{
"country": "brazil",
"state": null,
"city": "sao paulo",
"address": "Av. President Juscelino Kubitscheck, 1909",
"postal_code": "04551-065"
},
{
"country": "china",
"state": null,
"city": "中国",
"address": "海淀区，丹棱街5号",
"postal_code": "100080"
},
{
"country": "india",
"state": null,
"city": "gurgaon",
"address": "DLF Building No.5 (Epitome), Cyber City, DLF Phase III",
"postal_code": "122002"
},
{
"country": "mexico",
"state": null,
"city": "del álvaro obregón",
"address": "Av. Vasco de Quiroga 3200",
"postal_code": "01210"
},
{
"country": "russia",
"state": null,
"city": "москва",
"address": "Ул. Крылатская, 17",
"postal_code": "121614"
},
{
"country": "south africa",
"state": "gauteng",
"city": "johannesburg",
"address": "3012 William Nicol Drive",
"postal_code": "2191"
},
{
"country": "turkey",
"state": null,
"city": "i̇stanbul",
"address": "Aydın Sokak No:7",
"postal_code": "34340"
},
{
"country": "austria",
"state": null,
"city": "vienna",
"address": "Am Europlatz 3",
"postal_code": "1120"
},
{
"country": "hong kong",
"state": null,
"city": "hong kong",
"address": "100 Cyberport Road",
"postal_code": null
},
{
"country": "ireland",
"state": "dublin",
"city": "leopardstown",
"address": "One Microsoft Place",
"postal_code": "18 D18"
},
{
"country": "israel",
"state": null,
"city": "רעננה",
"address": "רחוב הפנינה 2",
"postal_code": "43107"
},
{
"country": "new zealand",
"state": null,
"city": "auckland",
"address": "22 Viaduct Harbour Avenue",
"postal_code": null
},
{
"country": "poland",
"state": null,
"city": "warszawa",
"address": "Al. Jerozolimskie 195a",
"postal_code": "02-222"
},
{
"country": "portugal",
"state": null,
"city": "lisboa",
"address": "Rua do Fogo de Santelmo, Lote 2.07.02",
"postal_code": "1990 – 110"
},
{
"country": "saudi arabia",
"state": null,
"city": "riyadh",
"address": "The Business Gate, Building A2 Airport Road, Cordobah",
"postal_code": "11552"
},
{
"country": "singapore",
"state": null,
"city": "singapore",
"address": "182 Cecil Street",
"postal_code": "069547"
},
{
"country": "slovakia",
"state": null,
"city": "bratislava",
"address": "Pradiareň 1900",
"postal_code": "821 08"
},
{
"country": "taiwan",
"state": "taipei city",
"city": "xinyi district",
"address": "Zhongxiao East Road Section 5 No. 68",
"postal_code": null
},
{
"country": "united arab emirates",
"state": null,
"city": "dubai",
"address": "Sheikh Zayed Road",
"postal_code": "52244"
},
{
"country": "argentina",
"state": null,
"city": "buenos aires",
"address": "1106 Federal Capital",
"postal_code": null
},
{
"country": "chile",
"state": null,
"city": "santiago",
"address": "Av. Vitacura 6844",
"postal_code": null
},
{
"country": "colombia",
"state": null,
"city": "bogota",
"address": "Calle 92 # 11 – 51",
"postal_code": null
},
{
"country": "egypt",
"state": null,
"city": "cairo",
"address": "Kilo 28, Cairo/Alex Desert Road",
"postal_code": null
},
{
"country": "indonesia",
"state": null,
"city": "jakarta",
"address": "Jl. Jend. Sudirman Kav. 52-53",
"postal_code": "12190"
},
{
"country": "malaysia",
"state": null,
"city": "kuala lumpur",
"address": "No. 211, Jalan Tun Sambanthan",
"postal_code": "50470"
},
{
"country": "philippines",
"state": null,
"city": "makati city",
"address": "Ayala Avenue cor Edsa, Brgy. San Lorenzo,",
"postal_code": "1223"
},
{
"country": "romania",
"state": null,
"city": "bucurești",
"address": "Bulevardul Iuliu Maniu nr. 6P",
"postal_code": "061103"
},
{
"country": "thailand",
"state": null,
"city": "เขตปทุมวัน กรุงเทพฯ",
"address": "87/2 ถนนวิทยุ แขวงลุมพินี",
"postal_code": "10330"
}
]
},
"meta": {
"confidence": 97,
"query": {
"query": "microsoft"
},
"credits": {
"charged": 2,
"remaining": 9862
}
}
}
```
## Related APIs
Enrich a company with full firmographics.
Map a company's subsidiaries.
Find local businesses via Google Maps.
Standardize and validate postal addresses.
# Company Lookalikes Finder API
Source: https://apidoc.cufinder.io/apis/company-lookalikes-finder
POST https://api.cufinder.io/v3/companies/lookalikes
Similar companies reveal untapped markets and competitor landscapes that fuel expansion strategies. The [Company Lookalikes Finder](https://cufinder.io/enrichment-engine/find-company-lookalikes) API returns a curated list of companies matching your target profile's industry, size, and characteristics with 98% confidence scores. Built for developers creating prospect intelligence and market analysis tools, this RESTful endpoint delivers comprehensive company profiles including employee counts, revenue estimates, and contact details that power account-based marketing, competitive research, and lead generation workflows across your sales stack.
## Common Use Cases
One strong customer can seed a whole list of similar accounts worth pursuing.
* **Account-Based Marketing:** Expanding a target list with companies like your best customers.
* **Prospecting:** Finding new accounts that match a proven ideal customer profile.
* **Market Research:** Discovering competitors and peers in a segment.
* **Partnership Sourcing:** Surfacing companies similar to existing partners.
* **List Building:** Growing campaign lists from a single seed company.
Credit usage is 5 credits per request.
## Attributes
Company domain, name, or LinkedIn URL, for example `stripe`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"companies": [
{
"name": "airbnb",
"domain": "airbnb.com",
"website_url": "http://airbnb.com",
"industry": "software development",
"type": "public company",
"employee_range": "5001-10000",
"followers_count": 3441743,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/airbnb.com",
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": "888 brannan street,san francisco, ca 94103, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/airbnb",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "anthropic",
"domain": "anthropic.com",
"website_url": "https://www.anthropic.com",
"industry": "research services",
"type": "privately held",
"employee_range": "501-1000",
"followers_count": 4592335,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/anthropic.com",
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/anthropicresearch",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "atlassian",
"domain": "atlassian.com",
"website_url": "https://atlassian.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": 2619337,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/atlassian.com",
"location": {
"country": "australia",
"state": "nsw",
"city": "sydney",
"address": "level 6/341 george st,sydney, nsw 2000, au",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/atlassian",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "databricks",
"domain": "databricks.com",
"website_url": "https://databricks.com",
"industry": "software development",
"type": "privately held",
"employee_range": "5001-10000",
"followers_count": 1350782,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/databricks.com",
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": "160 spear street,13th floor,san francisco, ca 94105, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/databricks",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "google",
"domain": "google.com",
"website_url": "https://google.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": 42334852,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/google.com",
"location": {
"country": "united states",
"state": "california",
"city": "mountain view",
"address": "1600 amphitheatre parkway,mountain view, ca 94043, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/google",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "paypal",
"domain": "paypal.com",
"website_url": "https://paypal.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": 1722232,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/paypal.com",
"location": {
"country": "united states",
"state": "california",
"city": "san jose",
"address": "2211 north first street,san jose, ca 95131, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/paypal",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "revolut",
"domain": "revolut.com",
"website_url": "https://www.revolut.com",
"industry": "financial services",
"type": "privately held",
"employee_range": "10001+",
"followers_count": 2211802,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/revolut.com",
"location": {
"country": "united kingdom",
"state": "england",
"city": "london",
"address": "30 s colonnade,london, england e14 5hx, gb",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/revolut",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "rippling",
"domain": "rippling.com",
"website_url": "https://www.rippling.com",
"industry": "software development",
"type": "privately held",
"employee_range": "1001-5000",
"followers_count": 469324,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/rippling.com",
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": "430 california st,san francisco, california 94104, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/rippling",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "salesforce",
"domain": "salesforce.com",
"website_url": "http://www.salesforce.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": 6738074,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/salesforce.com",
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": "415 mission st,san francisco, california 94105, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/salesforce",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "wise",
"domain": "wise.com",
"website_url": "https://wise.com",
"industry": "financial services",
"type": "public company",
"employee_range": "5001-10000",
"followers_count": 670223,
"linkedin_id": null,
"logo_url": "media.cufinder.io/domain_logo/wise.com",
"location": {
"country": "united kingdom",
"state": null,
"city": "london",
"address": "the tea building,56 shoreditch high street,london, e1 6jj, gb",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/wiseaccount",
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 95,
"query": {
"query": "stripe"
},
"credits": {
"charged": 5,
"remaining": 9862
}
}
}
```
## Related APIs
Search companies with multiple filters.
Identify a company's likely B2B customers.
Map a company's subsidiaries.
Enrich a company with full firmographics.
# Company Mission Statement API
Source: https://apidoc.cufinder.io/apis/company-mission-statement
POST https://api.cufinder.io/v3/companies/mission
Personalized outreach converts better when messaging aligns with a prospect's stated values and strategic direction. The Company Mission Statement API extracts verified mission statements from any company domain across CUFinder's 85M+ company database, returning full mission text with source URLs and extraction confidence scores that validate statement authenticity. Built for sales engagement platforms, personalization engines, and account research tools, this RESTful endpoint delivers mission statement data, including vision statements, core values, and purpose declarations, that power contextual email writing, pitch deck customization, and strategic account planning workflows across your customer acquisition systems.
## Common Use Cases
What a company says it stands for is useful context for any kind of personalized outreach.
* **Personalized Outreach:** Referencing a company's mission to warm up cold emails.
* **Account Research:** Understanding what an account cares about before a call.
* **Messaging:** Tailoring pitches to a company's stated values.
* **Data Enrichment:** Adding mission text to CRM records for context.
* **Market Research:** Comparing how companies in a sector position themselves.
Credit usage is 3 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"mission_statement": "We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems."
},
"meta": {
"confidence": 99,
"query": {
"query": "openai.com"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Get a quick company overview.
Enrich a company with full firmographics.
List the technologies a company uses.
Search companies with multiple filters.
# Company Name Normalizer API
Source: https://apidoc.cufinder.io/apis/company-name-normalizer
POST https://api.cufinder.io/v3/normalize/company-name
Duplicate records and CRM chaos emerge when company names appear as "ACME corp", "acme Corp.", and "Acme Corporation" across your database. The Company Name Normalizer API standardizes company names by capitalizing the first letter of each word and removing extra spaces, returning cleaned names with consistent formatting that ensure database integrity and deduplication accuracy. Built for data enrichment platforms, CRM hygiene tools, and marketing automation systems, this RESTful endpoint delivers normalized company name data, including whitespace corrections, capitalization standardization, and formatting consistency, that power duplicate detection, account matching accuracy, and clean data integration workflows across your sales and marketing stack.
## Common Use Cases
Inconsistent company names quietly corrupt matching and reporting, so most teams clean them up first.
* **Data Hygiene:** Cleaning inconsistent company names before they hit your database.
* **Deduplication:** Merging records that spell the same company different ways.
* **CRM Matching:** Aligning incoming names with existing accounts.
* **Analytics:** Standardizing names so reports group the same company together.
* **Data Integration:** Reconciling company names across systems and imports.
Free. This endpoint does not charge credits.
## Attributes
The company name to normalize, for example `google l.l.c`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"normalized": "Google L.L.C"
},
"meta": {
"confidence": 97,
"query": {
"value": "google l.l.c"
},
"credits": {
"charged": 0,
"remaining": 9862
}
}
}
```
## Related APIs
Resolve a company name to its verified website domain.
Get the company name behind a domain.
Standardize and validate postal addresses.
Standardize phone numbers to a clean format.
# Company Name to Domain API
Source: https://apidoc.cufinder.io/apis/company-name-to-domain
POST https://api.cufinder.io/v3/companies/name-to-domain
Company websites are the foundation of B2B lead enrichment and outreach campaigns. The Company Name to Domain API [transforms company names into verified website URLs](https://cufinder.io/enrichment-engine/company-name-to-domain) with 94-98% accuracy, pulling from CUFinder's database of 85M+ enriched profiles. Built for developers automating CRM enrichment and lead qualification, this RESTful endpoint returns official domain addresses with confidence scores that power data pipelines, webhook integrations, and real-time lookup systems across your sales stack.
## Common Use Cases
A verified domain is the anchor for most enrichment, so this endpoint shows up all over the stack.
* **CRM Enrichment:** Automatically populating empty website fields in HubSpot or Salesforce.
* **Lead Qualification:** Quickly uncovering the correct website for targeted B2B prospecting and cold outreach.
* **Brand Intelligence:** Connecting basic text inputs to richer datasets and logo libraries.
* **Data Cleanup:** Resolving company names to canonical domains for deduplication.
* **Pipeline Automation:** Feeding verified domains into downstream enrichment steps.
Credit usage is 1 credit per request.
## Attributes
Company name, for example `stripe`.
ISO country code to disambiguate the lookup. Defaults to `US`.
Company address, as an extra disambiguation hint.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"domain": "stripe.com"
},
"meta": {
"confidence": 95,
"query": {
"name": "stripe",
"country_code": "US"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Get the company name behind a domain.
Clean and standardize company names.
Find a company's LinkedIn page URL.
Enrich a company with full firmographics.
# Company Phone Finder API
Source: https://apidoc.cufinder.io/apis/company-phone-finder
POST https://api.cufinder.io/v3/companies/phone-finder
Phone numbers create direct lines to prospects that email alone can't match. The [Company Phone Finder](https://cufinder.io/enrichment-engine/company-name-to-company-phone) API returns up to two verified business phone numbers from company names, domains, or LinkedIn URLs with 93% confidence scores. Built for developers building CRM enrichment and customer support tools, this RESTful endpoint delivers validated contact numbers that power outbound calling systems, data completion workflows, and multi-channel outreach campaigns across your sales stack.
## Common Use Cases
When email alone falls flat, a verified company line gives reps another way to connect.
* **Sales Outreach:** Adding a phone number so reps can call instead of only emailing.
* **CRM Enrichment:** Filling empty phone fields across company records.
* **Lead Routing:** Equipping call teams with a verified company line.
* **Verification:** Confirming a company is reachable before outreach.
* **Data Hygiene:** Replacing disconnected or outdated numbers.
Credit usage is 2 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"phones": [
"+800046046",
"+800554533",
"+900150503"
]
},
"meta": {
"confidence": 98,
"query": {
"query": "apple"
},
"credits": {
"charged": 2,
"remaining": 9862
}
}
}
```
## Related APIs
Return verified role-based company emails.
Enrich a company with full firmographics.
Find a company's LinkedIn page URL.
Standardize phone numbers to a clean format.
# Company Revenue Finder API
Source: https://apidoc.cufinder.io/apis/company-revenue-finder
POST https://api.cufinder.io/v3/companies/revenue
Revenue estimates drive lead scoring decisions that separate high-value prospects from time-wasters. The [Company Revenue Finder](https://cufinder.io/enrichment-engine/find-company-annual-revenue) API returns annual revenue data from company names or domains with 96% confidence scores, pulling from CUFinder's continuously updated business intelligence database. Built for developers creating account-based marketing and sales qualification tools, this RESTful endpoint delivers verified financial estimates that power territory planning, deal prioritization, and market segmentation workflows across your sales stack.
## Common Use Cases
Revenue is one of the clearest signals of account value, so it drives both prioritization and planning.
* **Lead Scoring:** Prioritizing accounts by estimated revenue.
* **Segmentation:** Tiering prospects into revenue bands for targeting.
* **Territory Planning:** Balancing books of business by account value.
* **Data Enrichment:** Adding a revenue estimate to every company record.
* **Market Sizing:** Estimating the value of a segment or region.
Credit usage is 3 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"revenue": {
"annual_range": "$100-1000B"
}
},
"meta": {
"confidence": 93,
"query": {
"query": "google"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Get a company's current employee count.
Retrieve funding rounds and investors.
Enrich a company with full firmographics.
Search companies with multiple filters.
# Company SaaS Checker API
Source: https://apidoc.cufinder.io/apis/company-saas-checker
POST https://api.cufinder.io/v3/companies/saas-check
Qualifying software companies before outreach prevents wasted prospecting cycles on businesses outside your addressable market. The Company SaaS Checker API analyzes domain characteristics across CUFinder's 85M+ company database to determine whether a company operates a SaaS business model, returning boolean classifications with confidence scores and supporting indicators like subscription pricing signals and cloud infrastructure deployment. Built for sales intelligence platforms, market research tools, and vertical-specific prospecting systems, this RESTful endpoint delivers SaaS classification data, including product delivery model, pricing structure indicators, and technology stack markers, that power ICP filtering, competitive landscape mapping, and partner ecosystem identification workflows across your go-to-market operations.
## Common Use Cases
Knowing whether a company is SaaS sharpens targeting for any software-focused team.
* **List Segmentation:** Isolating SaaS companies for software-specific campaigns.
* **Lead Qualification:** Filtering prospects to those that fit a SaaS profile.
* **Market Research:** Sizing the SaaS share of a sector or region.
* **Data Enrichment:** Tagging records with a SaaS flag for filtering.
* **Partner Sourcing:** Finding SaaS vendors for integrations or resale.
Credit usage is 2 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"is_saas": true
},
"meta": {
"confidence": 93,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 2,
"remaining": 9862
}
}
}
```
## Related APIs
List the technologies a company uses.
Classify a company as B2B or B2C.
Enrich a company with full firmographics.
Search companies with multiple filters.
# Company Search API
Source: https://apidoc.cufinder.io/apis/company-search
POST https://api.cufinder.io/v3/companies/search
Advanced filtering transforms generic databases into laser-targeted prospect lists that sales teams actually convert. The Company Search API queries CUFinder's 85M+ company profiles using multiple parameters, including location, industry, employee size, funding, and founding year, returning filtered business data with 96% confidence scores. Built for developers creating prospecting tools and market intelligence platforms, this RESTful endpoint delivers granular company matches that power account-based marketing, lead generation, and competitive analysis workflows across your sales stack.
## Common Use Cases
The right combination of filters turns a blank page into a ready-to-work prospect list.
* **Lead Generation:** Building targeted company lists from combined filters.
* **Account-Based Marketing:** Finding every account that fits an ideal customer profile.
* **Market Research:** Exploring a segment by industry, size, location, and more.
* **Territory Building:** Pulling companies for a specific region or vertical.
* **List Building:** Generating fresh prospect lists on demand.
Credit usage is 3 credits per request.
## Attributes
Company name, matched as a substring. Required only when no other filter is set; at least one search filter is always required.
Company country. See the full [list of accepted countries](/apis/accepted-values#countries).
Company state. See the full [list of accepted states](/apis/accepted-values#states).
Company city. See the full [list of accepted cities](/apis/accepted-values#cities).
Company industry. See the full [list of accepted industries](/apis/accepted-values#industries).
Company size band. Allowed values: `1-10`, `11-50`, `51-200`, `201-500`, `501-1000`, `1001-5000`, `5001-10000`, `10001+`.
Minimum follower count.
Maximum follower count.
Company founded after this year.
Company founded before this year.
Minimum total funding, in USD.
Maximum total funding, in USD.
Minimum annual revenue, in USD.
Maximum annual revenue, in USD.
Products and services keywords, for example `["b2b"]`.
Whether the company is a school.
Page number for paginated results. Each page returns up to 10 records.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"companies": [
{
"name": "n8n",
"domain": "n8n.io",
"website_url": "https://n8n.io",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "be",
"city": "berlin",
"address": "novalisstraße 10,berlin, be 10115, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/n8n",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "join",
"domain": "join.com",
"website_url": "https://join.com",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": null,
"city": "berlin",
"address": "schönhauser allee 36,berlin, 10435, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/join-com",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "mgt-commerce gmbh",
"domain": "mgt-commerce.com",
"website_url": "http://www.mgt-commerce.com",
"industry": "software development",
"type": "self-owned",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "berlin",
"city": "berlin",
"address": "27 mendelssohnstr.,berlin, berlin 10405, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/mgt-commerce-gmbh",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "researchgate",
"domain": "researchgate.net",
"website_url": "https://www.researchgate.net",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": null,
"city": "berlin",
"address": "chausseestraße 20,berlin, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/researchgate",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "komoot",
"domain": "komoot.com",
"website_url": "https://komoot.com",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": null,
"city": "berlin",
"address": "berlin, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/komoot-gmbh",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "qdrant",
"domain": "qdrant.tech",
"website_url": "https://qdrant.tech",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "berlin",
"city": "berlin",
"address": "berlin, berlin 10115, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/qdrant",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "black forest labs",
"domain": "blackforestlabs.ai",
"website_url": "https://blackforestlabs.ai",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "baden-württemberg",
"city": "freiburg",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/bflai",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "plan a",
"domain": "plana.earth",
"website_url": "http://www.plana.earth",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": null,
"city": "berlin",
"address": "berlin, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/planaearth",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "360dialog",
"domain": "360dialog.com",
"website_url": "http://www.360dialog.com",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "bavaria",
"city": "grünwald",
"address": "torstraße 61,berlin, 10119, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/360dialog",
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "flip",
"domain": "getflip.com",
"website_url": "https://www.getflip.com",
"industry": "software development",
"type": "privately held",
"employee_range": "51-200",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "germany",
"state": "baden-württemberg",
"city": "stuttgart",
"address": "rotebühlstraße 50,stuttgart, baden-württemberg 70178, de",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/flip-app",
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 97,
"query": {
"industry": "software development",
"country": "germany",
"employee_range": "51-200",
"page": 1
},
"credits": {
"charged": 3,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Discover companies similar to a target.
Identify a company's likely B2B customers.
Search 1B+ profiles with combined filters.
Enrich a company with full firmographics.
# Company Snapshot API
Source: https://apidoc.cufinder.io/apis/company-snapshot
POST https://api.cufinder.io/v3/companies/snapshot
Understanding who your prospects sell to reveals positioning angles that generic company data cannot provide. The Company Snapshot API extracts strategic business intelligence from any company domain across CUFinder's 85M+ company database, returning ICP characteristics, target industries, buyer personas, and core value propositions with extraction confidence scores and supporting evidence. Built for competitive intelligence platforms, sales enablement tools, and market research systems, this RESTful endpoint delivers strategic positioning data, including customer segment descriptions, vertical market focus, decision-maker roles, and differentiation claims, that power competitive battlecards, personalized pitch development, and partnership alignment workflows across your go-to-market operations.
## Common Use Cases
A quick, structured overview saves reps from digging through tabs before every call.
* **Account Research:** Getting a fast overview before a sales call.
* **Lead Qualification:** Sizing up an inbound lead at a glance.
* **CRM Enrichment:** Adding a compact company profile to records.
* **Sales Briefings:** Prepping reps with the key facts on an account.
* **Data Enrichment:** Backfilling core company details in bulk.
Credit usage is 3 credits per request.
## Attributes
Company domain or website URL, for example `stripe.com`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"snapshot": {
"icp": "The company targets growing businesses across various stages, specifically small to mid-sized companies, startups, and enterprises, emphasizing flexibility and scalability.",
"target_industries": [
"Healthcare",
"Healthcare, Logistics, Transportation, and Automotive industries",
"Media, Entertainment, Creative Economy"
],
"target_personas": [
"business owners and decision-makers (such as CEOs, Founders, CTOs)",
"developers and technical teams",
"enterprise IT and operations managers"
],
"value_proposition": "We provide flexible, scalable financial infrastructure solutions that enable businesses of all sizes and industries to accept payments, build custom revenue models, expand internationally, manage risk, and accelerate gro …"
}
},
"meta": {
"confidence": 99,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Enrich a company with full firmographics.
List the technologies a company uses.
Estimate a company's annual revenue.
Retrieve funding rounds and investors.
# Company Subsidiaries Finder API
Source: https://apidoc.cufinder.io/apis/company-subsidiaries-finder
POST https://api.cufinder.io/v3/companies/subsidiaries
Subsidiary networks reveal organizational structures that enterprise sales teams need to map complete account hierarchies. The [Company Subsidiaries Finder](https://cufinder.io/enrichment-engine/find-child-companies) API returns comprehensive lists of child companies and divisions from parent company queries with 93% confidence scores. Built for developers creating enterprise sales and organizational mapping tools, this RESTful endpoint delivers verified subsidiary data that power account planning, relationship mapping, and multi-division outreach workflows across your sales stack.
## Common Use Cases
Seeing the full corporate family changes how you map an account and where you expand next.
* **Account Mapping:** Seeing the full corporate family behind a target account.
* **Whitespace Analysis:** Finding sibling companies to expand into.
* **Risk and Compliance:** Tracing ownership across subsidiaries.
* **Market Research:** Understanding a conglomerate's reach.
* **Data Enrichment:** Linking parent and subsidiary records in your CRM.
Credit usage is 3 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"companies": [
{
"name": "github",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "halo studios",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "m12, microsoft's venture fund",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "microsoft ai",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "flip",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "studios quality - xbox game studios",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
},
{
"name": "xbox",
"domain": null,
"website_url": null,
"industry": null,
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 99,
"query": {
"query": "microsoft"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Discover companies similar to a target.
List a company's office locations.
Enrich a company with full firmographics.
Identify a company's likely B2B customers.
# Company Tech Stack Finder API
Source: https://apidoc.cufinder.io/apis/company-tech-stack-finder
POST https://api.cufinder.io/v3/companies/tech-stack
Technology stacks expose software dependencies that SaaS sales teams use for hyper-targeted prospecting. The [Company Tech Stack Finder](https://cufinder.io/enrichment-engine/find-technology-stack) API returns comprehensive lists of technologies companies use, from CMS platforms to analytics tools, with 98% confidence scores. Built for developers creating sales intelligence and competitive analysis platforms, this RESTful endpoint delivers verified tech stack data that power lead scoring, competitor research, and technology-based segmentation workflows across your sales stack.
## Common Use Cases
The tools a company already runs reveal fit, timing, and plenty of talking points.
* **Competitive Displacement:** Targeting companies that use a rival's product.
* **Lead Qualification:** Filtering prospects by the tools they already run.
* **Integration Sales:** Finding accounts that use software you plug into.
* **Market Research:** Measuring adoption of a technology across a segment.
* **Personalized Outreach:** Referencing a prospect's stack in your pitch.
Credit usage is 3 credits per request.
## Attributes
Company domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"tech_stack": [
"Webpack",
"Sentry",
"Klarna Checkout",
"PayPal Credit",
"Stripe",
"Mastercard",
"Digest",
"hCaptcha",
"Substack",
"ZingChart",
"Bootstrap",
"Strato",
"Warp",
"PayPal",
"Ionic",
"GraphQL"
]
},
"meta": {
"confidence": 93,
"query": {
"query": "stripe.com"
},
"credits": {
"charged": 3,
"remaining": 9862
}
}
}
```
## Related APIs
Check whether a company is a SaaS business.
Enrich a company with full firmographics.
Get a quick company overview.
Classify a company as B2B or B2C.
# Contact Lookalike Finder API
Source: https://apidoc.cufinder.io/apis/contact-lookalike-finder
POST https://api.cufinder.io/v3/people/lookalikes
The Contact Lookalike Finder API returns 25 people who resemble a given contact. Pass a person's LinkedIn URL or email and get back similar profiles with their role, company, and firmographics, perfect for expanding from one great lead to a whole list.
## Common Use Cases
Give one great contact and get 25 more who look just like them.
* **Prospecting:** Expanding from a best customer to similar people.
* **Account-Based Marketing:** Finding more of the right personas in a segment.
* **Recruiting:** Sourcing candidates similar to a strong hire.
* **List Building:** Growing outreach lists from a single seed profile.
Credit usage is 10 credits per request.
## Attributes
The person's LinkedIn profile URL. At least one of `linkedin_url` or `email` is required.
The person's email address. At least one of `linkedin_url` or `email` is required.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"people": [
{
"first_name": "aidan",
"last_name": "bienstock",
"full_name": "aidan bienstock",
"avatar_url": "media.cufinder.io/person_profile/aidan-bienstock-159536239",
"job": {
"title": "engineering project manager",
"level": null,
"categories": [
"Engineering & Technical",
"Operations",
"Sales"
]
},
"company": {
"name": "5c",
"domain": null,
"website_url": "https://5c.ai",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/5cai",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "canada",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/aidan-bienstock-159536239",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 0,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "5c",
"size": "51-200",
"id": "5cai",
"founded": "2025",
"industry": "technology, information and internet",
"location": null,
"linkedin_url": "linkedin.com/company/5cai",
"linkedin_id": "105784960",
"facebook_url": null,
"twitter_url": null,
"website": "https://5c.ai"
},
"title": {
"name": "engineering project manager",
"role": null,
"sub_role": null,
"levels": []
},
"start_date": "2026-07",
"end_date": "Present",
"location_names": [],
"is_primary": true,
"summary": null
},
{
"company": {
"name": "McGill University",
"size": "10001+",
"id": "mcgill-university",
"founded": "1821",
"industry": "Higher Education",
"location": "Montreal, Qc",
"linkedin_url": "linkedin.com/school/mcgill-university",
"linkedin_id": "4855",
"facebook_url": "facebook.com/mcgilluniversity",
"twitter_url": "twitter.com/mcgillu",
"website": "http://www.mcgill.ca/about"
},
"location_names": [],
"end_date": "2026-07",
"start_date": "2022-06",
"title": {
"name": "student",
"role": null,
"sub_role": null,
"levels": []
},
"is_primary": false,
"summary": null
}
],
"educations": [
{
"school": {
"name": "mcgill university",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
"country": null,
"continent": null
},
"linkedin_url": "linkedin.com/school/mcgill-university",
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"start_date": "2024-06",
"end_date": "Present",
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
}
],
"skills": [],
"certifications": [],
"languages": []
},
{
"first_name": "ben",
"last_name": "miller",
"full_name": "ben miller",
"avatar_url": "media.cufinder.io/person_profile/ben-miller-5b012b50",
"job": {
"title": "engineering manager",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "together software",
"domain": null,
"website_url": "https://www.togetherplatform.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/togetherapp",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "canada",
"state": "ontario",
"city": "toronto",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/ben-miller-5b012b50",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 0,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "together software",
"size": "11-50",
"id": "togetherapp",
"founded": null,
"industry": "technology, information and internet",
"location": {
"name": null,
"locality": "toronto",
"region": "ontario",
"metro": null,
"country": "canada",
"continent": null,
"street_address": null,
"address_line_2": null,
"postal_code": null,
"geo": null
},
"linkedin_url": "linkedin.com/company/togetherapp",
"linkedin_id": "18713542",
"facebook_url": null,
"twitter_url": null,
"website": "https://www.togetherplatform.com"
},
"title": {
"name": "engineering manager",
"role": "operations",
"sub_role": null,
"levels": []
},
"start_date": "2023-12",
"end_date": "Present",
"location_names": [
"canada"
],
"is_primary": true,
"summary": null
}
],
"educations": [],
"skills": [],
"certifications": [],
"languages": []
},
{
"first_name": "bingyu(iris)",
"last_name": "li",
"full_name": "bingyu(iris) li",
"avatar_url": "media.cufinder.io/person_profile/bingyu-iris-li-978087178",
"job": {
"title": "engineering manager i",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "stackadapt",
"domain": null,
"website_url": "https://stackadapt.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/stackadapt",
"facebook_url": "facebook.com/stackadapt",
"twitter_url": "twitter.com/stackadapt"
}
},
"location": {
"country": "canada",
"state": "ontario",
"city": "toronto",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/bingyu-iris-li-978087178",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 388,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "stackadapt",
"size": "1001-5000",
"id": "stackadapt",
"founded": "2014",
"industry": "technology, information and internet",
"location": null,
"linkedin_url": "linkedin.com/company/stackadapt",
"linkedin_id": "5045143",
"facebook_url": null,
"twitter_url": null,
"website": "https://stackadapt.com"
},
"title": {
"name": "engineering manager i",
"role": null,
"sub_role": null,
"levels": []
},
"start_date": "Jan 2023",
"end_date": "Present",
"location_names": [],
"is_primary": true,
"summary": null
},
{
"company": {
"name": "scotiabank",
"size": "10001+",
"id": "scotiabank",
"founded": null,
"industry": "banking",
"location": null,
"linkedin_url": "linkedin.com/company/scotiabank",
"linkedin_id": "3139",
"facebook_url": null,
"twitter_url": null,
"website": "http://www.scotiabank.com"
},
"title": null,
"start_date": null,
"end_date": null,
"location_names": [],
"is_primary": false,
"summary": null
},
{
"company": {
"name": "university of regina",
"size": null,
"id": "university-of-regina",
"founded": null,
"industry": null,
"location": null,
"linkedin_url": "linkedin.com/company/university-of-regina",
"linkedin_id": null,
"facebook_url": null,
"twitter_url": null,
"website": null
},
"title": null,
"start_date": null,
"end_date": null,
"location_names": [],
"is_primary": false,
"summary": null
}
],
"educations": [
{
"school": {
"name": "university of regina",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
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"role": null,
"sub_role": null,
"levels": []
},
"is_primary": false,
"summary": null
},
{
"company": {
"name": "ericsson",
"size": "10001+",
"id": "ericsson",
"founded": "1876",
"industry": "it services and it consulting",
"location": "kista, stockholm",
"linkedin_url": "linkedin.com/company/ericsson",
"linkedin_id": "1060",
"facebook_url": "facebook.com/ericsson",
"twitter_url": "twitter.com/ericsson",
"website": "http://www.ericsson.com"
},
"location_names": [
"stockholm, stockholms lan, sweden"
],
"end_date": "2014-08",
"start_date": "2014-05",
"title": {
"name": "software developer",
"role": "engineering",
"sub_role": "software",
"levels": []
},
"is_primary": false,
"summary": null
},
{
"company": {
"name": "myseat.com media",
"size": null,
"id": null,
"founded": null,
"industry": null,
"location": null,
"linkedin_url": null,
"linkedin_id": null,
"facebook_url": null,
"twitter_url": null,
"website": null
},
"location_names": [],
"end_date": "2017-01",
"start_date": "2015-07",
"title": {
"name": "software developer",
"role": "engineering",
"sub_role": "software",
"levels": []
},
"is_primary": false,
"summary": null
},
{
"company": {
"name": "ayuda media systems",
"size": "11-50",
"id": "ayuda-media-systems",
"founded": "2003",
"industry": "computer software",
"location": {
"name": "montréal, quebec, canada",
"locality": "montréal",
"region": "quebec",
"metro": null,
"country": "canada",
"continent": "north america",
"street_address": null,
"address_line_2": null,
"postal_code": "h2y 2e1",
"geo": null
},
"linkedin_url": "linkedin.com/company/ayuda-media-systems",
"linkedin_id": "140198",
"facebook_url": "facebook.com/ayudasystems",
"twitter_url": "twitter.com/ayudasystems",
"website": "ayudasystems.com"
},
"location_names": [
"montréal, quebec, canada"
],
"end_date": "2017-09",
"start_date": "2016-09-01",
"title": {
"name": "full stack software developer, programmatic digital out of home",
"role": "engineering",
"sub_role": "software",
"levels": []
},
"is_primary": false,
"summary": "Full Stack Software Developer at Ayuda Media Systems"
}
],
"educations": [
{
"school": {
"name": "concordia university",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
"country": null,
"continent": null
},
"linkedin_url": "linkedin.com/school/concordia-university",
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"start_date": null,
"end_date": null,
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
},
{
"school": {
"name": "cegep john abbott college",
"type": "post-secondary institution",
"id": "1gBmtSOLxJDFazXnexMAMw_0",
"location": {
"name": "canada",
"locality": null,
"region": null,
"country": "canada",
"continent": "north america"
},
"linkedin_url": "linkedin.com/school/cegep-john-abbott-college",
"facebook_url": null,
"twitter_url": null,
"linkedin_id": "20481",
"website": "johnabbott.qc.ca",
"domain": "johnabbott.qc.ca"
},
"start_date": null,
"end_date": "2011",
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
},
{
"school": {
"name": "beaconsfield high school",
"type": "secondary school",
"id": null,
"location": null,
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"degrees": [],
"start_date": null,
"end_date": "2008",
"majors": [],
"minors": [],
"gpa": null,
"summary": null
}
],
"skills": [],
"certifications": [],
"languages": []
},
{
"first_name": "victor",
"last_name": "adelaja",
"full_name": "victor adelaja",
"avatar_url": "media.cufinder.io/person_profile/v-adelaja",
"job": {
"title": "engineering manager",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "priceline",
"domain": null,
"website_url": "http://www.priceline.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/priceline-com",
"facebook_url": "facebook.com/priceline",
"twitter_url": "twitter.com/priceline"
}
},
"location": {
"country": "canada",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/v-adelaja",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": null,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "priceline",
"size": "1001-5000",
"id": "priceline-com",
"founded": "1998",
"industry": "technology, information and internet",
"location": "800 connecticut avenue,norwalk, ct 06854, us",
"linkedin_url": "linkedin.com/company/priceline-com",
"linkedin_id": "7451",
"facebook_url": "facebook.com/priceline",
"twitter_url": "twitter.com/priceline",
"website": "http://www.priceline.com"
},
"title": {
"name": "engineering manager",
"role": null,
"sub_role": null,
"levels": []
},
"start_date": "2023-12",
"end_date": "Present",
"location_names": [],
"is_primary": true,
"summary": null
},
{
"company": {
"name": "scotiabank",
"size": "10001+",
"id": "scotiabank",
"founded": null,
"industry": "banking",
"location": null,
"linkedin_url": "linkedin.com/company/scotiabank",
"linkedin_id": "3139",
"facebook_url": null,
"twitter_url": null,
"website": "http://www.scotiabank.com"
},
"title": null,
"start_date": null,
"end_date": null,
"location_names": [],
"is_primary": false,
"summary": null
},
{
"company": {
"name": "niger delta exploration production plc",
"size": null,
"id": "niger-delta-exploration--production-plc",
"founded": null,
"industry": null,
"location": null,
"linkedin_url": "linkedin.com/company/niger-delta-exploration--production-plc",
"linkedin_id": null,
"facebook_url": null,
"twitter_url": null,
"website": null
},
"title": null,
"start_date": null,
"end_date": null,
"location_names": [],
"is_primary": false,
"summary": null
}
],
"educations": [
{
"school": {
"name": "robert gordon university",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
"country": null,
"continent": null
},
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"start_date": null,
"end_date": null,
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
}
],
"skills": [],
"certifications": [],
"languages": []
},
{
"first_name": "victoria",
"last_name": "martín",
"full_name": "victoria martín",
"avatar_url": "media.cufinder.io/person_profile/victoriamartinhernandez",
"job": {
"title": "engineering manager",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "league",
"domain": null,
"website_url": "https://league.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/league-inc-",
"facebook_url": "facebook.com/leagueinc",
"twitter_url": "twitter.com/joinleague"
}
},
"location": {
"country": "canada",
"state": "ontario",
"city": "toronto",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/victoriamartinhernandez",
"facebook_url": null,
"twitter_url": "twitter.com/nirvik66"
},
"summary": null,
"followers_count": 607,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "league",
"size": "501-1000",
"id": "league-inc-",
"founded": "2014",
"industry": "technology, information and internet",
"location": {
"name": null,
"locality": "toronto",
"region": "ontario",
"metro": null,
"country": "canada",
"continent": null,
"street_address": null,
"address_line_2": null,
"postal_code": null,
"geo": null
},
"linkedin_url": "linkedin.com/company/league-inc-",
"linkedin_id": "5401900",
"facebook_url": "facebook.com/leagueinc",
"twitter_url": "twitter.com/joinleague",
"website": "https://league.com"
},
"title": {
"name": "engineering manager",
"role": "engineering",
"sub_role": null,
"levels": []
},
"start_date": "2023-12",
"end_date": "Present",
"location_names": [
"canada"
],
"is_primary": true,
"summary": null
},
{
"company": {
"name": "universidad autonoma de madrid",
"size": null,
"id": "universidad-autonoma-de-madrid",
"founded": null,
"industry": null,
"location": null,
"linkedin_url": "linkedin.com/company/universidad-autonoma-de-madrid",
"linkedin_id": null,
"facebook_url": null,
"twitter_url": null,
"website": null
},
"title": null,
"start_date": null,
"end_date": null,
"location_names": [],
"is_primary": false,
"summary": null
},
{
"company": {
"name": "league",
"size": "1-10",
"id": "nivedya-",
"founded": "2014",
"industry": "marketing and advertising",
"location": "los angeles, california, united states",
"linkedin_url": "linkedin.com/company/nivedya-",
"linkedin_id": "3591845",
"facebook_url": null,
"twitter_url": null,
"website": "league-made.com"
},
"location_names": [],
"end_date": "2023-12",
"start_date": "2022-06",
"title": {
"name": "engineering manager",
"role": null,
"sub_role": null,
"levels": []
},
"is_primary": true,
"summary": null
},
{
"company": {
"name": "symbility intersect",
"size": "51-200",
"id": "symbility-intersect",
"founded": "2008",
"industry": "internet",
"location": {
"name": "toronto, ontario, canada",
"locality": "toronto",
"region": "ontario",
"metro": null,
"country": "canada",
"continent": "north america",
"street_address": "106 front street east",
"address_line_2": "suite 200",
"postal_code": "m5a 1e1",
"geo": "43.70,-79.41"
},
"linkedin_url": "linkedin.com/company/symbility-intersect",
"linkedin_id": "657497",
"facebook_url": null,
"twitter_url": null,
"website": "symbilityintersect.com"
},
"location_names": [],
"end_date": null,
"start_date": "2015-08",
"title": {
"name": "senior android developer",
"role": "engineering",
"sub_role": null,
"levels": [
"senior"
]
},
"is_primary": true,
"summary": null
},
{
"company": {
"name": "idealista",
"size": "501-1000",
"id": "idealista-com",
"founded": "2000",
"industry": "internet",
"location": {
"name": "spain",
"locality": null,
"region": null,
"metro": null,
"country": "spain",
"continent": "europe",
"street_address": null,
"address_line_2": null,
"postal_code": null,
"geo": null
},
"linkedin_url": "linkedin.com/company/idealista-com",
"linkedin_id": "32440",
"facebook_url": "facebook.com/idealista",
"twitter_url": "twitter.com/idealista",
"website": "idealista.com"
},
"location_names": [
"madrid, madrid, spain"
],
"end_date": "2014-01",
"start_date": "2013-03",
"title": {
"name": "android developer",
"role": "engineering",
"sub_role": null,
"levels": []
},
"is_primary": false,
"summary": "The leading real estate company in Spain. idealista.com is a website that allows the direct encounter between people looking for a home, either purchase or rent, and the advertisers. Similar companies around the world are Trulia (USA) or RightMove (UK) Development of the Android mobile application. Add new features to the app as well as taking care that everything runs smoothly and evolves to use the latest trends in android patterns in order to help people find a home. Use of agile methodologies for the development, SCRUM and KANVAN"
},
{
"company": {
"name": "echeckin services",
"size": "1-10",
"id": "echeckin-services",
"founded": "2011",
"industry": "information technology and services",
"location": {
"name": "spain",
"locality": null,
"region": null,
"metro": null,
"country": "spain",
"continent": "europe",
"street_address": null,
"address_line_2": null,
"postal_code": null,
"geo": null
},
"linkedin_url": "linkedin.com/company/echeckin-services",
"linkedin_id": "2354409",
"facebook_url": null,
"twitter_url": null,
"website": "echeckinservices.com"
},
"location_names": [
"madrid, madrid, spain"
],
"end_date": "2012-12",
"start_date": "2011-04",
"title": {
"name": "chief technology officer and co-founder",
"role": "engineering",
"sub_role": null,
"levels": [
"owner",
"cxo"
]
},
"is_primary": false,
"summary": "Service on the cloud that allows companies to control the presence or actions of their workers at specific points through the mobile phone, using QR Codes or NFC tags for their identification. > Project selected for Business Booster 2011-2012 acceleration program (Valencia, Spain) > Project Selected for Tetuan Valley Spring 2011 acceleration program (Madrid, Spain) > Development of the mobile applications used by the workers to register their presence for Android and iOS operating systems. > Responsible for the database system: model, performance, bulk load processes, maintenance, cleaning and recovering. MongoDB. > Development in JAVA of the web services used in the communications – web / mobile applications / databases – and open API so third parties can integrate the core in their solutions. > Responsible for making decisions about which technologies to use, which functionalities to add and other aspects about the evolution of the service"
},
{
"company": {
"name": "mobawa",
"size": "1-10",
"id": "mobawa",
"founded": "2011",
"industry": "information technology and services",
"location": null,
"linkedin_url": "linkedin.com/company/mobawa",
"linkedin_id": "2265549",
"facebook_url": null,
"twitter_url": null,
"website": "mobawa.com"
},
"location_names": [
"madrid, madrid, spain"
],
"end_date": "2011-04",
"start_date": "2010-11",
"title": {
"name": "chief technology officer and co-founder",
"role": "engineering",
"sub_role": null,
"levels": [
"owner",
"cxo"
]
},
"is_primary": false,
"summary": "Project with the aim of bringing closer the latest technologies to the companies. Development of applications that merge web and mobile applications (Android and iOS) as well as the generation and management of the web services and databases required. Mobawa is no longer available and I've recently picked up the project as personal under the name of Gravitup where in my free time I develop personal ideas and turn them into mobile applications. You can check some of them in the google play, https://play.google.com/st ore/apps/developer?id=GRAVITUP"
},
{
"company": {
"name": "datatronics mobility",
"size": "11-50",
"id": "datatronics-mobility",
"founded": "1997",
"industry": "telecommunications",
"location": {
"name": "madrid, madrid, spain",
"locality": "madrid",
"region": "madrid",
"metro": null,
"country": "spain",
"continent": "europe",
"street_address": null,
"address_line_2": null,
"postal_code": null,
"geo": "40.40,-3.69"
},
"linkedin_url": "linkedin.com/company/datatronics-mobility",
"linkedin_id": "1884060",
"facebook_url": null,
"twitter_url": null,
"website": null
},
"location_names": [
"madrid, madrid, spain"
],
"end_date": "2010-11",
"start_date": "2005-12",
"title": {
"name": "analyst and programmer at r+d department",
"role": "engineering",
"sub_role": null,
"levels": []
},
"is_primary": false,
"summary": "Company dedicated to the tracking and fleet management. Responsible for management of the database both its model and bulk load, maintenance, cleaning and recovering processes. Oracle, SQL Server. Programming in C++ when the GIS fleet tracking application was desktop and using Java to develop the web services required and adding new features when it was migrated to a full-web environment."
}
],
"educations": [
{
"school": {
"name": "universidad autónoma de madrid",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
"country": null,
"continent": null
},
"linkedin_url": "linkedin.com/school/universidad-autonoma-de-madrid",
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"start_date": "2026-01",
"end_date": null,
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
}
],
"skills": [],
"certifications": [],
"languages": []
},
{
"first_name": "vin",
"last_name": "rodrigues",
"full_name": "vin rodrigues",
"avatar_url": "media.cufinder.io/person_profile/vinrodrigues",
"job": {
"title": "engineering manager",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "loblaw digital",
"domain": null,
"website_url": "http://loblawdigital.co",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/loblaw-digital",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "canada",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/vinrodrigues",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 0,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "loblaw digital",
"size": "501-1000",
"id": "loblaw-digital",
"founded": "2012",
"industry": "technology, information and internet",
"location": "500 lake shore blvd w,toronto, ontario m5v, ca",
"linkedin_url": "linkedin.com/company/loblaw-digital",
"linkedin_id": "9450129",
"facebook_url": null,
"twitter_url": null,
"website": "http://loblawdigital.co"
},
"title": {
"name": "engineering manager",
"role": null,
"sub_role": null,
"levels": []
},
"start_date": null,
"end_date": "Present",
"location_names": [],
"is_primary": true,
"summary": null
}
],
"educations": [],
"skills": [],
"certifications": [],
"languages": []
}
]
},
"meta": {
"confidence": 97,
"query": {
"linkedin_url": "linkedin.com/in/iain-mckenzie"
},
"credits": {
"charged": 10,
"remaining": 9862
}
}
}
```
## Related APIs
Search 1B+ profiles with combined filters.
Enrich a person with full contact data.
Discover companies similar to a target.
Identify the person behind an email address.
# Domain to Company Name API
Source: https://apidoc.cufinder.io/apis/domain-to-company-name
POST https://api.cufinder.io/v3/companies/domain-to-name
Domain intelligence powers reverse lookup workflows that sales and marketing teams rely on daily. The [Domain to Company Name](https://cufinder.io/enrichment-engine/find-company-name-from-website) API converts website URLs into registered company names with 97% confidence scores, making it essential for lead qualification and visitor identification. Built for developers creating data enrichment pipelines, this RESTful endpoint returns verified company information from domains that fuel IP-to-company matching, form enrichment tools, and account-based marketing platforms across your tech stack.
## Common Use Cases
A bare domain becomes far more useful the moment it is tied to a real company name.
* **CRM Enrichment:** Turning a captured domain into a clean company name.
* **Lead Capture:** Identifying the company behind a signup email domain.
* **Data Cleanup:** Resolving domains to canonical company names.
* **Analytics:** Grouping web traffic and signups by company.
* **Pipeline Automation:** Naming accounts automatically during enrichment.
Credit usage is 1 credit per request.
## Attributes
Company domain, for example `cufinder.io`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"name": "cufinder"
},
"meta": {
"confidence": 98,
"query": {
"domain": "cufinder.io"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Resolve a company name to its verified website domain.
Enrich a company with full firmographics.
Find a company's LinkedIn page URL.
Get a quick company overview.
# Errors
Source: https://apidoc.cufinder.io/apis/errors
In this guide, we will talk about what happens when something goes wrong while you work with the API. Mistakes happen, and mostly they will be yours, not ours. Let's look at some status codes and error types you might encounter.
You can tell if your request was successful by checking the status code when receiving an API response. If a response comes back unsuccessful, you can use the error type and error message to figure out what has gone wrong and do some rudimentary debugging (before contacting support).
Before reaching out to support with an error, please be aware that 99% of
all reported errors are, in fact, user errors. Therefore, please carefully
check your code before contacting CUFinder support.
See every HTTP status code the API returns, what each one means, and how to resolve it.
## The error envelope
Whenever a request fails, the API returns `"success": false` with a machine-readable `error.code`, a human-readable `message`, and, on validation errors, a `details` array naming each problem field:
```json Error theme={null}
{
"success": false,
"error": {
"code": "validation_failed",
"message": "Please resolve validation errors",
"details": [
{ "field": "query", "issue": "query should not be empty" }
]
}
}
```
Handle errors by branching on the `success` flag and `error.code`; both are stable. Note that a not-found lookup returns HTTP `200` with `"success": false`, so the HTTP status alone is not enough. The `message` text is for humans and may change.
## Error codes
Your API key is missing or invalid. Check the `x-api-key` header.
Your account does not have enough credits. Nothing was charged and no work was performed.
The entity you addressed could not be resolved. Returned with HTTP `200` and `"success": false`, check the envelope, not the status code. An empty search result is not an error; searches return `"success": true` with an empty array.
The upstream lookup timed out. You were not charged, retry the request.
The request is missing required parameters or has invalid values. See `error.details` for the exact fields.
Too many requests. Slow down and retry later.
The request itself is malformed (for example, invalid JSON).
Something went wrong on our side. Credits are never charged for failed requests.
# Jobs API
Source: https://apidoc.cufinder.io/apis/jobs-api
POST https://api.cufinder.io/v3/jobs/search
The Jobs API returns live job postings that match your filters, each enriched with the hiring company's full firmographics. Search by job title, location, industry, company size, funding, revenue, and more across CUFinder's data, and get back structured job and company records you can act on. Built for developers building sourcing tools, talent intelligence platforms, and hiring-signal workflows.
## Common Use Cases
Open job postings are one of the clearest signals that a company is growing and spending.
* **Recruiting and Sourcing:** Tracking live openings for a role to find companies that are hiring.
* **Hiring Signals:** Spotting companies in hiring mode as a buying signal for sales.
* **Talent Market Research:** Mapping which companies are posting for a role across a region or industry.
* **Competitive Intelligence:** Watching how peers and competitors staff up over time.
* **List Building:** Generating targeted lists of hiring companies from combined filters.
Credit usage is 2 credits per request.
## Attributes
Job title, matched as a substring, for example `data scientist`.
Company country. See the full [list of accepted countries](/apis/accepted-values#countries).
Company state. See the full [list of accepted states](/apis/accepted-values#states).
Company city. See the full [list of accepted cities](/apis/accepted-values#cities).
Company industry. See the full [list of accepted industries](/apis/accepted-values#industries).
Company size band. Allowed values: `1-10`, `11-50`, `51-200`, `201-500`, `501-1000`, `1001-5000`, `5001-10000`, `10001+`.
Minimum follower count.
Maximum follower count.
Company founded after this year.
Company founded before this year.
Minimum total funding, in USD.
Maximum total funding, in USD.
Minimum annual revenue, in USD.
Maximum annual revenue, in USD.
Products and services keywords, for example `["b2b"]`.
Whether the company is a school.
Page number for paginated results. Each page returns up to 10 records.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"jobs": [
{
"job_id": "4444530118",
"title": "Nurse Scientist",
"url": "https://www.linkedin.com/jobs/view/nurse-scientist-at-american-nurses-association-4444530118",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-20T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4444530116",
"title": "Senior Director, Research and Quality",
"url": "https://www.linkedin.com/jobs/view/senior-director-research-and-quality-at-american-nurses-association-4444530116",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-20T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4449785935",
"title": "Systems Engineer",
"url": "https://www.linkedin.com/jobs/view/systems-engineer-at-american-nurses-association-4449785935",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-08T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4446448091",
"title": "Strat. Comms. & Marketing Spec., A.I. Progs.",
"url": "https://www.linkedin.com/jobs/view/strat-comms-marketing-spec-a-i-progs-at-american-nurses-association-4446448091",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-08T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4442086867",
"title": "Program Manager, AI, Nursing & Digital Health",
"url": "https://www.linkedin.com/jobs/view/program-manager-ai-nursing-digital-health-at-american-nurses-association-4442086867",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-01T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4444614596",
"title": "Program Management Support Staff Specialist",
"url": "https://www.linkedin.com/jobs/view/program-management-support-staff-specialist-at-american-nurses-association-4444614596",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-01T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4440631678",
"title": "Content Manager- American Nurses Association",
"url": "https://www.linkedin.com/jobs/view/content-manager-american-nurses-association-at-american-nurses-association-4440631678",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-08-01T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4440635581",
"title": "Content Manager- American Nurses Credentialing Center",
"url": "https://www.linkedin.com/jobs/view/content-manager-american-nurses-credentialing-center-at-american-nurses-association-4440635581",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-07-25T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4440293374",
"title": "Dir., Technical Operations & Infrastructure",
"url": "https://www.linkedin.com/jobs/view/dir-technical-operations-infrastructure-at-american-nurses-association-4440293374",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-07-23T20:09:57+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
},
{
"job_id": "4440243649",
"title": "Digital Marketing Specialist",
"url": "https://www.linkedin.com/jobs/view/digital-marketing-specialist-at-american-nurses-association-4440243649",
"location": {
"country": null,
"state": null,
"city": null,
"address": "Silver Spring, MD",
"postal_code": null
},
"posted_at": "2026-07-22T13:02:17+00:00",
"company": {
"name": "american nurses association",
"domain": null,
"website_url": "http://www.nursingworld.org",
"industry": "non-profit organizations",
"type": null,
"employee_range": "201-500",
"followers_count": 645661,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "maryland",
"city": "silver spring",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/american-nurses-association",
"facebook_url": null,
"twitter_url": null
}
}
}
]
},
"meta": {
"confidence": 94,
"query": {
"name": "nurse",
"country": "united states",
"page": 1
},
"credits": {
"charged": 2,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Search companies with multiple filters.
Locate a company's careers page.
Find employees working at a company.
Search 1B+ profiles with combined filters.
# LinkedIn Profile Email Finder API
Source: https://apidoc.cufinder.io/apis/linkedin-profile-email-finder
POST https://api.cufinder.io/v3/people/email-finder
LinkedIn profiles reveal professional context but hide the email addresses that outreach campaigns desperately need. The [LinkedIn Profile Email Finder](https://cufinder.io/enrichment-engine/person-email-finder) API extracts verified business email addresses from LinkedIn URLs with 94% confidence scores, matching profiles against CUFinder's 1B+ contact database. Built for developers creating B2B lead generation and recruitment automation tools, this RESTful endpoint delivers validated work emails that power cold outreach, talent acquisition, and partnership development workflows across your sales stack.
## Common Use Cases
A LinkedIn profile is easy to find; the usable email is usually the missing piece.
* **Sales Prospecting:** Getting a usable email from a LinkedIn profile for outreach.
* **Recruiting:** Reaching candidates found on LinkedIn over email.
* **CRM Enrichment:** Adding verified emails to contact records sourced socially.
* **Social Selling:** Moving a LinkedIn connection into an email sequence.
* **Lead Generation:** Converting profile lists into contactable leads.
Credit usage is 5 credits per request.
## Attributes
The person's LinkedIn profile URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"emails": [
{
"value": "rniwa@apple.com",
"type": "work"
}
]
},
"meta": {
"confidence": 98,
"query": {
"linkedin_url": "linkedin.com/in/rniwa"
},
"credits": {
"charged": 5,
"remaining": 9862
}
}
}
```
## Related APIs
Enrich a person from their LinkedIn profile.
Enrich a person with full contact data.
Identify the person behind an email address.
Return verified role-based company emails.
# LinkedIn Profile Enrichment API
Source: https://apidoc.cufinder.io/apis/linkedin-profile-enrichment
POST https://api.cufinder.io/v3/people/enrich-by-linkedin
LinkedIn URLs contain professional identities that sales teams need transformed into actionable contact data. The [LinkedIn Profile Enrichment](https://cufinder.io/enrichment-engine/contact-linkedin-to-info) API converts LinkedIn profile URLs into comprehensive person and company information, including name, title, employer, location, and social profiles, with 93% confidence scores. Built for developers creating recruiting tools and sales intelligence platforms, this RESTful endpoint delivers enriched professional profiles that power CRM personalization, candidate screening, and account-based marketing workflows across your sales stack.
## Common Use Cases
A single profile URL is enough to build a full, usable contact record.
* **Contact Enrichment:** Filling out a contact's title, company, and details from their profile.
* **Recruiting:** Building complete candidate records from a LinkedIn URL.
* **Lead Scoring:** Qualifying contacts with full professional context.
* **CRM Enrichment:** Backfilling contact records sourced from LinkedIn.
* **Personalized Outreach:** Tailoring messages with a contact's real background.
Credit usage is 1 credit per request.
## Attributes
The person's LinkedIn profile URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"person": {
"first_name": "iain",
"last_name": "mckenzie",
"full_name": "iain mckenzie",
"avatar_url": "media.cufinder.io/person_profile/iain-mckenzie",
"job": {
"title": "engineering",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "stripe",
"domain": null,
"website_url": "https://stripe.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/stripe",
"facebook_url": "facebook.com/stripepayments",
"twitter_url": "twitter.com/stripe"
}
},
"location": {
"country": "canada",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/iain-mckenzie",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 0,
"emails": [],
"phones": [],
"experiences": [
{
"company": {
"name": "stripe",
"size": "5001-10000",
"id": "stripe",
"founded": "2010",
"industry": "technology, information and internet",
"location": "354 oyster point blvd,south san francisco, california 94080, us",
"linkedin_url": "linkedin.com/company/stripe",
"linkedin_id": "2135371",
"facebook_url": "facebook.com/stripepayments",
"twitter_url": "twitter.com/stripe",
"website": "https://stripe.com"
},
"title": {
"name": "engineering",
"role": null,
"sub_role": null,
"levels": []
},
"start_date": "2025-07",
"end_date": "Present",
"location_names": [],
"is_primary": true,
"summary": null
}
],
"educations": [
{
"school": {
"name": "queen's university",
"type": null,
"id": null,
"location": {
"name": null,
"locality": null,
"region": null,
"country": null,
"continent": null
},
"linkedin_url": "linkedin.com/school/queen's-university",
"facebook_url": null,
"twitter_url": null,
"linkedin_id": null,
"website": null,
"domain": null
},
"start_date": null,
"end_date": "2026-06",
"gpa": null,
"degrees": [],
"majors": [],
"minors": [],
"summary": null
}
],
"skills": [],
"certifications": [],
"languages": []
}
},
"meta": {
"confidence": 99,
"query": {
"linkedin_url": "linkedin.com/in/iain-mckenzie"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Find an email from a LinkedIn profile.
Enrich a person with full contact data.
Search 1B+ profiles with combined filters.
Identify the person behind an email address.
# Local Business Search API
Source: https://apidoc.cufinder.io/apis/local-business-search-google-maps-search-api
POST https://api.cufinder.io/v3/local-businesses/search
Local businesses represent untapped markets that B2C and service-based companies overlook in traditional B2B databases. The Local Business Search API queries neighborhood businesses by location and industry, returning phones, emails, ratings, and social profiles, with 95% confidence scores. Built for developers creating local marketing and service provider platforms, this RESTful endpoint delivers comprehensive small business data including NAICS codes and Google Maps integration that power geo-targeted campaigns, competitor analysis, and community outreach workflows across your sales stack.
## Common Use Cases
Local lead generation starts with a clear picture of who operates in an area.
* **Local Lead Generation:** Building lists of businesses in a city or area.
* **Field Sales:** Planning on-site visits around nearby prospects.
* **Market Research:** Mapping the density of a category in a region.
* **Directory Building:** Populating listings with verified local business data.
* **Competitive Analysis:** Seeing who operates in a given market.
Credit usage is 5 credits per request.
## Attributes
Local business country, for example `united states`. See the full [list of accepted countries](/apis/accepted-values#countries).
Local business industry, for example `coffee shop` or `dentist`. See the full [list of accepted local business industries](/apis/accepted-values#local-business-industries), which is separate from the company industries list.
Local business name, matched as a substring, for example `coffee`.
Local business state. See the full [list of accepted states](/apis/accepted-values#states).
Local business city. See the full [list of accepted cities](/apis/accepted-values#cities).
Local business postal code.
Filter by SIC codes.
Filter by NAICS codes.
Filter by phone number type.
Only return businesses with a website.
Only return businesses with a phone number.
Only return businesses with a mobile number.
Only return businesses with an email address.
Only return businesses with a LinkedIn page.
Only return businesses with a Facebook page.
Only return businesses with an Instagram account.
Only return businesses with a Twitter account.
Only return businesses with a YouTube channel.
Only return businesses with a TikTok account.
Minimum review rating, from 0 to 5.
Minimum number of reviews.
Sort order. Allowed values: `relevance`, `rating`, `reviews`. Defaults to `relevance`.
Page number for paginated results. Each page returns up to 10 records.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"local_businesses": [
{
"id": "65de59db4fd71c8789abc704",
"name": "Alohana Acai Bowls & Coffee / Oceanside",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://alohanaacaibowlssd.com/",
"domain": "alohanaacaibowlssd.com",
"phone": "+17604217175",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "california",
"city": "oceanside",
"address": "212 n coast hwy, oceanside, ca 92054",
"postal_code": "92054"
},
"coordinates": {
"lat": 33.1964483,
"lng": -117.3796645
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.8,
"reviews": 93
}
},
{
"id": "65de59db4fd71c8789abcf28",
"name": "Cobble Hill Coffee Shop",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://www.cobblehillcoffeeshopny.com/",
"domain": "cobblehillcoffeeshopny.com",
"phone": "+17188521162",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": "info@cobblehillcoffeeshopny.com",
"location": {
"country": "united states",
"state": "new york",
"city": "brooklyn",
"address": "314 court st, brooklyn, ny 11231",
"postal_code": "11231"
},
"coordinates": {
"lat": 40.6834534,
"lng": -73.9955585
},
"social": {
"linkedin_url": null,
"facebook_url": "facebook.com/cobblehillcoffeeshop",
"twitter_url": null,
"instagram_url": "instagram.com/cobblehillcoffeeshop",
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.2,
"reviews": 445
}
},
{
"id": "65de59db4fd71c8789ac1694",
"name": "Greenhaus Coffee",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://www.greenhauscoffee.com/",
"domain": "greenhauscoffee.com",
"phone": "+19377109019",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": "info@greenhauscoffee.com",
"location": {
"country": "united states",
"state": "ohio",
"city": "sidney",
"address": "126 e poplar st, sidney, oh 45365",
"postal_code": "45365"
},
"coordinates": {
"lat": 40.2858075,
"lng": -84.1555775
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.7,
"reviews": 169
}
},
{
"id": "65de59db4fd71c8789ac28b3",
"name": "Sundara Coffee House and Grill",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "http://sundara-coffee-house.square.site/",
"domain": "square.site",
"phone": "+14095480074",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "texas",
"city": "groves",
"address": "4000 lincoln ave, groves, tx 77619",
"postal_code": "77619"
},
"coordinates": {
"lat": 29.9468154,
"lng": -93.9188912
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.8,
"reviews": 274
}
},
{
"id": "65de59db4fd71c8789ac3899",
"name": "The Coffee Bean & Tea Leaf",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://www.coffeebean.com/store/usa/rialto/rialto",
"domain": "coffeebean.com",
"phone": "+19095660363",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": "rquinto@coffeebean.com",
"location": {
"country": "united states",
"state": "california",
"city": "rialto",
"address": "1877 n riverside ave, rialto, ca 92376",
"postal_code": "92376"
},
"coordinates": {
"lat": 34.1337693,
"lng": -117.3700871
},
"social": {
"linkedin_url": "linkedin.com/company/coffee-bean",
"facebook_url": "facebook.com/thecoffeebean",
"twitter_url": "twitter.com/thecoffeebean",
"instagram_url": "instagram.com/thecoffeebean",
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.4,
"reviews": 741
}
},
{
"id": "65de59db4fd71c8789ac4e1d",
"name": "Carolina Coffee N Creamery",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": null,
"domain": null,
"phone": "+13368494120",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "north carolina",
"city": "yadkinville",
"address": "101 n state st, yadkinville, nc 27055, united states",
"postal_code": "27055"
},
"coordinates": {
"lat": 36.1346857,
"lng": -80.6600246
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4,
"reviews": 26
}
},
{
"id": "65de59db4fd71c8789ac588d",
"name": "Formally Naked Cat Coffee, LLC. Now Canyon Coffee",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "http://nakedcatcoffee.weebly.com/",
"domain": "weebly.com",
"phone": "+15038518502",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "oregon",
"city": "stayton",
"address": "433 n 3rd ave, stayton, or 97383",
"postal_code": "97383"
},
"coordinates": {
"lat": 44.7982445,
"lng": -122.7924994
},
"social": {
"linkedin_url": null,
"facebook_url": null,
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.8,
"reviews": 39
}
},
{
"id": "65de59db4fd71c8789aca0a7",
"name": "Timber Coffee Company",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://www.facebook.com/Timber-Coffee-Company-349666712789862/",
"domain": "facebook.com",
"phone": "+12182209739",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "minnesota",
"city": "silver bay",
"address": "98 outer dr, silver bay, mn 55614",
"postal_code": "55614"
},
"coordinates": {
"lat": 47.2927512,
"lng": -91.2704403
},
"social": {
"linkedin_url": null,
"facebook_url": "https://www.facebook.com/Timber-Coffee-Company-349666712789862/",
"twitter_url": null,
"instagram_url": null,
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.9,
"reviews": 245
}
},
{
"id": "65de59db4fd71c8789acb234",
"name": "Black Rock Coffee Bar",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "http://br.coffee/",
"domain": "br.coffee",
"phone": "+18338435776",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": null,
"location": {
"country": "united states",
"state": "oregon",
"city": "portland",
"address": "8128 se powell blvd, portland, or 97206",
"postal_code": "97206"
},
"coordinates": {
"lat": 45.4971706,
"lng": -122.5794061
},
"social": {
"linkedin_url": null,
"facebook_url": "facebook.com/blackrockcoffeebar",
"twitter_url": null,
"instagram_url": "instagram.com/blackrockcoffeebar",
"youtube_url": null,
"tiktok_url": "tiktok.com/@blackrock.coffeebar"
},
"rating": {
"score": 4.4,
"reviews": 542
}
},
{
"id": "65de59db4fd71c8789acb85a",
"name": "PJ's Coffee",
"industry": "coffee shop",
"naics_code": "722515",
"sic_code": "5812",
"website_url": "https://www.pjscoffee.com/menu/",
"domain": "pjscoffee.com",
"phone": "+13374567967",
"phone_type": "FIXED_LINE_OR_MOBILE",
"email": "traciej@pjscoffee.com",
"location": {
"country": "united states",
"state": "louisiana",
"city": "lafayette",
"address": "1501 w pinhook rd, lafayette, la 70503, united states",
"postal_code": "70503"
},
"coordinates": {
"lat": 30.1977883,
"lng": -92.0161349
},
"social": {
"linkedin_url": "linkedin.com/company/pj's-coffee-of-new-orleans",
"facebook_url": "facebook.com/pjscoffee",
"twitter_url": "twitter.com/pjscoffee",
"instagram_url": "instagram.com/pjscoffee",
"youtube_url": null,
"tiktok_url": null
},
"rating": {
"score": 4.2,
"reviews": 58
}
}
]
},
"meta": {
"confidence": 97,
"query": {
"country": "united states",
"industry": "coffee shop",
"company_name": "coffee",
"page": 1
},
"credits": {
"charged": 5,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Search companies with multiple filters.
List a company's office locations.
Find a company's phone numbers.
Enrich a company with full firmographics.
# Person Enrichment API
Source: https://apidoc.cufinder.io/apis/person-enrichment
POST https://api.cufinder.io/v3/people/enrich-by-name
Names and companies provide starting points that sales teams need expanded into complete professional profiles. The [Person Enrichment](https://cufinder.io/enrichment-engine/contact-enrichment) API transforms full names and company names into detailed contact records, including job title, email, phone, LinkedIn, and location, with 97% confidence scores. Built for developers creating lead completion and identity verification tools, this RESTful endpoint delivers comprehensive person data that power CRM enrichment, contact validation, and personalized outreach workflows across your sales stack.
## Common Use Cases
One identifier is often all it takes to complete a contact's full profile.
* **Contact Enrichment:** Completing a person's profile with title, company, and socials.
* **Lead Scoring:** Qualifying contacts with full professional detail.
* **CRM Enrichment:** Backfilling missing fields on people records.
* **Personalized Outreach:** Tailoring messages with accurate contact context.
* **Recruiting:** Building richer candidate records from a single identifier.
Credit usage is 10 credits per request.
## Attributes
The person's full name.
The person's company, domain, name, or LinkedIn URL.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"person": {
"first_name": "iain",
"last_name": "mckenzie",
"full_name": "iain mckenzie",
"avatar_url": "media.cufinder.io/person_profile/iain-mckenzie",
"job": {
"title": "engineering",
"level": null,
"categories": [
"Engineering & Technical",
"Sales"
]
},
"company": {
"name": "stripe",
"domain": null,
"website_url": "https://stripe.com",
"industry": "technology, information and internet",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/stripe",
"facebook_url": "facebook.com/stripepayments",
"twitter_url": "twitter.com/stripe"
}
},
"location": {
"country": "canada",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/iain-mckenzie",
"facebook_url": null,
"twitter_url": null
},
"summary": null,
"followers_count": 0,
"emails": [],
"phones": [],
"experiences": [],
"educations": [],
"skills": [],
"certifications": [],
"languages": []
}
},
"meta": {
"confidence": 98,
"query": {
"full_name": "iain mckenzie",
"company": "stripe"
},
"credits": {
"charged": 10,
"remaining": 9862
}
}
}
```
## Related APIs
Search 1B+ profiles with combined filters.
Enrich a person from their LinkedIn profile.
Identify the person behind an email address.
Find an email from a LinkedIn profile.
# Person Name Normalizer API
Source: https://apidoc.cufinder.io/apis/person-name-normalizer
POST https://api.cufinder.io/v3/normalize/person-name
The Person Name Normalizer API cleans a person's name into a consistent, standardized form. Strip stray titles, fix casing, and collapse messy whitespace so your contact records match and dedupe reliably.
## Common Use Cases
Inconsistent person names quietly break matching, deduplication, and personalization.
* **Data Hygiene:** Standardizing contact names before they enter your CRM.
* **Deduplication:** Matching records that spell the same person differently.
* **Personalization:** Keeping salutations clean in outreach at scale.
* **Data Integration:** Reconciling names across systems and imports.
Credit usage is 1 credit per request.
## Attributes
The person name to normalize.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"normalized": "DR. Morteza Heydari Phd"
},
"meta": {
"confidence": 97,
"query": {
"value": "DR. Morteza Heydari phd"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Clean and standardize company names.
Standardize phone numbers to a clean format.
Standardize and validate postal addresses.
Normalize URLs to a canonical form.
# Person Search API
Source: https://apidoc.cufinder.io/apis/person-search
POST https://api.cufinder.io/v3/people/search
Multi-filter searches separate precision prospecting from spray-and-pray outreach that wastes sales cycles. The Person Search API queries CUFinder's 1B+ professional profiles using combined filters, including name, company details, location, industry, and revenue, returning matching contact records with 99% confidence scores. Built for developers creating targeted prospecting and talent sourcing platforms, this RESTful endpoint delivers filtered person data including job titles, social profiles, and employer information that power account-based selling, executive search, and partnership identification workflows across your sales stack.
## Common Use Cases
Filtered search turns a target profile into a real list of people you can reach.
* **Lead Generation:** Building targeted contact lists from combined filters.
* **Recruiting and Sourcing:** Finding candidates by role, company, and location.
* **Account-Based Marketing:** Reaching the right people inside target accounts.
* **Market Research:** Exploring a talent pool or buyer segment.
* **List Building:** Generating fresh prospect lists on demand.
Credit usage is 5 credits per request.
## Attributes
Person full name, matched as a substring, for example `john doe`.
Person country. See the full [list of accepted countries](/apis/accepted-values#countries).
Person state. See the full [list of accepted states](/apis/accepted-values#states).
Person city. See the full [list of accepted cities](/apis/accepted-values#cities).
Person job title.
Seniority level. Allowed values: `cxo`, `vp`, `director`, `manager`, `senior`, `entry`, `owner`, `partner`, `training`.
Company domain.
Company name.
Company LinkedIn URL.
Company industry. See the full [list of accepted industries](/apis/accepted-values#industries).
Company size band. Allowed values: `1-10`, `11-50`, `51-200`, `201-500`, `501-1000`, `1001-5000`, `5001-10000`, `10001+`.
Minimum company annual revenue, in USD.
Maximum company annual revenue, in USD.
Page number for paginated results. Each page returns up to 10 records.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"people": [
{
"first_name": null,
"last_name": null,
"full_name": "alan baptista",
"avatar_url": null,
"job": {
"title": "product marketing director, go-to-market, and partner marketing.",
"level": null,
"categories": []
},
"company": {
"name": "veeam software",
"domain": null,
"website_url": "https://www.veeam.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "washington",
"city": "seattle",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/veeam-software",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "california",
"city": "los angeles",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/abaptista",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "adrienne chang",
"avatar_url": null,
"job": {
"title": "product marketing",
"level": null,
"categories": []
},
"company": {
"name": "meta",
"domain": null,
"website_url": "https://meta.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "menlo park",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/meta",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "california",
"city": "menlo park",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/adriennetchang",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "agusto alarco",
"avatar_url": null,
"job": {
"title": "strategy, marketing & analytics | mba | ex-capital one",
"level": null,
"categories": []
},
"company": {
"name": "comscore, inc.",
"domain": null,
"website_url": "https://www.comscore.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "virginia",
"city": "reston",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/comscore",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "virginia",
"city": "reston",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/agustoalarco",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alan letran",
"avatar_url": null,
"job": {
"title": "sr. marketing manager | digital advertising",
"level": null,
"categories": []
},
"company": {
"name": "amazon",
"domain": null,
"website_url": "amazon.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "washington",
"city": "seattle",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/amazon",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "washington",
"city": "seatac",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/alan-letran",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alena chiang",
"avatar_url": null,
"job": {
"title": "passionate about e-commerce, retail, marketing strategy, start-ups, and all things diy.",
"level": null,
"categories": []
},
"company": {
"name": "meta",
"domain": null,
"website_url": "https://meta.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "menlo park",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/meta",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "eau claire",
"city": "eau claire",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/alena-chiang",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alex mastrodonato",
"avatar_url": null,
"job": {
"title": "platform marketing leader",
"level": null,
"categories": []
},
"company": {
"name": "servicenow",
"domain": null,
"website_url": "http://www.servicenow.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "santa clara",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/servicenow",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/alexmastrodonato",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alex sosner",
"avatar_url": null,
"job": {
"title": "senior manager, influencer marketing",
"level": null,
"categories": []
},
"company": {
"name": "etsy",
"domain": null,
"website_url": "http://www.etsy.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new york",
"city": "brooklyn",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/etsy",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "new york",
"city": "new york",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/alexsosner",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alina pimentel",
"avatar_url": null,
"job": {
"title": "product marketing",
"level": null,
"categories": []
},
"company": {
"name": "hinge health",
"domain": null,
"website_url": "http://www.hingehealth.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "panama",
"state": "veraguas province",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/hinge-health",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/alina-pimentel-a320169b",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "alma bencomo",
"avatar_url": null,
"job": {
"title": "product marketing manager | cloud marketing",
"level": null,
"categories": []
},
"company": {
"name": "microsoft",
"domain": null,
"website_url": "https://news.microsoft.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "washington",
"city": "redmond",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/microsoft",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "arizona",
"city": "phoenix",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/almabencomo",
"facebook_url": null,
"twitter_url": null
}
},
{
"first_name": null,
"last_name": null,
"full_name": "amanda keay",
"avatar_url": null,
"job": {
"title": "event producer | experiential marketing |conference & corporate events | digital marketing | events manager @overclock labs",
"level": null,
"categories": []
},
"company": {
"name": "overclock labs, creators of akash network",
"domain": null,
"website_url": "http://akash.network",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/akash-network",
"facebook_url": null,
"twitter_url": null
}
},
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/amanda-keay-b355ba307",
"facebook_url": null,
"twitter_url": null
}
}
]
},
"meta": {
"confidence": 93,
"query": {
"country": "united states",
"job_title": "marketing",
"company_industry": "software development",
"page": 1
},
"credits": {
"charged": 5,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Enrich a person with full contact data.
Find employees working at a company.
Enrich a person from their LinkedIn profile.
Identify the person behind an email address.
# Phone Number Normalizer API
Source: https://apidoc.cufinder.io/apis/phone-number-normalizer
POST https://api.cufinder.io/v3/normalize/phone
Data quality issues start when phone numbers arrive in dozens of inconsistent formats across your contact database. The Phone Number Normalizer API standardizes phone numbers into E.164 international format, returning cleaned numbers with country codes, area codes, and validation status that ensure CRM integration compatibility and dialer system accuracy. Built for data enrichment platforms, contact management systems, and communication automation tools, this RESTful endpoint delivers normalized phone data, including format corrections, carrier type detection, and validity flags, that power click-to-dial functionality, SMS delivery optimization, and duplicate contact consolidation workflows across your sales and marketing stack.
## Common Use Cases
Consistent phone formatting keeps dialers, deduplication, and compliance running smoothly.
* **Data Hygiene:** Standardizing phone numbers to a consistent format before storage.
* **Deduplication:** Matching records that store the same number different ways.
* **Dialer Readiness:** Formatting numbers so calling tools dial correctly.
* **Compliance:** Keeping numbers in a valid format for messaging and consent.
* **Data Integration:** Reconciling phone formats across systems and imports.
Credit usage is 1 credit per request.
## Attributes
The phone number to normalize, for example `+1 (310) 807 3300`.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"normalized": "+1 310 807 3300"
},
"meta": {
"confidence": 95,
"query": {
"value": "+1 (310) 807 3300"
},
"credits": {
"charged": 1,
"remaining": 9862
}
}
}
```
## Related APIs
Standardize and validate postal addresses.
Clean and standardize company names.
Find a company's phone numbers.
Enrich a company with full firmographics.
# Quick Start
Source: https://apidoc.cufinder.io/apis/quick-start
This guide will get you all set up and ready to use the CUFinder API. We'll cover how to get started using one of our API endpoints and how to make your first API request. We'll also look at where to go next to find all the information you need to take full advantage of our powerful REST API.
Before you can make requests to the CUFinder API, you will need to grab your
API key from your dashboard. You find it under
Account » API key.
## Make your first request
Every CUFinder endpoint is a `POST` to `https://api.cufinder.io/v3/...` with a JSON body and your key in the `x-api-key` header. Try the Company Email Finder:
```bash theme={null}
curl -X POST 'https://api.cufinder.io/v3/companies/email-finder' \
--header 'Content-Type: application/json' \
--header 'x-api-key: YOUR_API_KEY' \
--data '{"query": "cufinder.io"}'
```
Every successful response uses the same envelope: your result in `data`, and request context in `meta`:
```json theme={null}
{
"success": true,
"data": {
"emails": ["info@cufinder.io", "marketing@cufinder.io"]
},
"meta": {
"confidence": 99,
"query": { "query": "cufinder.io" },
"credits": { "charged": 1, "remaining": 9896 }
}
}
```
* `data`: the endpoint's result. List endpoints also include `meta.pagination` with `{ "page", "has_more" }`.
* `meta.credits`: what this call cost (`charged`) and your balance after it (`remaining`). Credits are only charged on successful responses.
* `meta.query`: an echo of the inputs the API accepted.
## What's next ?
Great, you're now access to your API key and can make your first request to the API. Presented here is the exhaustive list of APIs, accompanied by the instructions on how to call them.
# Reverse Email Lookup API
Source: https://apidoc.cufinder.io/apis/reverse-email-lookup
POST https://api.cufinder.io/v3/people/enrich-by-email
Email addresses hide complete professional profiles that marketing and sales teams need for personalization. The [Reverse Email Lookup](https://cufinder.io/enrichment-engine/reverse-email-lookup) API transforms email addresses into detailed person and company data, including full name, job title, LinkedIn, employer, and location, with 98% confidence scores. Built for developers creating identity resolution and lead validation tools, this RESTful endpoint delivers comprehensive contact profiles that power email verification, account enrichment, and prospect intelligence workflows across your sales stack.
## Common Use Cases
An email address on its own says little until you know who is actually behind it.
* **Lead Capture:** Identifying the person behind an inbound email address.
* **CRM Enrichment:** Turning a bare email into a full contact record.
* **Verification:** Checking who is really behind a signup.
* **Sales Research:** Learning who you are talking to before you reply.
* **Data Enrichment:** Backfilling names and roles from email addresses.
Credit usage is 4 credits per request.
## Attributes
The person's email address.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"person": {
"first_name": "morteza",
"last_name": "heydari",
"full_name": "morteza heydari",
"avatar_url": "media.cufinder.io/person_profile/morteza-heydari-192567168",
"job": {
"title": "backend developer",
"level": null,
"categories": [
"Other"
]
},
"company": {
"name": "cufinder",
"domain": null,
"website_url": "https://cufinder.io",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/cufinder",
"facebook_url": "facebook.com/cufinder",
"twitter_url": "x.com/cu_finder"
}
},
"location": {
"country": "turkey",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/morteza-heydari-192567168",
"facebook_url": null,
"twitter_url": null
},
"summary": "typescriptjavascript (es5, es6, es7, es8, ...)node.jsexpress.jsnest.jsreact 19next.jsvue.jsnuxt.jsmaterial uiant design tailwind 4bootstrap 5sasshtml 5css 3",
"followers_count": 11131,
"emails": [],
"phones": [],
"experiences": [],
"educations": [],
"skills": [],
"certifications": [],
"languages": []
}
},
"meta": {
"confidence": 99,
"query": {
"email": "morteza.heydari@cufinder.io"
},
"credits": {
"charged": 4,
"remaining": 9862
}
}
}
```
## Related APIs
Enrich a person with full contact data.
Find an email from a LinkedIn profile.
Enrich a person from their LinkedIn profile.
Search 1B+ profiles with combined filters.
# Status Codes
Source: https://apidoc.cufinder.io/apis/status-codes
Every CUFinder API response returns a standard HTTP status code alongside a JSON envelope. Learn what each code means, why it happens, and how to resolve it.
Every request to the CUFinder API returns a standard **HTTP status code** together with a JSON envelope. Successful lookups are `200` with `"success": true`. Most failures use meaningful 4xx/5xx codes with `"success": false` and a stable, machine-readable `error.code`, with one exception: **a not-found lookup also returns HTTP `200`**, with `"success": false` and `error.code` of `not_found`. Always branch on the `success` flag (or `error.code`), not on the HTTP status alone.
## Response shape
```json Success (200) theme={null}
{
"success": true,
"data": {
"emails": ["info@cufinder.io"]
},
"meta": {
"confidence": 99,
"query": { "query": "cufinder.io" },
"credits": { "charged": 1, "remaining": 9896 }
}
}
```
```json Not found (200) theme={null}
{
"success": false,
"error": {
"code": "not_found",
"message": "Not Found"
}
}
```
```json Error (4xx / 5xx) theme={null}
{
"success": false,
"error": {
"code": "validation_failed",
"message": "Please resolve validation errors",
"details": [
{ "field": "query", "issue": "query should not be empty" }
]
}
}
```
The endpoint's result. List endpoints return plural arrays (`companies`, `people`, `jobs`, ...) and include `meta.pagination` with `{ "page", "has_more" }`.
`charged` is what this call cost; `remaining` is your balance after it. **Credits are only charged when `success` is `true`**, errors, including not-found lookups, never charge, and an insufficient-credits check runs before any work starts.
Present only on errors. A stable identifier you can branch on: `invalid_request`, `unauthorized`, `insufficient_credits`, `not_found`, `request_timeout`, `validation_failed`, `rate_limited`, `internal_error`.
## Quick reference
| Code | `error.code` | Meaning |
| ----- | ---------------------- | -------------------------------------------------------------------- |
| `200` | , | The request succeeded and `data` was returned. |
| `200` | `not_found` | The entity you looked up could not be resolved (`"success": false`). |
| `400` | `invalid_request` | The request is malformed (for example, invalid JSON). |
| `401` | `unauthorized` | The API key is missing or invalid. |
| `402` | `insufficient_credits` | Not enough credits to run this request. |
| `408` | `request_timeout` | The upstream lookup timed out. Retry the request. |
| `422` | `validation_failed` | A required field is missing or a value is invalid. |
| `429` | `rate_limited` | Too many requests. Slow down and retry later. |
| `500` | `internal_error` | Something went wrong on our side. |
## 200: OK
The request was processed successfully.
`data` contains your result and `meta.credits` reports the cost and your remaining balance. Note that **an empty search result is still a `200`**, list endpoints return an empty array rather than an error, and the request is charged normally.
## 400: Invalid request
The request body could not be processed at all, most commonly invalid JSON or a wrong content type.
**How to resolve**
* Send a JSON body with `Content-Type: application/json`.
* Validate your JSON before sending.
## 401: Unauthorized
The `x-api-key` header is missing, malformed, or invalid.
**How to resolve**
* Confirm you are sending the `x-api-key` header on every request.
* Copy your key exactly from **Account » API key** in the [dashboard](https://dashboard.cufinder.io), keys are case-sensitive and contain no spaces.
* Make sure the key has not been rotated or revoked.
```json Response theme={null}
{
"success": false,
"error": {
"code": "unauthorized",
"message": "API key is missing or invalid."
}
}
```
## 402: Insufficient credits
Your account does not have enough credits for this request. The check runs **before** any work is performed, so nothing is charged and no lookup happens.
**How to resolve**
* Review your remaining balance from the [dashboard](https://dashboard.cufinder.io) or from `meta.credits.remaining` on any successful call.
* Top up credits or upgrade your plan, then retry the request.
```json Response theme={null}
{
"success": false,
"error": {
"code": "insufficient_credits",
"message": "Not enough credits, please charge your account!"
}
}
```
## Not found (200 with `success: false`)
The specific entity you addressed, a company, a person, a domain, could not be resolved. The HTTP status is `200`, so **check the `success` flag or `error.code` to detect it**. You were not charged.
**How to resolve**
* Double-check the identifier: domains without typos, full LinkedIn URLs, exact spellings.
* Try an alternative identifier, for example a domain instead of a company name.
* Remember: search endpoints never return `not_found` for "no matches"; they return `"success": true` with an empty array.
```json Response theme={null}
{
"success": false,
"error": {
"code": "not_found",
"message": "Not Found"
}
}
```
## 408: Request timeout
The upstream data source took too long to answer. You were not charged.
**How to resolve**
* Retry the request, timeouts are usually transient.
* If one endpoint times out repeatedly for the same input, try again later or [contact support](https://cufinder.io/contact-sales).
```json Response theme={null}
{
"success": false,
"error": {
"code": "request_timeout",
"message": "The upstream request timed out. Please try again."
}
}
```
## 422: Validation failed
The request reached the API but a required field is missing or a value is invalid. The `details` array names each problem field.
**How to resolve**
* Read `error.details`, each entry has the `field` and the `issue`.
* Check the endpoint's **Attributes** section for required parameters and accepted values.
```json Response theme={null}
{
"success": false,
"error": {
"code": "validation_failed",
"message": "Please resolve validation errors",
"details": [
{ "field": "country", "issue": "country should not be empty" }
]
}
}
```
## 429: Rate limited
You are sending requests faster than your key allows.
**How to resolve**
* Slow your request rate and retry after a short delay.
* Spread batch jobs over time instead of bursting.
## 500: Server error
Something went wrong on our side. These are rare, and you are never charged for them.
**How to resolve**
* Retry the request after a short delay.
* If the error persists, [contact support](https://cufinder.io/contact-sales) with the request details so we can investigate.
Before contacting support, double-check your request against the endpoint's **Attributes** and the codes above, most errors are resolved by correcting the API key, credits, or parameters.
# URL Normalizer API
Source: https://apidoc.cufinder.io/apis/url-normalizer
POST https://api.cufinder.io/v3/normalize/url
The URL Normalizer API cleans a URL into a canonical form, dropping paths, parameters, and scheme noise so the same site always resolves to the same value. Free to call, so you can run it across entire datasets.
## Common Use Cases
The same website written five different ways is five records your systems treat as different companies.
* **Data Hygiene:** Canonicalizing website fields before storage.
* **Deduplication:** Matching records that store the same site with different URLs.
* **Enrichment Prep:** Producing clean domains to feed into enrichment endpoints.
* **Analytics:** Grouping traffic and records by canonical site.
Free. This endpoint does not charge credits.
## Attributes
The URL to normalize.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"normalized": "WWW.CuFinder.io"
},
"meta": {
"confidence": 99,
"query": {
"value": "WWW.CuFinder.io/path/?utm=x"
},
"credits": {
"charged": 0,
"remaining": 9862
}
}
}
```
## Related APIs
Resolve a company name to its domain.
Get the company name behind a domain.
Clean and standardize company names.
Standardize and validate postal addresses.
# Usage Limits
Source: https://apidoc.cufinder.io/apis/usage-limits
We define rate limits on a per-key basis and use a fixed-window rate limiting strategy. Our API key's rate limit is 100 requests per minute. It means, you can make 100 API calls at any interval within the 60-second window.
# Detection timeframes
Source: https://apidoc.cufinder.io/buying-signals/concepts/detection-timeframes
The time windows each signal is evaluated over, from instant snapshot diffs to six-month patterns.
Every signal is evaluated over a specific time window. Some fire the instant a field changes between two crawls. Others only fire once a pattern holds across weeks or months. Knowing the window tells you **how fresh a signal is** and **how much weight to give it**, a single-snapshot change is immediate but noisy, while a 90-day pattern is slower but far more reliable.
CUFinder evaluates signals across five practical windows.
Short-fuse patterns that need to be caught fast, like a cluster of crisis posts.
Rolling-activity windows: posting cadence, engagement shifts, and the nightly momentum score.
Quarter-scale patterns: vacancies, consolidations, churn spikes, and most composite events.
The longest patterns, like the pre-IPO buildout that unfolds over two quarters.
Two more windows sit outside the week-to-six-month scale: an **instant snapshot** diff (the change is detected the moment two crawls differ) and a **12-month lookback** used by the first-in-function and first-in-geography signals.
## Snapshot (instant)
These fire the moment a tracked field differs between two consecutive crawls. There is no waiting window, the change is the signal. They are the most immediate but also the most sensitive, so filter them by magnitude. This is the largest group.
**71 signals** use this window:
| Signal | Category | What it means |
| ------------------------------------------------------------------------------------------------- | -------------- | ----------------------------------------------------------------------- |
| [`employee_growth`](/buying-signals/signals/growth/employee-growth) | Growth | A company's employee count increased by at least one between snapshots. |
| [`employee_size_band_upgrade`](/buying-signals/signals/growth/employee-size-band-upgrade) | Growth | A company moved up a full Professional Network employee size band. |
| [`followers_growth`](/buying-signals/signals/growth/followers-growth) | Growth | The company's Professional Network follower count increased. |
| [`jobs_open_increase`](/buying-signals/signals/growth/jobs-open-increase) | Growth | The number of open job postings increased. |
| [`remote_jobs_share_change`](/buying-signals/signals/growth/remote-jobs-share-change) | Growth | The share of remote or hybrid postings shifted significantly. |
| [`employee_decrease`](/buying-signals/signals/decline/employee-decrease) | Decline | A company's employee count dropped by at least one. |
| [`employee_size_band_downgrade`](/buying-signals/signals/decline/employee-size-band-downgrade) | Decline | A company fell down a Professional Network employee size band. |
| [`hiring_freeze_signal`](/buying-signals/signals/decline/hiring-freeze-signal) | Decline | Open roles collapsed while headcount stayed flat or fell. |
| [`followers_decrease`](/buying-signals/signals/decline/followers-decrease) | Decline | The company's follower count dropped. |
| [`jobs_open_decrease`](/buying-signals/signals/decline/jobs-open-decrease) | Decline | The number of open job postings fell. |
| [`jobs_dropped_to_zero`](/buying-signals/signals/decline/jobs-dropped-to-zero) | Decline | A company that had open roles now has none. |
| [`name_change`](/buying-signals/signals/identity/name-change) | Identity | The company name string changed. |
| [`name_change_drastic`](/buying-signals/signals/identity/name-change-drastic) | Identity | A name change with almost no overlap to the old name. |
| [`dba_added`](/buying-signals/signals/identity/dba-added) | Identity | The name now references a former name (dba/fka). |
| [`tagline_change`](/buying-signals/signals/identity/tagline-change) | Identity | The company tagline text changed. |
| [`tagline_added`](/buying-signals/signals/identity/tagline-added) | Identity | A previously empty tagline is now populated. |
| [`tagline_removed`](/buying-signals/signals/identity/tagline-removed) | Identity | A populated tagline was cleared out. |
| [`description_change_minor`](/buying-signals/signals/identity/description-change-minor) | Identity | The company description was lightly edited. |
| [`description_change_major`](/buying-signals/signals/identity/description-change-major) | Identity | The company description was substantially rewritten. |
| [`description_keyword_added`](/buying-signals/signals/identity/description-keyword-added) | Identity | A tracked strategic keyword appeared in the description. |
| [`description_keyword_removed`](/buying-signals/signals/identity/description-keyword-removed) | Identity | A tracked strategic keyword disappeared from the description. |
| [`industry_change`](/buying-signals/signals/categorization/industry-change) | Categorization | The company's primary industry classification changed. |
| [`industry_added`](/buying-signals/signals/categorization/industry-added) | Categorization | A new industry was added to the company's list. |
| [`industry_removed`](/buying-signals/signals/categorization/industry-removed) | Categorization | An industry was dropped from the company's list. |
| [`specialty_added`](/buying-signals/signals/categorization/specialty-added) | Categorization | A new specialty was added. |
| [`specialty_removed`](/buying-signals/signals/categorization/specialty-removed) | Categorization | A specialty was dropped from the list. |
| [`specialties_count_increase`](/buying-signals/signals/categorization/specialties-count-increase) | Categorization | The total number of specialties grew notably. |
| [`specialties_count_decrease`](/buying-signals/signals/categorization/specialties-count-decrease) | Categorization | The total number of specialties shrank notably. |
| [`hq_change`](/buying-signals/signals/location/hq-change) | Location | The headquarters city or country changed. |
| [`hq_country_change`](/buying-signals/signals/location/hq-country-change) | Location | The headquarters country specifically changed. |
| [`location_added`](/buying-signals/signals/location/location-added) | Location | A new office location appeared. |
| [`location_removed`](/buying-signals/signals/location/location-removed) | Location | An office location disappeared. |
| [`country_expansion`](/buying-signals/signals/location/country-expansion) | Location | A company opened its first location in a new country. |
| [`multi_office_milestone`](/buying-signals/signals/location/multi-office-milestone) | Location | The company's office count crossed a milestone threshold. |
| [`office_consolidation`](/buying-signals/signals/location/office-consolidation) | Location | Multiple office closures in a short window. |
| [`company_type_change`](/buying-signals/signals/structure/company-type-change) | Structure | The company's entity type changed. |
| [`nonprofit_to_for_profit`](/buying-signals/signals/structure/nonprofit-to-for-profit) | Structure | The company changed between nonprofit and for-profit status. |
| [`subsidiary_status_change`](/buying-signals/signals/structure/subsidiary-status-change) | Structure | The company became or stopped being a subsidiary. |
| [`founded_year_added`](/buying-signals/signals/structure/founded-year-added) | Structure | A previously missing founded year was populated. |
| [`founded_year_change`](/buying-signals/signals/structure/founded-year-change) | Structure | The founded year value changed. |
| [`parent_company_change`](/buying-signals/signals/structure/parent-company-change) | Structure | The parent company changed to a different one. |
| [`parent_company_added`](/buying-signals/signals/structure/parent-company-added) | Structure | A parent company was set where there was none. |
| [`parent_company_removed`](/buying-signals/signals/structure/parent-company-removed) | Structure | A parent company relationship was cleared. |
| [`subsidiary_added`](/buying-signals/signals/structure/subsidiary-added) | Structure | A new subsidiary appeared in the company's list. |
| [`subsidiary_removed`](/buying-signals/signals/structure/subsidiary-removed) | Structure | A subsidiary was removed from the company's list. |
| [`showcase_page_added`](/buying-signals/signals/structure/showcase-page-added) | Structure | A new Professional Network showcase page was linked. |
| [`showcase_page_removed`](/buying-signals/signals/structure/showcase-page-removed) | Structure | A Professional Network showcase page was unlinked. |
| [`funding_round_announced`](/buying-signals/signals/funding/funding-round-announced) | Funding | A new funding round was announced. |
| [`last_funding_round_change`](/buying-signals/signals/funding/last-funding-round-change) | Funding | The company's funding stage advanced. |
| [`total_funding_increase`](/buying-signals/signals/funding/total-funding-increase) | Funding | The company's total funding raised increased. |
| [`page_reactivated`](/buying-signals/signals/activity/page-reactivated) | Activity | The company resumed posting after a dormant stretch. |
| [`post_topic_shift`](/buying-signals/signals/activity/post-topic-shift) | Activity | The dominant topic of recent posts changed. |
| [`employee_joined`](/buying-signals/signals/people/employee-joined) | People | A person's current company changed to this company. |
| [`employee_departed`](/buying-signals/signals/people/employee-departed) | People | A person left this company for another. |
| [`internal_promotion`](/buying-signals/signals/people/internal-promotion) | People | Someone was promoted to a more senior title at the same company. |
| [`lateral_title_change`](/buying-signals/signals/people/lateral-title-change) | People | Someone changed title at the same company without a seniority change. |
| [`c_suite_hire`](/buying-signals/signals/people/c-suite-hire) | People | A new C-level executive joined the company. |
| [`c_suite_departure`](/buying-signals/signals/people/c-suite-departure) | People | A C-level executive left the company. |
| [`founder_departure`](/buying-signals/signals/people/founder-departure) | People | A founder left the company. |
| [`vp_hire`](/buying-signals/signals/people/vp-hire) | People | A new VP-level leader joined the company. |
| [`vp_departure`](/buying-signals/signals/people/vp-departure) | People | A VP-level leader left the company. |
| [`first_role_hire`](/buying-signals/signals/people/first-role-hire) | People | A company made its first-ever hire for a role function. |
| [`leadership_churn_spike`](/buying-signals/signals/people/leadership-churn-spike) | People | Multiple senior leaders departed in a short window. |
| [`engineering_leader_hire`](/buying-signals/signals/people/engineering-leader-hire) | People | A new engineering leader joined the company. |
| [`sales_leader_hire`](/buying-signals/signals/people/sales-leader-hire) | People | A new sales leader joined the company. |
| [`talent_outflow_to_competitor`](/buying-signals/signals/people/talent-outflow-to-competitor) | People | An employee left for a known competitor. |
| [`talent_inflow_from_company`](/buying-signals/signals/people/talent-inflow-from-company) | People | A company hired multiple people from the same source company. |
| [`notable_hire`](/buying-signals/signals/people/notable-hire) | People | A high-profile or highly experienced person joined. |
| [`ipo_signal`](/buying-signals/signals/composite/ipo-signal) | Composite | A company went from private to public. |
| [`acquired_signal`](/buying-signals/signals/composite/acquired-signal) | Composite | A company was acquired. |
| [`risk_score`](/buying-signals/signals/composite/risk-score) | Composite | A nightly composite score ranking a company's decline and risk. |
## Crawl-over-crawl baselines
These compare the current crawl against a short rolling baseline (the previous 4 or 6 crawls) to catch anomalies like spikes and surges. They fire only when current activity breaks sharply from the recent norm.
**9 signals** use this window:
| Signal | Category | What it means |
| -------------------------------------------------------------------------------------------------- | --------- | ---------------------------------------------------------------- |
| [`followers_spike`](/buying-signals/signals/growth/followers-spike) | Growth | Follower growth ran sharply above the company's recent baseline. |
| [`jobs_open_spike`](/buying-signals/signals/growth/jobs-open-spike) | Growth | Open job postings jumped sharply above the recent baseline. |
| [`senior_hiring_increase`](/buying-signals/signals/growth/senior-hiring-increase) | Growth | The share of senior-level postings rose meaningfully. |
| [`engineering_hiring_surge`](/buying-signals/signals/growth/engineering-hiring-surge) | Growth | Engineering job postings doubled versus the prior crawl. |
| [`sales_hiring_surge`](/buying-signals/signals/growth/sales-hiring-surge) | Growth | Sales job postings doubled versus the prior crawl. |
| [`layoff_signal`](/buying-signals/signals/decline/layoff-signal) | Decline | Employee count dropped sharply in a single interval. |
| [`mass_layoff_signal`](/buying-signals/signals/decline/mass-layoff-signal) | Decline | Employee count dropped severely in a single interval. |
| [`followers_growth_decelerating`](/buying-signals/signals/decline/followers-growth-decelerating) | Decline | Follower growth slowed sharply while still positive. |
| [`affiliated_pages_count_change`](/buying-signals/signals/structure/affiliated-pages-count-change) | Structure | The net count of affiliated pages shifted notably. |
## Week (7 days)
Short-fuse patterns evaluated over a rolling 7-day window. Fast-moving by design, so they are caught and acted on quickly.
**1 signal** use this window:
| Signal | Category | What it means |
| ----------------------------------------------------------------------------------- | -------- | ---------------------------------------------------------------- |
| [`crisis_response_signal`](/buying-signals/signals/activity/crisis-response-signal) | Activity | A cluster of statement or apology-style posts in a short window. |
## 1 month (30 days)
Rolling 30-day windows, mostly social-activity rhythms compared against the preceding 30-day periods, plus the nightly momentum score that decays over 30-day steps.
**4 signals** use this window:
| Signal | Category | What it means |
| ----------------------------------------------------------------------------------------- | --------- | ---------------------------------------------------------------- |
| [`posting_activity_increase`](/buying-signals/signals/activity/posting-activity-increase) | Activity | The company is posting noticeably more on social. |
| [`posting_activity_decrease`](/buying-signals/signals/activity/posting-activity-decrease) | Activity | The company is posting noticeably less on social. |
| [`avg_engagement_change`](/buying-signals/signals/activity/avg-engagement-change) | Activity | Average post engagement shifted significantly. |
| [`momentum_score`](/buying-signals/signals/composite/momentum-score) | Composite | A nightly composite score ranking a company's upward trajectory. |
## 3 months (60 to 90 days)
Quarter-scale patterns. A vacancy that stays open 60 days, three closures or departures inside 90 days, or a composite event whose parts must land within a 60-to-90-day window. Slower to fire, but high-confidence.
**9 signals** use this window:
| Signal | Category | What it means |
| -------------------------------------------------------------------------------------- | --------- | ------------------------------------------------------ |
| [`page_dormant`](/buying-signals/signals/activity/page-dormant) | Activity | The company stopped posting for an extended period. |
| [`key_role_vacancy`](/buying-signals/signals/people/key-role-vacancy) | People | A senior role stayed unfilled for 60 days. |
| [`merger_signal`](/buying-signals/signals/composite/merger-signal) | Composite | Two companies merged under a common new parent. |
| [`pivot_signal`](/buying-signals/signals/composite/pivot-signal) | Composite | A company fundamentally changed what it does. |
| [`rebrand_signal`](/buying-signals/signals/composite/rebrand-signal) | Composite | A company executed a coordinated rebrand. |
| [`expansion_signal`](/buying-signals/signals/composite/expansion-signal) | Composite | A company is expanding into new markets while growing. |
| [`decline_signal`](/buying-signals/signals/composite/decline-signal) | Composite | A company shows a clear distress pattern. |
| [`restructuring_signal`](/buying-signals/signals/composite/restructuring-signal) | Composite | A company is undergoing major restructuring. |
| [`executive_team_buildout`](/buying-signals/signals/composite/executive-team-buildout) | Composite | A company rapidly built out its leadership team. |
## 6 months (180 days)
The longest patterns in the catalog. These need two quarters of evidence before they fire, which makes them rare and strong.
**2 signals** use this window:
| Signal | Category | What it means |
| ------------------------------------------------------------------------- | --------- | -------------------------------------------------- |
| [`headcount_recovery`](/buying-signals/signals/growth/headcount-recovery) | Growth | Employee growth returned after a recent decline. |
| [`pre_ipo_signal`](/buying-signals/signals/composite/pre-ipo-signal) | Composite | A company shows the early pattern of an IPO track. |
## 12-month lookback
These check whether something is happening for the first time in a year, a first job posting in a function or geography. The window is a backward lookback rather than a waiting period.
**3 signals** use this window:
| Signal | Category | What it means |
| ------------------------------------------------------------------------------- | -------- | ----------------------------------------------------------------------- |
| [`first_job_in_function`](/buying-signals/signals/growth/first-job-in-function) | Growth | A company posted a role in a function it hadn't hired for in 12 months. |
| [`first_job_in_country`](/buying-signals/signals/growth/first-job-in-country) | Growth | A company posted its first role in a new country. |
| [`first_job_in_city`](/buying-signals/signals/growth/first-job-in-city) | Growth | A company posted its first role in a new city. |
## Using timeframes in practice
Pair the window with magnitude. Act fastest on short-window, high-magnitude events (a funding round, a crisis cluster), and treat long-window composites as durable, high-trust signals worth a more considered approach. The longer the window a signal clears, the longer your outreach stays relevant.
The full pipeline behind snapshots, baselines, and windowed triggers.
# How detection works
Source: https://apidoc.cufinder.io/buying-signals/concepts/how-detection-works
Snapshots, deltas, and the emit logic behind every signal.
Every CUFinder signal is produced by the same underlying mechanism: we capture data over time, compare consecutive snapshots, and emit a signal whenever a meaningful change occurs.
## The detection pipeline
Company pages, job postings, employee profiles, and funding records are captured on a recurring schedule and stored as point-in-time snapshots.
Each new crawl is diffed against the previous one. Fields like `employee_count`, `job_count`, `name`, `description`, `locations`, and `funding rounds` are checked for change.
Each signal has a precise, deterministic trigger. Some fire on a single field change; others require a threshold, a rolling baseline, or a multi-day window.
Quantitative changes are sorted into magnitude buckets so you can prioritize. See [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
Many signals persist extra context in a `meta` object, the function that was hired for, the country expanded into, the keyword that was added, so the signal is immediately actionable.
## Three kinds of trigger logic
The simplest signals fire on any change to a tracked field. `name_change` fires when the name string differs. `employee_growth` fires when headcount rises by at least one. These are sensitive, so they are best filtered by magnitude.
Many signals compare the current crawl against a rolling baseline. `followers_spike` fires at 3× the previous six-crawl average. `jobs_open_spike` fires at 2× the previous four-crawl average. Thresholds filter out ordinary movement so only anomalies surface.
The highest-value signals look across time and across other signals. `office_consolidation` waits for three closures in 90 days. `acquired_signal` requires a new parent plus a name or description change within 60 days. Composite signals are built entirely from other signals firing together.
## Confidence and scrutiny
Not every change is equally trustworthy. Some signals are emitted with extra scrutiny or lower confidence because the underlying data is noisy.
`followers_decrease` is often a Professional Network-side correction rather than a real decline, so it is emitted with extra scrutiny. `founded_year_change` is usually a data correction, so it carries a confidence of 0.5 and requires confirmation before you act on it.
## Attribution
People signals are attributed to the right company automatically. When a person's `current_company` changes, `employee_joined` is attributed to the company they moved to, while `employee_departed` is attributed to the company they left. This keeps every people signal anchored to the correct account.
See how quantitative changes are bucketed into low, moderate, high, and hyper.
# Magnitude buckets
Source: https://apidoc.cufinder.io/buying-signals/concepts/magnitude-buckets
How we rank signal strength so you can filter ruthlessly.
Not every change carries the same weight. A 2% headcount bump is noise. A 40% jump is a story. Magnitude buckets let you separate the two.
For any signal driven by a quantitative change, CUFinder computes the percentage delta between snapshots and sorts it into one of four buckets:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why buckets matter
A raw event stream is a firehose. Magnitude buckets turn it into a prioritized queue.
Ignore every `low` blip and only review `high` and `hyper` events when you are time-constrained.
The percentage basis means a single hire at a 10-person company buckets far higher than one hire at a 5,000-person company, exactly as it should.
Sort a day's signals by magnitude to work the strongest events first.
Set alert thresholds per signal: maybe `hyper` only for headcount, but `moderate` and up for funding.
## Categorical and scored signals
Some signals are not percentage-based and are handled differently.
Events like `employee_size_band_upgrade`, `first_job_in_function`, and `name_change_drastic` are categorically meaningful and are treated as high-signal regardless of a raw percentage. Crossing a size band, for example, requires sustained net hiring, so it rarely fires on noise.
`momentum_score` and `risk_score` are not bucketed events. They are continuous, decaying scores recomputed nightly, designed to be sorted and ranked rather than bucketed. See the [composite signals](/buying-signals/signals/composite-signals).
A good starting policy: alert on `high` and `hyper` for everything, then widen to `moderate` for the handful of signals that map directly to your buyer.
Every signal, its category, and what it means, in one table.
# Full signal index
Source: https://apidoc.cufinder.io/buying-signals/concepts/signal-index
Every signal CUFinder tracks, grouped by category, in one reference table.
CUFinder tracks **99 buying signals** across 10 categories. Every signal below links to its full reference page.
## Growth (14)
Signals that point to a company adding people, attention, and open roles, usually meaning budget is loosening up.
| Signal | What it means |
| ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- |
| [`employee_growth`](/buying-signals/signals/growth/employee-growth) | A company's employee count increased by at least one between snapshots. |
| [`employee_size_band_upgrade`](/buying-signals/signals/growth/employee-size-band-upgrade) | A company moved up a full Professional Network employee size band. |
| [`headcount_recovery`](/buying-signals/signals/growth/headcount-recovery) | Employee growth returned after a recent decline. |
| [`followers_growth`](/buying-signals/signals/growth/followers-growth) | The company's Professional Network follower count increased. |
| [`followers_spike`](/buying-signals/signals/growth/followers-spike) | Follower growth ran sharply above the company's recent baseline. |
| [`jobs_open_increase`](/buying-signals/signals/growth/jobs-open-increase) | The number of open job postings increased. |
| [`jobs_open_spike`](/buying-signals/signals/growth/jobs-open-spike) | Open job postings jumped sharply above the recent baseline. |
| [`first_job_in_function`](/buying-signals/signals/growth/first-job-in-function) | A company posted a role in a function it hadn't hired for in 12 months. |
| [`first_job_in_country`](/buying-signals/signals/growth/first-job-in-country) | A company posted its first role in a new country. |
| [`first_job_in_city`](/buying-signals/signals/growth/first-job-in-city) | A company posted its first role in a new city. |
| [`senior_hiring_increase`](/buying-signals/signals/growth/senior-hiring-increase) | The share of senior-level postings rose meaningfully. |
| [`engineering_hiring_surge`](/buying-signals/signals/growth/engineering-hiring-surge) | Engineering job postings doubled versus the prior crawl. |
| [`sales_hiring_surge`](/buying-signals/signals/growth/sales-hiring-surge) | Sales job postings doubled versus the prior crawl. |
| [`remote_jobs_share_change`](/buying-signals/signals/growth/remote-jobs-share-change) | The share of remote or hybrid postings shifted significantly. |
## Decline (9)
Signals that flag contraction, cost-cutting, or instability, useful both for timing efficiency plays and for pausing accounts.
| Signal | What it means |
| ------------------------------------------------------------------------------------------------ | -------------------------------------------------------------- |
| [`employee_decrease`](/buying-signals/signals/decline/employee-decrease) | A company's employee count dropped by at least one. |
| [`employee_size_band_downgrade`](/buying-signals/signals/decline/employee-size-band-downgrade) | A company fell down a Professional Network employee size band. |
| [`layoff_signal`](/buying-signals/signals/decline/layoff-signal) | Employee count dropped sharply in a single interval. |
| [`mass_layoff_signal`](/buying-signals/signals/decline/mass-layoff-signal) | Employee count dropped severely in a single interval. |
| [`hiring_freeze_signal`](/buying-signals/signals/decline/hiring-freeze-signal) | Open roles collapsed while headcount stayed flat or fell. |
| [`followers_decrease`](/buying-signals/signals/decline/followers-decrease) | The company's follower count dropped. |
| [`followers_growth_decelerating`](/buying-signals/signals/decline/followers-growth-decelerating) | Follower growth slowed sharply while still positive. |
| [`jobs_open_decrease`](/buying-signals/signals/decline/jobs-open-decrease) | The number of open job postings fell. |
| [`jobs_dropped_to_zero`](/buying-signals/signals/decline/jobs-dropped-to-zero) | A company that had open roles now has none. |
## Identity (10)
Signals tracking how a company describes itself, name, tagline, and description, often hinting at rebrands, pivots, or M\&A.
| Signal | What it means |
| --------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |
| [`name_change`](/buying-signals/signals/identity/name-change) | The company name string changed. |
| [`name_change_drastic`](/buying-signals/signals/identity/name-change-drastic) | A name change with almost no overlap to the old name. |
| [`dba_added`](/buying-signals/signals/identity/dba-added) | The name now references a former name (dba/fka). |
| [`tagline_change`](/buying-signals/signals/identity/tagline-change) | The company tagline text changed. |
| [`tagline_added`](/buying-signals/signals/identity/tagline-added) | A previously empty tagline is now populated. |
| [`tagline_removed`](/buying-signals/signals/identity/tagline-removed) | A populated tagline was cleared out. |
| [`description_change_minor`](/buying-signals/signals/identity/description-change-minor) | The company description was lightly edited. |
| [`description_change_major`](/buying-signals/signals/identity/description-change-major) | The company description was substantially rewritten. |
| [`description_keyword_added`](/buying-signals/signals/identity/description-keyword-added) | A tracked strategic keyword appeared in the description. |
| [`description_keyword_removed`](/buying-signals/signals/identity/description-keyword-removed) | A tracked strategic keyword disappeared from the description. |
## Categorization (7)
Signals tracking how a company classifies its industry and specialties, revealing pivots and market expansion.
| Signal | What it means |
| ------------------------------------------------------------------------------------------------- | ------------------------------------------------------ |
| [`industry_change`](/buying-signals/signals/categorization/industry-change) | The company's primary industry classification changed. |
| [`industry_added`](/buying-signals/signals/categorization/industry-added) | A new industry was added to the company's list. |
| [`industry_removed`](/buying-signals/signals/categorization/industry-removed) | An industry was dropped from the company's list. |
| [`specialty_added`](/buying-signals/signals/categorization/specialty-added) | A new specialty was added. |
| [`specialty_removed`](/buying-signals/signals/categorization/specialty-removed) | A specialty was dropped from the list. |
| [`specialties_count_increase`](/buying-signals/signals/categorization/specialties-count-increase) | The total number of specialties grew notably. |
| [`specialties_count_decrease`](/buying-signals/signals/categorization/specialties-count-decrease) | The total number of specialties shrank notably. |
## Location (7)
Signals tracking where a company operates, new offices, HQ moves, and country expansion.
| Signal | What it means |
| ----------------------------------------------------------------------------------- | --------------------------------------------------------- |
| [`hq_change`](/buying-signals/signals/location/hq-change) | The headquarters city or country changed. |
| [`hq_country_change`](/buying-signals/signals/location/hq-country-change) | The headquarters country specifically changed. |
| [`location_added`](/buying-signals/signals/location/location-added) | A new office location appeared. |
| [`location_removed`](/buying-signals/signals/location/location-removed) | An office location disappeared. |
| [`country_expansion`](/buying-signals/signals/location/country-expansion) | A company opened its first location in a new country. |
| [`multi_office_milestone`](/buying-signals/signals/location/multi-office-milestone) | The company's office count crossed a milestone threshold. |
| [`office_consolidation`](/buying-signals/signals/location/office-consolidation) | Multiple office closures in a short window. |
## Structure (13)
Signals tracking the legal and organizational shape of a company, parents, subsidiaries, and entity types.
| Signal | What it means |
| -------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| [`company_type_change`](/buying-signals/signals/structure/company-type-change) | The company's entity type changed. |
| [`nonprofit_to_for_profit`](/buying-signals/signals/structure/nonprofit-to-for-profit) | The company changed between nonprofit and for-profit status. |
| [`subsidiary_status_change`](/buying-signals/signals/structure/subsidiary-status-change) | The company became or stopped being a subsidiary. |
| [`founded_year_added`](/buying-signals/signals/structure/founded-year-added) | A previously missing founded year was populated. |
| [`founded_year_change`](/buying-signals/signals/structure/founded-year-change) | The founded year value changed. |
| [`parent_company_change`](/buying-signals/signals/structure/parent-company-change) | The parent company changed to a different one. |
| [`parent_company_added`](/buying-signals/signals/structure/parent-company-added) | A parent company was set where there was none. |
| [`parent_company_removed`](/buying-signals/signals/structure/parent-company-removed) | A parent company relationship was cleared. |
| [`subsidiary_added`](/buying-signals/signals/structure/subsidiary-added) | A new subsidiary appeared in the company's list. |
| [`subsidiary_removed`](/buying-signals/signals/structure/subsidiary-removed) | A subsidiary was removed from the company's list. |
| [`showcase_page_added`](/buying-signals/signals/structure/showcase-page-added) | A new Professional Network showcase page was linked. |
| [`showcase_page_removed`](/buying-signals/signals/structure/showcase-page-removed) | A Professional Network showcase page was unlinked. |
| [`affiliated_pages_count_change`](/buying-signals/signals/structure/affiliated-pages-count-change) | The net count of affiliated pages shifted notably. |
## Funding (3)
The gold standard of buying signals, fresh capital means fresh budget.
| Signal | What it means |
| ---------------------------------------------------------------------------------------- | --------------------------------------------- |
| [`funding_round_announced`](/buying-signals/signals/funding/funding-round-announced) | A new funding round was announced. |
| [`last_funding_round_change`](/buying-signals/signals/funding/last-funding-round-change) | The company's funding stage advanced. |
| [`total_funding_increase`](/buying-signals/signals/funding/total-funding-increase) | The company's total funding raised increased. |
## Activity (7)
Signals tracking how engaged a company is on social channels, posting rhythm, engagement, and topic shifts.
| Signal | What it means |
| ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| [`posting_activity_increase`](/buying-signals/signals/activity/posting-activity-increase) | The company is posting noticeably more on social. |
| [`posting_activity_decrease`](/buying-signals/signals/activity/posting-activity-decrease) | The company is posting noticeably less on social. |
| [`page_dormant`](/buying-signals/signals/activity/page-dormant) | The company stopped posting for an extended period. |
| [`page_reactivated`](/buying-signals/signals/activity/page-reactivated) | The company resumed posting after a dormant stretch. |
| [`avg_engagement_change`](/buying-signals/signals/activity/avg-engagement-change) | Average post engagement shifted significantly. |
| [`post_topic_shift`](/buying-signals/signals/activity/post-topic-shift) | The dominant topic of recent posts changed. |
| [`crisis_response_signal`](/buying-signals/signals/activity/crisis-response-signal) | A cluster of statement or apology-style posts in a short window. |
## People (17)
Person-page derived signals, often the earliest and richest buying signals since a single hire can reshape a purchasing strategy.
| Signal | What it means |
| --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| [`employee_joined`](/buying-signals/signals/people/employee-joined) | A person's current company changed to this company. |
| [`employee_departed`](/buying-signals/signals/people/employee-departed) | A person left this company for another. |
| [`internal_promotion`](/buying-signals/signals/people/internal-promotion) | Someone was promoted to a more senior title at the same company. |
| [`lateral_title_change`](/buying-signals/signals/people/lateral-title-change) | Someone changed title at the same company without a seniority change. |
| [`c_suite_hire`](/buying-signals/signals/people/c-suite-hire) | A new C-level executive joined the company. |
| [`c_suite_departure`](/buying-signals/signals/people/c-suite-departure) | A C-level executive left the company. |
| [`founder_departure`](/buying-signals/signals/people/founder-departure) | A founder left the company. |
| [`vp_hire`](/buying-signals/signals/people/vp-hire) | A new VP-level leader joined the company. |
| [`vp_departure`](/buying-signals/signals/people/vp-departure) | A VP-level leader left the company. |
| [`first_role_hire`](/buying-signals/signals/people/first-role-hire) | A company made its first-ever hire for a role function. |
| [`key_role_vacancy`](/buying-signals/signals/people/key-role-vacancy) | A senior role stayed unfilled for 60 days. |
| [`leadership_churn_spike`](/buying-signals/signals/people/leadership-churn-spike) | Multiple senior leaders departed in a short window. |
| [`engineering_leader_hire`](/buying-signals/signals/people/engineering-leader-hire) | A new engineering leader joined the company. |
| [`sales_leader_hire`](/buying-signals/signals/people/sales-leader-hire) | A new sales leader joined the company. |
| [`talent_outflow_to_competitor`](/buying-signals/signals/people/talent-outflow-to-competitor) | An employee left for a known competitor. |
| [`talent_inflow_from_company`](/buying-signals/signals/people/talent-inflow-from-company) | A company hired multiple people from the same source company. |
| [`notable_hire`](/buying-signals/signals/people/notable-hire) | A high-profile or highly experienced person joined. |
## Composite (12)
High-confidence patterns that combine multiple individual signals into major company events.
| Signal | What it means |
| -------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| [`ipo_signal`](/buying-signals/signals/composite/ipo-signal) | A company went from private to public. |
| [`acquired_signal`](/buying-signals/signals/composite/acquired-signal) | A company was acquired. |
| [`merger_signal`](/buying-signals/signals/composite/merger-signal) | Two companies merged under a common new parent. |
| [`pivot_signal`](/buying-signals/signals/composite/pivot-signal) | A company fundamentally changed what it does. |
| [`rebrand_signal`](/buying-signals/signals/composite/rebrand-signal) | A company executed a coordinated rebrand. |
| [`expansion_signal`](/buying-signals/signals/composite/expansion-signal) | A company is expanding into new markets while growing. |
| [`decline_signal`](/buying-signals/signals/composite/decline-signal) | A company shows a clear distress pattern. |
| [`restructuring_signal`](/buying-signals/signals/composite/restructuring-signal) | A company is undergoing major restructuring. |
| [`executive_team_buildout`](/buying-signals/signals/composite/executive-team-buildout) | A company rapidly built out its leadership team. |
| [`pre_ipo_signal`](/buying-signals/signals/composite/pre-ipo-signal) | A company shows the early pattern of an IPO track. |
| [`momentum_score`](/buying-signals/signals/composite/momentum-score) | A nightly composite score ranking a company's upward trajectory. |
| [`risk_score`](/buying-signals/signals/composite/risk-score) | A nightly composite score ranking a company's decline and risk. |
***
Sign up free and enrich your lists with any of these signals, plus verified emails, phones, and firmographics.
# What are buying signals?
Source: https://apidoc.cufinder.io/buying-signals/concepts/what-are-buying-signals
Why timing beats static lists, and how signals change the way you prospect.
A static lead list tells you *who* a company is. It never tells you *when* to call them. In B2B, timing is most of the game, a perfect-fit account is worthless if you reach out during a hiring freeze instead of a hiring surge.
**Buying signals** flip that model. Instead of guessing, you watch for the observable events that historically precede a purchase.
Industry, size, location, tech stack. Stable attributes that describe a company but carry no timing.
Funding rounds, executive hires, hiring surges, expansions. Time-stamped events that signal need and budget right now.
## A signal is an observed change
Every signal in this catalog is defined the same way: a measurable change between two snapshots of a company or person. A company that just opened its first engineering role in a new country is expanding. A company that just added a CRO is about to overhaul its sales stack. These are not hunches. They are patterns.
## Three families of signals
CUFinder organizes signals into three families:
Derived from a company's public Professional Network page: growth and decline in headcount, identity and categorization changes, location moves, structural shifts, funding, and posting activity.
Derived from individual employee profiles: who joined, who left, who got promoted, and which executives moved. People moves are often the earliest and richest signals.
Combinations of the above that map to major events: IPOs, acquisitions, mergers, pivots, rebrands, expansions, restructurings, and rolling momentum and risk scores.
## Match signals to what you sell
The best signal depends entirely on your product. The point is to map signals to your specific buyer.
Watch `sales_leader_hire`, `sales_hiring_surge`, and `funding_round_announced`.
Watch `engineering_leader_hire`, `engineering_hiring_surge`, and `pre_ipo_signal`.
Watch `ipo_signal`, `company_type_change`, and `hq_country_change`.
Watch `expansion_signal`, `country_expansion`, and `first_job_in_country`.
Browse the full index, then sign up to enrich your lists with the ones that fit your motion.
# Introduction
Source: https://apidoc.cufinder.io/buying-signals/index
Real-time intent signals that tell you exactly when a company is ready to buy.
Most teams find out a company was in-market about three weeks too late. **Buying signals** fix that. They are real-world events, a funding round, a new VP of Sales, a first-ever job posting in a new function, that tell you a company just changed in a way that creates need, budget, or urgency.
CUFinder tracks **99 signals** across company pages, people moves, and composite patterns, refreshed daily against a base of 1B+ people profiles and 85M+ company profiles. This documentation is the complete reference: every signal has its own page with the exact trigger condition, stored fields, magnitude, interpretation, and an outreach playbook.
Why timing beats static lists, and how signals change the way you prospect.
Snapshots, deltas, and the emit logic behind every signal.
How we rank signal strength so you can filter ruthlessly.
Every signal in one searchable table, grouped by category.
## Signal families
Growth, decline, identity, categorization, location, structure, funding, and activity, derived from company Professional Network pages.
Hires, departures, promotions, and executive moves derived from individual profiles.
High-confidence patterns like IPO, acquisition, expansion, and momentum that combine smaller events.
## Start tracking signals
Sign up free and start enriching your lists with buying signals built in. No credit card required.
# Company Activity API
Source: https://apidoc.cufinder.io/buying-signals/signals-apis/company-activity
POST https://api.cufinder.io/v3/companies/activities
The Company Activity API returns a company's most recent social posts, including text, hashtags, media, and engagement counts. Pass a company name, domain, or LinkedIn URL and get back a structured feed you can mine for launches, hiring news, and campaigns.
## Common Use Cases
Company posts reveal what a business is talking about right now, launches, hiring, milestones, and campaigns.
* **Social Monitoring:** Pulling a company's latest posts in a single call.
* **Sales Timing:** Spotting launches or announcements worth reacting to.
* **Competitive Intelligence:** Watching how competitors position and message.
* **Content Research:** Studying what resonates for a company or peer set.
Credit usage is 2 credits per request.
## Attributes
Company name, company domain, or company LinkedIn URL.
Page number for paginated results.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"activities": [
{
"activity_id": "7462132400869888002",
"url": "https://www.linkedin.com/posts/cufinder_anthropic-alphabet-ai-activity-7462132400869888002-bCRi",
"text": "🚀 $9B → $2T: The Crossover Coming for Alphabet That's not a typo. Anthropic is projected to grow 222× by 2030 — and cross Alphabet's revenue by mid-2028. Joseph Jacks (OSS Capital) dropped the forecast. We animated it. 👇 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 THE PROJECTED PATH 🤖 Anthropic ARR 2025 → $9B (actual) 2026 → $100B 2027 → $340B 2028 → $850B 2029 → $1.4T 2030 → $2T 🔍 Alphabet GAAP Revenue 2025 → $403B (actual) → 2030 → $815B Locked at ~15% YoY in a mature ads-and-cloud business. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ THE CROSSOVER: MID-2028 @ ~$575B Joseph calls it \"a continuation of the curve already in evidence.\" Anthropic's ARR jumped 3.3× in a single 4-month window ($9B → $30B, Dec 2025 → Apr 2026). Why this isn't a bull case: 🔹 Enterprise customers (>$1M/yr) doubled 500 → 1,000 in <2 months post-Series G 🔹 Claude Code is the wedge dragging the whole platform into Fortune 2000 deployments 🔹 Compute supply finally unblocked (3.5GW Google + Broadcom, SpaceX partnership) 🔹 TAM isn't software ($800B). It's labor ($50T+). ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔍 SEE THE NEXT $1T COMPANY FIRST The next Anthropic isn't hiding. It's already in CUFinder. The CUFinder Company Revenue Filter slices 500M+ B2B records by exact revenue band: 🔹 $1M → $10M → early movers 🔹 $10M → $100M → scale-ups about to break out 🔹 $100M → $1B → pre-IPO, pre-acquisition gold 🔹 $1B+ → enterprise giants Combine with industry, country, employee count and growth rate. Build a list of every $50–200M AI company in Europe — in 30 seconds. 👉 cufinder.io Forecast: Joseph Jacks OSS Capital. #Anthropic #Alphabet #AI #ClaudeCode #B2B #CUFinder #EnterpriseAI #StartupGrowth #LeadGeneration #JosephJacks",
"posted_at": "2026-05-18T13:30:04.063Z",
"is_video": true,
"hashtags": [
"#AI",
"#Alphabet",
"#Anthropic",
"#B2B",
"#CUFinder",
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"#LeadGeneration",
"#StartupGrowth"
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"https://www.linkedin.com/embed/feed/update/urn:li:activity:7462132400869888002?compact=true"
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"reactions_count": 3,
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{
"activity_id": "7459958072573083649",
"url": "https://www.linkedin.com/posts/cufinder_from-65m-to-40-million-a-decade-of-linkedin-activity-7459958072573083649-uifT",
"text": "🚀 From 6.5M to 40 MILLION: A Decade of LinkedIn Power Shifts What does it actually take to dominate LinkedIn? We tracked the Top 20 most-followed people on LinkedIn — every single year from 2016 to 2026. The story is wilder than you'd think. 👇 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 THE GIANTS WHO HELD THE THRONE In 2016, Richard Branson 🇬🇧 ruled with 6.5M followers. Today? He's #2 with 18.6M. The new king is Bill Gates 🇺🇸 — who climbed from 2.5M → 40.3M. That's a 16× explosion. 🤯 Behind them stand the steady titans: 🔹 Satya Nadella — 12.0M 🔹 Jeff Weiner — 10.4M 🔹 Arianna Huffington — 9.6M Proof that consistent presence beats viral moments. Every. Single. Time. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 THE EARLY THOUGHT LEADERS (2016–2018) The names that BUILT the LinkedIn we know today: ✦ Jack Welch ✦ Deepak Chopra MD (official) ✦ Mark Cuban ✦ ✦ Gretchen Rubin ✦ David Edelman ✦ Craig Newmark ✦ ✦ Lou Adler ✦ Robert Herjavec 🇨🇦 ✦ Jeff Weiner Haden ✦ ✦ Daniel Burrus ✦ Bernard Marr 🇬🇧 ✦ Barbara Corcoran ✦ ✦ Travis Bradberry ✦ Eric Ries ✦ Daniel Goleman ✦ Some are STILL in the top 20. Others got eclipsed by the next wave. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌊 THE 2020 SURGE The pandemic flipped LinkedIn into a thought-leadership powerhouse. Suddenly, these voices broke through: ▸ Tony Robbins ▸ Justin Trudeau 🇨🇦 ▸ Melinda French Gates ▸ Ian Bremmer ▸ Narendra Modi 🇮🇳 ▸ Adam Grant ▸ Simon Sinek ▸ Guy Kawasaki ▸ James Caan CBE Caan 🇬🇧 Authors. CEOs. Heads of state. LinkedIn stopped being a résumé site — and became a media platform. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 THE NEW WAVE (2022–2026) The latest climbers prove LinkedIn rewards depth over decades: 🌟 Dr. Brigette Hyacinth Hyacinth 🇹🇹 🌟 Anthony J James 🇦🇺 🌟 Brené Brown 🌟 Kevin O'Leary 🇨🇦 🌟 Gary Vaynerchuk 🌟 Derek Shen 🇨🇳 🌟 Lin Zhengang 🇭🇰 🌟 Barack Obama 🇺🇸 (yes, the LinkedIn debut hits hard) The biggest individual jump? 👉 Adam Grant: 200K → 5.4M in 10 years. The recipe wasn't follower hacks. It was original ideas + relentless consistency. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔍 SO HOW DO YOU FIND THE NEXT BREAKOUT VOICE? That's exactly why we built the ✨ Influencer Filter ✨ ✅ Filter by follower count (1K → 40M+) ✅ Country, industry, role, seniority ✅ Spot decision-makers AND rising voices ✅ Your shortcut to the right people, at the right scale Whether you're prospecting, hiring, or building partnerships — find them in seconds, not days. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ #LinkedInInfluencers #ThoughtLeadership #B2BMarketing #LeadGeneration #CUFinder #InfluencerMarketing #SocialSelling #LinkedInTips #DataDriven #SalesProspecting #PersonalBranding #ContentMarketing #LinkedInGrowth #MarketingTrends #BusinessLeadership",
"posted_at": "2026-05-12T13:30:03.796Z",
"is_video": true,
"hashtags": [
"#B2BMarketing",
"#BusinessLeadership",
"#CUFinder",
"#ContentMarketing",
"#DataDriven",
"#InfluencerMarketing",
"#LeadGeneration",
"#LinkedInGrowth",
"#LinkedInInfluencers",
"#LinkedInTips",
"#MarketingTrends",
"#PersonalBranding",
"#SalesProspecting",
"#SocialSelling",
"#ThoughtLeadership"
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"https://www.linkedin.com/embed/feed/update/urn:li:activity:7459958072573083649?compact=true"
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"reactions_count": 4,
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},
{
"activity_id": "7344999453751173122",
"url": "https://www.linkedin.com/posts/cufinder_how-to-target-the-right-decision-maker-in-activity-7344999453751173122--B0C",
"text": "CUFinder helps businesses connect with real decision-makers — fast. Our platform offers powerful tools for B2B lead generation, data enrichment, and contact search so you can find emails, phone numbers, LinkedIn profiles, and company domains in seconds. Whether you need to enrich an existing lead list or generate new prospects, CUFinder helps you target by job title, industry, company size, and location, with clean, verified data that fuels smarter outreach and faster growth. No more guessing. Just targeting. Start now at https://cufinder.io #B2BLeadGeneration #DataEnrichment #EmailFinder #LinkedInFinder #SalesTools #LeadGeneration #GrowthHacking #CUFinder #B2BSales #OutreachTools",
"posted_at": "2025-06-29T08:05:33.236Z",
"is_video": true,
"hashtags": [
"#B2BLeadGeneration",
"#B2BSales",
"#CUFinder",
"#DataEnrichment",
"#EmailFinder",
"#GrowthHacking",
"#LeadGeneration",
"#LinkedInFinder",
"#OutreachTools",
"#SalesTools"
],
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]
},
"meta": {
"confidence": 94,
"query": {
"query": "linkedin.com/company/cufinder",
"page": 1
},
"credits": {
"charged": 2,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": false
}
}
}
```
## Related APIs
Companies that just triggered a signal.
Get a quick company overview.
Enrich a company with full firmographics.
People at companies that triggered a signal.
# Company Signals API
Source: https://apidoc.cufinder.io/buying-signals/signals-apis/company-signals
POST https://api.cufinder.io/v3/signals/companies
The Company Signals API returns the companies where a chosen buying signal just fired. Filter by signal name, look-back window, and signal strength, and get back companies with their firmographics and the signal that triggered.
## Common Use Cases
Turn any buying signal into a live list of the companies where it just fired.
* **Signal-Based Targeting:** Building account lists from a chosen signal.
* **Buying-Intent Detection:** Finding companies in a moment of change.
* **Market Monitoring:** Tracking how many companies hit a signal over time.
* **Account Prioritization:** Ranking accounts by signal strength and recency.
Credit usage is 2 credits per request.
## Attributes
The company buying signal to filter by. This endpoint uses the 82 company-related signals; the 17 People-category signals belong to the [People Signals API](/buying-signals/signals-apis/people-signals) instead. See the full list below.
| Signal name | Category | What it detects |
| ------------------------------- | -------------- | ----------------------------------------------------------------------- |
| `avg_engagement_change` | Activity | Average post engagement shifted significantly. |
| `crisis_response_signal` | Activity | A cluster of statement or apology-style posts in a short window. |
| `page_dormant` | Activity | The company stopped posting for an extended period. |
| `page_reactivated` | Activity | The company resumed posting after a dormant stretch. |
| `post_topic_shift` | Activity | The dominant topic of recent posts changed. |
| `posting_activity_decrease` | Activity | The company is posting noticeably less on social. |
| `posting_activity_increase` | Activity | The company is posting noticeably more on social. |
| `industry_added` | Categorization | A new industry was added to the company's list. |
| `industry_change` | Categorization | The company's primary industry classification changed. |
| `industry_removed` | Categorization | An industry was dropped from the company's list. |
| `specialties_count_decrease` | Categorization | The total number of specialties shrank notably. |
| `specialties_count_increase` | Categorization | The total number of specialties grew notably. |
| `specialty_added` | Categorization | A new specialty was added. |
| `specialty_removed` | Categorization | A specialty was dropped from the list. |
| `acquired_signal` | Composite | A company was acquired. |
| `decline_signal` | Composite | A company shows a clear distress pattern. |
| `executive_team_buildout` | Composite | A company rapidly built out its leadership team. |
| `expansion_signal` | Composite | A company is expanding into new markets while growing. |
| `ipo_signal` | Composite | A company went from private to public. |
| `merger_signal` | Composite | Two companies merged under a common new parent. |
| `momentum_score` | Composite | A nightly composite score ranking a company's upward trajectory. |
| `pivot_signal` | Composite | A company fundamentally changed what it does. |
| `pre_ipo_signal` | Composite | A company shows the early pattern of an IPO track. |
| `rebrand_signal` | Composite | A company executed a coordinated rebrand. |
| `restructuring_signal` | Composite | A company is undergoing major restructuring. |
| `risk_score` | Composite | A nightly composite score ranking a company's decline and risk. |
| `employee_decrease` | Decline | A company's employee count dropped by at least one. |
| `employee_size_band_downgrade` | Decline | A company fell down a Professional Network employee size band. |
| `followers_decrease` | Decline | The company's follower count dropped. |
| `followers_growth_decelerating` | Decline | Follower growth slowed sharply while still positive. |
| `hiring_freeze_signal` | Decline | Open roles collapsed while headcount stayed flat or fell. |
| `jobs_dropped_to_zero` | Decline | A company that had open roles now has none. |
| `jobs_open_decrease` | Decline | The number of open job postings fell. |
| `layoff_signal` | Decline | Employee count dropped sharply in a single interval. |
| `mass_layoff_signal` | Decline | Employee count dropped severely in a single interval. |
| `funding_round_announced` | Funding | A new funding round was announced. |
| `last_funding_round_change` | Funding | The company's funding stage advanced. |
| `total_funding_increase` | Funding | The company's total funding raised increased. |
| `employee_growth` | Growth | A company's employee count increased by at least one between snapshots. |
| `employee_size_band_upgrade` | Growth | A company moved up a full Professional Network employee size band. |
| `engineering_hiring_surge` | Growth | Engineering job postings doubled versus the prior crawl. |
| `first_job_in_city` | Growth | A company posted its first role in a new city. |
| `first_job_in_country` | Growth | A company posted its first role in a new country. |
| `first_job_in_function` | Growth | A company posted a role in a function it hadn't hired for in 12 months. |
| `followers_growth` | Growth | The company's Professional Network follower count increased. |
| `followers_spike` | Growth | Follower growth ran sharply above the company's recent baseline. |
| `headcount_recovery` | Growth | Employee growth returned after a recent decline. |
| `jobs_open_increase` | Growth | The number of open job postings increased. |
| `jobs_open_spike` | Growth | Open job postings jumped sharply above the recent baseline. |
| `remote_jobs_share_change` | Growth | The share of remote or hybrid postings shifted significantly. |
| `sales_hiring_surge` | Growth | Sales job postings doubled versus the prior crawl. |
| `senior_hiring_increase` | Growth | The share of senior-level postings rose meaningfully. |
| `dba_added` | Identity | The name now references a former name (dba/fka). |
| `description_change_major` | Identity | The company description was substantially rewritten. |
| `description_change_minor` | Identity | The company description was lightly edited. |
| `description_keyword_added` | Identity | A tracked strategic keyword appeared in the description. |
| `description_keyword_removed` | Identity | A tracked strategic keyword disappeared from the description. |
| `name_change` | Identity | The company name string changed. |
| `name_change_drastic` | Identity | A name change with almost no overlap to the old name. |
| `tagline_added` | Identity | A previously empty tagline is now populated. |
| `tagline_change` | Identity | The company tagline text changed. |
| `tagline_removed` | Identity | A populated tagline was cleared out. |
| `country_expansion` | Location | A company opened its first location in a new country. |
| `hq_change` | Location | The headquarters city or country changed. |
| `hq_country_change` | Location | The headquarters country specifically changed. |
| `location_added` | Location | A new office location appeared. |
| `location_removed` | Location | An office location disappeared. |
| `multi_office_milestone` | Location | The company's office count crossed a milestone threshold. |
| `office_consolidation` | Location | Multiple office closures in a short window. |
| `affiliated_pages_count_change` | Structure | The net count of affiliated pages shifted notably. |
| `company_type_change` | Structure | The company's entity type changed. |
| `founded_year_added` | Structure | A previously missing founded year was populated. |
| `founded_year_change` | Structure | The founded year value changed. |
| `nonprofit_to_for_profit` | Structure | The company changed between nonprofit and for-profit status. |
| `parent_company_added` | Structure | A parent company was set where there was none. |
| `parent_company_change` | Structure | The parent company changed to a different one. |
| `parent_company_removed` | Structure | A parent company relationship was cleared. |
| `showcase_page_added` | Structure | A new Professional Network showcase page was linked. |
| `showcase_page_removed` | Structure | A Professional Network showcase page was unlinked. |
| `subsidiary_added` | Structure | A new subsidiary appeared in the company's list. |
| `subsidiary_removed` | Structure | A subsidiary was removed from the company's list. |
| `subsidiary_status_change` | Structure | The company became or stopped being a subsidiary. |
Look-back window in days. Allowed values: `7`, `30`, `90`, or `180`.
Signal strength. Allowed values: `low`, `moderate`, `high`, or `hyper`.
Page number for paginated results.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"companies": [
{
"name": "google",
"domain": "google.com",
"website_url": "https://google.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "mountain view",
"address": "1600 amphitheatre parkway,mountain view, ca 94043, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/google",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "amazon",
"domain": "amazon.com",
"website_url": "http://amazon.com",
"industry": "software development",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": null,
"address": "2127 7th ave.,seattle, wa 98109, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/amazon",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "deloitte",
"domain": "deloitte.com",
"website_url": "http://www.deloitte.com",
"industry": "business consulting and services",
"type": "privately held",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": null,
"state": null,
"city": "worldwide",
"address": "worldwide,worldwide, oo",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/deloitte",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "unilever",
"domain": "unilever.com",
"website_url": "http://www.unilever.com",
"industry": "manufacturing",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united kingdom",
"state": "london",
"city": "blackfriars",
"address": "100 victoria embankment,blackfriars, london ec4y 0dy, gb",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/unilever",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "ibm",
"domain": "ibm.com",
"website_url": "http://www.ibm.com",
"industry": "it services and it consulting",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new york",
"city": "new york",
"address": "international business machines corp.,new orchard road,armonk, new york, ny 10504, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/ibm",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "nestlé",
"domain": "nestle.com",
"website_url": "http://www.nestle.com",
"industry": "food and beverage services",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "switzerland",
"state": null,
"city": "vevey",
"address": "av. nestlé 55,vevey, 1800, ch",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/nestle-s-a-",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "apple",
"domain": "apple.com",
"website_url": "https://apple.com",
"industry": "computers and electronics manufacturing",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "cupertino",
"address": "1 apple park way,cupertino, california 95014, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/apple",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "forbes",
"domain": "forbes.com",
"website_url": "http://www.forbes.com",
"industry": "book and periodical publishing",
"type": "privately held",
"employee_range": "201-500",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new jersey",
"city": "jersey city",
"address": "jersey city, nj, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/forbes-magazine",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "harvard business review",
"domain": "hbr.org",
"website_url": "http://www.hbr.org",
"industry": "book and periodical publishing",
"type": "nonprofit",
"employee_range": "201-500",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "massachusetts",
"city": "brighton",
"address": "20 guest street,200,brighton, ma 02135, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/harvard-business-review",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
},
{
"name": "tesla",
"domain": "tesla.com",
"website_url": "https://tesla.com",
"industry": "motor vehicle manufacturing",
"type": "public company",
"employee_range": "10001+",
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "texas",
"city": "austin",
"address": "13101 harold green rd,austin, texas 78725, us",
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/tesla-motors",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "employee_growth",
"time_frame": 30,
"bucket": "low"
}
}
]
},
"meta": {
"confidence": 99,
"query": {
"signal_name": "employee_growth",
"time_frame": 30,
"bucket": "low",
"page": 1
},
"credits": {
"charged": 2,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
People at companies that triggered a signal.
Recent social posts for a company.
Search companies with multiple filters.
Enrich a company with full firmographics.
# Signals APIs
Source: https://apidoc.cufinder.io/buying-signals/signals-apis/introduction
The Signals APIs turn CUFinder's buying-signal graph into live, queryable endpoints. Pull a company's social activity, list the people or companies that just triggered a specific signal, and track job changes across any date range.
The **Signals APIs** expose CUFinder's buying-signal data as simple REST endpoints. Instead of browsing signals one page at a time, you can query them programmatically and feed the results straight into your CRM, outreach, or data pipeline.
Every endpoint is a `POST` to `https://api.cufinder.io/v3/...`, authenticated with your `x-api-key` header, and returns the standard `{ "success": true, "data": { ... }, "meta": { ... } }` envelope with credits and pagination in `meta`. All four endpoints support a `page` parameter for pagination.
## Endpoints
Recent social posts for a company.
People at companies that just triggered a signal.
Companies that just triggered a signal.
New jobs, promotions, and title moves over a date range.
## Authentication
Send your API key in the `x-api-key` header on every request. You can find it under **Account » API key** in the [dashboard](https://dashboard.cufinder.io/auth/signup/).
Start free and query buying signals in minutes. No credit card required.
# Job Changes API
Source: https://apidoc.cufinder.io/buying-signals/signals-apis/job-changes
POST https://api.cufinder.io/v3/signals/job-changes
The Job Changes API returns people who changed roles within a date range, new jobs, promotions, lateral title moves, and first jobs. Each record includes the person's LinkedIn URL and their from/to company and title.
## Common Use Cases
Track when people change roles, new jobs, promotions, and title moves, across any date range.
* **Champion Tracking:** Following contacts who move to new companies.
* **New-Buyer Alerts:** Reaching decision-makers who just started a role.
* **Recruiting:** Spotting promotions and moves for sourcing.
* **Relationship Mapping:** Watching talent flow between companies.
Credit usage is 1 credit per request.
## Attributes
Start of the date range, formatted `YYYY-MM-DD`.
End of the date range, formatted `YYYY-MM-DD`.
The kind of job change to return. Allowed values:
| Type | Meaning |
| ----------------------- | -------------------------------------------------- |
| `company_change` | Person moved from company A to company B. |
| `promotion` | Same company, seniority or title jumped up. |
| `lateral_title_change` | Same company, title changed but no seniority jump. |
| `no_company_to_company` | Person had no company and now has one. |
Page number for paginated results.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"job_changes": [
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/muhammad-usman-mansab-498196267",
"detected_at": "2026-08-22T23:59:57.989Z",
"from": {
"company_linkedin_url": "linkedin.com/company/red-star-technologies",
"company_linkedin_id": "38159812",
"company_name": "red star technologies",
"title": "search engine optimization specialist"
},
"to": {
"company_linkedin_url": "linkedin.com/company/red-star-technologies",
"company_linkedin_id": "38159812",
"company_name": "red star technologies",
"title": "senior seo specialist | technical seo | local seo | off-page seo | aeo | geo"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/matthew-hensley-b80007226",
"detected_at": "2026-08-22T23:59:57.246Z",
"from": {
"company_linkedin_url": "linkedin.com/company/cticlinical",
"company_linkedin_id": "705469",
"company_name": "cti clinical trial and consulting services",
"title": "third year student"
},
"to": {
"company_linkedin_url": "linkedin.com/company/cticlinical",
"company_linkedin_id": "705469",
"company_name": "cti clinical trial and consulting services",
"title": "senior"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/meghnaashok",
"detected_at": "2026-08-22T23:59:55.047Z",
"from": {
"company_linkedin_url": "linkedin.com/company/metricstream",
"company_linkedin_id": "164954",
"company_name": "metricstream",
"title": "global events & brand experience strategist | 14+ years building brands through experiences"
},
"to": {
"company_linkedin_url": "linkedin.com/company/metricstream",
"company_linkedin_id": "164954",
"company_name": "metricstream",
"title": "senior manager"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/pushpendra-kumar-m",
"detected_at": "2026-08-22T23:59:54.684Z",
"from": {
"company_linkedin_url": "linkedin.com/company/hdfcergo",
"company_linkedin_id": "270704",
"company_name": "hdfc ergo general insurance",
"title": "insurance sales & client retention specialist with 4+ years’ experience | 85%+ renewal conversion | bancassurance, cross-selling & crm expertise | rising star award winner | pursuing mba"
},
"to": {
"company_linkedin_url": "linkedin.com/company/hdfcergo",
"company_linkedin_id": "270704",
"company_name": "hdfc ergo general insurance",
"title": "senior associate skilled in problem solving and customer retention.(mba in marketing management)"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/ajmckinney",
"detected_at": "2026-08-22T23:59:51.823Z",
"from": {
"company_linkedin_url": "linkedin.com/company/evangel-presbyterian-church",
"company_linkedin_id": "9803557",
"company_name": "evangel presbyterian church (pca)",
"title": "pastoral assistant & elder"
},
"to": {
"company_linkedin_url": "linkedin.com/company/evangel-presbyterian-church",
"company_linkedin_id": "9803557",
"company_name": "evangel presbyterian church (pca)",
"title": "director of discipleship & administration"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/mahmoodalim",
"detected_at": "2026-08-22T23:59:42.370Z",
"from": {
"company_linkedin_url": "linkedin.com/company/al--othman-holding-company",
"company_linkedin_id": "673271",
"company_name": "al- othman holding company",
"title": "digital transformation/it planning/it pmo/it governance/business solutions/enterprise architecture/cybersecurity"
},
"to": {
"company_linkedin_url": "linkedin.com/company/al--othman-holding-company",
"company_linkedin_id": "673271",
"company_name": "al- othman holding company",
"title": "it director/cio/cto/idc cio excellency/idc cio50/global cio200"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/jayendra-kasture-1bb5a72ba",
"detected_at": "2026-08-22T23:59:38.479Z",
"from": {
"company_linkedin_url": "linkedin.com/company/vellore-institute-of-technology---chennai-campus",
"company_linkedin_id": "97331650",
"company_name": "vellore institute of technology - chennai campus",
"title": "board-ready legal academic"
},
"to": {
"company_linkedin_url": "linkedin.com/company/vellore-institute-of-technology---chennai-campus",
"company_linkedin_id": "97331650",
"company_name": "vellore institute of technology - chennai campus",
"title": "iica certified independent director | corporate governance | corporate and commercial law | esg and sustainability | legal risk & compliance | board advisor |"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/malik-laouadi-m-a-39b886198",
"detected_at": "2026-08-22T23:59:37.269Z",
"from": {
"company_linkedin_url": "linkedin.com/company/vosker",
"company_linkedin_id": "19025028",
"company_name": "vosker",
"title": "product analyst | product vision and strategy | customer focus & market analysis | business revenue growth"
},
"to": {
"company_linkedin_url": "linkedin.com/company/vosker",
"company_linkedin_id": "19025028",
"company_name": "vosker",
"title": "senior product analyst | product vision and strategy | customer focus & market analysis | business revenue growth"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/carlo-pererano-7975b3117",
"detected_at": "2026-08-22T23:59:29.692Z",
"from": {
"company_linkedin_url": "linkedin.com/company/msc-cruises",
"company_linkedin_id": "1205858",
"company_name": "msc cruises",
"title": "it infrastructure technical project manager"
},
"to": {
"company_linkedin_url": "linkedin.com/company/msc-cruises",
"company_linkedin_id": "1205858",
"company_name": "msc cruises",
"title": "it infrastructure technical lead"
}
},
{
"type": "promotion",
"linkedin_url": "linkedin.com/in/sindhu-raja-627502172",
"detected_at": "2026-08-22T23:59:25.628Z",
"from": {
"company_linkedin_url": "linkedin.com/company/saymoshimoshi",
"company_linkedin_id": "7938955",
"company_name": "moshi moshi - the communication company",
"title": "growth marketing executive | entrepreneur"
},
"to": {
"company_linkedin_url": "linkedin.com/company/saymoshimoshi",
"company_linkedin_id": "7938955",
"company_name": "moshi moshi - the communication company",
"title": "senior performance marketing executive | paid media | meta ads | google ads | e-commerce | campaign optimization | roas & roi optimization | e-commerce | app marketing | lead generation"
}
}
]
},
"meta": {
"confidence": 97,
"query": {
"start_date": "2026-05-23",
"end_date": "2026-08-23",
"type": "promotion",
"page": 1
},
"credits": {
"charged": 1,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
People at companies that triggered a signal.
Enrich a person with full contact data.
Search 1B+ profiles with combined filters.
Enrich a person from their LinkedIn profile.
# People Signals API
Source: https://apidoc.cufinder.io/buying-signals/signals-apis/people-signals
POST https://api.cufinder.io/v3/signals/people
The People Signals API returns the people at companies where a chosen buying signal just fired. Filter by signal name, look-back window, and signal strength, and get back contacts with their role, company, and location.
## Common Use Cases
Turn any buying signal into a live list of the people at companies where it just fired.
* **Signal-Based Prospecting:** Reaching people at companies hitting a chosen signal.
* **Timely Outreach:** Contacting decision-makers while the signal is still fresh.
* **Territory Building:** Pulling contacts by signal, strength, and recency.
* **Account Prioritization:** Focusing on accounts showing the strongest signals.
Credit usage is 2 credits per request.
## Attributes
The people buying signal to filter by. This endpoint uses the 17 People-category signals, such as `employee_joined` or `c_suite_hire`; company-level signals belong to the [Company Signals API](/buying-signals/signals-apis/company-signals) instead. See the full list below.
| Signal name | What it detects |
| ------------------------------ | --------------------------------------------------------------------- |
| `c_suite_departure` | A C-level executive left the company. |
| `c_suite_hire` | A new C-level executive joined the company. |
| `employee_departed` | A person left this company for another. |
| `employee_joined` | A person's current company changed to this company. |
| `engineering_leader_hire` | A new engineering leader joined the company. |
| `first_role_hire` | A company made its first-ever hire for a role function. |
| `founder_departure` | A founder left the company. |
| `internal_promotion` | Someone was promoted to a more senior title at the same company. |
| `key_role_vacancy` | A senior role stayed unfilled for 60 days. |
| `lateral_title_change` | Someone changed title at the same company without a seniority change. |
| `leadership_churn_spike` | Multiple senior leaders departed in a short window. |
| `notable_hire` | A high-profile or highly experienced person joined. |
| `sales_leader_hire` | A new sales leader joined the company. |
| `talent_inflow_from_company` | A company hired multiple people from the same source company. |
| `talent_outflow_to_competitor` | An employee left for a known competitor. |
| `vp_departure` | A VP-level leader left the company. |
| `vp_hire` | A new VP-level leader joined the company. |
Look-back window in days. Allowed values: `7`, `30`, `90`, or `180`.
Signal strength. Allowed values: `low`, `moderate`, `high`, or `hyper`.
Page number for paginated results.
## Response
```json Response theme={null}
{
"success": true,
"data": {
"people": [
{
"first_name": null,
"last_name": null,
"full_name": "ann forslund",
"avatar_url": null,
"job": {
"title": "scientific director, translational medicine team lead, precision medicine and clinical biomarkers oncology",
"level": null,
"categories": []
},
"company": {
"name": "bristol myers squibb",
"domain": null,
"website_url": "http://www.bms.com",
"industry": "pharmaceutical manufacturing",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new jersey",
"city": "lawrence township",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/bristol-myers-squibb",
"facebook_url": "facebook.com/bristolmyerssquibb",
"twitter_url": "twitter.com/bmsnews"
}
},
"location": {
"country": "united states",
"state": "new jersey",
"city": "princeton",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/00001",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "ahmed ahmed",
"avatar_url": null,
"job": {
"title": "senior operations specialist",
"level": null,
"categories": []
},
"company": {
"name": "marsh mclennan",
"domain": null,
"website_url": "http://www.corporate.marsh.com",
"industry": "professional services",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new york",
"city": "new york",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/marshmclennan",
"facebook_url": "facebook.com/marsh-mclennan-companies-inc-250780238285607",
"twitter_url": "twitter.com/mmcbrm"
}
},
"location": {
"country": "poland",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/079ahmed-ahmed",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "brian oetken",
"avatar_url": null,
"job": {
"title": "vp",
"level": null,
"categories": []
},
"company": {
"name": "wells fargo",
"domain": null,
"website_url": "http://www.wellsfargo.com",
"industry": "financial services",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "san francisco",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/wellsfargo",
"facebook_url": "facebook.com/wellsfargo/",
"twitter_url": "twitter.com/wellsfargo/"
}
},
"location": {
"country": "united states",
"state": "louisiana",
"city": "iowa",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/0etkenb",
"facebook_url": "facebook.com/brian.oetken",
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "dharvinder singh",
"avatar_url": null,
"job": {
"title": "manager",
"level": null,
"categories": []
},
"company": {
"name": "bose corporation",
"domain": null,
"website_url": "http://www.bose.com",
"industry": "computers and electronics manufacturing",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "massachusetts",
"city": "framingham",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/bose-corporation",
"facebook_url": "facebook.com/bose",
"twitter_url": "twitter.com/bose"
}
},
"location": {
"country": "united states",
"state": "georgia",
"city": "atlanta",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/100nu",
"facebook_url": null,
"twitter_url": "twitter.com/tweetsonu"
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "gary skarr",
"avatar_url": null,
"job": {
"title": "benefits director",
"level": null,
"categories": []
},
"company": {
"name": "rush university medical center",
"domain": null,
"website_url": "https://www.rush.edu",
"industry": "hospitals and health care",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "illinois",
"city": "chicago",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/rush-university-medical-center",
"facebook_url": "facebook.com/RushUniversity",
"twitter_url": "twitter.com/rushmedical"
}
},
"location": {
"country": "united states",
"state": "illinois",
"city": "naperville",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/102753",
"facebook_url": "facebook.com/gary.skarr",
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "massimiliano pierantoni",
"avatar_url": null,
"job": {
"title": "gucci precious lg, specific mix and special event produc. control & procurement senior manager gucci–ww lg & shoe operations",
"level": null,
"categories": []
},
"company": {
"name": "gucci",
"domain": null,
"website_url": "https://careers.gucci.com",
"industry": "retail luxury goods and jewelry",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "italy",
"state": "florence",
"city": "casellina di scandicci",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/gucci",
"facebook_url": "facebook.com/gucci",
"twitter_url": "twitter.com/gucci"
}
},
"location": {
"country": "italy",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/140967",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "ca dipali gupta",
"avatar_url": null,
"job": {
"title": "specialist calypso | social ambassador",
"level": null,
"categories": []
},
"company": {
"name": "nasdaq",
"domain": null,
"website_url": "https://www.nasdaq.com",
"industry": "financial services",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new york",
"city": "new york",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/nasdaq",
"facebook_url": "facebook.com/nasdaq/",
"twitter_url": "twitter.com/nasdaq"
}
},
"location": {
"country": "india",
"state": null,
"city": null,
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/1996-ca-dipali-gupta",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "david rosca",
"avatar_url": null,
"job": {
"title": "software engineer",
"level": null,
"categories": []
},
"company": {
"name": "curtiss-wright corporation",
"domain": null,
"website_url": "https://curtisswright.com",
"industry": "defense and space manufacturing",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "north carolina",
"city": "davidson",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/curtiss-wright-corporation",
"facebook_url": "facebook.com/onecwcareers/",
"twitter_url": "twitter.com/curtisswright"
}
},
"location": {
"country": "united states",
"state": "california",
"city": "placentia",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/1dave",
"facebook_url": "facebook.com/dave.rosca",
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "mandy dhaliwal",
"avatar_url": null,
"job": {
"title": "evp and cmo | board director",
"level": null,
"categories": []
},
"company": {
"name": "nutanix",
"domain": null,
"website_url": "http://www.nutanix.com",
"industry": "software development",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "california",
"city": "san jose",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/nutanix",
"facebook_url": "facebook.com/nutanix",
"twitter_url": "twitter.com/nutanix"
}
},
"location": {
"country": "united states",
"state": "california",
"city": "san jose",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/1mandydhaliwal",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
},
{
"first_name": null,
"last_name": null,
"full_name": "farida kafaei",
"avatar_url": null,
"job": {
"title": "compliance specialist",
"level": null,
"categories": []
},
"company": {
"name": "the estée lauder companies inc.",
"domain": null,
"website_url": "http://www.elcompanies.com",
"industry": "personal care product manufacturing",
"type": null,
"employee_range": null,
"followers_count": null,
"linkedin_id": null,
"logo_url": null,
"location": {
"country": "united states",
"state": "new york",
"city": "new york",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/company/the-estee-lauder-companies-inc",
"facebook_url": "facebook.com/elcompanies",
"twitter_url": "twitter.com/aveda"
}
},
"location": {
"country": "canada",
"state": "alberta",
"city": "calgary",
"address": null,
"postal_code": null
},
"social": {
"linkedin_url": "linkedin.com/in/200906",
"facebook_url": null,
"twitter_url": null
},
"signal": {
"name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper"
}
}
]
},
"meta": {
"confidence": 96,
"query": {
"signal_name": "c_suite_hire",
"time_frame": 90,
"bucket": "hyper",
"page": 1
},
"credits": {
"charged": 2,
"remaining": 9862
},
"pagination": {
"page": 1,
"has_more": true
}
}
}
```
## Related APIs
Companies that just triggered a signal.
New jobs, promotions, and title moves.
Search 1B+ profiles with combined filters.
Enrich a person with full contact data.
# Avg Engagement Change
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/avg-engagement-change
Average post engagement shifted significantly.
`avg_engagement_change`
Activity
Company Professional Network page (engagement metrics), rolling baseline.
Average post engagement shifted significantly.
## When it fires
Average likes plus comments per post in the last 30 days changes by ≥ 30% versus the rolling average.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | -------------------------------------------------- |
| `meta.direction` | up or down, the direction of the engagement shift. |
## Magnitude
Moderate (30% threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Engagement swings reflect changing market attention. A 30%+ shift in average engagement, up or down, indicates the company's content is resonating more or less, which tracks brand momentum and audience interest.
## How to use Avg Engagement Change Signal?
**The setup.** You run GTM at an employee-advocacy platform; your product turns a company's employees into its distribution channel. Your sale has a peculiar shape: you are not selling social media, you are selling **rescue or amplification of a social investment the buyer has already made**. The pitch depends entirely on which of those two moments the buyer is in.
**What you want.** To know, per target account, whether their content engine is compounding or stalling, because the same product gets bought for opposite reasons at those two moments.
**The signal fires.** `avg_engagement_change` fires twice in the same week, in opposite directions. Juno Freight's average engagement per post has fallen hard despite steady posting; Ottavia Labs' engagement has jumped well above baseline.
**Reading it.** Engagement-per-post is the honest metric behind the vanity ones: it measures whether anyone actually cares. Juno is shouting into a thinning room, a team posting diligently while the algorithm and audience drift away, frustration building at every marketing review. Ottavia has the opposite condition: content that works, constrained only by reach. **One needs a lifeline; the other needs a multiplier.** Your product is both, but the email that wins each is entirely different.
**The play.**
1. Split your sequences by direction of change; a single generic pitch wastes both moments.
2. To the declining account, sell rescue: posting hard with falling engagement usually means distribution, not content, is broken, and employee networks reach five to ten times the company page.
3. To the rising account, sell leverage: their content earns attention, and advocacy pours fuel on what already burns.
4. In both cases, cite their actual trajectory, **buyers trust a pitch that demonstrates you watched before you wrote**.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `avg_engagement_change`, bucketed by direction, feeds the two sequences automatically.
**Why it lands.** Most vendors pitch a product. You are pitching **an answer to the exact chart their marketing lead stared at this Monday**.
## How to read it
Engagement tracks how much the market is responding.
meta.direction shows whether interest is rising or falling.
Sustained up-shifts reflect growing brand pull.
## Outreach playbook
Momentum context. Up-shifts pair well with funding or launch signals.
## Related signals
The company is posting noticeably more on social.
Follower growth ran sharply above the company's recent baseline.
The dominant topic of recent posts changed.
***
Surface `avg_engagement_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Crisis Response Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/crisis-response-signal
A cluster of statement or apology-style posts in a short window.
`crisis_response_signal`
Activity
Company Professional Network page (post content), keyword analysis.
A cluster of statement or apology-style posts in a short window.
## When it fires
≥ 3 posts in 7 days containing statement, apology, or press-release language (statement, response, 'we want to address', regarding, incident, transparency).
## Magnitude
High, crisis clustering is a distinct, urgent pattern.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A crisis cluster tells you to handle the account with care, and timing. When a company posts three or more statement-style messages in a week, it's managing a public situation, a breach, a controversy, an incident. This shapes both whether and how to reach out.
## How to use Crisis Response Signal?
**The scenario.** You are a partner at a crisis-communications consultancy. Your paradox is brutal: the clients who need you most have never heard of you, because **companies do not shop for crisis help until they are already in one**, and by then they are choosing from whoever they can find in a panicked afternoon.
**The goal.** Reach companies in the opening hours of a public incident, when the response is still being improvised and professional help still changes the outcome.
**The signal fires.** `crisis_response_signal` fires for Ferrow Foods, a regional food manufacturer: a cluster of statement-style posts in a short window, an apology, a clarification, a promise of investigation. The pattern behind the signal is unmistakable, this is a company responding to something, and the something appears to be a product recall.
**Reading it.** The statement cluster tells you two things. First, the crisis is real and public. Second, and more usefully, **the response is being written in-house, in real time, by people doing it for the first time**, you can read the inexperience in the posts themselves: defensive phrasing, over-explanation, the classic mistakes. Companies in this state either stabilize with help in days or compound the damage for weeks.
**The play.**
1. Move in hours, with genuine usefulness and zero ambulance-chasing tone:
> Watching your team handle a hard week. One observation from someone who does this professionally: the next 48 hours of communication matter more than the last 48. If a second set of eyes on the next statement would help, I am available today, no engagement needed.
2. Offer the first review free, **trust built during the fire becomes the retainer after it**.
3. Never critique publicly or reference specifics in writing beyond what they have published.
4. Afterward, propose the preparedness engagement; companies that survived one improvised crisis are the readiest buyers of never-improvising-again.
**Automate it.** A daily [Company Signals API](/buying-signals/signals-apis/company-signals) check on `crisis_response_signal` across your region and industries is, functionally, your entire prospecting system, this market only exists in these windows.
**Why it lands.** In crisis work, credibility is proven by showing up calm while it burns. The signal tells you where it is burning.
## How to read it
Clustered statements mean the company is managing a situation.
Outreach timing and tone matter enormously here.
Security or comms tools may be acutely relevant.
## Outreach playbook
Sensitive. Pause generic outreach; for security/comms tools, this can be a fit.
## Related signals
The company is posting noticeably more on social.
The company's follower count dropped.
***
Surface `crisis_response_signal` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Activity signals
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/overview
Signals tracking how engaged a company is on social channels, posting rhythm, engagement, and topic shifts.
Signals tracking how engaged a company is on social channels, posting rhythm, engagement, and topic shifts.
The activity category contains **7 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
The company is posting noticeably more on social.
The company is posting noticeably less on social.
The company stopped posting for an extended period.
The company resumed posting after a dormant stretch.
Average post engagement shifted significantly.
The dominant topic of recent posts changed.
A cluster of statement or apology-style posts in a short window.
# Page Dormant
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/page-dormant
The company stopped posting for an extended period.
`page_dormant`
Activity
Company Professional Network page (post history), 90-day windows.
The company stopped posting for an extended period.
## When it fires
No new posts detected for ≥ 90 days when the prior 90-day window had ≥ 1 post.
## Magnitude
Moderate to high, prolonged silence is meaningful.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Dormancy is a caution flag for timing. A page going quiet for 90 days after prior activity can indicate a downsized marketing team, an acquisition transition, or distress, it's a component of the decline\_signal composite.
## How to use Page Dormant Signal?
**Where you sit.** You run SDR operations at a B2B data company. Your team sends thousands of emails a month, and you are measured on two numbers in permanent tension: volume and reply rate. The silent killer of the second is **dead accounts, companies that look real in the database but have functionally stopped operating or communicating**, absorbing touches that can never convert.
**The mission.** Systematically detect and suppress zombie accounts, so every SDR hour lands on companies that are actually alive.
**The signal fires.** `page_dormant` fires for Mullen & Frost, a consulting firm sitting in three of your active sequences: their company page has posted nothing for an extended period. Once-monthly updates simply stopped. A spot check finds their website unchanged in a year and their last known hire two years back.
**Reading it.** Corporate silence at that depth rarely reverses on its own. The realistic explanations, wind-down, acqui-hire, a pivot to lifestyle business, a shell awaiting dissolution, share one property: **nobody inside is reading vendor email**. Every sequence touch against the account costs deliverability, pollutes metrics, and wastes the scarcest thing you manage, SDR attention.
**The play.**
1. Wire the signal to a suppression queue, not an instant purge; a human confirms with a sixty-second check before the account is retired.
2. Tag retired accounts with the signal and date, keeping the suppression auditable and reversible.
3. Route the freed capacity deliberately: every hundred suppressed zombies is real hours returned to accounts that breathe.
4. Watch for the paired signal, **`page_reactivated` on a suppressed account is a resurrection worth knowing about**, and the pair costs you nothing to monitor.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `page_dormant` across your entire CRM runs the graveyard shift automatically; most teams find low single-digit percentages of their database is already dead.
**Why it lands.** Everyone optimizes messaging to raise reply rates. Deleting the audience that can never reply is **cheaper, faster, and permanent**.
## How to read it
90 days of no posts after activity is notable.
Often reflects reorg, acquisition, or trouble.
Feeds decline\_signal.
## Outreach playbook
Caution flag. Component of decline\_signal; verify account health before outreach.
## Related signals
The company resumed posting after a dormant stretch.
The company is posting noticeably less on social.
A company shows a clear distress pattern.
***
Surface `page_dormant` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Page Reactivated
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/page-reactivated
The company resumed posting after a dormant stretch.
`page_reactivated`
Activity
Company Professional Network page (post history).
The company resumed posting after a dormant stretch.
## When it fires
First post detected after a `page_dormant` state.
## Magnitude
Moderate to high, reactivation marks a turning point.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A page waking up after a quiet stretch often coincides with new leadership or a relaunch. Reactivation is a positive turning point, the company is re-engaging, frequently driven by a new marketing leader, a funding event, or a strategic reset.
## How to use Page Reactivated Signal?
**The scenario.** You do business development for a video production studio. Your buyers are marketing teams with budget and ambition, and your timing problem is chronic: teams already producing video have an incumbent studio, while teams doing nothing have no budget. The gap in between, **the moment a quiet company decides to get loud**, is where new studio relationships are born.
**The goal.** Catch companies at the restart: dormant brands that have just begun communicating again, before they have production partners for what comes next.
**The signal fires.** `page_reactivated` fires for Copperfield Estates, a property developer: after a long dormant stretch, their page is posting again. The early posts are the usual restart material, a leadership welcome, a project update, all static images and stock-adjacent design.
**Reading it.** Reactivation is a decision with a budget behind it. Somewhere at Copperfield, marketing was rebooted, a new hire, a new agency, a new strategy, and reboots follow a predictable content ladder: text posts first, then design, then photography, then video. They are on rung one. **Your product is rung four, and you want to be in the room before they climb there with someone else.**
**The play.**
1. Verify the restart is staffed: a new marketing hire visible on the team roughly confirms the budget is real.
2. Reach the marketing lead while the restart is young, generous first:
> Noticed Copperfield is back to publishing, good to see. Restarted brands usually hit the video question within a quarter. Here is a two-minute reel of what we have done for three property brands, for whenever that conversation starts.
3. Offer a small first project, a single walkthrough film, **restart budgets buy pilots, not retainers**, and pilots become retainers by themselves.
4. Track their content ladder monthly via their actual posts; when photography appears, video is one decision away, and your follow-up writes itself.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `page_reactivated`, with the [Company Activity API](/buying-signals/signals-apis/company-activity) showing what they are posting, turns brand restarts into a timed pipeline.
**Why it lands.** Incumbents own the companies already loud. The restarts have **budget, ambition, and no incumbent**, if you arrive on rung one.
## How to read it
Reactivation signals renewed momentum.
Often driven by a new marketing or comms hire.
May coincide with a product or brand relaunch.
## Outreach playbook
Positive timing signal. Check for a new marketing leader driving the revival.
## Related signals
The company stopped posting for an extended period.
The company is posting noticeably more on social.
A new VP-level leader joined the company.
***
Surface `page_reactivated` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Post Topic Shift
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/post-topic-shift
The dominant topic of recent posts changed.
`post_topic_shift`
Activity
Company Professional Network page (post content embeddings).
The dominant topic of recent posts changed.
## When it fires
Dominant topic cluster (from embeddings) of recent posts differs from the previous window.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------ | ---------------------------------------------- |
| `meta` | Old and new topic labels stored on the signal. |
## Magnitude
Moderate to high.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A topic pivot can signal a strategic repositioning. When a company's content suddenly shifts focus, from, say, recruiting to product, or one technology to another, it often reflects a change in priorities, messaging, or market focus.
## How to use Post Topic Shift Signal?
**Where you sit.** You lead business development at a penetration-testing firm. Security services have a strange demand curve: companies buy urgently right after deciding security matters, and almost never before. Your entire pipeline question reduces to one thing: **which companies just started caring?**
**The mission.** Detect the public moment a company's attention turns to security and compliance, because attention precedes budget by about one quarter.
**The signal fires.** `post_topic_shift` fires for Gable & Young Software, a healthcare-IT vendor: the dominant topic of their recent posts has changed. For years they posted product updates and hiring. The last two months are SOC 2 milestones, a data-protection webinar, and a leadership post about "earning customer trust."
**Reading it.** Companies broadcast what they are being asked about. A topic shift toward security almost always means pressure arrived from somewhere, enterprise deals stalling on security review, a close call, a big customer's audit demands, and the company is responding in public before its capabilities have caught up in private. That gap between **the story they have started telling and the security posture they actually have** is precisely what a pen-testing engagement closes.
**The play.**
1. Read the actual posts for the driver; SOC 2 posts mean an audit path is chosen and testing is mandatory, trust language means enterprise deals are forcing the issue.
2. Approach the CTO with alignment, not fear: they are clearly investing in security credibility, and a third-party test is the artifact that makes the claims stand up in a sales cycle.
3. Position the deliverable commercially, **a clean pen-test report is sales collateral for them, not just an audit checkbox**, which reframes your fee as pipeline spend.
4. Move within the quarter; post-topic shifts mark the start of budget formation, and the vendors present at formation get written into it.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `post_topic_shift`, with the [Company Activity API](/buying-signals/signals-apis/company-activity) supplying the posts themselves, tells you not just that attention moved, but exactly where.
**Why it lands.** Security selling fails against indifference. This signal finds companies **mid-conversion from indifferent to anxious**, which is the only market that buys.
## How to read it
A topic shift reflects changed priorities.
Stored labels show the direction of the shift.
Often precedes or accompanies a positioning change.
## Outreach playbook
Positioning clue. Compare old and new topics for the strategic story.
## Related signals
The company description was substantially rewritten.
Average post engagement shifted significantly.
A company fundamentally changed what it does.
***
Surface `post_topic_shift` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Posting Activity Decrease
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/posting-activity-decrease
The company is posting noticeably less on social.
`posting_activity_decrease`
Activity
Company Professional Network page (post frequency), rolling baseline.
The company is posting noticeably less on social.
## When it fires
Posts in the last 30 days ≤ 0.5× the rolling average of the preceding three 30-day windows.
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A communications slowdown can hint at internal turbulence. A sharp drop in posting may reflect a marketing team in flux, a quiet period before an announcement, or broader distraction, useful as a soft early-warning signal.
## How to use Posting Activity Decrease Signal?
**The setup.** You are a CSM at a social-advertising platform. Your accounts spend monthly budgets amplifying their content, and your renewals have a leading indicator nobody formally tracks: **the health of the customer's own marketing operation**. When their organic engine slows, the paid budget follows it down, usually two quarters later.
**What you want.** An early-warning system for marketing-team decay inside your book of business, visible before it reaches your revenue dashboard.
**The signal fires.** `posting_activity_decrease` fires for Tidebrook Resorts, a hospitality group and a six-figure annual account: their organic posting cadence has fallen well below baseline. Daily posts have become weekly. Your platform data shows their campaign refresh rate slipping in parallel.
**Reading it.** Marketing teams go quiet for reasons, a departed social manager, a budget freeze, an agency transition, a reorg, and none of those reasons are good for your renewal. Organic silence is the earliest public symptom; **the ad spend cut is the same disease arriving at your P\&L later**. A CSM who calls at the symptom stage can help; one who calls at the budget-cut stage can only discount.
**The play.**
1. Check your internal data first: login frequency, campaign edits, support tickets. Silence inside and outside together means act now.
2. Call your champion with curiosity, not alarm: the posting dip is your opener to ask what changed on the team.
3. Adapt the save to the cause, **a departed social manager means you offer managed services and templates; a budget freeze means you protect the renewal with a right-sized plan proposed early**.
4. Log the signal date against the renewal; over a year, you will learn your book's true lag between organic decay and churn, and staff accordingly.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `posting_activity_decrease` across your account list, piped into health scores, converts a public behavior into renewal foresight.
**Why it lands.** Churn postmortems always find the warning signs were visible. This one makes them **visible on time**.
## How to read it
Halved posting cadence is a notable pullback.
Can reflect marketing churn or internal distraction.
Use as context, not a standalone trigger.
## Outreach playbook
Soft caution. Watch for progression toward page\_dormant.
## Related signals
The company is posting noticeably more on social.
The company stopped posting for an extended period.
Follower growth slowed sharply while still positive.
***
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# Posting Activity Increase
Source: https://apidoc.cufinder.io/buying-signals/signals/activity/posting-activity-increase
The company is posting noticeably more on social.
`posting_activity_increase`
Activity
Company Professional Network page (post frequency), rolling baseline.
The company is posting noticeably more on social.
## When it fires
Posts in the last 30 days ≥ 1.5× the rolling average of the preceding three 30-day windows.
## Magnitude
Moderate to high (1.5× threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A company suddenly talking more usually has something to announce. Increased posting cadence often precedes or accompanies launches, funding news, hiring pushes, or events, it's a sign the company is in an active, outward-facing phase.
## How to use Posting Activity Increase Signal?
**The scenario.** You sell sponsorships for a major industry conference in the data and analytics space. Your buyers are marketing leaders, and the ones who say yes share a state of mind: **they are already in visibility mode**, spending energy on being seen. Pitching sponsorships to quiet companies is pushing rope; pitching to loud ones is finishing their sentence.
**The goal.** Rank your sponsorship prospect list by current marketing aggression, so your team calls companies while their appetite for attention is hot.
**The signal fires.** `posting_activity_increase` fires for Jura Analytics, a mid-size data-quality vendor: their posting cadence has jumped well above their own baseline. Three posts a week where there used to be three a month, product teasers, hiring posts, founder commentary.
**What it tells you.** A company does not triple its posting by accident; content velocity is budgeted behavior. A ramp like this usually means a launch approaching, a new marketing leader proving themselves, or a funded push for category presence. All three of those buyers have the same problem your booth solves: **content reaches their existing audience, but events reach the audience they do not have yet**.
**The play.**
1. Read the ramp before calling: what are they posting about? A product teaser stream means launch, and launches need stages.
2. Pitch the complement, not the category: they are clearly investing in being visible, and a keynote slot puts that same story in front of four thousand people who do not follow them yet.
3. Offer launch-timed packages, **a booth the month of their launch is worth triple a booth in a quiet quarter**, and price accordingly.
4. Deprioritize accounts whose activity is flat; the same pitch lands differently on a team in hibernation.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `posting_activity_increase` across your prospect list, with the [Company Activity API](/buying-signals/signals-apis/company-activity) supplying the actual posts for context, gives every seller a ranked, researched call sheet.
**Why it lands.** Sponsorship is bought with momentum money. This signal finds **the companies currently spending it**.
## How to read it
More posting means active announcements or campaigns.
Companies post more during growth and launch periods.
Check what they're posting about for the why.
## Outreach playbook
Engagement opportunity. Read recent posts to find the conversation hook.
## Related signals
The company is posting noticeably less on social.
Average post engagement shifted significantly.
The dominant topic of recent posts changed.
The company resumed posting after a dormant stretch.
***
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# Industry Added
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/industry-added
A new industry was added to the company's list.
`industry_added`
Categorization
Company Professional Network page (industries array).
A new industry was added to the company's list.
## When it fires
A new entry appears in the `industries` array.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | ---------------------------- |
| `meta.industry` | The industry that was added. |
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Adding an industry means broadening reach. Unlike a primary industry change, adding a secondary industry signals expansion into an adjacent market while keeping the core, growth through diversification.
## How to use Industry Added Signal?
**The setup.** You run RevOps at a compliance-automation vendor that sells exclusively to financial services. Your TAM is defined, your lists are built, and everyone treats the ICP as a fixed universe. It is not. **Companies migrate into your ICP all the time**, fintech-adjacent firms adding lending, software companies adding payments, and nobody owns noticing.
**What you want.** A standing process that catches companies entering your addressable market at the moment of entry, when their compliance burden is brand-new and unserved.
**The signal fires.** `industry_added` fires for Glenrock Data, an analytics company that has added "financial services" to its industry list. A quick look confirms why: they have launched a lending-analytics product that touches regulated data.
**Reading it.** A company adding your industry to its identity is announcing a market entry, and market entrants inherit the market's obligations without the market's experience. Glenrock now faces compliance requirements their team has likely never operated under, with no incumbent vendor relationships, no legacy processes, and **a strong preference for whoever explains the new rules before the first audit does**.
**The play.**
1. Auto-add the account to your TAM and route it to the team that owns new-entrant plays.
2. Reach out with orientation, not product: a plain-language guide to the compliance obligations that come with the market they just entered.
3. Sell the newness explicitly, incumbents in your space compete on switching; **with market entrants you compete only against confusion**, which is a much better opponent.
4. Measure ICP inflow quarterly. If a segment keeps flowing toward you, marketing should get there earlier with content aimed at companies considering the move.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `industry_added`, filtered to your industry values, turns TAM from a static spreadsheet into a growing list.
**Why this works.** Everyone fights over the companies already in the market. The ones just arriving have **no incumbents, fresh budgets, and the humility of beginners**, and this signal hands them to you first.
## How to read it
The company is entering an adjacent market.
Adding industries reflects a broadening strategy.
A new industry footprint may mean new tooling needs.
## Outreach playbook
Signals market expansion. Check meta.industry for new-market fit with your product.
## Related signals
The company's primary industry classification changed.
An industry was dropped from the company's list.
A new specialty was added.
***
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# Industry Change
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/industry-change
The company's primary industry classification changed.
`industry_change`
Categorization
Company Professional Network page (primary industry field).
The company's primary industry classification changed.
## When it fires
`industry_primary` value changes between snapshots.
## Magnitude
High, reclassifying the core industry is significant.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A company reclassifying itself is rethinking its core market. Primary industry is fundamental, when it changes, the company has genuinely shifted what business it's in, a strong pivot indicator that feeds pivot\_signal.
## How to use Industry Change Signal?
**The scenario.** You are a commercial-lines insurance broker. Your book runs on renewals, and renewals run on a quiet assumption: that each client is still the business you originally underwrote. Except businesses drift. The machine shop starts doing design consulting; the distributor starts manufacturing. And **coverage written for the old business does not reliably pay claims for the new one**.
**The goal.** Catch client and prospect industry drift when it happens, because a reclassified company is either an underinsured client to protect or a mid-transition prospect nobody else is calling correctly.
**The signal fires.** `industry_change` fires for Barnaby Works, a longtime client: their primary industry classification has moved from metal fabrication to engineering services. Nobody called you about it. They never do.
**What it tells you.** An industry reclassification means the center of gravity of the business moved. For a client, that means exposures their current policy never contemplated, professional liability for a company insured as a fabricator, for instance. For a prospect, it means their incumbent broker is probably still renewing them **on autopilot, against a risk profile that no longer exists**.
**The play.**
1. For clients: book a coverage review framed as a service touch, not a sale. Walk through what the business actually does now versus what the policy says.
2. Document the gap in writing; if a claim ever lands in it, that letter is the difference between a difficult call and a lawsuit.
3. For prospects: lead with the reclassification itself, "your business changed categories; has your coverage?", which beats any generic quote-me pitch.
4. Re-check limits and class codes at every fire of this signal, **premium adjustments from reclassification cut both ways, and finding savings builds as much trust as finding gaps**.
**Automate it.** Run your whole book plus prospect list through the [Company Signals API](/buying-signals/signals-apis/company-signals) on `industry_change` quarterly, and let hits generate review tasks automatically.
**Why it lands.** Every broker promises annual reviews. You are the one whose reviews are **triggered by the client's actual life, not the calendar**.
## How to read it
Primary industry is foundational; a change is a real pivot.
The company now competes and buys in a different space.
Part of pivot\_signal.
## Outreach playbook
Strong pivot indicator. Re-qualify the account against the new industry.
## Related signals
A new industry was added to the company's list.
An industry was dropped from the company's list.
A company fundamentally changed what it does.
***
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# Industry Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/industry-removed
An industry was dropped from the company's list.
`industry_removed`
Categorization
Company Professional Network page (industries array).
An industry was dropped from the company's list.
## When it fires
An entry disappears from the `industries` array.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | ------------------------------ |
| `meta.industry` | The industry that was removed. |
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Dropping an industry usually signals focus or a pivot away from a segment. Companies prune industries when they exit a market or sharpen their positioning, the stored industry tells you exactly what they're leaving behind.
## How to use Industry Removed Signal?
**Where you sit.** You manage an SDR team at a vertical SaaS for construction. Your sequences convert because they are savagely specific: job-site language, construction math, trade references. Which means they convert to exactly no one else, and **every non-construction company still sitting in your lists is wasted touches, polluted metrics, and deliverability risk**.
**The mission.** Keep the outbound universe honest by removing companies the moment they stop being your market, with the same rigor everyone applies to adding them.
**The signal fires.** `industry_removed` fires for Kite & Crown, a mid-size firm your team has sequenced twice this year: "construction" has been dropped from their industry list. Their remaining classification says facilities management, a business you do not serve.
**What it tells you.** The company has redefined itself out of your ICP. Continuing to work the account now costs you three ways: SDR hours spent on unwinnable conversations, reply-rate metrics dragged down invisibly, and the slow spam-folder tax of emailing people who have no reason to engage. The exit signal is **as operationally valuable as an entry signal, and almost nobody automates it**.
**The play.**
1. Auto-suppress the account from active sequences and tag the reason with the signal name and date, so the suppression is auditable, not mysterious.
2. Sweep the account's contacts out of nurture tracks built on construction pain points.
3. Keep a light-touch exception path: if there is an open opportunity on the account, alert the rep instead of suppressing silently.
4. Review the exit cohort quarterly, **if many accounts exit toward the same adjacent industry, that is either churn in your market or a product signal worth escalating**.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) job on `industry_removed`, matched against your CRM, runs list hygiene continuously instead of during the annual cleanup nobody enjoys.
**Why it lands.** Outbound teams obsess over who to add and ignore who to remove. The remove side is where **reply rates, sender reputation, and SDR morale quietly go to die**.
## How to read it
Removing an industry means leaving or de-emphasizing it.
Pruning reflects a deliberate narrowing.
meta.industry shows the abandoned segment.
## Outreach playbook
Signals focus or exit. Useful context for understanding strategic narrowing.
## Related signals
A new industry was added to the company's list.
The company's primary industry classification changed.
The total number of specialties shrank notably.
A company fundamentally changed what it does.
***
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# Categorization signals
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/overview
Signals tracking how a company classifies its industry and specialties, revealing pivots and market expansion.
Signals tracking how a company classifies its industry and specialties, revealing pivots and market expansion.
The categorization category contains **7 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
The company's primary industry classification changed.
A new industry was added to the company's list.
An industry was dropped from the company's list.
A new specialty was added.
A specialty was dropped from the list.
The total number of specialties grew notably.
The total number of specialties shrank notably.
# Specialties Count Decrease
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/specialties-count-decrease
The total number of specialties shrank notably.
`specialties_count_decrease`
Categorization
Company Professional Network page (specialties array length).
The total number of specialties shrank notably.
## When it fires
Total `specialties.length` decreased by ≥ 3.
## Magnitude
Moderate to high.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A company narrowing its scope is making deliberate strategic cuts. A net decrease of 3 or more specialties signals focusing or pivoting, the company is sharpening its message, often after a strategy reset or new leadership.
## How to use Specialties Count Decrease Signal?
**The scenario.** You are an analyst at a growth-equity fund. Between diligence sprints, your job is watching a few hundred companies for the moments that change a thesis. One of the least-watched moments: **a company deliberately narrowing what it claims to do**.
**The goal.** Treat public focus-narrowing as the strategic event it usually is, and time outreach to companies that have just made the hard choices investors love to see already made.
**The signal fires.** `specialties_count_decrease` fires for Meridian Robotics, a company on your watchlist: their specialties list has shrunk from eleven entries to four. The four that remain are all warehouse automation. The seven that vanished were the scattered experiments, consulting, custom hardware, a vision API.
**Reading it.** Companies add specialties casually and remove them painfully; every deleted line item was someone's project. A cut this deep is the fingerprint of a strategic reset, new leadership discipline, a board conversation about focus, or **a company cleaning up its story before raising**. Focused companies with a clarified core are exactly what growth equity wants to find, ideally a quarter before the banker process starts.
**The play.**
1. Diff the lists and read the survivors; what a company keeps under pressure is the truest statement of strategy it will ever publish.
2. Cross-check the narrative: headcount steady plus specialties shrinking reads as focus; both shrinking reads as retreat. **The same signal points opposite directions depending on its neighbors.**
3. If it reads as focus, open the relationship now with a thesis-led note referencing the warehouse-automation market specifically, founders answer specificity.
4. Log the narrowing date; if a raise announcement follows within two quarters, you have calibrated the signal's lead time for the next one.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) review of `specialties_count_decrease` across your watchlist sectors turns focus events into a standing sourcing queue.
**Why this works.** Everyone can see a company after it raises. The narrowing that precedes the raise is public too, **just illegible to anyone not watching for it**.
## How to read it
Fewer specialties means a tighter, sharper positioning.
Often follows a strategy change or new leadership.
A 3+ drop is intentional, not incidental.
## Outreach playbook
Focusing indicator. Strong context for pivot\_signal interpretation.
## Related signals
A specialty was dropped from the list.
The total number of specialties grew notably.
A company fundamentally changed what it does.
***
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# Specialties Count Increase
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/specialties-count-increase
The total number of specialties grew notably.
`specialties_count_increase`
Categorization
Company Professional Network page (specialties array length).
The total number of specialties grew notably.
## When it fires
Total `specialties.length` increased by ≥ 3.
## Magnitude
Moderate to high, a jump of 3+ reflects deliberate broadening.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Companies casting a wider net are often in a build phase. A net increase of 3 or more specialties signals broadening positioning, the company is expanding its declared capabilities, common during growth and diversification.
## How to use Specialties Count Increase Signal?
**Where you sit.** You own a white-label development shop. Your clients are agencies that sell more than they can build, and your entire pipeline question is: **which agencies are about to sell more than they can build?**
**The mission.** Spot agencies in expansion mode, visibly widening what they offer, because a widening offer almost always outruns the team behind it, and that gap is your product.
**The signal fires.** `specialties_count_increase` fires for Foxglove Creative, a 25-person brand agency. Their specialties list has grown notably: alongside the usual brand and design entries, there are now e-commerce builds, motion, and app development. Three new service lines, no visible hiring to match.
**Reading it.** An agency that broadens its list faster than its team is making a bet: promise the capability, win the work, figure out delivery later. It is a rational bet, and "later" arrives on a signed contract with a deadline. When it does, they need delivery capacity that does not exist in-house, immediately, quietly, and at agency-friendly rates. **You are the "later".**
**The play.**
1. Compare the new specialties against your service menu; pitch only the overlap, agencies smell generic partner spam instantly.
2. Reach the founder peer-to-peer, and normalize the gap rather than exposing it:
> Noticed Foxglove is expanding into e-comm and app work, exciting move. Most agencies scale those lines with a white-label bench before hiring for them. We are that bench for four agencies your size; happy to share how the handoff works.
3. Offer a small pilot project, **the first overflow project is the whole sale**; agencies that use a bench once rarely stop.
4. Time follow-ups a month or two after the specialty change; that is roughly when the first oversold contract lands.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `specialties_count_increase`, filtered to agencies in your size band, is a feed of companies manufacturing their own delivery gap.
**Why it lands.** Every agency expansion creates a quiet capacity crisis. You are simply arriving **the week the promise gets ahead of the payroll**.
## How to read it
More specialties means a wider declared capability set.
Often coincides with hiring and product expansion.
The company is widening its market appeal.
## Outreach playbook
Growth-mode indicator. Pair with hiring signals to confirm a build phase.
## Related signals
A new specialty was added.
The total number of specialties shrank notably.
A company's employee count increased by at least one between snapshots.
***
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# Specialty Added
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/specialty-added
A new specialty was added.
`specialty_added`
Categorization
Company Professional Network page (specialties array).
A new specialty was added.
## When it fires
A new entry appears in `specialties`.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | ----------------------------- |
| `meta.specialty` | The specialty that was added. |
## Magnitude
Low to moderate (individual specialty).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
New specialties point to expanding capabilities. A company adding a specialty is advertising a new competency or service line, useful for spotting product expansion early. Feeds pivot\_signal.
## How to use Specialty Added Signal?
**The scenario.** You run channel partnerships at a CRM platform. Your partner program grows one certified agency at a time, and the best recruits share a trait: **they have just decided to invest in your ecosystem's skills, but have not yet formalized a partnership with anyone**, including your competitors.
**The goal.** Find agencies at the moment they add your technology to their service list, when their commitment is fresh and their partner allegiance is still up for grabs.
**The signal fires.** `specialty_added` fires for Bluewater Digital, a 40-person consultancy: they have added "CRM implementation" and your platform's name to their specialties. Until last month, they were a web-development shop.
**Reading it.** An agency adds a specialty when it has staffed for it, usually one or two hires with your certification, and wants the market to know. That means they are actively hunting their first implementation clients right now, with no case studies, no partner-tier benefits, and no lead flow. An agency in that position **needs exactly what a partner program provides, and knows it**, which makes this the single easiest recruiting conversation in channel work.
**The play.**
1. Verify the substance: check their team for certified staff and their site for the new service page. Real investment beats aspirational keywords.
2. Reach the founder within the month, while the new practice is hungry:
> Saw Bluewater added CRM implementation to the roster. New practices live or die on their first three clients, and our partner program exists to supply exactly those. Worth twenty minutes?
3. Lead with lead flow, not logo placement, **new practices need revenue, not badges**.
4. Fast-track their tier review after the first delivery; early wins convert opportunistic partners into ecosystem loyalists.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `specialty_added`, filtered to your technology's keywords, is a partner-recruiting pipeline that staffs itself.
**Why it lands.** Channel teams usually recruit agencies that are already someone's partner. This signal finds them **the week before they become one**.
## How to read it
An added specialty advertises a new competency.
Often precedes a new product or offering launch.
Part of pivot\_signal.
## Outreach playbook
Spot capability expansion. Check meta.specialty for relevance to your offering.
## Related signals
A specialty was dropped from the list.
The total number of specialties grew notably.
A new industry was added to the company's list.
A company fundamentally changed what it does.
***
Surface `specialty_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Specialty Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/categorization/specialty-removed
A specialty was dropped from the list.
`specialty_removed`
Categorization
Company Professional Network page (specialties array).
A specialty was dropped from the list.
## When it fires
An entry is removed from `specialties`.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | ------------------------------- |
| `meta.specialty` | The specialty that was removed. |
## Magnitude
Low to moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Pruning specialties often means tightening positioning. Removing a specialty signals the company is narrowing its focus or sunsetting a capability, the inverse of specialty\_added. Feeds pivot\_signal.
## How to use Specialty Removed Signal?
**The setup.** You lead business development at a cloud-migration consultancy. Your best projects come from a scenario nobody tracks systematically: **a service provider exits a line of business, and its clients get orphaned**. The provider moves on; the clients are left running on something nobody wants to maintain.
**What you want.** To detect service-line exits among IT providers in your region, because every exit strands a book of clients who will need to move, soon, with help.
**The signal fires.** `specialty_removed` fires for Ironbark IT, a managed-services firm two towns over: "on-premise hosting" has been dropped from their specialties. Their remaining list is all cloud. The strategic read is obvious, they are winding down the data-center business.
**Reading it.** A dropped specialty is a company quietly announcing what it will stop renewing. Ironbark's hosting clients, dozens of businesses running on servers Ironbark no longer wants to own, will hear about this over the next year through worsening service and awkward renewal conversations. Each of them faces a migration project, and **the migration will be sold by whoever shows up before the panic**, which is not Ironbark.
**The play.**
1. Map the stranded market: Ironbark's typical client profile tells you who is sitting on their racks.
2. Consider the direct route too, call Ironbark. Providers exiting a line often **prefer referring clients to a credible successor over abandoning them**, and a referral partnership beats cold outreach into their book.
3. Position around continuity for the end clients: assessment, migration path, timeline, before their current arrangement degrades around them.
4. Watch for the same signal across every provider in your region; each fire is a new stranded book.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `specialty_removed` across regional IT providers, filtered for the legacy keywords you migrate away from, maps orphaned demand continuously.
**Why it lands.** You are not creating migration demand. **Someone else's strategic retreat created it**; the signal just tells you where.
## How to read it
Removing specialties sharpens the company's positioning.
May reflect a discontinued service line.
Part of pivot\_signal.
## Outreach playbook
Signals narrowing. Useful context, especially as part of pivot\_signal.
## Related signals
A new specialty was added.
The total number of specialties shrank notably.
An industry was dropped from the company's list.
A company fundamentally changed what it does.
***
Surface `specialty_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Company Signals
Source: https://apidoc.cufinder.io/buying-signals/signals/company-signals
Signals derived from a company's public page: growth, decline, identity, categorization, location, structure, funding, and activity.
Company signals are derived from a company's public Professional Network page. They track what's happening to the organization as a whole, how fast it's growing or shrinking, how it describes and classifies itself, where it operates, how it's structured, what it has raised, and how actively it shows up online.
There are **70 company signals** across **8 categories**. Each category has its own overview page and a detailed page per signal.
## The eight categories
Headcount, followers, and open roles climbing, usually budget loosening up.
Contraction, cost-cutting, and instability, for timing efficiency plays or pausing accounts.
Name, tagline, and description changes that hint at rebrands, pivots, or M\&A.
Industry and specialty changes that reveal pivots and market expansion.
New offices, HQ moves, and country expansion.
Parents, subsidiaries, and entity-type changes tied to M\&A and restructuring.
Fresh capital, the clearest budget signal there is.
Posting rhythm, engagement, and topic shifts that track momentum.
## How to use company signals
Growth and funding signals tell you where budget is opening up. Decline signals tell you where to pause, or where to reposition around efficiency. Identity, categorization, and structure signals tell you when a company has fundamentally changed and needs re-qualifying. Pair any of them with [magnitude](/buying-signals/concepts/magnitude-buckets) and [timeframe](/buying-signals/concepts/detection-timeframes) to prioritize.
The most common starting point for finding in-market accounts.
# Composite Signals
Source: https://apidoc.cufinder.io/buying-signals/signals/composite-signals
High-confidence patterns that combine multiple individual signals into major company events.
Composite signals combine several individual signals into a single high-confidence event. Where one signal might be noise, three firing together within a window is a pattern, so composites are the most reliable signals in the catalog and the ones worth alerting on directly.
There are **12 composite signals**, including two continuous scores recomputed nightly.
## Events and scores
Patterns like IPO, acquisition, merger, pivot, rebrand, expansion, decline, and restructuring that fire when their component signals align inside a time window.
`momentum_score` and `risk_score` roll all positive or all negative signals into one decaying number, recomputed nightly, so you can rank an entire territory by trajectory.
## Every composite signal
| Signal | What it means |
| -------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| [`ipo_signal`](/buying-signals/signals/composite/ipo-signal) | A company went from private to public. |
| [`acquired_signal`](/buying-signals/signals/composite/acquired-signal) | A company was acquired. |
| [`merger_signal`](/buying-signals/signals/composite/merger-signal) | Two companies merged under a common new parent. |
| [`pivot_signal`](/buying-signals/signals/composite/pivot-signal) | A company fundamentally changed what it does. |
| [`rebrand_signal`](/buying-signals/signals/composite/rebrand-signal) | A company executed a coordinated rebrand. |
| [`expansion_signal`](/buying-signals/signals/composite/expansion-signal) | A company is expanding into new markets while growing. |
| [`decline_signal`](/buying-signals/signals/composite/decline-signal) | A company shows a clear distress pattern. |
| [`restructuring_signal`](/buying-signals/signals/composite/restructuring-signal) | A company is undergoing major restructuring. |
| [`executive_team_buildout`](/buying-signals/signals/composite/executive-team-buildout) | A company rapidly built out its leadership team. |
| [`pre_ipo_signal`](/buying-signals/signals/composite/pre-ipo-signal) | A company shows the early pattern of an IPO track. |
| [`momentum_score`](/buying-signals/signals/composite/momentum-score) | A nightly composite score ranking a company's upward trajectory. |
| [`risk_score`](/buying-signals/signals/composite/risk-score) | A nightly composite score ranking a company's decline and risk. |
Composites are your highest-signal alerts. Because each one already requires multiple corroborating events, you can act on them with far less filtering than single snapshot signals.
Catch IPOs, acquisitions, and expansions the moment the pattern completes. Start free, no credit card required.
# Acquired Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/acquired-signal
A company was acquired.
`acquired_signal`
Composite
Composite of Parent Company Added with name or description change.
A company was acquired.
## When it fires
`parent_company_added` AND (`name_change` OR `description_change_major`) within a 60-day window.
## Magnitude
Hyper, a confirmed acquisition.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
That combination is a near-certain acquisition. A new parent appearing alongside a name or description change is the structural fingerprint of an acquisition, far more reliable than any single signal, and it reshapes who controls budget and vendor decisions.
## How to use Acquired Signal?
**Where you sit.** You are an enterprise CSM at a data-integration platform. Wrenfield Data, a healthy seven-year account, mid-six figures annually, sits in your book. Renewal is in ten months. You have a good champion, solid usage, and, as of this morning, a problem you did not know about.
**The mission.** Treat acquisitions of your customers as first-class renewal events, because **an acquired customer is not the same customer anymore**, whatever the logo says.
**The signal fires.** `acquired_signal` fires for Wrenfield: the composite has detected the acquisition pattern, a new parent, Halcyon Group, plus the identity changes that follow. Your champion mentions it two days later, with the airy optimism of someone who has not yet met the integration team.
**Reading it.** Every acquired account's contracts enter a sorting process: keep, consolidate, or kill. Halcyon has its own preferred vendors, maybe a competing integration platform among them, and somewhere an integration office will eventually compare overlapping tools on a spreadsheet you will never see. Sitting still means letting that spreadsheet decide your renewal. **The accounts that survive integrations are the ones that made themselves annoying to remove before the comparison started.**
**The play.**
1. Reforecast honestly: flag the renewal as at-risk-by-structure regardless of current health, and tell your leadership why now.
2. Deepen technical entrenchment immediately, more integrations live, more teams onboarded, more workflows dependent; usage is your only vote in the parent's process.
3. Ask your champion for the map: who at Halcyon owns vendor consolidation, and can you present the platform's footprint to them proactively.
4. Explore the upside path too, **Halcyon has other subsidiaries with the same data problems**, and a parent-level agreement turns your biggest risk into your biggest expansion.
**Automate it.** Run your whole book through the [Company Signals API](/buying-signals/signals-apis/company-signals) on `acquired_signal` weekly; every fire should open a renewal-risk play automatically, not after the QBR.
**Why it lands.** Integration spreadsheets kill quietly and on schedule. This signal gives you **two or three quarters to become unkillable**.
## How to read it
The signal combination strongly confirms an acquisition.
The parent may centralize or release purchasing.
Acquirers often consolidate vendors post-deal.
## Outreach playbook
Top-tier. Determine whether the parent controls budget; expect stack consolidation.
## Related signals
A parent company was set where there was none.
The company name string changed.
The company description was substantially rewritten.
Two companies merged under a common new parent.
The company became or stopped being a subsidiary.
***
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# Decline Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/decline-signal
A company shows a clear distress pattern.
`decline_signal`
Composite
Composite of headcount, hiring, and activity signals.
A company shows a clear distress pattern.
## When it fires
`employee_decrease` AND `jobs_dropped_to_zero` AND `page_dormant` within 90 days.
## Magnitude
High, a clear distress pattern.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A clear distress pattern to flag. Shrinking headcount, zero open roles, and a silent page together paint an unambiguous picture of contraction, a strong signal to pause expansion outreach or, for efficiency tools, to reframe around survival and cost.
## How to use Decline Signal?
**The setup.** You run credit and finance operations at a wholesale electronics distributor. Your customers buy on net-60 terms, and your bad-debt losses have a pattern that haunts the postmortems: **the warning signs were public for months before the invoice went unpaid**, shrinking teams, quiet channels, leadership walking out. You just were not looking.
**What you want.** A systematic distress screen across your receivables book, so credit decisions use tomorrow's information instead of last year's financials.
**The signal fires.** `decline_signal` fires for Redgate Retail, a customer carrying a mid-six-figure credit line: the composite has aggregated sustained headcount decline, hiring collapse, and audience decay into a clear distress pattern. Their payment behavior, notably, is still fine, invoices land a few days late at worst.
**Reading it.** Payment behavior is a lagging indicator; companies pay their suppliers right up until the week they cannot. The composite is the leading version of the same story, and the gap between the two is your entire window to act. **Credit risk managed at the distress-signal stage costs terms adjustments; managed at the missed-payment stage, it costs write-offs and lawyers.**
**The play.**
1. Tighten quietly and gradually: shorten new-order terms, lower the exposure ceiling, require deposits on unusual volumes, ordinary-course moves that protect you without humiliating them.
2. Have sales stay close; a struggling customer treated respectfully is a loyal customer if they recover, and **an orderly wind-down of exposure if they do not**.
3. Cross-check against your own data, order frequency and size trends, and escalate to credit-hold criteria only when both public and internal signals agree.
4. Log every fire and outcome; within a year you will know the signal's real precision on your book and can set policy on evidence.
**Automate it.** Your receivables list against the [Company Signals API](/buying-signals/signals-apis/company-signals) on `decline_signal`, reviewed monthly with credit committee discipline, turns public decay into portfolio protection.
**Why it lands.** Every write-off postmortem finds visible warnings. This signal moves them **from the postmortem to the agenda**.
## How to read it
Three aligned decline signals confirm real contraction.
Net-new spend is unlikely in this state.
Cost-cutting tools may still find a fit.
## Outreach playbook
Strong caution. Pause expansion pitches; consider cost-reduction positioning.
## Related signals
A company's employee count dropped by at least one.
A company that had open roles now has none.
The company stopped posting for an extended period.
A nightly composite score ranking a company's decline and risk.
***
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# Executive Team Buildout
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/executive-team-buildout
A company rapidly built out its leadership team.
`executive_team_buildout`
Composite
Composite of executive and VP hire signals.
A company rapidly built out its leadership team.
## When it fires
≥ 2 `c_suite_hire` events AND ≥ 1 `vp_hire` event within 90 days.
## Magnitude
High, a common pre-funding or pre-IPO pattern.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is a common pre-funding or pre-IPO pattern, and a great proactive signal. When a company adds multiple executives and a VP in a quarter, it's professionalizing leadership ahead of a major event, often a raise or IPO, and those new leaders bring fresh tooling mandates.
## How to use Executive Team Buildout Signal?
**The scenario.** You founded a leadership-development firm that coaches executive teams through scale. Your ideal client is not a company with one new executive; it is **a company assembling several at once**, because a leadership team built in a hurry is a team of strangers holding a rocket.
**The goal.** Find companies mid-buildout, multiple senior hires landing within quarters of each other, when the team's dysfunction is still forming and still preventable.
**The signal fires.** `executive_team_buildout` fires for Farrow & Vale, a fintech fresh off a growth round: the composite has counted the pattern, a new CFO, CRO, and VP People, all within five months, layered onto a founder team that ran flat for six years.
**Reading it.** A buildout like this is a predictable collision: veteran founders with tribal knowledge and no process, meeting imported executives with process and no context. The first two quarters decide whether this becomes a leadership team or a set of competing fiefdoms, and the failure mode is expensive, **the average mis-hired executive costs a year and a re-search, and the average unaligned team costs the strategy itself**. Boards know this; founders learn it live.
**The play.**
1. Reach the CEO within the buildout window, framing the risk as normal, not shameful: every team assembled this fast hits the same three frictions, and there is a repeatable way through them.
2. Offer a team-launch engagement, a structured alignment sprint for the new executive group, rather than open-ended coaching, **founders buy defined programs, not ongoing dependency**.
3. Use the investor angle where it exists; the board that funded the buildout will happily sponsor what protects it.
4. Anchor follow-on work to the calendar: the two-quarter mark, when first conflicts surface, is your natural expansion conversation.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) review of `executive_team_buildout`, filtered to recently funded companies, finds leadership teams at the moment of assembly.
**Why it lands.** Coaching sold to a settled team is maintenance. Sold during assembly, **it is the difference between a team and a cast of expensive strangers**, and the CEO can already feel which way it is tipping.
## How to read it
Rapid leadership buildout precedes funding or IPO.
Each new leader brings tool-evaluation appetite.
Catch the company before the big event lands.
## Outreach playbook
Proactive top signal. Multiple new execs mean multiple new buying windows.
## Related signals
A new C-level executive joined the company.
A new VP-level leader joined the company.
A company shows the early pattern of an IPO track.
A new funding round was announced.
***
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# Expansion Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/expansion-signal
A company is expanding into new markets while growing.
`expansion_signal`
Composite
Composite of location, hiring, and headcount signals.
A company is expanding into new markets while growing.
## When it fires
`location_added` AND `first_job_in_country` AND `employee_growth` within 60 days.
## Magnitude
High, unmistakable growth-mode expansion.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is unmistakable growth-mode expansion. A new office, a first hire in a new country, and rising headcount all within 60 days is the clearest possible picture of a company scaling geographically, with all the local tooling and operational needs that brings.
## How to use Expansion Signal?
**The scenario.** You are an AE at a corporate travel-management platform. Travel programs get bought at a threshold: below it, companies book ad hoc and nobody cares; above it, an ops leader realizes travel has become a cost center with no owner. The threshold is crossed by one kind of company, **the kind growing in several directions at once**.
**The goal.** Find companies mid-expansion, new markets, new offices, growing teams, because multi-front growth manufactures exactly the travel complexity your product manages.
**The signal fires.** `expansion_signal` fires for Panora Foods, a specialty-food producer: the composite has aligned the ingredients, headcount growth, new locations, first hires in new countries, into a single verdict: coordinated expansion, not incidental drift.
**Reading it.** A company expanding on multiple fronts is generating travel faster than governance: founders visiting the new market monthly, sales teams crossing regions, new-office onboarding trips, all booked on personal cards and reconciled in spreadsheet purgatory. Finance sees the cost ballooning without seeing the bookings. That gap has a predictable breaking point, **the quarter someone senior asks "what are we actually spending on travel?"**, and your best position is having answered it before it is asked.
**The play.**
1. Size the motion from the signal's ingredients: country expansion plus office adds means recurring corridor travel, the highest-value pattern for you.
2. Approach the finance or ops leader with the question they are about to ask: companies scaling into new markets typically discover travel is their fastest-growing unmanaged cost; you can show the number and cap it.
3. Offer visibility first, **a free spend analysis converts better than a platform demo**, because the number does the selling.
4. Anchor pricing to the growth: every new office and market they add makes the unmanaged version worse, which makes your renewal automatic.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `expansion_signal` filters your territory down to companies in coordinated growth, the only cohort whose travel problem is compounding this quarter.
**Why it lands.** Travel management sells poorly as policy and brilliantly as **relief for a cost curve the CFO just noticed bending**.
## How to read it
Three aligned growth signals confirm active expansion.
The company is committing to new markets.
Expansion drives local compliance and tooling demand.
## Outreach playbook
Top growth signal. Route to local teams; position for scaling needs.
## Related signals
A new office location appeared.
A company posted its first role in a new country.
A company opened its first location in a new country.
A company's employee count increased by at least one between snapshots.
***
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# IPO Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/ipo-signal
A company went from private to public.
`ipo_signal`
Composite
Composite of Company Type Change.
A company went from private to public.
## When it fires
`company_type_change` fires going from any private type to Public Company.
## Magnitude
Hyper, an IPO is a defining corporate event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
An IPO reshapes budget, scrutiny, and tooling all at once. Newly public companies face new compliance, reporting, and governance requirements, and they often have fresh capital and pressure to professionalize their entire operations stack.
## How to use IPO Signal?
**The scenario.** You are an AE at an equity-management platform, cap tables, ESPP administration, insider-trading compliance. Your product's most urgent buyer is a company that has just crossed the line from private to public, because **going public converts equity administration from a spreadsheet nuisance into a regulatory obligation overnight**.
**The goal.** Reach companies within the first quarter after their transition to public status, while the new obligations are still being staffed and tooled.
**The signal fires.** `ipo_signal` fires for Lanthorn Biosciences: the composite has detected their shift from private to public. Eight hundred employees, hundreds of them now holding equity that trades, and a finance team that has never run a blackout window in their lives.
**What it tells you.** Newly public companies inherit a stack of recurring obligations at once: 10b5-1 plan administration, insider lists, blackout enforcement, ESPP mechanics, Section 16 filings. The IPO project team handled the offering itself, but **the permanent operational machinery is usually half-built at listing**, run on spreadsheets by exhausted people who just survived the roadshow. That gap closes within two or three quarters, with somebody's platform.
**The play.**
1. Target the stock-plan administrator or controller, not the CFO; the person drowning in the new mechanics is your champion.
2. Lead with the next deadline they face, the first blackout window, the first ESPP purchase period, and how the platform makes it routine.
3. Bring newly-public references, **this buyer wants to hear from peers one year ahead of them**, not from decade-old public giants.
4. Move fast but expect procurement rigor; public companies buy carefully, then stay for years, and the LTV justifies the cycle.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `ipo_signal` gives you every private-to-public transition as it lands; a quarter's worth of fires is a full pipeline in this category.
**Why it lands.** Most vendors chase companies before the bell rings. The bigger, calmer market is **the mess that starts the morning after**.
## How to read it
Going public transforms the company's obligations.
New reporting and governance tooling becomes mandatory.
IPO proceeds fund a wave of investment.
## Outreach playbook
Top-tier. Target compliance, finance, and governance tooling needs.
## Related signals
The company's entity type changed.
The headquarters country specifically changed.
A company shows the early pattern of an IPO track.
***
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# Merger Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/merger-signal
Two companies merged under a common new parent.
`merger_signal`
Composite
Composite across two companies' parent relationships.
Two companies merged under a common new parent.
## When it fires
Two distinct companies acquire the same new `parent_id` within 60 days of each other. Emits once on the second company.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------ | ---------------------------------- |
| `meta.merged_with` | References both merging companies. |
## Magnitude
Hyper, mergers are major consolidation events.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Mergers create massive consolidation and re-evaluation of vendors. When two companies come under one new parent, overlapping tools get cut and surviving vendors get scrutinized, a high-stakes moment for both incumbents and challengers.
## How to use Merger Signal?
**The scenario.** You run an HR-systems consultancy specializing in consolidation: taking two companies' payroll, HRIS, and benefits stacks and making them one. Your projects are born in exactly one kind of event, and the event announces itself: **two companies becoming one company, with two of everything**.
**The goal.** Reach merged organizations in the window between deal close and systems decision, when both legacy stacks still run in parallel and the pain of duplication grows weekly.
**The signal fires.** `merger_signal` fires: Beaumont Logistics and Carver Freight, two regional carriers of similar size, have merged under a new common parent. Composite confirmation, structure changes plus the identity pattern, not just rumor.
**Reading it.** A merger of equals is the hardest systems scenario and the best consulting scenario: neither side's stack is the obvious winner, both HR teams are defending their own, and every payroll cycle runs twice. This standoff has a natural clock, **duplicated systems cost real money monthly, and the first combined benefits enrollment is a hard deadline**, but no natural referee. Your entire value is being the referee with a method.
**The play.**
1. Time the approach four to eight weeks post-close, after the executive dust settles, before either HR team wins by attrition.
2. Approach the combined entity's CHRO, or the deal's integration lead, with neutrality as the pitch: an objective assessment of both stacks against the merged company's needs, with a migration plan either way.
3. Lead with the enrollment deadline; naming the immovable date that makes deferral impossible is half the close.
4. Fix the payroll consolidation first, **the workstream with a monthly cost makes your fee self-justifying**, then expand into the full roadmap.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) check on `merger_signal` in your regions produces a handful of qualified events a year, and each one is a six-month engagement.
**Why it lands.** Merged companies do not need convincing that the duplication hurts. They need **a referee neither side can accuse of picking favorites**, which is precisely what an outsider is for.
## How to read it
Mergers eliminate redundant tools and vendors.
Surviving systems face re-evaluation.
meta.merged\_with references both companies.
## Outreach playbook
High-stakes. Position as the consolidation winner; map both entities' stacks.
## Related signals
A company was acquired.
A parent company was set where there was none.
A name change with almost no overlap to the old name.
***
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# Momentum Score
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/momentum-score
A nightly composite score ranking a company's upward trajectory.
`momentum_score`
Composite
Composite rollup of all positive signals, recomputed nightly.
A nightly composite score ranking a company's upward trajectory.
## When it fires
Recomputed nightly per company. Weighted rolling score: +2 per growth signal, +1 per activity-increase signal, +3 per executive hire, decayed by age (× 0.5 per 30 days). Persisted as one document per company per day with `to_value` = score.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------- | ----------------------------------------- |
| `to_value` | The computed momentum score for that day. |
## Magnitude
Continuous score rather than a bucketed event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
It gives you one number to rank your whole list by upward trajectory. Instead of watching dozens of individual signals, momentum\_score rolls all the positive ones into a single, decaying score, so you can sort your entire territory by who's heating up right now.
## How to use Momentum Score?
**The setup.** You are the RevOps director at a B2B software company, and it is territory-planning season. The annual ritual: carve six hundred accounts across eight reps using employee count and industry, then spend the year watching some "A-tier" accounts sit dead while nominal C-tier accounts explode. Static firmographics answer "how big?", never **"which of these companies is actually going somewhere?"**
**What you want.** A trajectory dimension in the territory model, so account scores reflect direction of travel, not just size.
**The signal fires.** Not as an event but as a ranking: `momentum_score` is computed nightly for every company, aggregating the upward signals, hiring, growth, funding, audience, into a single comparable number. You pull it for all six hundred accounts and sort. The reshuffle is immediate: a 90-person logistics-tech firm outscores three enterprise accounts your model had gilded for years.
**Reading it.** Momentum answers the question size cannot: which accounts will have new budgets, new initiatives, and new pain this year. High-momentum small accounts become mid-market accounts inside your fiscal year, **and the vendor already in the building when that happens wins by default**. Low-momentum large accounts still matter, but as retention work, not growth bets, and staffing them like growth bets is how territories quietly fail.
**The play.**
1. Add momentum as a scoring axis next to size and fit, weight it visibly so the model is arguable rather than mystical.
2. Balance territories by momentum, not just count, **every rep gets a fair share of companies going somewhere**, which is also the fairest quota logic you will ever ship.
3. Route the top decile into a fast-touch motion regardless of current size; today's momentum is next year's segment upgrade.
4. Re-pull quarterly; momentum decays and spikes, and the model should breathe with it.
**Automate it.** A scheduled [Company Signals API](/buying-signals/signals-apis/company-signals) job on `momentum_score` across your account universe keeps the ranking current with zero analyst hours.
**Why it lands.** Every competitor sizes accounts by what they are. Scoring them by **what they are becoming** is the cheapest unfair advantage in territory design.
## How to read it
One number summarizes all positive momentum.
The 30-day decay keeps the score current.
Rank an entire territory by trajectory at a glance.
## Outreach playbook
Use as your primary sort. Work the highest-momentum accounts first.
## Related signals
A nightly composite score ranking a company's decline and risk.
A company's employee count increased by at least one between snapshots.
A new C-level executive joined the company.
A new funding round was announced.
***
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# Pivot Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/pivot-signal
A company fundamentally changed what it does.
`pivot_signal`
Composite
Composite of description, specialty, and industry changes.
A company fundamentally changed what it does.
## When it fires
`description_change_major` AND (`specialty_added` OR `specialty_removed`) AND `industry_change` within 60 days.
## Magnitude
High, a genuine business pivot.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
That's a company genuinely reinventing what it does. When the description, specialties, and industry all shift together, the company isn't tweaking its message, it's changing its business, which creates entirely new needs and discards old ones.
## How to use Pivot Signal?
**The setup.** You lead customer success at an API-infrastructure company. Usage-based revenue has a blind spot: the dashboard tells you *how much* a customer uses you, not *why*, and when a customer's business fundamentally changes direction, **your dashboard is the last place the change appears and the first place the damage lands**.
**What you want.** Advance notice when a customer's company is becoming a different company, so the account plan changes before the usage does.
**The signal fires.** `pivot_signal` fires for Skylark Devices, a solid mid-tier account: the composite has stacked the evidence, a rewritten description, a drastic repositioning, shifted hiring, into a verdict that Skylark is fundamentally changing what it does. Reading closer: they are moving from consumer hardware to a fleet-management services model.
**Reading it.** A pivoting customer is simultaneously your biggest churn risk and your biggest expansion candidate, and you rarely know which until it is over. The old product's API usage will decay with the old business. The new business may need you three times more, or not at all. **The deciding variable is usually whether you are in the room when the new architecture gets drawn**, and pivots draw their architecture early.
**The play.**
1. Book a roadmap conversation immediately, framed as partnership, not renewal defense: what is Skylark becoming, and what does its infrastructure need to become alongside it?
2. Map your product against the new direction honestly, fleet services means telemetry ingestion and uptime SLAs, and bring that mapping to the call.
3. Renegotiate the contract shape early if usage will dip before it grows, **a bridge plan that keeps a pivoting customer beats a rigid one that loses them**.
4. Flag the account for weekly signal review until the pivot settles; direction changes compound.
**Automate it.** Your account list against the [Company Signals API](/buying-signals/signals-apis/company-signals) on `pivot_signal`, wired into CS alerting, turns strategy-level surprises into calendar invitations.
**Why it lands.** CS teams manage usage curves. Pivots break the curve, **this signal is the only warning that arrives before the break**.
## How to read it
Three aligned changes confirm a genuine pivot.
A pivoted company needs different tools.
Tools tied to the old model may be dropped.
## Outreach playbook
Re-qualify against the new business. Old assumptions no longer apply.
## Related signals
The company description was substantially rewritten.
The company's primary industry classification changed.
A new specialty was added.
A specialty was dropped from the list.
The dominant topic of recent posts changed.
***
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# Pre-IPO Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/pre-ipo-signal
A company shows the early pattern of an IPO track.
`pre_ipo_signal`
Composite
Composite of leadership, sales, and engineering signals.
A company shows the early pattern of an IPO track.
## When it fires
`executive_team_buildout` AND `sales_hiring_surge` AND (`engineering_hiring_surge` OR `engineering_leader_hire`) within 180 days.
## Magnitude
Hyper, the strongest early IPO-track indicator.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
It's the strongest early indicator of an IPO track. A leadership buildout plus a sales surge plus engineering scaling over six months is the classic shape of a company preparing to go public, well-funded, scaling fast, and professionalizing across the board.
## How to use Pre-IPO Signal?
**The setup.** You are a partner at an audit-readiness consultancy; SOX compliance, controls documentation, financial-reporting hygiene. Your engagements are large, long, and bought exactly once in a company's life: **in the one-to-two-year runway before going public**. Find the runway, find the revenue.
**What you want.** To identify companies entering IPO preparation before they announce anything, because by the confidential filing, the readiness firm is long since hired.
**The signal fires.** `pre_ipo_signal` fires for Quillon Payments, a fintech that has kept impressively quiet: the composite has assembled the pattern, the executive build-out with public-company pedigrees, the structural cleanups, the growth trajectory, into a probability their trajectory points at a listing.
**Reading it.** No single ingredient proves intent; a CFO hire is just a hire. The composite's value is the accumulation, **companies assembling all of these pieces at once are building toward something, and that something usually files an S-1 within eighteen months**. At this stage, Quillon's finance leadership knows exactly what work is coming. They may not yet know who will do it.
**The play.**
1. Verify the tell that matters most for you: a recently hired CFO or Chief Accounting Officer with prior public-company experience is the closest thing to a confession.
2. Approach that executive peer-to-peer; they have run readiness before and are budgeting it now, so skip the education and lead with capacity and timeline.
3. Sell the calendar, **readiness started two years out is a program; started one year out it is a crisis**, and your pitch is simply which one they would prefer to buy.
4. Be discreet in writing; pre-IPO companies despise vendors speculating about their plans on the record. "Companies at your stage" does the work without saying the word.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) review of `pre_ipo_signal` fires, cross-checked against your existing relationships, keeps the partner team pointed at next year's engagements.
**Why it lands.** Readiness work is won one to two years before the event everyone else reacts to. This composite is **the eighteen-month head start, systematized**.
## How to read it
This pattern marks a company on a path to public markets.
Leadership, sales, and engineering all grow.
Pre-IPO companies invest heavily in their stack.
## Outreach playbook
Top-tier proactive signal. Position for enterprise-grade, IPO-readiness tooling.
## Related signals
A company rapidly built out its leadership team.
Sales job postings doubled versus the prior crawl.
Engineering job postings doubled versus the prior crawl.
A new engineering leader joined the company.
A company went from private to public.
***
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# Rebrand Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/rebrand-signal
A company executed a coordinated rebrand.
`rebrand_signal`
Composite
Composite of name, tagline, and description changes.
A company executed a coordinated rebrand.
## When it fires
`name_change` AND `tagline_change` AND `description_change_major` within 60 days.
## Magnitude
High, a deliberate, coordinated rebrand.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A coordinated rebrand signals a fresh strategic chapter. When name, tagline, and core description all change together within 60 days, it's a deliberate, funded brand overhaul, often tied to new leadership, a raise, or a repositioning.
## How to use Rebrand Signal?
**Where you sit.** You are an AE at a digital-asset-management platform. Your product keeps a company's logos, templates, and brand files organized and current, which sounds like a nice-to-have exactly until the day it is not: **the day everything the company owns has to change at once**.
**The mission.** Find companies mid-rebrand, when the old assets are officially wrong, the new ones are multiplying uncontrolled, and brand chaos has a budget attached.
**The signal fires.** `rebrand_signal` fires for Trellick, formerly Morrow & Co, a commercial property firm: the composite has confirmed the full pattern, new name, new tagline, rewritten description, refreshed showcase presence. This is not a logo tweak; it is a coordinated identity replacement.
**Reading it.** Mid-rebrand companies live a specific operational nightmare: two brand systems exist simultaneously, four hundred employees have the old deck template, regional offices are printing yesterday's logo, and the brand manager fields the same "which version?" email nine times a day. Rebrand budgets always include rollout, and rollout is where DAM gets bought, **not as software, but as the mechanism that makes the new brand actually stick**.
**The play.**
1. Reach the brand or marketing-ops lead during the rollout itself, when the pain is daily and named.
2. Pitch enforcement, not storage:
> Congrats on the Trellick rebrand. The hard part starts now: making four hundred people stop using Morrow & Co files. That enforcement problem is literally what we sell.
3. Offer migration as onboarding, **loading their new brand system into the platform is the implementation**, which turns setup cost into the rollout plan.
4. Land before rollout completes; after everyone has the new files scattered across drives, the moment, and the budget, are gone.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `rebrand_signal` in your segments delivers companies at peak brand-chaos, which is peak receptivity.
**Why it lands.** DAM pitched to a stable brand is infrastructure nobody prioritizes. Pitched mid-rebrand, **it is the answer to this morning's angriest email**.
## How to read it
Three aligned identity changes mean a real rebrand.
Rebrands require budget and executive sponsorship.
Often coincides with new leadership or funding.
## Outreach playbook
Engagement opportunity. Rebrands signal a company investing in its next phase.
## Related signals
The company name string changed.
The company tagline text changed.
The company description was substantially rewritten.
A company fundamentally changed what it does.
***
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# Restructuring Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/restructuring-signal
A company is undergoing major restructuring.
`restructuring_signal`
Composite
Composite of churn, headcount, and consolidation signals.
A company is undergoing major restructuring.
## When it fires
`leadership_churn_spike` AND `employee_decrease` AND (`office_consolidation` OR `mass_layoff_signal`) within 90 days.
## Magnitude
High, major restructuring underway.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Major restructuring is underway. Leadership churn, headcount loss, and either office consolidation or mass layoffs together signal a company in deep transition, the buying committee, budgets, and priorities are all in flux.
## How to use Restructuring Signal?
**Where you sit.** You are a principal at an organizational-design consultancy. Companies hire you to redesign how they are structured, and they do it at one moment: mid-upheaval, when the old org has been half-dismantled and the new one exists mostly as a diagram the executive team argues about. The engagement problem is that **upheaval is exactly when companies stop answering cold outreach**.
**The mission.** Identify companies in active restructuring early, and arrive as the calm methodology in a building full of open questions.
**The signal fires.** `restructuring_signal` fires for Belmont Circuits, a 1,200-person electronics manufacturer: the composite has assembled leadership churn, workforce reduction, and structural reshuffling into a single verdict, this is not a bad quarter, it is a reorganization in progress.
**Reading it.** Companies mid-restructure are making dozens of irreversible decisions, spans of control, function consolidation, who reports to whom, at speed, under stress, usually with no framework beyond the CFO's cost target. The result, two years later, is predictable: a structure optimized for last year's crisis rather than next year's business. **The window to prevent that is the middle of the restructuring itself**, which is precisely when this signal fires.
**The play.**
1. Approach the CEO or CHRO mid-storm with structure, not sympathy: restructurings driven purely by cost targets tend to need a second restructuring within two years, and there is a method that prevents the rework.
2. Offer a compressed engagement, **a two-week organization review that plugs into decisions they are making this month**, not a six-month study that arrives after the org chart is frozen.
3. Bring pattern knowledge as the credential: what companies their size in their industry consolidated well and what they regretted.
4. Position for the follow-on: post-restructure implementation, leadership onboarding, operating-model tuning, is where the relationship compounds.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) review of `restructuring_signal` in your target industries surfaces companies at the exact decision-density your practice monetizes.
**Why it lands.** Org design sold to a stable company is philosophy. Sold mid-restructuring, **it is a handrail, and everyone on the executive floor is currently reaching for one**.
## How to read it
Multiple decline signals confirm major restructuring.
Committee, budget, and priorities are all changing.
Pause until the dust settles, or pitch cost reduction.
## Outreach playbook
Caution. Re-qualify entirely once the restructuring settles.
## Related signals
Multiple senior leaders departed in a short window.
A company's employee count dropped by at least one.
Multiple office closures in a short window.
Employee count dropped severely in a single interval.
A nightly composite score ranking a company's decline and risk.
***
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# Risk Score
Source: https://apidoc.cufinder.io/buying-signals/signals/composite/risk-score
A nightly composite score ranking a company's decline and risk.
`risk_score`
Composite
Composite rollup of all negative signals, recomputed nightly.
A nightly composite score ranking a company's decline and risk.
## When it fires
Mirror of `momentum_score` computed over decline signals and negative people signals (departures, churn).
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------- | ------------------------------------- |
| `to_value` | The computed risk score for that day. |
## Magnitude
Continuous score rather than a bucketed event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Together, the two scores let you sort accounts by opportunity and by risk in a single view. risk\_score is the inverse of momentum, it rolls decline and negative people signals into one number so you can spot at-risk accounts and protect existing customers before churn hits.
## How to use Risk Score?
**Where you sit.** You lead CS operations at a subscription-software company. Your health-score model is honest about what it can see, product usage, support tickets, NPS, and blind to what it cannot: **the customer's own business condition**. Which is why the postmortem on every shock churn says the same thing: usage looked fine, and then the company itself hit the wall.
**The mission.** Add an outside-in dimension to customer health, so the model catches customers whose companies are declining even while their logins look healthy.
**The signal fires.** As a nightly ranking rather than an event: `risk_score` aggregates each company's decline-side signals, shrinking teams, collapsed hiring, audience decay, leadership exits, into a single comparable number. You pull it across your customer base and join it to health scores. The intersection tells the real story: fourteen accounts sit in the healthy-usage, high-risk quadrant, **the exact quadrant your current model cannot see**.
**Reading it.** Usage measures whether users like the product; risk measures whether the company writing the check is okay. A power user at a sinking company still churns, through budget cuts, headcount loss, or acquisition, and no in-app metric warns you. The two-axis view separates four different renewals that your one-axis model treats as two.
**The play.**
1. Blend risk into the health score with real weight, and label the quadrants so CSMs act differently in each.
2. For healthy-usage, high-risk accounts, shift the conversation from adoption to value defense: multi-year pricing, right-sizing before they ask, executive alignment on ROI.
3. Stop over-investing rescue effort in low-usage accounts at healthy companies, **those are adoption problems, not survival problems**, and they respond to different plays.
4. Feed both scores to finance for renewals forecasting; the blended number forecasts materially better than either alone.
**Automate it.** A nightly [Company Signals API](/buying-signals/signals-apis/company-signals) sync of `risk_score` across the book, joined to health data in your CS platform, runs the whole system unattended.
**Why it lands.** Churn models built only on product data can watch the ship's instruments while missing the iceberg. This score is **the lookout, watching the water**.
## How to read it
One number summarizes all decline signals.
Rising risk on a customer flags retention work.
Together they give a full opportunity-and-risk picture.
## Outreach playbook
Use for retention. Rising risk on a customer account triggers a save play.
## Related signals
A nightly composite score ranking a company's upward trajectory.
A company shows a clear distress pattern.
A company is undergoing major restructuring.
Multiple senior leaders departed in a short window.
***
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# Employee Decrease
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/employee-decrease
A company's employee count dropped by at least one.
`employee_decrease`
Decline
Company Professional Network page (employee count field).
A company's employee count dropped by at least one.
## When it fires
`employee_count` decreases by ≥ 1 between snapshots.
## Magnitude
Bucketed on percentage change in `employee_count`.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
Mild on its own, but it feeds the heavier decline patterns (layoff\_signal, decline\_signal, restructuring\_signal). A single departure is normal churn; sustained decreases across crawls indicate genuine contraction.
## How to use Employee Decrease Signal?
**The scenario.** You run CS operations at a per-seat collaboration SaaS. Renewal risk in your book has a pattern nobody likes to say out loud: **when a customer's headcount shrinks, your seat count is next**. The account team usually finds out at the renewal call, which is the worst possible place to find out.
**The goal.** See customer shrinkage the month it starts, so the save motion begins two quarters before the renewal instead of two weeks.
**The signal fires.** `employee_decrease` fires for Brightline Media, a 350-seat customer up for renewal in seven months. Their employee count has dropped for the second consecutive period. Your product usage dashboard confirms it: weekly active seats are drifting down in step.
**What it tells you.** Shrinking companies audit their software line items, and per-seat tools with empty seats are the first thing highlighted in that audit. Left alone, this renewal arrives as a procurement-led downgrade demand. Handled early, it can be **a right-sizing conversation you control instead of a discount conversation they control**.
**The play.**
1. Alert the account's CSM automatically and open a save plan now, seven months out.
2. Get ahead of the audit: proactively propose trimming genuinely unused seats. Giving up phantom seats early **buys the credibility that protects the seats that matter**.
3. Shift the value story from breadth to depth: fewer people relying on the product more is a survivable narrative; the same licenses with fewer users is not.
4. Flag the account out of expansion campaigns; nothing reads worse to a shrinking customer than an upsell email.
**Automate it.** Sync your customer list against the [Company Signals API](/buying-signals/signals-apis/company-signals) on `employee_decrease` monthly, and pipe hits into your health-score model with a meaningful weight.
**Why this works.** Churn prevention mostly fails for being late. This signal moves the starting line back by half a year, **and the save rate difference between month seven and month one is the whole game**.
## How to read it
One drop is churn; repeated drops are contraction.
Feeds layoff\_signal, decline\_signal, and restructuring\_signal.
Contracting companies sometimes buy efficiency tools, read the context.
## Outreach playbook
Don't alert alone. Use as a component of decline composites or to pause expansion-focused outreach.
## Related signals
Employee count dropped sharply in a single interval.
A company fell down a Professional Network employee size band.
A company shows a clear distress pattern.
A company is undergoing major restructuring.
***
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# Employee Size Band Downgrade
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/employee-size-band-downgrade
A company fell down a Professional Network employee size band.
`employee_size_band_downgrade`
Decline
Company Professional Network page (employee size band field).
A company fell down a Professional Network employee size band.
## When it fires
`employee_band` moves down a Professional Network band.
## Magnitude
Inherently high, crossing a band downward reflects sustained loss.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
That's a meaningful contraction, not a rounding error. Dropping a band signals real downsizing, the company has shed enough people to cross a recognized threshold, which usually means budget tightening and vendor consolidation.
## How to use Employee Size Band Downgrade Signal?
**The setup.** You are an AE at a spend-management platform. Your product pays for itself by finding waste, which creates a strange sales dynamic: **comfortable companies do not care, and struggling companies care intensely**. Your pitch lands best inside active cost-cutting mandates.
**What you want.** A reliable way to find companies that have just entered serious belt-tightening, without waiting for a layoff headline that may never come.
**The signal fires.** `employee_size_band_downgrade` fires for Osprey Logistics, a freight forwarder that has slipped from the 201-500 band down into 51-200. Band downgrades are slow, structural events; a company does not shed that share of its workforce by accident.
**Reading it.** A band drop means the cost conversation already happened at board level and the painful lever, people, has already been pulled. What follows, almost mechanically, is the hunt for every non-headcount saving: software, suppliers, travel, facilities. **There is a person at Osprey right now with a spreadsheet titled something like "cost review", and your product is built to fill it.**
**Your move.**
1. Lead with respect and specificity, not vulture energy. These buyers can smell opportunism.
2. Aim at the CFO with an offer shaped like help:
> Most freight companies your size are sitting on 8 to 12% of addressable spend leakage, duplicate SaaS, unmanaged vendors, off-contract buying. Given the year you are navigating, a free spend scan might be worth an hour.
3. Price against their mandate, **your fee versus the savings found is the only slide that matters**.
4. Move quickly but expect scrutiny; cost-cutting companies buy carefully, then loyally.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `employee_size_band_downgrade` builds a cohort of companies in exactly this posture, refreshed as the economy moves.
**Why it lands.** Selling savings to a company in growth mode is pushing rope. Selling savings to a company mid-downsize is **handing a drowning swimmer the thing they were already reaching for**.
## How to read it
Band downgrades require sustained net losses, not noise.
Expect spend freezes and vendor consolidation.
Cost-saving tools can still win here, reframe the pitch around ROI.
## Outreach playbook
Caution flag for net-new expansion deals. Opportunity for cost-reduction tooling.
## Related signals
A company's employee count dropped by at least one.
Employee count dropped sharply in a single interval.
Multiple office closures in a short window.
***
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# Followers Decrease
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/followers-decrease
The company's follower count dropped.
`followers_decrease`
Decline
Company Professional Network page (followers field).
The company's follower count dropped.
## When it fires
`followers` count decreases between snapshots.
## Magnitude
Usually low. Emitted with extra scrutiny.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
It's rare, so we emit it with extra scrutiny since it's often a Professional Network-side correction rather than a real decline. Genuine follower loss can reflect reputational issues, but data corrections are the more common cause, so treat this as low-confidence.
## How to use Followers Decrease Signal?
**Where you sit.** You are the strategy director at a brand studio. Rebrand projects are big-ticket and rare, and they almost never start from a cold pitch. They start when a leadership team privately admits **the brand has stopped pulling its weight**, and starts looking for evidence.
**The mission.** Find brands where the evidence is already accumulating in public, an audience actively voting with its feet, and be the ones who name the problem first.
**The signal fires.** `followers_decrease` fires for Juniper Foods, a mid-size packaged-goods brand. Their follower count is not merely flat; it is shrinking. People are actively choosing to stop listening, which is rarer and more damning than being ignored.
**What it tells you.** Audiences almost never unfollow over one bad post. A sustained decrease means the brand's content, voice, or relevance has genuinely decayed, and the marketing team is likely either aware and stuck, or unaware and exposed. Either version is an opening, because **a shrinking audience is the one chart a CMO cannot spin upward**.
**Your move.**
1. Gather the surrounding evidence before outreach: content cadence, engagement trend, how their category peers are moving. One signal opens the door; a pattern closes the meeting.
2. Approach the CMO with a diagnostic frame, not an accusation: audiences drift when a brand's story falls behind its business, and there are three usual causes worth ruling out.
3. Offer a brand-health audit as the entry product, **small commitment, structured findings, natural bridge to the rebrand**, if the findings justify one.
4. Never lead with the unfollow number in writing; let it surface in the room, where context travels with it.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `followers_decrease` across consumer categories keeps a shortlist of brands accumulating quiet evidence.
**Why it lands.** You are not selling a rebrand. You are arriving with a diagnosis **just as the patient starts feeling the symptoms**.
## How to read it
Professional Network periodically purges bot followers, causing artificial drops.
Don't act on this alone, corroborate with other decline signals.
Real, sustained loss can hint at a PR problem.
## Outreach playbook
Rarely actionable. Investigate only if paired with crisis\_response\_signal.
## Related signals
Follower growth slowed sharply while still positive.
A cluster of statement or apology-style posts in a short window.
***
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# Followers Growth Decelerating
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/followers-growth-decelerating
Follower growth slowed sharply while still positive.
`followers_growth_decelerating`
Decline
Company Professional Network page, evaluated against a 6-crawl rolling baseline.
Follower growth slowed sharply while still positive.
## When it fires
Follower growth rate in the current crawl \< 50% of the rolling average of the previous 6 crawls, AND that average was positive.
## Magnitude
Low to moderate, this is a momentum signal, not an absolute decline.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Momentum is cooling even if the absolute number still climbs. Deceleration is an early, subtle indicator that brand attention is fading, useful as a leading indicator before harder decline signals appear.
## How to use Followers Growth Decelerating Signal?
**The scenario.** You founded a content studio that builds editorial engines for B2B brands: strategy, series, distribution. Your natural customer is not a brand with no content, it is a brand whose content **used to work and quietly stopped**, because they already believe, they already budget, and they already feel the plateau.
**The goal.** Detect the plateau while it is still deceleration, before it becomes decline and the team starts doubting the channel entirely.
**The signal fires.** `followers_growth_decelerating` fires for Lumen Pay, a fintech that spent two years as a content darling. Still growing, but the growth rate has fallen off sharply against their own trend. The curve is bending, and inside the company, someone is watching it bend at every marketing review.
**What it tells you.** Deceleration at a former high-flyer usually means the playbook aged: the format that won them their first fifty thousand followers cannot win the next fifty. Teams in this spot have tried posting more, seen it not help, and are quietly out of ideas. **They do not need convincing that content matters; they need a second act**, which is precisely the product you sell.
**The play.**
1. Do the homework their team is too close to do: what changed in their mix, what their fastest-growing peer does differently, where the format fatigue shows.
2. Open with the curve, kindly:
> Your audience growth is still positive but bending, the classic sign a playbook has reached its ceiling. We have rebuilt second acts for three fintech content teams; happy to share what the inflection usually means.
3. Propose a format-refresh sprint, not a takeover, **teams that built a successful engine want it renovated, not replaced**.
4. Benchmark the recovery openly; the same curve that opened the deal proves the retainer.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `followers_growth_decelerating` across your verticals finds every bending curve without you eyeballing charts.
**Why it lands.** Nobody hires a studio at the peak. They hire at the plateau, and this signal is the plateau, timestamped.
## How to read it
Still growing, but slower, an early softening signal.
Can precede broader decline before headcount moves.
Low intensity; use as context, not a primary trigger.
## Outreach playbook
Context only. Watch for it stacking with activity decreases.
## Related signals
The company's follower count dropped.
The company's Professional Network follower count increased.
The company is posting noticeably less on social.
***
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# Hiring Freeze Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/hiring-freeze-signal
Open roles collapsed while headcount stayed flat or fell.
`hiring_freeze_signal`
Decline
Company Professional Network page and jobs, combined condition.
Open roles collapsed while headcount stayed flat or fell.
## When it fires
`job_count` drops ≥ 50% AND `employee_count` is flat or down in the same interval.
## Magnitude
High, the 50% posting collapse is a strong threshold.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Classic hiring-freeze behavior, and a clear sign to time your outreach carefully. A freeze means budget scrutiny is high and new purchases face extra approval. It's a precursor that often appears before layoffs or after a missed quarter.
## How to use Hiring Freeze Signal?
**The setup.** You are a partner at a fractional-CFO firm serving companies between five and fifty million in revenue. Your clients hire you in one emotional state above all others: **the moment growth-at-all-costs turns into make-the-cash-last**. The trouble is finding companies at that moment from the outside.
**What you want.** A data signature of the shift into cash discipline, visible before the company ever announces anything, because companies never announce it.
**The signal fires.** `hiring_freeze_signal` fires for Copper & Twine, a DTC homewares brand. The pattern behind the signal is specific: open roles collapsed to near zero while headcount stayed flat. Nobody was laid off; the company simply stopped adding. That is not drift, that is **a decision, and decisions like that come from a board meeting about runway**.
**Reading it.** A freeze without layoffs is the signature of a leadership team buying time: preserving the team, cutting optionality, and almost certainly managing cash on a spreadsheet the founder built at midnight. Companies in this state need forecasting discipline, scenario models, and a credible cash story for investors, exactly the deliverables of a fractional CFO, and they need them **before the next board meeting, not after**.
**Your move.**
1. Approach the founder or CEO directly; in this state, finance decisions have re-centralized to them.
2. Respect the unspoken: never say "freeze". Say discipline: most founders navigating a tighter market want a 13-week cash view and three scenarios they trust.
3. Offer a fixed-scope runway review as the wedge, small, fast, and immediately useful in the next investor conversation.
4. Convert to fractional from there; **the runway review that survives one board meeting becomes a retainer by the second**.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `hiring_freeze_signal`, filtered to your revenue band's typical headcount range, surfaces companies entering the exact state you serve.
**Why it lands.** Every fractional CFO says "we help you extend runway". You are the one saying it to companies **the same month they privately decided runway was the problem**.
## How to read it
Freezes mean every purchase faces tighter approval.
Often precedes layoffs, an early caution flag.
Either delay outreach or lead with cost justification.
## Outreach playbook
Deprioritize for net-new spend. Revisit when hiring signals return.
## Related signals
A company that had open roles now has none.
Employee count dropped sharply in a single interval.
A company's employee count dropped by at least one.
***
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# Jobs Dropped to Zero
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/jobs-dropped-to-zero
A company that had open roles now has none.
`jobs_dropped_to_zero`
Decline
Company Professional Network jobs (open postings count).
A company that had open roles now has none.
## When it fires
`job_count` was > 0 previously and is 0 now.
## Magnitude
High, a full stop on hiring is decisive.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A full stop on hiring is a strong caution flag. Going from active hiring to zero open roles signals a freeze, a budget lockdown, or distress. It's a key component of the decline\_signal composite.
## How to use Jobs Dropped to Zero Signal?
**Where you sit.** You are an AE at a workflow-automation platform. Your strongest ROI story was never "grow faster", it is **"do more with the team you have"**, and there is one kind of company for whom that sentence is currently the entire strategy.
**The mission.** Find companies that have explicitly stopped adding people, because leadership there has already accepted the premise your product monetizes: output must grow while headcount cannot.
**The signal fires.** `jobs_dropped_to_zero` fires for Grangeworth Manufacturing, a 500-person industrial components maker. They had open roles continuously for years; as of this crawl, they have none. Not fewer. Zero.
**What it tells you.** Zero is a statement. Companies at zero postings have frozen the org chart, which means every operational problem previously solved by "hire someone" now needs a different answer. Managers there are being asked for the same output with a fixed team, and they are looking, right now, for **anything that makes their existing people faster**, because it is the only lever they are still allowed to pull.
**The play.**
1. Rebuild your pitch for the moment: no growth language, no scale-up case studies. This buyer's hero story is efficiency, so open with hours recovered per person.
2. Target the operations or plant leaders, the people holding the fixed-team mandate personally.
3. Quantify in their currency, **one automated workflow that saves two hours a day equals a hire they are not allowed to make**.
4. Keep procurement expectations realistic: frozen companies buy slowly, but they renew, because efficiency wins compound.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `jobs_dropped_to_zero` builds the cohort; cross it with your ideal industries and watch it refresh as freezes spread or thaw with the cycle.
**Why it lands.** Most software sells growth to companies that want growth. You are selling leverage to companies **that have been forbidden everything else**.
## How to read it
Zero open roles after active hiring is a hard freeze.
Often accompanies financial pressure or restructuring.
Feeds decline\_signal alongside employee\_decrease and page\_dormant.
## Outreach playbook
Caution flag. Strong component of decline\_signal; pause expansion outreach.
## Related signals
Open roles collapsed while headcount stayed flat or fell.
The number of open job postings fell.
A company shows a clear distress pattern.
***
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# Jobs Open Decrease
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/jobs-open-decrease
The number of open job postings fell.
`jobs_open_decrease`
Decline
Company Professional Network jobs (open postings count).
The number of open job postings fell.
## When it fires
`job_count` decreases by ≥ 1 between snapshots.
## Magnitude
Bucketed on percentage change in `job_count`.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
A small pullback in hiring appetite. On its own it's mild, roles get filled and removed routinely, but a sustained downward trend in open roles indicates cooling growth and tightening budgets.
## How to use Jobs Open Decrease Signal?
**The scenario.** You lead competitive intelligence at a B2B software company. Your two main rivals publish nothing, announce nothing, and deny everything, and yet their hiring pages have been telling you their strategy for years. You just have not been listening systematically.
**The goal.** Turn competitor hiring contraction into two concrete motions: sharper win-back targeting of their customers, and better-armed sales conversations against them.
**The signal fires.** `jobs_open_decrease` fires for Vexel, your closest competitor. Their open-roles count has fallen for the third consecutive period, and the composition is telling: the cuts are concentrated in customer success and product, while sales postings remain.
**What it tells you.** A sustained posting decline with that shape suggests **Vexel is protecting bookings while starving delivery**, the classic pattern of a company managing to a number. If their CS team is thinning, their customers' experience is about to thin with it: slower support, stalled roadmap, longer onboarding. Those customers do not know it yet. You do.
**The play.**
1. Brief your sales team with the pattern, carefully framed, no fabrication, just the public trajectory: fewer delivery roles usually precedes slower delivery.
2. Refresh the battlecard: roadmap-confidence and support-quality questions become your reps' discovery weapons against Vexel deals.
3. Warm up the win-back list: accounts you lost to Vexel in the last two years get a low-pressure "how is it going" touch this quarter, **timed to land as the service decay becomes noticeable**.
4. Alert recruiting; a competitor slowing hiring is a competitor whose pipeline of interviewed, qualified candidates is going spare.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) watch on `jobs_open_decrease` for your named competitors turns their careers page into a strategy feed you never have to check manually.
**Why this works.** Competitors tell you their pressures through what they stop doing. Hiring is simply **the first thing companies stop doing in public**.
## How to read it
One fewer role is normal; a trend is a slowdown.
Sustained decreases signal a hiring pullback.
Low intensity unless it accelerates toward zero.
## Outreach playbook
Low priority alone. Watch the trajectory toward jobs\_dropped\_to\_zero.
## Related signals
A company that had open roles now has none.
Open roles collapsed while headcount stayed flat or fell.
The number of open job postings increased.
***
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# Layoff Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/layoff-signal
Employee count dropped sharply in a single interval.
`layoff_signal`
Decline
Company Professional Network page (employee count field).
Employee count dropped sharply in a single interval.
## When it fires
`employee_count` drops ≥ 10% in a single crawl interval.
## Magnitude
High by definition (≥10% threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A drop that sharp almost always means a layoff event. Layoffs reshape budgets, freeze new purchases, and shuffle the buying committee. Knowing a layoff happened lets you pause cold outreach or, for efficiency products, reframe around doing more with less.
## How to use Layoff Signal?
**The setup.** You lead talent acquisition at a growing fintech. Your hardest roles, senior engineers, compliance specialists, take five months to fill through normal pipelines, mostly because the people you want are employed, content, and not looking. Except sometimes, suddenly, through no fault of their own, they are looking.
**What you want.** To know, within days, when a company employing your target profiles has a layoff, so you can be **early, respectful, and genuinely useful** to excellent people having a terrible week.
**The signal fires.** `layoff_signal` fires for Skybridge Travel, a travel-tech company whose engineering org overlaps heavily with your stack. Their employee count dropped sharply in a single interval, the signature of a layoff event rather than attrition.
**Reading it.** A layoff releases, in one day, the exact people you spend months trying to reach: employed-caliber talent, now motivated, with a natural reason to talk. It also starts a clock. **The best people from any layoff are gone within three weeks**, and the recruiters who treat them like humans in week one beat the ones who blast them like leads in week three.
**Your move.**
1. Map the overlap first: which Skybridge teams match your open roles, and who in your company knows someone there for a warm referral.
2. Reach out with dignity as the default. One human note beats any campaign:
> Saw the news about Skybridge, rough week, and their loss frankly. We are hiring for two roles your background fits. No pressure and no process games, happy to just talk if useful.
3. Fast-track the process visibly, **certainty is the product this candidate pool is buying**, so compress your usual five stages to two.
4. Keep notes on who you contacted; layoff cohorts remember who treated them well, and they refer each other for years.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `layoff_signal`, filtered to companies whose tech stack or industry matches yours, gives your sourcers a same-week head start.
**Why it lands.** Recruiting is timing plus respect. This signal supplies the timing; the respect part is on you.
## How to read it
Layoffs usually come with a spending freeze, time outreach carefully.
Decision-makers may have left, re-map the account.
Tools that cut cost or replace headcount can still land.
## Outreach playbook
Pause expansion pitches. For cost-saving tools, lead with consolidation and ROI.
## Related signals
Employee count dropped severely in a single interval.
A company's employee count dropped by at least one.
Open roles collapsed while headcount stayed flat or fell.
A company shows a clear distress pattern.
***
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# Mass Layoff Signal
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/mass-layoff-signal
Employee count dropped severely in a single interval.
`mass_layoff_signal`
Decline
Company Professional Network page (employee count field).
Employee count dropped severely in a single interval.
## When it fires
`employee_count` drops ≥ 20% in a single crawl interval.
## Magnitude
Hyper by definition (≥20% threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Major restructuring is happening, and the buying committee may be in flux. A 20%+ cut is a company-defining event, often tied to financial distress, a pivot, or acquisition. Treat it as a strong caution signal and a trigger to fully re-qualify the account.
## How to use Mass Layoff Signal?
**Where you sit.** You do business development for an outplacement firm; companies hire you to help the employees they let go land somewhere new. It is a strange market: your buyer is an HR leader having one of the hardest months of their career, and your service is bought **during the event, almost never before it**.
**The mission.** Reach HR leadership at companies undergoing a major reduction within the narrow window when outplacement decisions are actually made, without being one more vulture in their inbox.
**The signal fires.** `mass_layoff_signal` fires for Tundra Retail Group, a national retailer: a severe single-interval drop in employee count, the signature of a large restructuring event. The scale matters, this is not a trimmed team, it is hundreds of people entering the job market at once.
**Reading it.** At that scale, the company's HR team is underwater: notifications, severance mechanics, legal review, morale of the people staying. Outplacement is on their checklist, often unbudgeted and unresearched, and it gets decided fast, **usually within two to three weeks of the event**, by an exhausted CHRO choosing between whoever shows up credibly.
**Your move.**
1. Contact the CHRO within the week, with empathy that is real rather than performed:
> Weeks like this one are brutal on the team managing them. If outplacement support is on your list, we can stand up a program for a group this size in five days, and I can share exactly what it costs and covers in one page.
2. Lead with the one-pager, not a meeting request, **decision-ready beats discovery-call when your buyer has no spare hours**.
3. Reference outcomes for the departing employees, placement rates and time-to-land, because that is the story the CHRO needs to tell internally.
4. Afterward, stay in touch; HR leaders change companies, and they rehire the outplacement partner that made the worst month survivable.
**Automate it.** A daily check of the [Company Signals API](/buying-signals/signals-apis/company-signals) on `mass_layoff_signal` is, bluntly, your entire demand-generation system, the market only exists in these windows.
**Why it lands.** In this category, being helpful and being first are the same thing.
## How to read it
A fifth of the workforce gone signals crisis or radical restructuring.
Assume your champion may be gone and the org chart is different.
Feeds restructuring\_signal when paired with churn and consolidation.
## Outreach playbook
Strong caution. Re-verify contacts before any outreach; the buying committee likely changed.
## Related signals
Employee count dropped sharply in a single interval.
A company is undergoing major restructuring.
Multiple senior leaders departed in a short window.
A company shows a clear distress pattern.
***
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# Decline signals
Source: https://apidoc.cufinder.io/buying-signals/signals/decline/overview
Signals that flag contraction, cost-cutting, or instability, useful both for timing efficiency plays and for pausing accounts.
Signals that flag contraction, cost-cutting, or instability, useful both for timing efficiency plays and for pausing accounts.
The decline category contains **9 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
A company's employee count dropped by at least one.
A company fell down a Professional Network employee size band.
Employee count dropped sharply in a single interval.
Employee count dropped severely in a single interval.
Open roles collapsed while headcount stayed flat or fell.
The company's follower count dropped.
Follower growth slowed sharply while still positive.
The number of open job postings fell.
A company that had open roles now has none.
# Funding Round Announced
Source: https://apidoc.cufinder.io/buying-signals/signals/funding/funding-round-announced
A new funding round was announced.
`funding_round_announced`
Funding
Funding records (rounds list).
A new funding round was announced.
## When it fires
A new entry appears in the funding rounds list.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------ | ------------------------------------------------------------------- |
| `meta` | Round details (amount, stage, date, and more) stored on the signal. |
## Magnitude
Hyper-priority, the single clearest budget event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A new round is the single clearest 'they have budget now' event. Fresh capital is earmarked for growth, hiring, and tooling, companies that just raised are actively shopping and have the budget approved. This is the highest-intent signal in the catalog.
## How to use Funding Round Announced Signal?
**The scenario.** You founded an outbound agency that builds sales pipelines for B2B startups. Your service costs real money, and your buyers pay for it most happily in one specific season: **the weeks after new capital lands**, when growth targets have just been rewritten upward and the team to hit them has not been hired yet.
**The goal.** Reach every freshly funded startup in your niche within days of the announcement, ahead of the flood, because every agency reads the same funding news and by day ten the founder's inbox is a graveyard of congratulations.
**The signal fires.** `funding_round_announced` fires for Plumline, a workflow-automation startup: a new Series B. The math writes your pitch for you: a Series B implies aggressive pipeline targets, and their careers page shows exactly two salespeople.
**What it tells you.** Fresh funding compresses time. The board that approved the round attached expectations to it, and the founder now faces a gap between the growth curve they sold and the go-to-market machine they have. Hiring closes that gap in six months; **an agency closes it in three weeks**. That arbitrage is your entire pitch, but it only works while the gap is fresh and the panic is quiet.
**The play.**
1. Be in the inbox within 72 hours; speed is most of the win in a moment everyone can see.
2. Skip congratulations, lead with the gap:
> A Series B usually means the pipeline target tripled while the sales team did not. We build outbound engines for exactly this quarter of a company's life, and can be generating meetings inside three weeks while you hire properly.
3. Anchor to their timeline: results before their next board meeting is the deadline that matters to them.
4. Tier by round size and stage fit, **a Series B in your niche outranks a mega-round outside it**, always.
**Automate it.** A daily [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `funding_round_announced`, filtered to your verticals, delivers the list before the congratulations pile up.
**Why it lands.** Funded founders do not need convincing to spend, the board did that. They need **somewhere effective to spend it, fast**, and you arrived first.
## How to read it
A raise means approved capital to deploy.
Newly funded companies buy fast to deploy capital.
Underpins pre\_ipo\_signal and pairs with hiring surges.
## Outreach playbook
Top-tier alert. Reach out within days; budget is fresh and decisions move fast.
## Related signals
The company's funding stage advanced.
The company's total funding raised increased.
Open job postings jumped sharply above the recent baseline.
A company rapidly built out its leadership team.
***
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# Last Funding Round Change
Source: https://apidoc.cufinder.io/buying-signals/signals/funding/last-funding-round-change
The company's funding stage advanced.
`last_funding_round_change`
Funding
Funding records (last round stage).
The company's funding stage advanced.
## When it fires
`last_funding_round` advances (Seed → Series A → Series B → and so on).
## Magnitude
High, each stage advance means more capital.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Each step up means a bigger war chest and bigger ambitions. Advancing from Seed to Series A, or Series A to B, marks a maturity leap, larger budgets, more formal procurement, and a buying committee that's growing more sophisticated.
## How to use Last Funding Round Change Signal?
**The setup.** You lead growth at a cloud-infrastructure startup with usage-based pricing. Seed-stage companies love you and spend nothing; growth-stage companies spend seriously but are locked into contracts they signed at their last stage. Your best customers, you have learned, are caught at the boundary: **companies whose stage just changed, whose infrastructure needs are about to outgrow their setup**.
**What you want.** A stage-transition feed: every company in your ecosystem whose funding stage advanced, the moment it advances, so your ICP tiers update themselves.
**The signal fires.** `last_funding_round_change` fires for Coralbrook AI, a machine-learning tools company already in your CRM as a free-tier user: their last funding round has moved from seed to Series A.
**Reading it.** A stage change is a different event from a funding announcement, it marks the company graduating into a new operating class. Series A companies hire faster, ship faster, and hit infrastructure ceilings on a schedule you can practically calendar: the free tier stops fitting around month two, the scaling conversation happens by month six. Coralbrook is **already inside your product with a spending curve about to inflect**, which makes this the cheapest expansion opportunity you will ever get.
**The play.**
1. Wire the signal to your account tiers: any account whose stage advances gets auto-promoted into the managed tier, with an owner assigned.
2. Have that owner reach out about scale before the ceiling hits, **capacity planning offered early feels like partnership; the same call after an outage feels like upselling**.
3. Offer the growth-stage package with migration included; companies fresh off a raise trade money for time in every deal.
4. Track conversion by stage-transition cohort; it will outperform every static segment you have, and the data makes next year's planning honest.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `last_funding_round_change`, matched against your user base, turns stage graduation into an automated expansion trigger.
**Why this works.** Everyone chases funding announcements for new logos. The quieter win is **watching your own free users cross the stage line**, one query, zero acquisition cost.
## How to read it
Each stage means more capital and more structure.
Later stages buy enterprise-grade tooling.
Buying gets more process-driven at each stage.
## Outreach playbook
High-value. Match your offering to the new stage's budget and sophistication.
## Related signals
A new funding round was announced.
The company's total funding raised increased.
A company rapidly built out its leadership team.
***
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# Funding signals
Source: https://apidoc.cufinder.io/buying-signals/signals/funding/overview
The gold standard of buying signals, fresh capital means fresh budget.
The gold standard of buying signals, fresh capital means fresh budget.
The funding category contains **3 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
A new funding round was announced.
The company's funding stage advanced.
The company's total funding raised increased.
# Total Funding Increase
Source: https://apidoc.cufinder.io/buying-signals/signals/funding/total-funding-increase
The company's total funding raised increased.
`total_funding_increase`
Funding
Funding records (cumulative total).
The company's total funding raised increased.
## When it fires
`total_funding_usd` increases between snapshots.
## Magnitude
High, any increase reflects fresh capital.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Even without a named round, rising total funding signals capital inflow worth chasing. Total funding can rise via extensions, bridge rounds, or debt that don't always register as a named round, this catches capital events the round-name signal might miss.
## How to use Total Funding Increase Signal?
**Where you sit.** You do business development for a recruitment firm specializing in engineering talent for venture-backed companies. Your fees are premium, and premium fees need clients with two properties: **money in the bank and hiring pressure they cannot solve alone**. Total funding is your cleanest public proxy for both.
**The mission.** Keep a running, ranked view of which companies in your patch just got materially richer, including the quiet ones whose rounds never made the tech press.
**The signal fires.** `total_funding_increase` fires for Northgale Robotics: their total funding has stepped up meaningfully. No press release accompanied it, an unannounced extension or a quiet strategic investment, which is precisely the kind of event your competitors' Google Alerts will never catch.
**Reading it.** A total-funding jump without headlines is arguably better than a splashy round: the company has fresh capital and is **not currently drowning in vendor pitches**, because nobody else noticed. Capital converts to headcount on a predictable lag, robotics companies especially, where the roadmap is gated on scarce mechatronics and controls engineers your firm happens to specialize in.
**The play.**
1. Verify the shape of the raise where possible; extensions fund existing plans (hiring soon), strategics fund partnerships (hiring specific teams).
2. Approach the founder or VP Engineering with quiet knowledge as credibility:
> Noticed Northgale's funding position moved recently, congratulations, and no, it was not in the press. Teams usually convert that into engineering hires within a quarter. Our bench of controls engineers is the strongest it has been in two years; worth a conversation before the roadmap needs them?
3. Preempt the timeline: offer to build the candidate pipeline now so offers can go out the week roles are approved, **compressing their time-to-hire is the premium you charge for**.
4. Rank all fires by increase size relative to company size; a doubling matters more than a rounding error.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `total_funding_increase` across your territory catches the quiet money your competitors' news alerts structurally miss.
**Why it lands.** Loud rounds bring loud competition. Quiet capital plus early outreach is **the closest thing recruiting has to an uncontested market**.
## How to read it
Captures raises that don't show as a named round.
Any increase means new money to deploy.
Use alongside funding\_round\_announced for confirmation.
## Outreach playbook
High-value. Treat like a funding round; verify the source of the increase.
## Related signals
A new funding round was announced.
The company's funding stage advanced.
***
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# Employee Growth
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/employee-growth
A company's employee count increased by at least one between snapshots.
`employee_growth`
Growth
Company Professional Network page (employee count field).
A company's employee count increased by at least one between snapshots.
## When it fires
`employee_count` increases by ≥ 1 between two consecutive snapshots.
## Magnitude
Bucketed on the percentage change in `employee_count`. A single hire at a 10-person company (10%) buckets far higher than one hire at a 5,000-person company.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
It's the most basic growth marker. On its own it's gentle, but it's the foundation many composite signals (expansion\_signal, headcount\_recovery) build on. Sustained employee\_growth over several crawls is a reliable proxy for a company in build mode with loosening budget.
## How to use Employee Growth Signal?
**The scenario.** You run RevOps at a payroll and benefits platform for companies under 500 employees. Your product's value scales with headcount, and your churn data says something useful: **customers who were actively growing when they signed stay twice as long** as ones that were flat.
**The goal.** Stop treating all inbound-fit accounts equally, and build your outbound universe from companies that are demonstrably adding people, because every new hire is another payslip your product runs.
**The signal fires.** Over six weeks, `employee_growth` fires four times for Fernwood Clinics, a healthcare group that was at 80 employees and is now at 96. No press release, no funding news, just steady, compounding hiring.
**What it tells you.** One firing of this signal is noise; a single hire moves it. A repeated pattern is the tell. **Sustained employee growth means onboarding pain, payroll complexity, and benefits administration are all getting worse at that company every month**, which is precisely the pain you price on.
**The play.**
1. Build a "3-of-4" rule: only accounts where the signal fired in at least three of the last four periods enter the queue. This one filter removes most of the noise.
2. Route survivors to an SDR sequence that names the growth plainly: adding sixteen people in six weeks means someone is doing benefits enrollment by hand.
3. Prioritize by magnitude bucket, **a 20% headcount jump at an 80-person company is a different conversation than 1% at a 5,000-person one**.
4. Pass the growth context to the AE so discovery starts from evidence, not guesswork.
**Scale it.** A scheduled pull from the [Company Signals API](/buying-signals/signals-apis/company-signals) with `signal_name=employee_growth`, filtered to your size band, feeds the cohort automatically. Your SDRs work a list that **rebuilds itself every week**.
**Why this works.** Firmographics tell you who could buy. Repeated employee growth tells you whose problem is actively getting bigger, and those are rarely the same list.
## How to read it
Repeated firing across crawls signals a company actively scaling teams.
For large enterprises, a +1 change is statistically meaningless. Filter by magnitude bucket.
Feeds expansion\_signal and headcount\_recovery, so even low-magnitude events have downstream value.
## Outreach playbook
Don't alert on this alone. Use it as a filter: surface accounts with employee\_growth in 3+ of the last 4 crawls, then enrich for fit.
## Related signals
A company moved up a full Professional Network employee size band.
Employee growth returned after a recent decline.
The number of open job postings increased.
A company is expanding into new markets while growing.
***
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# Employee Size Band Upgrade
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/employee-size-band-upgrade
A company moved up a full Professional Network employee size band.
`employee_size_band_upgrade`
Growth
Company Professional Network page (employee size band field).
A company moved up a full Professional Network employee size band.
## When it fires
`employee_band` moves up a Professional Network band (for example, 51-200 → 201-500).
## Magnitude
Inherently high-signal. Crossing a band requires sustained net hiring, so it rarely fires on noise. Treated as a high-magnitude event regardless of the raw delta.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Crossing a band is a bigger deal than raw count because it often unlocks new budget lines, new headcount tiers, and new buying committees. A company moving from 51-200 to 201-500 is typically formalizing functions (first dedicated ops, security, or enablement roles).
## How to use Employee Size Band Upgrade Signal?
**The setup.** You are an AE at a flexible-workspace provider with buildings in four cities. Your best move-ins share a story: a company that grew past its office, tried squeezing desks into corridors for a quarter, then moved in a hurry, annoyed and ready to sign.
**What you want.** To reach companies **after the growth is undeniable but before the corridor-desk quarter**, when a space decision is inevitable and nobody has started the search.
**The signal fires.** `employee_size_band_upgrade` fires for Quantis Labs, a biotech tools company in your city. They have crossed from the 11-50 band into 51-200. Band jumps are rare and hard to fake, which is exactly what makes this firing worth attention.
**Reading it.** A band upgrade is not one hire, it is **a category change in how the company operates**. Somewhere in that jump, the office stopped fitting: meeting rooms are booked solid, the lease's headcount assumptions are broken, and the operations lead has started dreading Mondays. Companies in this moment decide on space within two quarters, almost mechanically.
**Your move.**
1. Find the person who owns the pain, usually operations or the office manager, not the CEO.
2. Open with the math of their moment:
> Congrats on the growth. Most teams that cross fifty people discover the office stops working before the lease does. If desks are getting creative at Quantis, I can show you what a two-year flexible setup looks like two blocks away.
3. Offer a tour tied to their actual numbers, **capacity for where they will be in a year, not where they are today**.
4. If they just signed a lease, log a reminder for the next band. Growth like this rarely stops on schedule.
**Put it on autopilot.** The [Company Signals API](/buying-signals/signals-apis/company-signals) filtered to `employee_size_band_upgrade` in your metro areas gives you a short, high-intent list, **a dozen real prospects a month beats a thousand cold ones**.
**Why it lands.** You are timing your pitch to a decision the building has already made for them.
## How to read it
New bands often correspond to new annual budget approvals and tooling standardization.
Companies crossing into 201-500 and beyond start replacing ad-hoc tools with platforms.
Unlike a single hire, a band upgrade reflects a trend, so the timing window stays open longer.
## Outreach playbook
High-priority alert. Pair with first\_job\_in\_function to see which new teams the growth is funding.
## Related signals
A company's employee count increased by at least one between snapshots.
Employee growth returned after a recent decline.
The company's office count crossed a milestone threshold.
***
Surface `employee_size_band_upgrade` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Engineering Hiring Surge
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/engineering-hiring-surge
Engineering job postings doubled versus the prior crawl.
`engineering_hiring_surge`
Growth
Company Professional Network jobs, engineering function count.
Engineering job postings doubled versus the prior crawl.
## When it fires
Count of engineering postings ≥ 2× the previous crawl.
## Magnitude
High by design (2× threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A surge in engineering hiring usually points to product investment and technical scaling. Companies don't double engineering headcount casually, it signals a major build, a platform migration, or a new technical direction, all of which drive tooling decisions.
## How to use Engineering Hiring Surge Signal?
**The scenario.** You do growth at a CI/CD platform. Your product sells best to engineering orgs in a specific moment: the team is doubling, the build pipeline that suited ten engineers is buckling under thirty, and **every merge conflict is suddenly a meeting**.
**The goal.** Catch engineering orgs in the middle of that doubling, when pipeline pain is spiking week over week and tooling budgets get unlocked to make it stop.
**The signal fires.** `engineering_hiring_surge` fires for Bexley Systems, a Series B logistics-software company: engineering postings have doubled against their prior baseline. Fifteen open engineering roles at a company with forty engineers.
**What it tells you.** A hiring surge of that shape means a build-out, a new product line, a replatform, or a post-funding scale push. Whatever the cause, the consequence is the same: **in six months, twice as many engineers will be fighting over the same build and deploy infrastructure**. The wise time to sell the fix is before the fire, and the team knows it is coming.
**The play.**
1. Verify the surge is core engineering, not a one-off data or IT batch, by scanning the roles' functions.
2. Target the platform or DevEx lead, the person whose job the surge quietly doubles.
3. Make the pitch about the roadmap they are already inside: onboarding thirty engineers onto a pipeline built for ten is a known cliff, and you have the before-and-after numbers from similar teams.
4. Time follow-ups to their hiring curve, **the pain peaks when the new cohort actually starts**, roughly a quarter after the postings.
**Scale it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `engineering_hiring_surge`, joined with your product-usage data for existing accounts, feeds both new business and expansion motions from one signal.
**Why this works.** Developer-tools buyers rarely respond to interruption. They respond to **someone who shows up already understanding the exact week their infrastructure started hurting**.
## How to read it
Doubling engineering means building, not maintaining.
More engineers means more dev tools, infra, security, and data spend.
Engineering surges frequently follow a raise, check funding signals.
## Outreach playbook
Prime signal for dev-tool, infra, and security sellers. Pair with engineering\_leader\_hire.
## Related signals
Open job postings jumped sharply above the recent baseline.
A new engineering leader joined the company.
A company shows the early pattern of an IPO track.
A new funding round was announced.
***
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# First Job in City
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/first-job-in-city
A company posted its first role in a new city.
`first_job_in_city`
Growth
Company Professional Network jobs, evaluated against 12-month posting history by city.
A company posted its first role in a new city.
## When it fires
A posting appears in a city with no postings in the previous 12 months.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | ----------------------------------------- |
| `meta.city_new` | The city that just saw its first posting. |
## Magnitude
Moderate to high, city-level granularity is useful but less decisive than country-level.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Same idea as first\_job\_in\_country at city granularity, useful for territory-based reps who care about local presence. A new city often precedes a physical office (watch for a paired location\_added).
## How to use First Job in City Signal?
**Where you sit.** You are a tenant-representation broker in Austin. Your livelihood depends on knowing which companies will need Austin office space before they have engaged anyone, because by the time a requirement hits the market, six brokers are already circling it.
**The mission.** Find out-of-town companies at the very first breadcrumb of an Austin expansion, **months before a space requirement officially exists**.
**The signal fires.** `first_job_in_city` fires for Nordlicht GmbH, a Hamburg-based industrial software firm. They have posted their first-ever Austin role: a regional sales director. One posting, first time, new city.
**Reading it.** Companies do not post a first role in a new city casually; it clears an internal debate about market entry that you never saw. The typical sequence from here is predictable: first hire works remote, three more follow within two quarters, then someone in Germany asks where everyone will sit. **The company that posts a first job in a city is eighteen months from a lease, and nobody is advising them yet.**
**Your move.**
1. Research the company's expansion pattern in other cities; some go serviced-office first, some straight to leases, and the history tells you which pitch fits.
2. Reach the operations or finance leader, not the new local hire, with a market briefing offer: what Austin costs, which submarkets fit industrial software, what their competitors pay.
3. Stay useful through the remote-working phase. **The broker relationship is usually decided before the space search begins.**
4. Watch the account for further Austin postings; the third local role is your trigger to move from useful to formal.
**Put it on autopilot.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `first_job_in_city`, filtered to your metro, is a pipeline of future requirements **no listing service will ever show you**.
**Why it lands.** In brokerage, the winner is almost always whoever was already in the room. This signal tells you which rooms to be in, a year early.
## How to read it
City-level hiring hints at a coming office or regional hub.
Best for reps with city or metro-level territories.
Frequently followed by a location\_added event within a few months.
## Outreach playbook
Useful for local field teams. Watch for a follow-on location\_added to confirm a real office.
## Related signals
A company posted its first role in a new country.
A new office location appeared.
The company's office count crossed a milestone threshold.
***
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# First Job in Country
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/first-job-in-country
A company posted its first role in a new country.
`first_job_in_country`
Growth
Company Professional Network jobs, evaluated against 12-month posting history by country.
A company posted its first role in a new country.
## When it fires
A posting appears in a country with no postings in the previous 12 months.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------ | -------------------------------------------- |
| `meta.country_new` | The country that just saw its first posting. |
## Magnitude
High-signal, geographic firsts are deliberate strategic moves.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
That's geographic expansion in real time. A company hiring in a new country is establishing local presence, which brings local compliance, local tooling, and local vendor needs. It also feeds the composite expansion\_signal.
## How to use First Job in Country Signal?
**The setup.** You are an AE at a global employment platform, an EOR that lets companies hire abroad without opening a legal entity. Your perfect prospect is not "a company that hires internationally". It is **a company hiring in a new country for the very first time**, staring down entity setup, foreign payroll, and compliance it has never touched.
**What you want.** A feed of exactly those first-time moments, in the countries where your coverage is strongest.
**The signal fires.** `first_job_in_country` fires for Maple & March, a 140-person Toronto design-tools company. The role: a senior developer, located in Portugal. The signal confirms this is their first Portuguese posting ever.
**Reading it.** Right now, someone at Maple & March is discovering what hiring one person in Portugal actually involves: an entity or a workaround, social contributions, a compliant contract in a legal system nobody in Toronto knows. **The question "how do we even employ this person" has a two-week shelf life**, and whichever answer arrives first tends to win.
**Your move.**
1. Speed over polish. This signal is a same-week signal, not a this-quarter signal.
2. Write to the People or Finance lead with the exact problem, not the category:
> Saw you are hiring your first developer in Portugal. Standing up a Portuguese entity for one hire takes about four months; we can have them compliantly employed in a week. Happy to walk through the math either way.
3. Include the real cost comparison, entity versus EOR, for one hire in that specific country. **Concreteness is the whole pitch.**
4. Win the one hire, then expand: first-country hires are how distributed teams start, not how they end.
**Put it on autopilot.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `first_job_in_country`, filtered to your top coverage countries, delivers prospects at the precise moment your product is the answer to a question they asked this morning.
**Why it lands.** Most of your market does not know your category exists until the day this problem appears. This signal is that day, on a list.
## How to read it
First hire in a country signals genuine expansion, not a one-off remote role.
New geographies bring data residency, compliance, and localization requirements.
Critical for region-based teams, route the account to the right local rep.
## Outreach playbook
Route to the territory owner for that country. Pair with country\_expansion for confirmation.
## Related signals
A company posted its first role in a new city.
A company opened its first location in a new country.
A new office location appeared.
A company is expanding into new markets while growing.
***
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# First Job in Function
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/first-job-in-function
A company posted a role in a function it hadn't hired for in 12 months.
`first_job_in_function`
Growth
Company Professional Network jobs, evaluated against 12-month posting history.
A company posted a role in a function it hadn't hired for in 12 months.
## When it fires
A job posting appears for a function (engineering, sales, marketing, product, ai-ml, security, finance, hr, operations, legal) that had zero postings in the previous 12 months.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | --------------------------------------------- |
| `meta.function` | The function that just saw its first posting. |
## Magnitude
Treated as high-signal regardless of count, the first hire in a function is categorically meaningful.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is one of the most valuable growth signals. A first-ever sales hire means they're about to build a sales motion and will need tools to support it. A first AI/ML role means a new technical initiative. Each first-in-function event marks the birth of a new buying center that didn't exist before.
## How to use First Job in Function Signal?
**The scenario.** You are the founder of a marketing platform built for one buyer: the very first marketing hire at a company that never had one. Your onboarding, pricing, and templates all assume a team of one. Your problem has never been product, it has been **finding companies at the exact moment marketing gets invented there**.
**The goal.** A repeatable feed of companies posting their first-ever marketing role, before that new hire arrives and cobbles together a stack of free trials.
**The signal fires.** `first_job_in_function` fires for Saltgrass Outdoor, a 60-person outdoor-equipment maker. They have posted a Marketing Manager role, and the signal's meaning is precise: no role in this function in at least twelve months, effectively a brand-new function.
**What it tells you.** A company hiring its first marketer has just admitted that founder-led promotion has hit its ceiling. The incoming hire will arrive to **no stack, no processes, no historical data, and a mandate to show results in a quarter**. People in that seat do not run tooling evaluations; they grab whatever makes them look competent fastest.
**The play.**
1. Act twice: once now, once at the hire. Before the hire, the founder is the buyer; after, the new marketer is.
2. To the founder, position around derisking the hire: give your new marketer a working setup on day one instead of a quarter of tool shopping.
3. When the marketer starts, and `first_role_hire` will often tell you, welcome them with your starter kit for solo marketing teams. **Be the first tool they succeed with, and you become the stack they build on.**
4. Watch the same signal for other functions your product touches; a first sales hire changes your buyer map at the same account.
**Scale it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `first_job_in_function` is effectively a birth registry for new departments; filter it to marketing and your company-size band.
**Why this works.** Every competitor is fighting to displace tools. You are arriving **where no tool has ever existed**, which is the cheapest market entry there is.
## How to read it
A brand-new function means a brand-new budget and a brand-new decision-maker.
First hires build from scratch, they're shopping with no incumbent vendor.
Map the function to your product: first sales hire → sales tools, first security hire → security tooling.
## Outreach playbook
Top-tier alert when the function matches your buyer. Reach the hiring manager before the role is even filled.
## Related signals
A company posted its first role in a new country.
Sales job postings doubled versus the prior crawl.
Engineering job postings doubled versus the prior crawl.
A company made its first-ever hire for a role function.
A company is expanding into new markets while growing.
***
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# Followers Growth
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/followers-growth
The company's Professional Network follower count increased.
`followers_growth`
Growth
Company Professional Network page (followers field).
The company's Professional Network follower count increased.
## When it fires
`followers` count increases between snapshots.
## Magnitude
Bucketed on the follower percentage change. Most steady growth is low magnitude; spikes are split into a dedicated signal.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
Rising follower counts signal market attention and brand momentum. On its own it's a soft signal, but it corroborates harder events, a funding announcement or product launch usually shows up as a follower bump too.
## How to use Followers Growth Signal?
**Where you sit.** You own a small social-media agency. Cold-pitching "we will grow your audience" gets deleted on sight, because every agency says it. What actually opens doors, you have learned, is **showing a prospect their own competitive gap with real numbers**.
**The mission.** Use audience-growth data as the hook itself: find industry pairs where one company's social presence is compounding and its direct competitor's is flat, then sell to the one falling behind.
**The signal fires.** `followers_growth` fires, repeatedly and in rising buckets, for Gritline Tools, a challenger brand in professional hand tools. Their established rival, Harlan Tools, shows no growth signals at all. Two comparable companies, one widening gap.
**What it tells you.** Steady follower growth at a challenger means content is being invested in and it is working. For the incumbent, that is tomorrow's problem arriving quietly: **share of audience precedes share of market in categories where buyers research socially**. Harlan's marketing team may not have noticed. Their leadership definitely has not.
**Your move.**
1. Build the two-line chart: Gritline's audience curve against Harlan's flat one, over twelve months.
2. Send it to Harlan's marketing lead with one sentence:
> Your closest competitor has grown their audience 4x faster than you over the past year. Here is the chart, and three things they are doing that you are not.
3. The pitch is the diagnosis, **the retainer is the treatment**, and the chart does the emotional work a cold pitch never could.
4. Repeat the pattern across your niche: every industry has a Gritline and a Harlan.
**Put it on autopilot.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `followers_growth` across your target verticals surfaces the fast movers; their flat competitors are your prospect list.
**Why it lands.** Nobody buys audience growth in the abstract. Everybody buys **not losing to the specific rival in the chart**.
## How to read it
Follower growth tracks brand awareness and content reach.
Best used to confirm other signals rather than as a primary trigger.
Steady, low-magnitude growth is normal and rarely actionable by itself.
## Outreach playbook
Treat as supporting evidence. Escalate only when it co-occurs with funding or hiring signals.
## Related signals
Follower growth ran sharply above the company's recent baseline.
The company is posting noticeably more on social.
Average post engagement shifted significantly.
***
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# Followers Spike
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/followers-spike
Follower growth ran sharply above the company's recent baseline.
`followers_spike`
Growth
Company Professional Network page, evaluated against a 6-crawl rolling baseline.
Follower growth ran sharply above the company's recent baseline.
## When it fires
Current-crawl follower growth rate ≥ 3× the rolling average of the previous 6 crawls.
## Magnitude
By definition a high or hyper event, the 3× threshold filters out ordinary growth.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A spike usually means a launch, a viral moment, or a funding announcement worth investigating. It's the sharp, anomalous version of followers\_growth, and it's far more actionable because it marks a specific moment in time rather than a trend.
## How to use Followers Spike Signal?
**The setup.** You run client development at a PR and communications agency. The hardest retainers to win are with brands that just had their moment: something went viral, coverage poured in, and for about two weeks the company has more attention than it knows how to use. By the time you hear about the moment on the news, **their inbox is already full of agencies**.
**What you want.** To detect the viral moment from the data, not the press, and be in the conversation on day one instead of day ten.
**The signal fires.** `followers_spike` fires for Marloe Hotels, a boutique hospitality group. Their follower growth is running far above their baseline, the signature of a single post or story catching fire. A quick look confirms it: a guest's video about their staff went everywhere over the weekend.
**Reading it.** A spike is a moment in time, and moments decay. The brand now faces a problem it has never had: **attention it did not plan for and cannot keep without help**. Founders in this window are unusually receptive, because they can feel the moment slipping while they answer interview requests one by one.
**Your move.**
1. Move within 48 hours; this is the fastest-decaying signal in the catalog.
2. Lead with the thing they are losing:
> That video bought you about two weeks of attention most brands pay years for. Here are three ways to turn it into bookings before it fades, whether or not you work with us.
3. Offer a one-week sprint, not an annual retainer, **match the offer to the shape of the moment**, then convert the sprint later.
4. Watch the reactions data for who amplified them; that list is raw material for the campaign you are about to propose.
**Put it on autopilot.** A daily check of the [Company Signals API](/buying-signals/signals-apis/company-signals) on `followers_spike` in your verticals is your newsroom, surfacing the moment **hours after it starts instead of days after it peaks**.
**Why it lands.** Every brand believes their viral moment should have become more than it did. You are selling the "more", while it is still possible.
## How to read it
Spikes pinpoint a date, find the announcement that drove it.
Most spikes trace back to a product launch, funding news, or major PR.
The attention window is short, reach out while the company is still in the spotlight.
## Outreach playbook
Alert immediately. Check recent posts and news to identify the trigger, then reference it in outreach.
## Related signals
The company's Professional Network follower count increased.
A new funding round was announced.
The dominant topic of recent posts changed.
***
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# Headcount Recovery
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/headcount-recovery
Employee growth returned after a recent decline.
`headcount_recovery`
Growth
Company Professional Network page, evaluated against 180-day signal history.
Employee growth returned after a recent decline.
## When it fires
`employee_growth` fires AND at least one prior `employee_decrease` exists in the last 180 days for the same company.
## Magnitude
Bucketed on the recovery delta. A sharp rebound (hyper) is a stronger story than a slow crawl back.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
A company clawing back from a dip is often re-investing, which makes for warm timing. Recovery follows restructuring, a pivot, or a tough quarter, and the rebuild phase brings fresh tooling decisions as the company resets its stack.
## How to use Headcount Recovery Signal?
**The scenario.** You own a staffing agency specialized in aviation and aerospace. The brutal part of your market is timing: during downturns nobody hires, and by the time recovery is obvious in the trade press, **every agency is calling the same reopened doors**.
**The goal.** Catch individual companies at the turn itself, the first months where headcount stops shrinking and starts climbing again, before the recovery is public knowledge.
**The signal fires.** `headcount_recovery` fires for Kestrel Aviation Services, an MRO provider that cut deep two years ago. The signal's definition is exactly the pattern you want: decline, then a sustained return to growth. They are rebuilding.
**What it tells you.** A company in recovery hires differently than a company in boom. They are cautious, budget-scarred, and short on recruiting muscle, because **the first thing cut in the downturn was usually the talent team**. They need people faster than they can rebuild the machine that finds people. That gap is your entire business.
**The play.**
1. Confirm the shape: check that the recovery is months deep, not a one-crawl blip, using the signal's recurrence.
2. Approach the operations leader with the constraint framed sympathetically: rebuilding a workforce with a skeleton TA team is slow exactly when speed matters most.
3. Offer contract-to-hire first. **Recovery-phase companies fear fixed costs more than they fear vacancies**, and contract staffing meets them where their risk tolerance actually is.
4. Grow with them: the agency that staffed the recovery usually keeps the account through the boom.
**Scale it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `headcount_recovery` across your industry codes gives you the turn, company by company, **without waiting for it to become a headline**.
**Why this works.** Everyone can sell into a boom. The margin is in being the partner who showed up during the fragile first quarter of the climb.
## How to read it
Signals a company exiting a difficult period and re-entering growth, often with new leadership.
Post-recovery rebuilds frequently re-evaluate vendors chosen during the lean period.
Always look at what caused the prior decrease, layoffs vs seasonal vs reorg change the pitch.
## Outreach playbook
Cross-reference the prior decline cause. A recovery after layoffs is a re-platforming opportunity.
## Related signals
A company's employee count increased by at least one between snapshots.
A company's employee count dropped by at least one.
A company is undergoing major restructuring.
***
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# Jobs Open Increase
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/jobs-open-increase
The number of open job postings increased.
`jobs_open_increase`
Growth
Company Professional Network jobs (open postings count).
The number of open job postings increased.
## When it fires
`job_count` increases between snapshots.
## Magnitude
Bucketed on percentage change in `job_count`.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
More open roles means more spending intent across the board. Hiring is one of the most direct expressions of budget, and rising open headcount often precedes tooling purchases by 30 to 90 days as new hires need to be equipped.
## How to use Jobs Open Increase Signal?
**Where you sit.** You sell recruitment advertising for a niche job board in logistics and supply chain. Your buyers are talent-acquisition teams, and their budget has one honest predictor: **how many roles they are trying to fill right now**.
**The mission.** Rank your entire prospect universe by current hiring pressure, so your team always calls the companies with the most open seats and the most reason to spend.
**The signal fires.** `jobs_open_increase` fires for Norvik Freight, a 3PL with distribution centers in three countries. Their open-roles count has climbed meaningfully against the last crawl, and the bucket says the jump is well above their normal fluctuation.
**What it tells you.** Every open role represents a daily cost of vacancy that someone in that building can feel: trucks without drivers, shifts without supervisors. When open roles rise faster than fills, the TA team is losing ground, and **teams losing ground buy reach**, more channels, more visibility, more speed. Your product is precisely that reach, in their exact niche.
**Your move.**
1. Sort this week's queue by magnitude bucket, and put rising accounts above every static one, regardless of size.
2. Reference the pressure, not the product, in the first line: fifteen open roles in a driver-shortage market is a sentence that buys you the second sentence.
3. Pitch the niche advantage: a logistics-only audience fills logistics roles faster than the generalist boards they are already burning budget on.
4. Set a decay rule, **if the open count falls for two consecutive periods, drop the account down the queue**; the budget urgency falls with it.
**Put it on autopilot.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `jobs_open_increase` across your logistics segment produces the ranked call list before Monday standup.
**Why it lands.** You stop selling job advertising to companies that might hire, and start selling it to companies **visibly failing to hire fast enough**.
## How to read it
Open roles cost money and the tools to support those roles cost money too.
Postings precede start dates, giving you a head start on outreach.
Pair with first\_job\_in\_function to see where the spending is going.
## Outreach playbook
Useful as a base layer. Combine with function-specific signals to target the right buyer.
## Related signals
Open job postings jumped sharply above the recent baseline.
A company posted a role in a function it hadn't hired for in 12 months.
Sales job postings doubled versus the prior crawl.
***
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# Jobs Open Spike
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/jobs-open-spike
Open job postings jumped sharply above the recent baseline.
`jobs_open_spike`
Growth
Company Professional Network jobs, evaluated against a 4-crawl rolling baseline.
Open job postings jumped sharply above the recent baseline.
## When it fires
`job_count` ≥ 2× the rolling average of the previous 4 crawls.
## Magnitude
High by design, the 2× threshold excludes routine hiring.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A hiring spike is one of the strongest near-term budget indicators you'll find. Doubling open roles in a single window signals an aggressive growth push, often funded by a recent raise or a new strategic mandate.
## How to use Jobs Open Spike Signal?
**The setup.** You do business development for an RPO firm, recruitment process outsourcing. Your best contracts start in a very specific emergency: a company's hiring load suddenly triples, their two internal recruiters are triaging instead of recruiting, and leadership is discovering that **you cannot hire your way out of a hiring problem fast enough**.
**What you want.** To find that emergency in the data the week it begins, not the month the backlog becomes an executive escalation.
**The signal fires.** `jobs_open_spike` fires for Cobalt Health, a 900-person healthcare provider: open postings have jumped far beyond their rolling baseline. A new facility announcement two weeks ago explains why. Forty open clinical and support roles, a TA team built for eight a month.
**Reading it.** A spike is different from growth. Growth is planned; **a spike is a workload the existing team was never sized for**, with a business deadline attached, in this case a facility that must open staffed. Internal recruiting throughput cannot triple on command. Either the deadline slips or capacity comes from outside.
**Your move.**
1. Reach the VP of People with the math, not the brochure:
> Forty open roles against a team that fills eight a month is a five-month backlog for a facility opening in three. We can put four dedicated recruiters on it next week and hand the process back when the surge is done.
2. Sell the surge, not the outsourcing, **temporary capacity is an easy yes; replacing the team is a hard one**, and the easy yes renews itself.
3. Anchor pricing to the cost of the deadline slipping, which their CFO has already calculated.
4. Deliver the surge well, and be standing there when the next spike comes. It always comes.
**Put it on autopilot.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `jobs_open_spike`, filtered to `high` and `hyper` buckets, is a direct feed of hiring emergencies in progress.
**Why it lands.** RPO is bought under pressure or not at all. The spike is the pressure, timestamped.
## How to read it
Sudden doubling means a company is scaling fast and deliberately.
Hiring spikes frequently follow funding rounds, check for a recent raise.
New hires at scale need onboarding, tooling, and enablement, all budget lines.
## Outreach playbook
High-priority alert. Look for a co-occurring funding signal to confirm the budget source.
## Related signals
The number of open job postings increased.
A new funding round was announced.
Sales job postings doubled versus the prior crawl.
Engineering job postings doubled versus the prior crawl.
***
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# Growth signals
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/overview
Signals that point to a company adding people, attention, and open roles, usually meaning budget is loosening up.
Signals that point to a company adding people, attention, and open roles, usually meaning budget is loosening up.
The growth category contains **14 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
A company's employee count increased by at least one between snapshots.
A company moved up a full Professional Network employee size band.
Employee growth returned after a recent decline.
The company's Professional Network follower count increased.
Follower growth ran sharply above the company's recent baseline.
The number of open job postings increased.
Open job postings jumped sharply above the recent baseline.
A company posted a role in a function it hadn't hired for in 12 months.
A company posted its first role in a new country.
A company posted its first role in a new city.
The share of senior-level postings rose meaningfully.
Engineering job postings doubled versus the prior crawl.
Sales job postings doubled versus the prior crawl.
The share of remote or hybrid postings shifted significantly.
# Remote Jobs Share Change
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/remote-jobs-share-change
The share of remote or hybrid postings shifted significantly.
`remote_jobs_share_change`
Growth
Company Professional Network jobs, remote/hybrid tag analysis.
The share of remote or hybrid postings shifted significantly.
## When it fires
Share of postings tagged remote or hybrid changes by ≥ 10 percentage points.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | ---------------------------------------------------- |
| `meta.direction` | up or down, the direction of the remote-share shift. |
## Magnitude
Bucketed on the percentage-point shift.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
Big shifts in remote policy can signal organizational change worth a conversation. A swing toward remote often accompanies geographic expansion and distributed-team tooling needs; a swing back toward office can signal consolidation or culture resets.
## How to use Remote Jobs Share Change Signal?
**The scenario.** You are a GTM lead at a managed IT provider specializing in distributed workforces: device shipping, remote onboarding, endpoint management, the whole stack a company needs when its people stop sharing a building.
**The goal.** Spot companies in the middle of a workforce-model shift, when the IT assumptions their office was built on are breaking one laptop at a time.
**The signal fires.** `remote_jobs_share_change` fires for Petrel Insurance, a 400-person regional insurer. The share of remote and hybrid roles in their postings has moved sharply upward against their baseline. A year ago they hired exclusively on-site; this quarter, most new roles are remote-eligible.
**What it tells you.** A shift like that is policy, not coincidence, and policy shifts arrive before infrastructure does. Somewhere at Petrel, IT is now imaging laptops one by one, shipping them from a spare desk, and fielding security questions their on-premise setup was never designed to answer. **The workforce model changed by decision; the IT model has to change by project**, and that project needs a partner.
**The play.**
1. Confirm direction and scale from the signal's magnitude, a small drift is culture, a large swing is strategy.
2. Target the IT manager with the operational reality, not the trend piece: remote hiring at their pace means dozens of device setups a quarter with no process behind them.
3. Lead with one painful, concrete workflow you fix, **laptop-to-new-hire in three days, anywhere**, rather than the full catalog.
4. Time follow-ups to their hiring: each remote cohort that starts makes the gap more visible and your pitch more obviously overdue.
**Scale it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `remote_jobs_share_change` in your regions finds insurers, law firms, and accountants making the same shift on the same schedule.
**Why this works.** Companies buy distributed-IT services when distribution stops being an experiment. The posting mix is where that decision shows up first.
## How to read it
A 10-point swing reflects a deliberate workforce strategy change.
More remote means more collaboration, security, and HR tooling needs.
Up and down tell very different stories, always check meta.direction.
## Outreach playbook
Niche but useful for collaboration, HR, and security tooling sellers. Read the direction first.
## Related signals
The number of open job postings increased.
Multiple office closures in a short window.
A company opened its first location in a new country.
***
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# Sales Hiring Surge
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/sales-hiring-surge
Sales job postings doubled versus the prior crawl.
`sales_hiring_surge`
Growth
Company Professional Network jobs, sales function count.
Sales job postings doubled versus the prior crawl.
## When it fires
Count of sales postings ≥ 2× the previous crawl.
## Magnitude
High by design (2× threshold).
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
When a company doubles its sales hiring, it's gearing up to sell harder, and that means sales-enablement budget. A sales surge is the clearest signal for anyone selling CRM, sales intelligence, enablement, or outbound tooling, the buyers and the budget arrive together.
## How to use Sales Hiring Surge Signal?
**Where you sit.** You are an AE at a sales-onboarding platform. Your product cuts new-rep ramp time from five months to three, which means it is worth the most to one buyer in one moment: **a sales leader who just committed to hiring a lot of reps, fast**.
**The mission.** Find sales organizations at the start of a hiring wave, when ramp math is suddenly a board-level number and every week of faster onboarding is measurable pipeline.
**The signal fires.** `sales_hiring_surge` fires for Vantage Peak Software: sales postings have doubled against their prior crawl. Ten open AE and SDR roles at a company whose sales team, until last month, was about twenty people.
**Reading it.** Someone signed off on aggressive growth, which means someone also signed up for the consequence: ten new reps who will produce almost nothing for their first several months. At standard ramp times, that surge represents **a seven-figure trough of unproductive quota capacity**, and the VP of Sales who owns the number has probably not finished doing that math yet. You get to do it for them.
**Your move.**
1. Build the mini business case before the first touch: their hiring volume, standard ramp benchmarks for their segment, and the value of shaving eight weeks off each rep.
2. Open with their number:
> Ten sales hires this quarter means roughly forty months of combined ramp ahead of you. Cutting that by a third is worth more than most tools in your stack combined. Happy to show the model.
3. Sell to the enablement lead if one exists, the VP if not, **whoever owns the ramp chart owns your budget line**.
4. Land before the cohort starts; onboarding tools bought after week one are retrofits, and retrofits stall.
**Put it on autopilot.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `sales_hiring_surge`, checked weekly, catches every hiring wave in your segment while the offer letters are still going out.
**Why it lands.** You are not selling software; you are selling months of quota back to the person being measured on them.
## How to read it
Doubling sales headcount means an aggressive revenue expansion.
New reps need data, tools, and training, all purchased fast.
Sales surges are part of the pre\_ipo\_signal pattern.
## Outreach playbook
Top signal for sales-tech sellers. Reach the new sales leader before the team is fully ramped.
## Related signals
Open job postings jumped sharply above the recent baseline.
A new sales leader joined the company.
A company shows the early pattern of an IPO track.
A company posted a role in a function it hadn't hired for in 12 months.
***
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# Senior Hiring Increase
Source: https://apidoc.cufinder.io/buying-signals/signals/growth/senior-hiring-increase
The share of senior-level postings rose meaningfully.
`senior_hiring_increase`
Growth
Company Professional Network jobs, title-pattern analysis on posting share.
The share of senior-level postings rose meaningfully.
## When it fires
Share of postings titled Director / VP / Head / Chief / Lead rises by ≥ 5 percentage points crawl-over-crawl.
## Magnitude
Bucketed on the percentage-point shift in senior posting share.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
Senior hiring often precedes strategic shifts and new initiatives. When a company tilts its hiring toward leadership roles, it's building out decision-making capacity, often ahead of a new product line, a funding round, or a market expansion.
## How to use Senior Hiring Increase Signal?
**The setup.** You lead business development at a retained executive-search firm. Your service is expensive, slow, and unmatched for roles that companies cannot afford to get wrong. The prospects worth your time are not companies that hire; they are **companies whose hiring is visibly tilting senior**, because senior roles are where retained search wins.
**What you want.** A structural read on which companies are entering a leadership build-out phase, so your partners spend their networking hours where the mandates will be.
**The signal fires.** `senior_hiring_increase` fires for Halewood Energy, a renewables developer. The share of senior-level postings in their mix has risen meaningfully: where they used to post engineers and analysts, they are now posting directors and heads-of.
**Reading it.** A senior-heavy mix means the organization is building its next layer, usually ahead of expansion, a raise, or a strategic turn. Companies fill the first one or two of those roles through job boards and their own networks, and then discover what every search partner knows: **senior pipelines are thin, slow, and full of candidates who are not actually looking**. That discovery, usually around month two of a stalled director search, is when retained search stops sounding expensive.
**The play.**
1. Map which senior roles they have posted and how long each has been open; the aging ones are your entry point.
2. Approach the CEO or CHRO consultatively, with market intelligence as the gift: compensation benchmarks for the roles they are struggling with, and where that talent currently sits.
3. Position against the stall, not the job board, **"your director search is in month three because the people you want are not applying to postings"** is a sentence that lands.
4. Win the hardest single mandate first. Retained relationships are built one impossible role at a time.
**Put it on autopilot.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) review of `senior_hiring_increase` across your practice areas tells each partner which three companies to get close to this quarter.
**Why it lands.** By the time a search firm is called, the shortlist of firms is already set. This signal puts you at the table **while the org chart is still being drawn**.
## How to read it
Leadership hiring precedes execution, the strategy is being staffed.
A wave of senior roles often maps to a new business unit or bet.
More senior hires means more budget owners to sell to soon.
## Outreach playbook
Monitor as a strategic-intent signal. Strong when paired with executive\_team\_buildout.
## Related signals
A new C-level executive joined the company.
A new VP-level leader joined the company.
A company rapidly built out its leadership team.
***
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# DBA Added
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/dba-added
The name now references a former name (dba/fka).
`dba_added`
Identity
Company Professional Network page (name field, pattern match).
The name now references a former name (dba/fka).
## When it fires
New name contains formerly, (prev., fka, dba, or a parenthetical with the old name.
## Magnitude
Moderate to high, an explicit transition marker.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
It's a clear breadcrumb pointing to a transition. When a company appends 'formerly X' or 'fka X', it's openly signaling a recent rebrand or acquisition while preserving search recognition, a confirmation that an identity shift just happened.
## How to use DBA Added Signal?
**Where you sit.** You handle business development for an intellectual-property law firm. Trademark work has a discovery problem: **by the time a company publicly launches a new brand, the filing decisions that needed a lawyer were due months earlier**, and either someone else got the work or, worse for the client, nobody did.
**The mission.** Spot brand transitions in their in-between phase, when a company is operating under both an old and a new name and the legal checklist is longest.
**The signal fires.** `dba_added` fires for Reed Analytics, which now presents itself as "Reed Analytics dba Parsec". The dual identity is the tell: a new brand exists, the old one is still legally load-bearing, and the transition between them is live right now.
**Reading it.** A dba phase is when trademark exposure peaks. The new name needs clearance and registration in every operating market; contracts, licenses, and registrations reference the old entity; and if the company skipped a proper clearance search before falling in love with "Parsec", **they may be building equity in a name they cannot keep**. Companies in this phase either have counsel handling it, or they have a to-do list nobody owns.
**The play.**
1. Run a quick public clearance scan on the new name yourself; if you find a conflict, your first email writes itself.
2. Approach the general counsel, or the CEO where none exists, with the checklist framed as a courtesy: the six legal items every dba transition needs closed, most of which have deadlines people discover late.
3. Offer a fixed-fee transition package, **predictable pricing wins exactly the mid-size clients big firms ignore**.
4. The transition work is the entry; the ongoing portfolio, renewals, and enforcement are the relationship.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `dba_added` in your jurisdictions surfaces brand transitions while the filings are still open questions.
**Why it lands.** Legal services sell badly as interruptions and brilliantly as deadlines. The dba phase is nothing but deadlines.
## How to read it
The company is telling you a change happened.
dba/fka markers preserve brand recall during a transition.
These markers are usually added right after a change.
## Outreach playbook
Confirms a recent rebrand or acquisition. Note both names in your records.
## Related signals
The company name string changed.
A name change with almost no overlap to the old name.
A company executed a coordinated rebrand.
***
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# Description Change Major
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/description-change-major
The company description was substantially rewritten.
`description_change_major`
Identity
Company Professional Network page (description field), similarity analysis.
The company description was substantially rewritten.
## When it fires
`description` Levenshtein-normalized similarity \< 0.7.
## Magnitude
High, a major rewrite reflects strategic change.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A major rewrite of how a company explains itself is a strong strategic signal. Companies rewrite their core description when they pivot, expand into new markets, or reposition after a funding round, all high-value timing events. It feeds pivot\_signal, rebrand\_signal, and acquired\_signal.
## How to use Description Change Major Signal?
**The setup.** You lead business development at a strategy consultancy focused on go-to-market transformation. Your buyers hire you mid-metamorphosis: new market, new model, new story. The frustration is that **by the time a repositioning is announced, the transformation budget is already spent**, on someone else.
**What you want.** To catch companies in the middle of rewriting themselves, when the direction is chosen but the execution, the org, the messaging, the GTM motion, is still unbuilt.
**The signal fires.** `description_change_major` fires for Vireo Mobility, a fleet-telematics company. The description was not edited, it was replaced: the old text sold hardware trackers; the new text sells a "fleet intelligence platform" with outcomes language and a subscription framing. That is a business-model migration written in public.
**Reading it.** A wholesale description rewrite is the visible tip of an internal decision stack: leadership has agreed on a new identity, marketing has committed it to text, and now the entire company has to become the thing the text claims. Hardware companies becoming platforms hit the same walls in the same order, **sales teams that cannot sell subscriptions, pricing nobody trusts, customers confused about what they bought**. Those walls are your service catalog.
**Your move.**
1. Diff the descriptions and name the migration precisely; "hardware to platform" earns a different pitch than "product to services".
2. Approach the CEO or CRO with the walls, not the theory:
> The new positioning reads like a hardware-to-platform shift, and companies making it usually hit the same three walls in year one. We have taken four industrial firms through this exact migration; happy to share what breaks first.
3. Offer a transformation-readiness assessment as the entry engagement, sized to be approved without a committee.
4. Land while the rewrite is fresh; **the gap between new story and old reality is widest, and most budgeted, in the first two quarters**.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `description_change_major` across your industries is a metamorphosis detector running around the clock.
**Why it lands.** You are not proposing change to a comfortable company. The signal only fires on companies **already mid-leap**, looking for someone who has landed it before.
## How to read it
Sub-0.7 similarity means the core story changed.
Often accompanies a genuine business shift.
Feeds pivot\_signal, rebrand\_signal, and acquired\_signal.
## Outreach playbook
High-value. Read the new description for the pivot story and tailor outreach to it.
## Related signals
The company description was lightly edited.
A company fundamentally changed what it does.
A company executed a coordinated rebrand.
A company was acquired.
***
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# Description Change Minor
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/description-change-minor
The company description was lightly edited.
`description_change_minor`
Identity
Company Professional Network page (description field), similarity analysis.
The company description was lightly edited.
## When it fires
`description` Levenshtein-normalized similarity is between 0.7 and 0.95.
## Magnitude
Low, minor wording updates.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Small wording updates, worth noting in aggregate. A single minor edit is rarely meaningful, but repeated description tweaks over time can show a company iterating on its positioning before a bigger shift.
## How to use Description Change Minor Signal?
**The scenario.** You run ABM at an enterprise-software vendor with a 300-account target list. Every account has a research brief: what they do, what they emphasize, what language their executives use. The briefs were accurate the week they were written. Six months later, **a third of them are quietly wrong**, and nobody has time to re-research 300 companies on a schedule.
**The goal.** Keep account intelligence continuously fresh with near-zero analyst effort, by refreshing only the accounts that actually changed something.
**The signal fires.** `description_change_minor` fires for Ondine Marine, one of your tier-two accounts. The edit is small, a few phrases reworked, a product term swapped, nothing strategic on its own. Which is exactly the point: minor edits are routine, but they are also **the only public notice you will ever get that the company touched its own story**.
**What it tells you.** Individually, a minor edit means little. Operationally, it is a perfect maintenance trigger: the account's self-description drifted, so the copy of it in your brief just went stale. And occasionally the "minor" edit is a leading fragment, a new product noun appearing a quarter before the launch, that your team will be glad to have caught.
**The play.**
1. Wire the signal to a review task, not an alert; minor changes deserve a queue, not a ping.
2. Have the automation diff old against new description and paste both into the account brief, so the human review takes thirty seconds.
3. Watch for new nouns, **fresh product names and market terms in a description edit often front-run announcements**, and flag those to the account team.
4. Track edit frequency per account; a company that suddenly edits monthly is fidgeting with its identity, which usually precedes a bigger move worth escalating.
**Automate it.** A scheduled [Company Signals API](/buying-signals/signals-apis/company-signals) job on `description_change_minor` across your account list keeps every brief self-updating for the cost of one API call a week.
**Why this works.** ABM loses to stale intelligence more often than to bad strategy. This signal makes freshness **a property of the system instead of a heroic quarterly project**.
## How to read it
A small refresh, not a strategic rewrite.
Repeated minor edits hint at active repositioning.
Use as context, not a trigger.
## Outreach playbook
Context only. Aggregate over time to spot iterative repositioning.
## Related signals
The company description was substantially rewritten.
A tracked strategic keyword appeared in the description.
The company tagline text changed.
***
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# Description Keyword Added
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/description-keyword-added
A tracked strategic keyword appeared in the description.
`description_keyword_added`
Identity
Company Professional Network page (description field), keyword tracking.
A tracked strategic keyword appeared in the description.
## When it fires
A tracked keyword (AI, agentic, platform, enterprise, SaaS, LLM, acquired, merger, Series A/B/C, and more) appears in the current description but not the previous one.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| -------------- | ------------------------------------ |
| `meta.keyword` | The specific keyword that was added. |
## Magnitude
Moderate to high depending on the keyword.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
The added keyword tells you exactly what shifted. A company adding 'AI' or 'agentic' is staking a claim in a new category; adding 'enterprise' signals a move upmarket; adding 'Series B' confirms a raise. The stored keyword makes this signal precisely actionable.
## How to use Description Keyword Added Signal?
**Where you sit.** You run GTM at a GPU-cloud provider. Your buyers are companies standing up real AI workloads, and your timing problem is acute: reach them before the first big training run and you are the infrastructure partner; reach them after and **you are quoting against an incumbent bill they have already rationalized**.
**The mission.** Detect the moment a company publicly commits to an AI direction, which reliably happens in their marketing language before it happens in their infrastructure spend.
**The signal fires.** `description_keyword_added` fires for Ridgeway Software, a supply-chain planning vendor: the tracked keyword "AI-powered" has appeared in their company description for the first time.
**What it tells you.** Companies do not add "AI-powered" to their self-description for fun; legal and marketing argue about that phrase. Its arrival means an AI initiative has been funded and blessed loudly enough to become identity. And every funded AI initiative walks the same road: hire ML engineers, prototype on a laptop, discover the compute wall, sign an infrastructure deal. The keyword is mile zero of that road, and **your entire business is winning the deal at the end of it**.
**The play.**
1. Corroborate with hiring: check the account for ML and data-engineering postings; keyword plus roles means the initiative is real, not aspirational.
2. Reach the technical leader early with a builder's offer, credits, benchmarks, an architecture session, not a sales sequence. **Infrastructure loyalty is formed at the prototype stage.**
3. Track the account's progression; when the engineering hires start, the compute wall is one to two quarters away, which is exactly your window.
4. Keep a tracked-keyword watchlist that matches your thesis: "AI-powered" today, whatever the next platform shift is tomorrow.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `description_keyword_added` turns a marketing-language change into a pipeline trigger with about a two-quarter head start on the spend.
**Why it lands.** Everyone sells to companies doing AI. The edge is selling to companies **the week they decided to become one**.
## How to read it
New keywords mark a deliberate positioning move.
meta.keyword tells you exactly which direction.
'enterprise' means upmarket; 'AI' means a tech pivot.
## Outreach playbook
Filter by keyword to match your ICP. 'enterprise' added = upmarket motion to support.
## Related signals
A tracked strategic keyword disappeared from the description.
The company description was substantially rewritten.
A company fundamentally changed what it does.
***
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# Description Keyword Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/description-keyword-removed
A tracked strategic keyword disappeared from the description.
`description_keyword_removed`
Identity
Company Professional Network page (description field), keyword tracking.
A tracked strategic keyword disappeared from the description.
## When it fires
A tracked keyword present in the previous description is absent now.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| -------------- | -------------------------------------- |
| `meta.keyword` | The specific keyword that was removed. |
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Both directions tell a story about where the company is heading. Dropping a keyword like 'startup' or a specific technology can signal maturation or a move away from a positioning, the inverse of the added-keyword signal.
## How to use Description Keyword Removed Signal?
**The scenario.** You manage the partner ecosystem at an e-commerce platform. Hundreds of agencies and app developers build on you, describe themselves as your partners, and drive a real share of your merchants' success. Ecosystem loyalty looks permanent right up until it is not, and **partners never announce that they are drifting to a rival platform. They just quietly stop mentioning you.**
**The goal.** Catch ecosystem drift at the identity level, when a partner edits you out of their self-description, months before the certification lapses or the co-sell numbers dip.
**The signal fires.** `description_keyword_removed` fires for Hollis & Frame, a 60-person agency that has been a top-tier partner for five years. The tracked keyword removed from their description: your platform's name. Their new description talks about "composable commerce" and, on closer reading, name-checks a competitor.
**Reading it.** An agency's description is its sales pitch, and agencies pitch what they want more of. Removing your platform from that pitch means new business development has been redirected, existing capability is being repositioned, and their best people are probably already training on the alternative. The revenue you see from them today is **momentum, not commitment**, and momentum decays.
**The play.**
1. Verify the drift: check their recent case studies, event presence, and job postings for the competing stack before assuming the worst.
2. Call the relationship owner honestly, partners respect directness: the description change was noticed, is this strategic or housekeeping, and what would it take to keep them invested.
3. If the answer is strategic, negotiate for the middle: **a multi-platform partner who still leads with you beats a defector**, and lead-flow commitments are your leverage.
4. Feed the account into succession planning either way; ecosystem coverage should never depend on any single agency's strategy.
**Automate it.** Run your partner directory through the [Company Signals API](/buying-signals/signals-apis/company-signals) on `description_keyword_removed` with your brand as the tracked keyword. Ecosystem health becomes observable instead of anecdotal.
**Why this works.** Partner churn is slow, silent, and expensive. The description edit is **the one moment the silence becomes visible**.
## How to read it
Removing a keyword distances the company from it.
Dropping 'startup' or early-stage terms signals growth.
meta.keyword shows what they're moving away from.
## Outreach playbook
Read meta.keyword for the de-positioning story. Useful paired with the added signal.
## Related signals
A tracked strategic keyword appeared in the description.
The company description was substantially rewritten.
***
Surface `description_keyword_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Name Change
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/name-change
The company name string changed.
`name_change`
Identity
Company Professional Network page (name field).
The company name string changed.
## When it fires
`name` string changed between snapshots.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------ | -------------------------- |
| `from_value` | The previous company name. |
| `to_value` | The new company name. |
## Magnitude
Treated as high-signal; names rarely change without a reason.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Names rarely change without a reason, a rebrand, an acquisition, or a legal restructuring. A name change is a flag to re-investigate everything you know about the account, and it feeds the rebrand\_signal and acquired\_signal composites.
## How to use Name Change Signal?
**The scenario.** You run an SEO and web agency, and your highest-margin service is migration protection: keeping a company's search traffic alive while its domain, brand, and site all change underneath it. The catch is timing. **Companies call you after the rebrand ships and the traffic is already gone**, when the cheap version of the project would have been beforehand.
**The goal.** Catch renames the week they surface, while redirects, rankings, and brand equity can still be protected instead of resurrected.
**The signal fires.** `name_change` fires for Harbourline, a B2B logistics platform now presenting itself as Arclight. The website still lives on the old domain, which tells you the migration is mid-flight, the most dangerous and most billable possible moment.
**What it tells you.** A company mid-rename has a dozen technical decisions in front of it that marketing teams reliably underestimate: domain strategy, redirect mapping, backlink outreach, brand-query cannibalization. Handled well, the rename costs a few percent of organic traffic for a quarter. Handled naively, **it costs half their inbound pipeline for a year**, and someone gets blamed.
**The play.**
1. Confirm the migration state: new name live, old domain still primary means you are early enough to matter.
2. Write to the marketing lead with the risk quantified and the fix framed as insurance:
> Congrats on the Arclight rebrand. The riskiest part is still ahead: renamed companies that migrate domains without a redirect plan typically lose 30 to 60% of organic traffic. We protect exactly this transition, happy to sanity-check your plan for free either way.
3. The free sanity-check is the wedge; **almost no in-house plan survives it intact**, and the gaps become the scope.
4. Deliver the migration, then keep the retainer, renamed companies spend on visibility for a year after.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `name_change` in your market is a standing list of companies entering the exact two-month window your service exists for.
**Why it lands.** Rebrand budgets are emotional; migration budgets are insurance. You are selling the insurance **while the truck is still loading**.
## How to read it
Companies don't rename casually; something structural happened.
The two most common causes are acquisition and rebranding.
Feeds rebrand\_signal and acquired\_signal.
## Outreach playbook
Investigate the cause. Update CRM records and re-confirm the account's status.
## Related signals
A name change with almost no overlap to the old name.
The name now references a former name (dba/fka).
A company executed a coordinated rebrand.
A company was acquired.
***
Surface `name_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Name Change Drastic
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/name-change-drastic
A name change with almost no overlap to the old name.
`name_change_drastic`
Identity
Company Professional Network page, similarity analysis on name word sets.
A name change with almost no overlap to the old name.
## When it fires
`name_change` fires AND Jaccard similarity on word sets \< 0.3.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------ | -------------------------- |
| `from_value` | The previous company name. |
| `to_value` | The new company name. |
## Magnitude
Hyper, near-total name change is the strongest identity event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A near-total name change usually means a full rebrand or an M\&A event rather than a cosmetic tweak. When the old and new names share almost no words, the company has fundamentally redefined itself, a top-priority investigation trigger.
## How to use Name Change Drastic Signal?
**The setup.** You are an analyst at a mid-market private equity firm. Your deal-sourcing edge is noticing corporate events before they reach databases and bankers' books, and one event class is chronically underwatched: **companies that change their name so completely that the old identity disappears**.
**What you want.** An automated tripwire for identity breaks, because a drastic rename is rarely cosmetic. It usually marks an acquisition being absorbed, a pivot being formalized, or a company distancing itself from its own history, and all three are interesting to a fund.
**The signal fires.** `name_change_drastic` fires: Pinewood Systems, a modest industrial-IoT firm you had shortlisted last year, is now called Juvo. No overlap between the names, no transition branding, a clean break.
**Reading it.** Unlike a normal rename, a drastic one is a fact pattern worth pulling on. Check the adjacent evidence: new leadership signals suggest an acquirer installing management; a rewritten description suggests a pivot; a new parent company confirms M\&A that never hit the wires. **The rename is not the event, it is the smoke above the event**, and your job is finding the fire.
**The play.**
1. Open a mini-diligence file the day the signal fires: what changed alongside the name, ownership, leadership, description, structure.
2. If it reads as a quiet acquisition, map the acquirer; unannounced roll-ups often mean **a platform strategy you can either join or compete with**.
3. If it reads as a pivot, re-underwrite your old thesis; the company you shortlisted may no longer exist, or may have become more interesting.
4. Either way, refresh the relationship with a soft touch to management; identity breaks often precede capital needs.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `name_change_drastic` across your sectors runs the tripwire continuously, feeding a review queue that takes an hour a week.
**Why this works.** Everyone reads the deal announcements. The funds with an edge read **the events that were structured not to be announced**.
## How to read it
Sub-0.3 similarity means a wholesale identity change.
Drastic changes frequently accompany acquisitions or mergers.
Escalate above ordinary name changes.
## Outreach playbook
High-priority. Strong evidence of M\&A or major rebrand; re-qualify entirely.
## Related signals
The company name string changed.
A company executed a coordinated rebrand.
A company was acquired.
Two companies merged under a common new parent.
***
Surface `name_change_drastic` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Identity signals
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/overview
Signals tracking how a company describes itself, name, tagline, and description, often hinting at rebrands, pivots, or M&A.
Signals tracking how a company describes itself, name, tagline, and description, often hinting at rebrands, pivots, or M\&A.
The identity category contains **10 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
The company name string changed.
A name change with almost no overlap to the old name.
The name now references a former name (dba/fka).
The company tagline text changed.
A previously empty tagline is now populated.
A populated tagline was cleared out.
The company description was lightly edited.
The company description was substantially rewritten.
A tracked strategic keyword appeared in the description.
A tracked strategic keyword disappeared from the description.
# Tagline Added
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/tagline-added
A previously empty tagline is now populated.
`tagline_added`
Identity
Company Professional Network page (tagline field).
A previously empty tagline is now populated.
## When it fires
`tagline` was empty or null and is now populated.
## Magnitude
Low to moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A new tagline can signal a fresh go-to-market push. Adding a tagline where there was none often means the company is investing in its brand presence, common in early-stage companies professionalizing or after a leadership change.
## How to use Tagline Added Signal?
**The scenario.** You are an independent positioning consultant. Your clients are technical founders who spent years selling through demos and word of mouth, and have just realized the words on their website matter. The eternal question is finding them **at the moment of that realization**, not before it, when they do not care, and not after, when an agency already owns the project.
**The goal.** Detect the first public flicker of a company starting to take its own story seriously.
**The signal fires.** `tagline_added` fires for Ostrom Robotics, a 45-person warehouse-automation firm. For years their profile had no tagline at all, the classic mark of an engineering-led company. Now, suddenly, there is one, and it is exactly what first attempts always are: vague, feature-shaped, and written by committee.
**What it tells you.** A company that goes from no tagline to a tagline has crossed an internal threshold: someone, a founder, a first marketer, a board member, decided the company needs to explain itself. That decision almost never stops at one line. It cascades into messaging, website, deck, and category questions, and right now **the appetite exists but the expertise usually does not**. The mediocre first tagline is the proof.
**The play.**
1. Check the adjacent signals: a recent first marketing hire or a new tagline together mean the messaging project is already funded.
2. Reach the founder with generosity rather than critique, nobody loves hearing their new line is weak:
> Noticed Ostrom just added a tagline, which usually means positioning has made it onto the roadmap. I help robotics companies get from first-draft language to a story that closes deals, happy to share three quick observations either way.
3. The "three observations" do the selling; **a specific critique of their actual words beats any portfolio**.
4. Scope a positioning sprint, then let the sprint output justify the retainer.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `tagline_added`, filtered to technical industries, is a feed of companies at the exact top of your funnel.
**Why it lands.** You are not convincing anyone that messaging matters. The signal only fires for companies **who just convinced themselves**.
## How to read it
Adding a tagline shows new attention to positioning.
Often seen as startups formalize their messaging.
A new CMO or marketing lead may drive it.
## Outreach playbook
Soft signal. More useful for early-stage targets professionalizing their brand.
## Related signals
The company tagline text changed.
A populated tagline was cleared out.
A new VP-level leader joined the company.
***
Surface `tagline_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Tagline Change
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/tagline-change
The company tagline text changed.
`tagline_change`
Identity
Company Professional Network page (tagline field).
The company tagline text changed.
## When it fires
`tagline` text changed and was previously non-empty.
## Magnitude
Moderate, taglines shift for repositioning.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Tagline shifts often reflect repositioning. The tagline is a company's elevator pitch, when it changes, the company is rethinking how it wants to be perceived, often alongside a broader strategic shift.
## How to use Tagline Change Signal?
**The setup.** You are an AE at a healthcare-data platform, staring at your stalled-deals list. Waypoint Health sits near the top: great discovery calls last year, real pain, then a polite freeze, "priorities have shifted". Every rep has twenty of these, and **the standard check-in email ("just bumping this") has roughly a zero percent resurrection rate**.
**What you want.** A legitimate, specific reason to reopen a frozen conversation, something that shows the account has changed rather than that your quarter needs help.
**The signal fires.** `tagline_change` fires for Waypoint Health. Their old line was about care coordination. The new one leads with "value-based care, powered by data". That is not wordsmithing; that is **the company announcing a strategy in nine words**.
**Reading it.** Companies change taglines when the story they tell the market changes, and the story changes when strategy does. If Waypoint is now selling itself on data-driven value-based care, then somewhere inside, priorities were re-cut, budgets moved, and initiatives that were frozen last year may have thawed under a new name. Your stalled deal was priced against the old strategy. **The new strategy deserves a new conversation.**
**Your move.**
1. Reread your old discovery notes against the new positioning; find the overlap between what they now claim and what you actually do.
2. Reopen with the change itself as the reason:
> Noticed Waypoint's messaging just shifted toward value-based care. When we spoke last year, your data infrastructure was the blocker for exactly that. Worth a fresh look at how the two connect?
3. Treat it as a new deal, not a resumed one, **new stakeholders may own the new strategy**, and the old champion may not be the right door anymore.
4. Apply the same replay across your stalled list whenever this signal fires on any of them.
**Automate it.** Sync your stalled and closed-lost accounts to a weekly [Company Signals API](/buying-signals/signals-apis/company-signals) check on `tagline_change`; every hit is a pre-written reopener.
**Why it lands.** "Just checking in" says your pipeline changed. Referencing their new story says **their company changed**, and that is a conversation people take.
## How to read it
A new tagline signals a new market message.
Read the new wording for hints about direction.
Part of rebrand\_signal when combined with name and description changes.
## Outreach playbook
Read the new tagline for positioning clues. Strong when part of rebrand\_signal.
## Related signals
A previously empty tagline is now populated.
A populated tagline was cleared out.
A company executed a coordinated rebrand.
The company description was substantially rewritten.
***
Surface `tagline_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Tagline Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/identity/tagline-removed
A populated tagline was cleared out.
`tagline_removed`
Identity
Company Professional Network page (tagline field).
A populated tagline was cleared out.
## When it fires
`tagline` was populated and is now empty or null.
## Magnitude
Low to moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Sometimes precedes a larger identity overhaul. Removing a tagline can be a transitional state during a rebrand, the old message is gone before the new one is ready, or it can reflect page neglect.
## How to use Tagline Removed Signal?
**Where you sit.** You are a freelance brand strategist. Your best projects come from companies in identity limbo, too big for their founding story, not yet sure of the next one. The problem is that limbo is invisible from the outside. Companies in it do not issue press releases saying "we no longer know what we are".
**The mission.** Find the public fingerprint of identity limbo, and reach those companies while the question is open, before an agency with a bigger logo gets the call.
**The signal fires.** `tagline_removed` fires for Solmar Foods, a specialty-ingredients company. They had a tagline for years, and now the field is simply blank. Nothing replaced it. Someone deliberately deleted the company's one-line explanation of itself and shipped the emptiness.
**What it tells you.** Removals are stranger and more meaningful than changes. A swapped tagline means a new story was ready; **a deleted tagline means the old story died before its successor was born**. Inside Solmar, some combination of leadership change, portfolio shift, or market repositioning has made the old line untrue, and nobody can yet agree on a true one. That standing disagreement is precisely the job you get hired for.
**The play.**
1. Timestamp the vacuum and watch the account; if the blank persists for weeks, the internal deadlock is real.
2. Look for the cause in surrounding signals, new leadership, description edits, structural changes, so you arrive with a hypothesis instead of a question.
3. Approach the CEO with the observation, gently. The blank space is your icebreaker: companies delete their tagline when the old story stops being true, and the new one usually needs an outside facilitator precisely because insiders are too close.
4. Sell the alignment workshop first, **the deliverable is the decision, not the words**; copy is what falls out of it.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `tagline_removed` gives you a shortlist of companies mid-identity-crisis, refreshed as they enter and resolve it.
**Why it lands.** Most consultants pitch answers to companies not asking questions. This signal finds the companies **already stuck on the question**.
## How to read it
Often a midpoint during a rebrand.
Can also reflect a poorly maintained page.
A new tagline or name change may follow.
## Outreach playbook
Low priority. Watch for a follow-on rebrand if combined with other identity signals.
## Related signals
The company tagline text changed.
A previously empty tagline is now populated.
The company stopped posting for an extended period.
***
Surface `tagline_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Country Expansion
Source: https://apidoc.cufinder.io/buying-signals/signals/location/country-expansion
A company opened its first location in a new country.
`country_expansion`
Location
Company Professional Network page (locations array), country-level check.
A company opened its first location in a new country.
## When it fires
`location_added` fires AND the new country was not previously represented anywhere in `locations`.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------ | ------------------------------------------ |
| `meta.country_new` | The new country the company expanded into. |
## Magnitude
High, international expansion is a strategic milestone.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is genuine international expansion, and a prime outreach moment. A first office in a new country means the company is committing to a new market with all the local compliance, data-residency, and tooling needs that entails. Feeds expansion\_signal.
## How to use Country Expansion Signal?
**The setup.** You lead business development at a localization agency: translation, cultural adaptation, regional QA. Your buyers only need you at specific moments, and the biggest one is unambiguous: **a company entering a country whose language and market it does not understand**.
**What you want.** A feed of companies at the start of a first-time market entry, before they have discovered how much more than translation the entry requires, and before the big localization platforms get the RFP.
**The signal fires.** `country_expansion` fires for Hearthside Games, a US studio: their first-ever location in Japan. For a games company, Japan is the classic high-stakes entry, an enormous market with famously low tolerance for lazy localization.
**Reading it.** A first office in a new country means the company has committed real money to the entry, which means the supporting spend, localization included, is budgeted or about to be. It also means they are early enough to still believe machine translation plus a contractor will do. That belief typically survives until the first embarrassing review, and **your pitch is cheaper than that review**.
**The play.**
1. Lead with the market's specific dangers, not your service list. For Japan and games: honorifics, text expansion breaking UI, culturalization beyond language.
2. Reach the publishing or product lead with a credibility artifact:
> Congrats on the Japan office. Before your first release there, it is worth twenty minutes on the five localization mistakes Western studios make in the Japanese market, we have fixed all five, expensively, for others.
3. Offer a small paid pilot, one title, one storefront, **beachhead pricing for a beachhead moment**.
4. Track their release calendar; localization urgency spikes at every launch.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `country_expansion`, filtered to your language pairs, catches every first entry into the markets you serve.
**Why it lands.** Companies buy localization reactively, after something flops. You are arriving **while the entry is still a plan and the budget still has room for doing it right**.
## How to read it
First office in a country is a real market commitment.
Brings data residency, compliance, and localization needs.
Feeds expansion\_signal alongside hiring and growth.
## Outreach playbook
Prime outreach moment. Route to local rep; pair with first\_job\_in\_country.
## Related signals
A new office location appeared.
A company posted its first role in a new country.
The headquarters country specifically changed.
A company is expanding into new markets while growing.
***
Surface `country_expansion` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# HQ Change
Source: https://apidoc.cufinder.io/buying-signals/signals/location/hq-change
The headquarters city or country changed.
`hq_change`
Location
Company Professional Network page (HQ fields).
The headquarters city or country changed.
## When it fires
`hq_city` or `hq_country` changed between snapshots.
## Magnitude
High, an HQ move is a major operational event.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
An HQ move is a major operational event. Relocating headquarters reflects growth (a bigger space), cost optimization (a cheaper region), or strategic repositioning (closer to talent or customers), all of which carry tooling and vendor implications.
## How to use HQ Change Signal?
**The scenario.** You do business development for an office design and fit-out firm. Your projects are lumpy, six-figure, and tied to one trigger above all others: **a company physically moving its headquarters**. Miss the move, miss the project. And companies plan moves quietly.
**The goal.** Learn about headquarters relocations as early as possible in the decision chain, ideally when the new space is chosen but still a shell.
**The signal fires.** `hq_change` fires for Larkspur Games, a 200-person game studio: their headquarters has moved across town, from a converted warehouse to a larger downtown address. The new address is the tell that matters, it is a building you know, and it delivers as open-plan concrete.
**What it tells you.** A company that has just changed its registered HQ is somewhere on a fit-out journey: either racing to make the new space usable, or living in it half-finished and hating it. Studios in particular carry strong opinions about space, sound rooms, playtest areas, dark rooms for art review, that generic landlord fit-outs never deliver. **Someone at Larkspur is fielding daily complaints about the new office right now.**
**The play.**
1. Move the week the signal fires; fit-out budgets are spent fastest in the first quarter after a move.
2. Pitch the operations lead with the studio-specific angle, not generic workplace design: what a games company needs that the landlord did not build.
3. Offer a phased plan, **companies fresh off moving costs respond to staged spending**, the acoustic fixes now, the showcase reception next quarter.
4. If they moved recently but signed elsewhere, stay warm; post-move regret projects surface within a year.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `hq_change` in your metro keeps a live list of companies mid-move, which beats hearing about relocations from the commercial property press a quarter late.
**Why it lands.** Nobody budgets for office design in the abstract. They budget for it **standing in an empty new headquarters, listening to it echo**.
## How to read it
Companies don't move HQ lightly.
Moves reflect either expansion or cost optimization.
A country-level move is escalated separately.
## Outreach playbook
Notable operational change. Check whether the move is growth-driven or cost-driven.
## Related signals
The headquarters country specifically changed.
A new office location appeared.
Multiple office closures in a short window.
***
Surface `hq_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# HQ Country Change
Source: https://apidoc.cufinder.io/buying-signals/signals/location/hq-country-change
The headquarters country specifically changed.
`hq_country_change`
Location
Company Professional Network page (HQ country field).
The headquarters country specifically changed.
## When it fires
`hq_country` changed specifically (flagged higher priority).
## Magnitude
Hyper-priority, country-level HQ moves are rare and consequential.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A country-level HQ move often signals re-domiciling or a regulatory shift, both of which carry big implications. Companies re-domicile for tax, regulatory, IPO-readiness, or major investor reasons, this is among the most consequential location signals.
## How to use HQ Country Change Signal?
**The setup.** You are a partner at a cross-border tax and legal advisory. Your highest-value engagements come from one rare, complex event: **a company redomiciling, moving its headquarters from one country to another**. Redomiciliations are announced to tax authorities long before they are announced to advisors like you, unless you find them yourself.
**What you want.** To detect HQ country moves the moment they become visible, because every one of them drags a chain of obligations the company has usually underestimated.
**The signal fires.** `hq_country_change` fires for Nimbus Analytics, a 300-person data company: headquarters moved from Boston to London. Whether the driver is investors, markets, or a founder's relocation, the compliance consequences are identical and enormous.
**Reading it.** A cross-border HQ move touches everything your practice sells: corporate structure, transfer pricing, payroll for staff left behind, equity-plan treatment across jurisdictions, permanent-establishment risk in the country they left. Companies mid-redomiciliation usually have counsel for the transaction itself, but **the eighteen months of operational tax cleanup afterward is chronically unowned**, and that is the engagement worth winning.
**The play.**
1. Confirm the move's shape: full redomiciliation, or a new legal HQ with operations unchanged? The signal starts the question; a registry check answers it.
2. Approach the CFO with the aftermath, not the transaction: the five obligations that surface in the first year after a US-to-UK move, and which ones have statutory deadlines.
3. Offer a post-move compliance audit as the entry engagement, **fixed scope, fast, and almost guaranteed to find something**, because these moves always leave loose ends.
4. Mine the pattern: companies that redomicile once often restructure again; stay on the account.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `hq_country_change`, watched monthly across your corridor countries, surfaces a handful of high-value events a quarter, which is all a practice like yours needs.
**Why it lands.** Redomiciling companies do not need convincing that they have a problem. They need **someone who has cleaned up after this exact move before**.
## How to read it
Country moves often relate to tax or regulatory strategy.
Some moves precede listing in a new jurisdiction.
Escalated above ordinary HQ changes.
## Outreach playbook
High-priority. Investigate the regulatory or IPO motivation behind the move.
## Related signals
The headquarters city or country changed.
A company opened its first location in a new country.
The company's entity type changed.
A company went from private to public.
***
Surface `hq_country_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Location Added
Source: https://apidoc.cufinder.io/buying-signals/signals/location/location-added
A new office location appeared.
`location_added`
Location
Company Professional Network page (locations array).
A new office location appeared.
## When it fires
A new office appears in the `locations` array.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | ---------------------------- |
| `meta.location` | The location that was added. |
## Magnitude
Moderate to high.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
New offices mean local growth and local budget. Opening an office is a tangible expansion commitment, more so than a remote hire, and it brings local needs around facilities, compliance, and tooling. Feeds country\_expansion and expansion\_signal.
## How to use Location Added Signal?
**Where you sit.** You are a regional sales manager at a commercial security company: access control, cameras, alarm monitoring. Every new office that opens in your territory needs what you sell, by policy, by insurance requirement, and usually by a deadline tied to move-in. The problem was never demand. It is **finding out about new offices before the incumbent vendor from the company's other sites gets the call by default**.
**The mission.** Catch office openings in your region at announcement, when the security contract is still unawarded.
**The signal fires.** `location_added` fires for Corvid Biotech, a Bay Area company: a new office location has appeared, in Denver, your patch. Lab-adjacent tenants are gold for security work: badge access, after-hours monitoring, compliance-grade audit trails.
**Reading it.** A new location on a company's profile means the lease is signed and the fit-out clock is running. Security systems get decided mid-fit-out, after IT cabling, before furniture, which gives you a window measured in weeks. The company's incumbent provider from headquarters has the inside track but a **known weakness: they are remote, and local service-level promises from two time zones away ring hollow**.
**The play.**
1. Identify the local decision chain: the office launch usually has a project manager, and facilities decisions run through them, not headquarters.
2. Pitch the local angle explicitly, response times, local techs, same-day service, against the incumbent's distance.
3. Bundle for the biotech profile: badge access plus compliance reporting beats generic packages, **sell to what the tenant is, not what the building is**.
4. If the incumbent wins anyway, book the twelve-month follow-up; first-year service failures at remote sites are common and switching happens at renewal.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `location_added`, filtered to your metro, is a permanent feed of fit-outs in progress, better than the building-permit lists everyone else works from.
**Why it lands.** Local vendors beat national incumbents in exactly one moment: **before the default choice gets made**. This signal is that moment.
## How to read it
A physical office is a real commitment to a market.
New locations come with local operational spend.
Feeds country\_expansion and expansion\_signal.
## Outreach playbook
Expansion signal. Route to the local territory and check for country\_expansion.
## Related signals
An office location disappeared.
A company opened its first location in a new country.
The company's office count crossed a milestone threshold.
A company is expanding into new markets while growing.
***
Surface `location_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Location Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/location/location-removed
An office location disappeared.
`location_removed`
Location
Company Professional Network page (locations array).
An office location disappeared.
## When it fires
An office disappears from the `locations` array.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | ------------------------------ |
| `meta.location` | The location that was removed. |
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Closures can signal consolidation. A single removed office may be a lease move, but repeated removals indicate cost-cutting, this signal feeds office\_consolidation when three or more occur in a window.
## How to use Location Removed Signal?
**The scenario.** You are an account manager at a corporate catering company. Your revenue is a bundle of standing contracts, weekly office lunches, meeting catering, pantry stocking, each tied to a physical office. Which means your revenue has a failure mode nobody at your company tracks: **client offices that quietly close**.
**The goal.** Get ahead of office closures among your clients, so you can rescue the relationship even when the location disappears, and know your true revenue risk before the finance team feels it.
**The signal fires.** `location_removed` fires for Palmetto Legal, a law firm you serve at three sites: their midtown office has vanished from the company's locations. That office is your second-largest single contract with them.
**What it tells you.** An office removal means people were relocated, downsized, or moved remote, and the standing services attached to that office are about to be cancelled by someone in procurement working down a checklist. If the first you hear of it is the cancellation email, you are a line item. If you call first, you are a partner helping them transition, and **partners get the consolidated contract at the surviving offices**.
**The play.**
1. Call your Palmetto contact the day the signal fires, lead with service, not alarm: how should catering adapt to the office changes?
2. Propose the migration: shift the midtown budget toward the two surviving sites, larger weekly orders, event catering for the newly combined teams.
3. Reforecast the account internally now, **losing a location without losing the client is a fine outcome, but only if finance saw it coming**.
4. Watch the pattern: one closed office is logistics; the same signal firing twice in a year means the client is shrinking, and the whole account needs a risk plan.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) check on `location_removed` across your client list turns silent revenue risk into a call sheet.
**Why it lands.** Location-dependent vendors always learn about closures last. This signal moves you **from the cancellation list to the planning conversation**.
## How to read it
Office closures often reflect cost reduction.
One removal is minor; three in 90 days is a pattern.
Feeds office\_consolidation.
## Outreach playbook
Watch for clustering. Three removals in 90 days triggers office\_consolidation.
## Related signals
A new office location appeared.
Multiple office closures in a short window.
A company fell down a Professional Network employee size band.
***
Surface `location_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Multi Office Milestone
Source: https://apidoc.cufinder.io/buying-signals/signals/location/multi-office-milestone
The company's office count crossed a milestone threshold.
`multi_office_milestone`
Location
Company Professional Network page (locations array length).
The company's office count crossed a milestone threshold.
## When it fires
Total `locations.length` crosses one of 5, 10, 25, or 50.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | ----------------------------------------------- |
| `meta.milestone` | The milestone value crossed (5, 10, 25, or 50). |
## Magnitude
High at every threshold; higher milestones are more significant.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Crossing a milestone marks a real scaling threshold. Going from 9 to 10 offices, or 24 to 25, reflects sustained geographic growth and a maturing operations function that standardizes tooling across sites.
## How to use Multi Office Milestone Signal?
**Where you sit.** You are an AE at an HR-compliance platform. Your product exists because employment law is local: five offices can mean five jurisdictions, five leave policies, five sets of posting requirements. Companies do not buy you at one office. They buy you at the milestone where **the spreadsheet that managed compliance stops surviving contact with reality**.
**The mission.** Find companies crossing exactly that operational threshold, the moment their office count makes manual HR compliance visibly untenable.
**The signal fires.** `multi_office_milestone` fires for Drift & Dune Apparel, a retail brand that has just crossed five locations, now spread across three states. Their HR team, judging by their profile, is two people.
**Reading it.** Office-count milestones are compliance milestones wearing a party hat. Three states means three payroll tax regimes, diverging sick-leave rules, and posting requirements that differ by location. At five offices, a two-person HR team is **exactly one resignation or one audit away from crisis**, and someone in that team already suspects it. Milestone companies are also celebrating, which makes them unusually reachable; congratulations open doors that cold pitches cannot.
**The play.**
1. Open with the milestone, sincerely, then pivot to what it quietly changed: five locations across three states puts them past the threshold where regulators expect systematic compliance.
2. Sell to the HR lead with a jurisdiction map of their actual footprint, **their own locations, their own overlapping rules**, which does more convincing than any demo.
3. Price against the audit, not the software category; one multi-state wage-and-hour finding costs more than years of your product.
4. Set milestone-based expansion triggers: every future `location_added` at the account is a natural touchpoint.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `multi_office_milestone` gives you companies at the threshold; filter by your state coverage for instant fit.
**Why it lands.** Complexity thresholds create buyers overnight. This signal is the overnight, **caught while they are still proud of it rather than buried by it**.
## How to read it
Milestones mark sustained multi-site growth.
Multi-office companies standardize and centralize tooling.
Crossing 25 or 50 is more significant than crossing 5.
## Outreach playbook
Scaling indicator. Higher milestones signal enterprise-grade tooling needs.
## Related signals
A new office location appeared.
A company moved up a full Professional Network employee size band.
A company opened its first location in a new country.
***
Surface `multi_office_milestone` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Office Consolidation
Source: https://apidoc.cufinder.io/buying-signals/signals/location/office-consolidation
Multiple office closures in a short window.
`office_consolidation`
Location
Company Professional Network page (locations array), 90-day window analysis.
Multiple office closures in a short window.
## When it fires
≥ 3 `location_removed` events for the same company within a 90-day window. Emits on the third event.
## Magnitude
High, three closures in 90 days is a deliberate pattern.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Consolidation often accompanies cost-cutting or restructuring. Three or more office closures in a quarter is a clear efficiency-driven contraction, it feeds restructuring\_signal and flags an account for cost-reduction conversations.
## How to use Office Consolidation Signal?
**The scenario.** You are a tenant-representation broker specializing in sublease and disposition work, the unglamorous side of commercial real estate. Your clients are companies holding space they no longer want, and your business has a discovery problem: **companies shed offices quietly, and by the time surplus space hits the market formally, the owner has already chosen an advisor**.
**The goal.** Identify companies in active consolidation while the surplus space is still an internal headache rather than a listed problem.
**The signal fires.** `office_consolidation` fires for Granite Peak Insurance, a regional carrier: multiple office closures within a short window. Three locations gone from their profile in a quarter, with headquarters and two hubs remaining.
**Reading it.** A consolidation pattern means a real-estate strategy is being executed right now, remote work absorbed the desks, or costs are being cut, or both. Behind each closed office is likely a lease that has not expired: space being paid for and not used. Somewhere at Granite Peak, a finance leader looks at those obligations monthly, and **an unsolicited, credible plan for making them stop would get read today**.
**The play.**
1. Do the homework first: public lease records and listing history for the closed locations tell you which obligations likely survive.
2. Write to the CFO or head of real estate with a specific, numbers-first note: three recent closures usually means residual lease liability; here is what comparable space is achieving on the sublease market right now, and what a disposition strategy would look like.
3. Offer the analysis free, **the disposition mandate is the product**; the analysis is the audition.
4. Assume more closures are coming, consolidations run in waves, so position for the program, not the single listing.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `office_consolidation` across your market is a deal-origination engine for exactly the mandates that never get competitively shopped.
**Why it lands.** In disposition work, the winning broker is usually just **the first credible one to acknowledge the problem out loud**. This signal tells you where the problem lives.
## How to read it
Consolidation reduces real-estate and operational overhead.
Often part of a broader reorganization.
Feeds restructuring\_signal.
## Outreach playbook
Caution for expansion deals. Opportunity for cost-consolidation tooling.
## Related signals
An office location disappeared.
A company is undergoing major restructuring.
Employee count dropped severely in a single interval.
***
Surface `office_consolidation` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Location signals
Source: https://apidoc.cufinder.io/buying-signals/signals/location/overview
Signals tracking where a company operates, new offices, HQ moves, and country expansion.
Signals tracking where a company operates, new offices, HQ moves, and country expansion.
The location category contains **7 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
The headquarters city or country changed.
The headquarters country specifically changed.
A new office location appeared.
An office location disappeared.
A company opened its first location in a new country.
The company's office count crossed a milestone threshold.
Multiple office closures in a short window.
# People Signals
Source: https://apidoc.cufinder.io/buying-signals/signals/people-signals
Signals derived from individual profiles: hires, departures, promotions, and executive moves.
People signals are derived from individual employee profiles rather than the company page. They are often the earliest and richest buying signals, a single executive hire can reshape an entire purchasing strategy months before it shows up in headcount or funding data.
There are **17 people signals**. Every signal is attributed to the right company automatically: a join is attributed to the company the person moved to, a departure to the company they left.
## Why people signals matter
A new CRO or VP of Engineering signals a coming stack overhaul before any budget moves.
A first-ever hire in a function creates a brand-new budget owner with no incumbent vendor.
Departures and churn reshape who you need to win over.
A champion who moves companies is a warm lead at their new employer.
## Every people signal
| Signal | What it means |
| --------------------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
| [`employee_joined`](/buying-signals/signals/people/employee-joined) | A person's current company changed to this company. |
| [`employee_departed`](/buying-signals/signals/people/employee-departed) | A person left this company for another. |
| [`internal_promotion`](/buying-signals/signals/people/internal-promotion) | Someone was promoted to a more senior title at the same company. |
| [`lateral_title_change`](/buying-signals/signals/people/lateral-title-change) | Someone changed title at the same company without a seniority change. |
| [`c_suite_hire`](/buying-signals/signals/people/c-suite-hire) | A new C-level executive joined the company. |
| [`c_suite_departure`](/buying-signals/signals/people/c-suite-departure) | A C-level executive left the company. |
| [`founder_departure`](/buying-signals/signals/people/founder-departure) | A founder left the company. |
| [`vp_hire`](/buying-signals/signals/people/vp-hire) | A new VP-level leader joined the company. |
| [`vp_departure`](/buying-signals/signals/people/vp-departure) | A VP-level leader left the company. |
| [`first_role_hire`](/buying-signals/signals/people/first-role-hire) | A company made its first-ever hire for a role function. |
| [`key_role_vacancy`](/buying-signals/signals/people/key-role-vacancy) | A senior role stayed unfilled for 60 days. |
| [`leadership_churn_spike`](/buying-signals/signals/people/leadership-churn-spike) | Multiple senior leaders departed in a short window. |
| [`engineering_leader_hire`](/buying-signals/signals/people/engineering-leader-hire) | A new engineering leader joined the company. |
| [`sales_leader_hire`](/buying-signals/signals/people/sales-leader-hire) | A new sales leader joined the company. |
| [`talent_outflow_to_competitor`](/buying-signals/signals/people/talent-outflow-to-competitor) | An employee left for a known competitor. |
| [`talent_inflow_from_company`](/buying-signals/signals/people/talent-inflow-from-company) | A company hired multiple people from the same source company. |
| [`notable_hire`](/buying-signals/signals/people/notable-hire) | A high-profile or highly experienced person joined. |
Surface hires, departures, and promotions across millions of profiles, with verified contact data attached. Start free.
# C-Suite Departure
Source: https://apidoc.cufinder.io/buying-signals/signals/people/c-suite-departure
A C-level executive left the company.
`c_suite_departure`
People
Person Professional Network profile (title), left-company attribution.
A C-level executive left the company.
## When it fires
`employee_departed` AND the old title matches a C-level pattern.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------------- | ----------------------------------- |
| `meta.role` | The C-level role vacated. |
| `meta.new_company_id` | Where the executive went, if known. |
## Magnitude
Hyper-priority, executive exits reshape committees.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Executive exits create both risk and opportunity. A departing C-level leader may have been your champion (risk) or a blocker (opportunity), and the vacancy reshapes the buying committee. It's a key input to leadership\_churn\_spike and feeds key\_role\_vacancy.
## How to use C-Suite Departure Signal?
**The setup.** You are an enterprise AE at a network-infrastructure vendor, four months into a six-figure deal with Okene Water, a 900-person utility company. Your champion is their Director of IT, and the executive sponsor who approved the budget is the CTO.
**What you want.** Deals do not usually die from losing; they die from silence. You want to **know the moment your buying committee changes shape**, before your forecast does.
**The signal fires.** Mid-quarter, `c_suite_departure` fires for Okene Water. The CTO, the person whose name is on your business case, has left the company. Nobody at the account mentioned it on your last call.
**Reading it.** An executive departure mid-deal is not automatically bad news, but it is **always a re-qualification event**. The budget your sponsor approved may not survive the transition, and an incoming CTO tends to freeze inherited projects until they have formed their own view.
**Your move.**
1. Flag the opportunity in your pipeline review and **move it out of commit until the sponsor question is answered**.
2. Call your champion the same week. Ask directly who inherits the initiative and whether the budget line survives.
3. Multi-thread now, not later: get introduced to the interim decision-maker while the org chart is still soft.
4. When the successor is named, treat them as a brand-new stakeholder. Re-run discovery instead of forwarding the old deck, because **the new executive owes nothing to their predecessor's decisions**.
**Put it on autopilot.** Feed your open-opportunity account list into a weekly [People Signals API](/buying-signals/signals-apis/people-signals) check on `c_suite_departure`. Any hit on an active deal posts an alert into your team's deal-risk channel, so **no rep ever learns about a lost sponsor from a lost renewal**.
**Why it lands.** Every quarter, some slice of your pipeline quietly loses its executive air cover. The reps who find out in week one reposition and survive it; the ones who find out at contract time write the loss report.
## How to read it
A departing exec may be a lost champion or a removed blocker.
The buying committee changes when an exec leaves.
meta.new\_company\_id may surface a new opportunity.
## Outreach playbook
High-priority. Re-map the committee; follow the exec to their new company.
## Related signals
A new C-level executive joined the company.
A founder left the company.
A senior role stayed unfilled for 60 days.
Multiple senior leaders departed in a short window.
***
Surface `c_suite_departure` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# C-Suite Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/c-suite-hire
A new C-level executive joined the company.
`c_suite_hire`
People
Person Professional Network profile (title), joined-company attribution.
A new C-level executive joined the company.
## When it fires
`employee_joined` AND the new title matches a C-level pattern (Chief \* Officer, CEO, CFO, CTO, CMO, CRO, COO, CISO, CPO, CDO).
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ----------- | --------------------------------- |
| `meta.role` | The C-level role that was filled. |
## Magnitude
Hyper-priority, executive hires reshape strategy.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A new executive almost always brings new initiatives and new vendors. New C-suite leaders have a mandate to change things and a budget to do it, the first 90 days are a prime window for tool evaluation. This feeds executive\_team\_buildout and pre\_ipo\_signal.
## How to use C-Suite Hire Signal?
**The scenario.** You are the GTM manager at a company that sells clinical-trial analytics software. Your customers are mid-size pharmaceutical and biotech companies, 200 to 2,000 employees, mostly in the US and Europe. Looking back at your closed-won deals, one pattern keeps repeating: **the best ones started shortly after the buyer brought in a new executive** who wanted to modernize how the company works.
**Your goal.** Stop learning about executive changes weeks late from a rep's LinkedIn scrolling. Instead, **reach every new C-level hire inside your ICP during their first weeks on the job**, while they are still setting their agenda and actively evaluating tools.
**The signal fires.** On a Tuesday, `c_suite_hire` fires for Althea Biotech, a 400-person pharma company in your territory. The stored `meta.role` field tells you the role that was filled: **CFO**. You check your CRM and find Althea is a dormant opportunity, a deal that went quiet eight months ago.
**How you read it.** This is not a cold lead anymore. The previous CFO was likely the reason the deal stalled, and the new one owns none of those past decisions. **New executives arrive with a mandate to change things and a budget to do it**, and **the first 90 days are when they pick their tools**. **A dormant account plus a fresh CFO is one of the warmest combinations a signal can hand you.**
**The play.**
1. Enrich the new CFO with a verified email and full profile using [Person Enrichment](/apis/person-enrichment).
2. Re-open the Althea opportunity and assign it to the rep who owned it before, they already know the account's history.
3. Enroll the CFO in a sequence **keyed to the role change, not to your feature list**. The first email might open like this:
> Congratulations on the new role at Althea. Most CFOs we work with spend their first quarter figuring out where trial spend actually goes, so I wanted to share how two companies your size cut reporting time from weeks to days...
4. Tag the opportunity with the signal name and date, so next quarter you can compare win rates on signal-triggered outreach against cold outreach.
**Scale it.** Once the manual version works, automate the intake: a weekly job calls the [People Signals API](/buying-signals/signals-apis/people-signals) with `signal_name=c_suite_hire`, keeps only rows where `meta.role`, industry, and company size match your ICP, and pushes the survivors straight into your CRM. Your reps open a **ready-made queue every Monday** instead of building lists by hand.
**Why this works.** **Timing beats volume.** The same email that gets ignored in month twelve of a CFO's tenure gets a reply in week three, because you arrived inside the window when change was the whole point of their job.
## How to read it
Executives arrive with a charter to make changes.
New leaders often bring or seek new tools.
Underpins executive\_team\_buildout and pre\_ipo\_signal.
## Outreach playbook
Top-tier. Reach out in the first 90 days while the new exec is setting direction.
## Related signals
A C-level executive left the company.
A new VP-level leader joined the company.
A company rapidly built out its leadership team.
A new sales leader joined the company.
A new engineering leader joined the company.
***
Surface `c_suite_hire` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Employee Departed
Source: https://apidoc.cufinder.io/buying-signals/signals/people/employee-departed
A person left this company for another.
`employee_departed`
People
Person Professional Network profile (current company field).
A person left this company for another.
## When it fires
A person's `current_company` changed from this company to another. Attributed to the company they left.
## Magnitude
Bucketed on role seniority; executive departures rank far higher.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
Departures can open gaps and create urgency. A departure may remove a champion or open a role you can influence, and it's the foundation for c\_suite\_departure, founder\_departure, and the churn composites.
## How to use Employee Departed Signal?
**Where you sit.** You are an SDR at a market-data platform. A third of your booked meetings come from a handful of engaged contacts, and when one of them leaves their company, two things break at once: the relationship and the pipeline attached to it.
**The mission.** Turn every contact departure into **two plays instead of one problem**: backfill the empty seat, and follow the person who left.
**The signal fires.** `employee_departed` fires on your best contact at Quarry Analytics, the research lead who opened your last three meetings there. She has left the company.
**What it tells you.** First, the Quarry relationship is now unowned, and whoever replaces her will pick their own vendors. Second, she is about to start somewhere new, where **she can champion you from day one, before any competitor knows she exists**.
**The play.**
1. At Quarry: find the interim owner of research, reference the work you did together, and re-establish the thread before the account goes cold.
2. With the leaver: wait until her new role is public, then reach out personally. **People remember the vendors who treated them like people, not job titles.**
3. When her new company appears, qualify it. If it fits, you have a warm account; if it does not, you still kept a relationship that will fit eventually.
4. Record both motions under the same signal tag so you can see, in a quarter, how much pipeline departures actually generate.
**Automate it.** Run your named contacts through the [Job Changes API](/buying-signals/signals-apis/job-changes) with `type=company_change` monthly, and let the [People Signals API](/buying-signals/signals-apis/people-signals) catch the rest. The output is a fresh follow-the-champion list with zero manual tracking.
**Why this works.** Churned contacts are usually written off as bad luck. Tracked properly, **they are one of the cheapest sources of net-new accounts you will ever get**.
## How to read it
Departures can remove champions or open influence windows.
Underpins c\_suite\_departure, founder\_departure, churn signals.
Lost champions mean re-qualifying the account.
## Outreach playbook
Base layer. Track executive departures closely; they reshape buying committees.
## Related signals
A C-level executive left the company.
A founder left the company.
A senior role stayed unfilled for 60 days.
Multiple senior leaders departed in a short window.
***
Surface `employee_departed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Employee Joined
Source: https://apidoc.cufinder.io/buying-signals/signals/people/employee-joined
A person's current company changed to this company.
`employee_joined`
People
Person Professional Network profile (current company field).
A person's current company changed to this company.
## When it fires
A person's `current_company` changed to this company. Attributed to the joining company.
## Magnitude
Bucketed on role seniority and volume; individual joins are low, waves are higher.
Buckets are assigned from the percentage change between snapshots:
| Bucket | Condition |
| ---------- | --------------------------- |
| `low` | 1% ≤ \|delta\_pct\| \< 5% |
| `moderate` | 5% ≤ \|delta\_pct\| \< 15% |
| `high` | 15% ≤ \|delta\_pct\| \< 30% |
| `hyper` | \|delta\_pct\| ≥ 30% |
## Why it matters
New hires bring new needs and new perspectives. Every join is a potential new user, champion, or budget owner, and joins are the foundation for the high-value people composites like c\_suite\_hire, vp\_hire, and talent\_inflow\_from\_company.
## How to use Employee Joined Signal?
**The scenario.** You lead growth at a contract-management SaaS. Your happiest users are legal-ops people, and you have noticed something about them: **when they change jobs, they bring their tools along**. You have never systematically acted on it.
**The goal.** Build a champion-tracking motion: every time a power user from a customer account surfaces at a new company, treat that company as a warm lead the same week.
**The signal fires.** `employee_joined` fires: a legal-ops manager who ran your product for three years at Saffron Grid has just joined Birchmoor Health, a 1,200-person hospital group that has never been in your pipeline.
**What it tells you.** Somewhere inside a brand-new account, **there is now a person who already knows your product, likes it, and has credibility to recommend it**. That is worth more than fifty cold emails.
**The play.**
1. Check the new company against your ICP. Birchmoor fits: right size, right compliance burden.
2. Reach out to your former user first, not the buyer. Congratulate them, no pitch:
> Saw you landed at Birchmoor, congrats. If contract review over there still lives in shared drives, happy to give your new team the same setup you had at Saffron Grid.
3. Let them open the door internally. **A warm internal referral converts at a different order of magnitude than outbound.**
4. Log the account with a "champion moved" source tag so you can measure this motion against your other channels.
**Automate it.** Export the active users from your customer accounts, then watch them with the [People Signals API](/buying-signals/signals-apis/people-signals) and the [Job Changes API](/buying-signals/signals-apis/job-changes). Every match lands in a spreadsheet your team reviews each Friday.
**The payoff.** Your customers' alumni become a permanent, self-refreshing source of warm accounts, and **you are the first vendor to say congratulations instead of the fifth to say "quick question"**.
## How to read it
Every hire is a potential user or internal advocate.
Underpins c\_suite\_hire, vp\_hire, and notable\_hire.
Waves of joins signal scaling; single joins are routine.
## Outreach playbook
Base layer. Filter by role and seniority to surface the joins that matter.
## Related signals
A new C-level executive joined the company.
A new VP-level leader joined the company.
A high-profile or highly experienced person joined.
A company hired multiple people from the same source company.
***
Surface `employee_joined` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Engineering Leader Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/engineering-leader-hire
A new engineering leader joined the company.
`engineering_leader_hire`
People
Person Professional Network profile (title), joined-company attribution.
A new engineering leader joined the company.
## When it fires
`employee_joined` AND the title matches engineering leadership (VP Engineering, Head of Engineering, CTO, Director of Engineering).
## Magnitude
High, engineering leaders drive technical tooling decisions.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A new engineering leader usually means a wave of technical tooling decisions. Engineering leaders evaluate infrastructure, dev tools, security, and data platforms, often re-architecting the stack they inherit. Feeds pre\_ipo\_signal.
## How to use Engineering Leader Hire Signal?
**The scenario.** You are an AE at an observability platform. Your product usually replaces a self-hosted monitoring stack that nobody loves but nobody owns strongly enough to replace. What breaks the stalemate, in deal after deal, is **a new engineering leader who did not build the old stack and feels no loyalty to it**.
**Your goal.** Get in front of every new VP of Engineering or Head of Platform in your segment during the window when they are auditing what they inherited.
**The signal fires.** `engineering_leader_hire` fires for Stonebridge Media, a 600-person streaming company. A new VP of Engineering has just started, hired in from a company you happen to have as a customer.
**How you read it.** New engineering leaders run a stack review in their first quarter, almost without exception. Tools survive the review by being defensible; tools get replaced when **the only argument for them is "it is what we already have"**. Better still, this VP comes from a shop where your product was the default, so the switching pitch is already half made.
**The play.**
1. Confirm the overlap: check whether the VP's previous company is an active customer and which of your features their old team leaned on.
2. Send one short, technical message. No deck, no meeting ask:
> Congrats on the new role. If the Stonebridge stack review puts monitoring on the table, happy to share the exact setup your old team ran, dashboards included.
3. If there is no reply in two weeks, route a second touch through your champion at their previous company.
4. Tag the opportunity with the signal so your team learns **which leadership hires convert and which never do**.
**Scale it.** A weekly [People Signals API](/buying-signals/signals-apis/people-signals) pull on `engineering_leader_hire`, cross-referenced against your customer alumni, keeps this queue full without anyone scrolling job-change posts.
**Why this works.** You are not interrupting a happy owner; you are **arriving exactly when ownership changed hands** and everything is negotiable.
## How to read it
New eng leaders re-evaluate infra, dev, and security tools.
They often replace what they inherit.
Feeds pre\_ipo\_signal with hiring surges.
## Outreach playbook
Prime for dev-tool and infra sellers. Reach out in the first 90 days.
## Related signals
Engineering job postings doubled versus the prior crawl.
A new C-level executive joined the company.
A new VP-level leader joined the company.
A company shows the early pattern of an IPO track.
***
Surface `engineering_leader_hire` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# First Role Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/first-role-hire
A company made its first-ever hire for a role function.
`first_role_hire`
People
Person Professional Network profile (title), cross-referenced against current employees.
A company made its first-ever hire for a role function.
## When it fires
`employee_joined` AND no other currently-employed person holds that role function (for example, first-ever CISO or first Head of AI).
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | -------------------------------------------------- |
| `meta.function` | The role function being filled for the first time. |
## Magnitude
High, a first-of-its-kind hire creates a new buying center.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A first-of-its-kind hire means a brand-new buying center is forming. The first CISO will build a security program from scratch; the first Head of AI will stand up new infrastructure, no incumbent vendors, greenfield budget, and a leader hungry to establish their function.
## How to use First Role Hire Signal?
**Where you sit.** You do BD at a partner-management platform. Your product is only relevant to companies that have decided partnerships are a real channel, and the unmistakable marker of that decision is a hire: **the day a company employs its first-ever Head of Partnerships**.
**The mission.** Meet every first partnerships hire in your market during their honeymoon quarter, when they have a mandate, a blank slate, and not a single tool.
**The signal fires.** `first_role_hire` fires for Eastbrook Fintech, a 250-person payments company. They have hired a Head of Partnerships, and the signal confirms what makes it special: the company has never employed this function before.
**Reading it.** A first hire into a new function is a different animal from a replacement hire. There is no incumbent tool to displace, no predecessor's process to respect, and no committee, just **one person who must build everything from scratch and will remember whoever helped**. They are also, quietly, a little overwhelmed.
**Your move.**
1. Lead with usefulness, not product. Send the resource you wish every new partnerships leader had:
> Congrats on the new role. Most first partnership leads spend month one building the same three spreadsheets. Here is a template pack that skips that month, no strings.
2. Offer a working session on partner-program design, product optional.
3. When the tooling conversation comes, and it will, **you are the vendor who showed up before there was budget**, which is a category of one.
4. Track cohort timing: first hires typically buy tooling in months three to six. Sequence accordingly rather than pushing in week two.
**Put it on autopilot.** The [People Signals API](/buying-signals/signals-apis/people-signals) filtered to `first_role_hire` gives you every new-function hire in your segment; keep the partnerships ones, discard the rest.
**Why it lands.** Every other signal finds buyers replacing something. This one finds **buyers at the exact moment the category gets created inside their company**.
## How to read it
A first-ever role creates a brand-new budget owner.
No incumbent vendor means an open competition.
First-in-role leaders move fast to build their function.
## Outreach playbook
Top-tier when the function fits. Be first to reach a greenfield buyer.
## Related signals
A company posted a role in a function it hadn't hired for in 12 months.
A new C-level executive joined the company.
A new VP-level leader joined the company.
A high-profile or highly experienced person joined.
***
Surface `first_role_hire` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Founder Departure
Source: https://apidoc.cufinder.io/buying-signals/signals/people/founder-departure
A founder left the company.
`founder_departure`
People
Person Professional Network profile (title), left-company attribution.
A founder left the company.
## When it fires
`employee_departed` AND the old title contains founder, co-founder, or cofounder. Priority escalated.
## Magnitude
Hyper-priority, founder exits reshape company direction.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is a major signal and we escalate its priority, since founder exits reshape company direction. A founder leaving often marks a leadership transition, an acquisition aftermath, or a strategic inflection, all of which change how and what the company buys.
## How to use Founder Departure Signal?
**The setup.** You manage the partner ecosystem at a SaaS platform. Thirty implementation partners deliver most of your enterprise rollouts, and the smaller ones are **founder-shaped: the founder is the quality bar, the roadmap, and half the delivery capacity**.
**What you want.** Early warning when a key partner is about to wobble, so customer projects never inherit the wobble.
**The signal fires.** `founder_departure` fires for Lumenpath Consulting, an eight-year partner that currently owns two of your largest in-flight implementations. Their founder, the person every escalation ultimately reached, has left.
**What it tells you.** Founder departures at services firms are followed, with uncomfortable frequency, by senior staff departures, delivery slowdowns, and sometimes a quiet pivot or sale. None of that is certain here, but **hoping is not a partner-management strategy** when live customer projects are attached.
**The play.**
1. Call the partner directly, the same week. Ask about continuity: who owns delivery now, and what changes for certified staff.
2. Review every active project staffed by Lumenpath and identify which have single points of failure.
3. Quietly line up a second certified partner for the affected regions. **You are not replacing anyone, you are removing the single point of failure.**
4. Watch the account for follow-on signals over the next two quarters; a spike in departures after a founder exit is the confirmation you act on.
**Automate it.** Keep your partner directory synced against the [People Signals API](/buying-signals/signals-apis/people-signals) on `founder_departure` and the leadership signals around it. Partner risk becomes a dashboard, not a rumor.
**Why this works.** Ecosystem teams usually learn about partner instability from a failed project. This way you learn about it from a signal, **while every option is still on the table**.
## How to read it
Founder exits mark significant transitions.
Founders often leave after an acquisition vests.
New leadership may redirect strategy and spend.
## Outreach playbook
Escalate. Investigate the cause; founder exits often follow acquisitions.
## Related signals
A C-level executive left the company.
Multiple senior leaders departed in a short window.
A company was acquired.
***
Surface `founder_departure` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Internal Promotion
Source: https://apidoc.cufinder.io/buying-signals/signals/people/internal-promotion
Someone was promoted to a more senior title at the same company.
`internal_promotion`
People
Person Professional Network profile (title and company fields).
Someone was promoted to a more senior title at the same company.
## When it fires
Same `current_company`, `current_title` changed, AND the new title contains a seniority keyword (senior, lead, principal, staff, head, director, vp, chief) the old title didn't.
## Magnitude
Moderate to high depending on the new seniority level.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Promotions often come with expanded authority and budget. A newly promoted leader frequently gets a mandate, a budget, and a desire to make their mark, prime conditions for evaluating new tools. A champion's promotion can also strengthen an existing deal.
## How to use Internal Promotion Signal?
**The scenario.** You are a CSM at an expense-management platform. At your mid-market accounts, the person who actually runs your product is rarely the person who signed the contract, and **your best expansion deals have always started with a champion who gained power**.
**The goal.** Catch the moment a hands-on user at one of your accounts gets promoted into budget authority, and convert that promotion into expansion.
**The signal fires.** `internal_promotion` fires at Gorse & Gray, a 450-person architecture firm and a healthy but flat account. Your day-to-day contact, the finance manager who runs every report, is now Finance Director.
**What it tells you.** Your product just gained an executive advocate without a single meeting. Newly promoted leaders want **early, visible wins in their first quarter**, and expanding a tool they already trust is the lowest-risk win available to them.
**The play.**
1. Congratulate them like a human first. Then, separately, book a "what changes in your new role" call.
2. Come armed with their own usage data: the hours saved, the close time improvement, the adoption curve. **Hand them the internal business case pre-written.**
3. Propose the expansion they always wanted but could not approve: the extra modules, the second department, the annual plan.
4. Update the account map. Your old champion seat is now empty one level down, so start cultivating their successor too.
**Automate it.** Sync your named contacts at customer accounts to the [People Signals API](/buying-signals/signals-apis/people-signals) and the [Job Changes API](/buying-signals/signals-apis/job-changes) with `type=promotion`. Every promotion inside your book of business lands in your CS inbox the week it happens.
**The payoff.** Expansion revenue usually waits for a renewal conversation. Promotion-triggered expansion happens mid-cycle, at higher win rates, because **the buyer is selling for you**.
## How to read it
Promotions expand budget and decision power.
New leaders want early wins and may buy to get them.
An existing champion's promotion strengthens your position.
## Outreach playbook
Warm signal. If the promoted person is a known contact, re-engage with the new mandate.
## Related signals
Someone changed title at the same company without a seniority change.
A new VP-level leader joined the company.
A new C-level executive joined the company.
***
Surface `internal_promotion` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Key Role Vacancy
Source: https://apidoc.cufinder.io/buying-signals/signals/people/key-role-vacancy
A senior role stayed unfilled for 60 days.
`key_role_vacancy`
People
Person profiles, 60-day vacancy tracking.
A senior role stayed unfilled for 60 days.
## When it fires
A C-level or VP-level person departed AND no replacement joined within 60 days. Emits on day 60.
## Magnitude
Moderate to high, prolonged vacancies affect decisions.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A prolonged vacancy can stall or accelerate decisions depending on the gap. Sixty days without a replacement means functional decisions may be on hold, or an interim leader may be making expedient choices, either way, it's a timing signal worth knowing.
## How to use Key Role Vacancy Signal?
**The setup.** You do business development for an interim-executive marketplace. Your product is a person: a seasoned operator who can hold a leadership seat for six months while the company searches properly. Your entire pipeline is other companies' empty chairs.
**What you want.** To find **leadership seats the moment they go empty**, because every week a key role sits vacant, the pain compounds and the willingness to consider an interim rises.
**The signal fires.** `key_role_vacancy` fires for Danforth Logistics, a 700-person freight company. Their Head of Operations has left, and no successor has appeared. For a logistics company, an empty operations seat is not an org-chart gap, it is **a daily operational risk with a name on it**.
**Reading it.** Companies tolerate vacancies in support functions for months. They cannot tolerate them in the functions that run the core business, and the timeline pressure works entirely in your favor: a retained search takes four to six months, and the CEO knows it.
**Your move.**
1. Move fast; this signal decays faster than almost any other. Week one is ideal, week four is late.
2. Write to the CEO or COO with the trade-off framed plainly:
> Filling a Head of Operations seat properly takes five months. I can have a freight-experienced interim holding it in two weeks, so the search can take the time it needs to be right.
3. Attach one anonymized case study from the same industry. **Specificity beats scale in a message like this.**
4. If they decline, stay close: vacancies that "the team is covering" have a way of becoming urgent around week eight.
**Put it on autopilot.** A weekly [People Signals API](/buying-signals/signals-apis/people-signals) pull on `key_role_vacancy`, filtered by function and company size, is effectively **a live map of your addressable market**, refreshed for the price of an API call.
**Why it lands.** You are not creating a need. The empty chair does that every single morning.
## How to read it
An empty senior seat often pauses functional buying.
An acting leader may make fast, pragmatic decisions.
The eventual hire reopens the buying window.
## Outreach playbook
Monitor. Decisions may be paused; re-engage when the role is filled.
## Related signals
A C-level executive left the company.
A VP-level leader left the company.
Multiple senior leaders departed in a short window.
***
Surface `key_role_vacancy` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Lateral Title Change
Source: https://apidoc.cufinder.io/buying-signals/signals/people/lateral-title-change
Someone changed title at the same company without a seniority change.
`lateral_title_change`
People
Person Professional Network profile (title and company fields).
Someone changed title at the same company without a seniority change.
## When it fires
Same `current_company`, `current_title` changed, no seniority change detected.
## Magnitude
Low to moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Often a reorg signal. A lateral move, same level, different role, frequently reflects an internal reorganization, which reshuffles responsibilities and can change who owns a buying decision.
## How to use Lateral Title Change Signal?
**Where you sit.** You run field marketing at an endpoint-security vendor. Your segmentation lives and dies on personas: IT operations gets one message track, risk and compliance gets a very different one. And personas, it turns out, **do not sit still**.
**The mission.** Keep your contact database's personas true to reality, so the right people stop getting the wrong campaigns.
**The signal fires.** `lateral_title_change` fires for a long-time contact at Pellworth Insurance. Same company, same seniority, but she has moved from IT Operations Manager to Compliance Program Manager. No promotion, no announcement, just a sideways step your CRM would never have caught.
**What it tells you.** A lateral move is a quiet signal with loud implications: **her budget, priorities, and pain points just changed even though her seniority did not**. The patch-management webinars you have been sending her are now irrelevant; the audit-readiness content she has never received is now exactly her job.
**The play.**
1. Update the persona field on her contact record, and let your marketing automation move her to the compliance nurture track.
2. Have her account's owner send a one-line human acknowledgment; a small touch, but it signals you actually pay attention.
3. Check the account map: her old seat in IT Operations is now open, so **the lateral move hands you a persona to re-recruit as well as one to re-message**.
4. Review which active campaigns still target her old role at that account, and swap the content before the next send.
**Automate it.** A monthly [Job Changes API](/buying-signals/signals-apis/job-changes) sweep with `type=lateral_title_change` across your database, wired to persona-update rules, keeps segmentation honest **without a single list-cleaning project**.
**Why it lands.** Everyone tracks promotions and departures. Lateral moves are where databases silently rot, and where the marketers who notice quietly outperform the ones who do not.
## How to read it
Lateral moves often reflect internal reorganization.
The person's remit and budget may change.
Confirm whether the buying owner changed.
## Outreach playbook
Reorg context. Re-confirm who owns the relevant budget after the move.
## Related signals
Someone was promoted to a more senior title at the same company.
A person's current company changed to this company.
A senior role stayed unfilled for 60 days.
***
Surface `lateral_title_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Leadership Churn Spike
Source: https://apidoc.cufinder.io/buying-signals/signals/people/leadership-churn-spike
Multiple senior leaders departed in a short window.
`leadership_churn_spike`
People
Person profiles, 90-day window analysis.
Multiple senior leaders departed in a short window.
## When it fires
≥ 3 c\_suite\_departure or vp\_departure events for the same company within a 90-day window. Emits on the third event.
## Magnitude
High, clustered executive exits signal instability.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Heavy churn signals instability worth understanding before you engage. Three or more senior departures in a quarter points to turmoil, a failed strategy, an acquisition shake-out, or culture problems. It feeds restructuring\_signal and the risk\_score.
## How to use Leadership Churn Spike Signal?
**The scenario.** You are a vendor-risk analyst at a regional bank. Among the two hundred vendors in your register is Corminster Software, which processes a meaningful slice of your loan documentation. Your annual reviews check their financials and certifications. They do not check whether **the people running the company are heading for the exits**.
**The goal.** Add a leading indicator to vendor monitoring, something that moves months before an audited financial statement ever would.
**The signal fires.** `leadership_churn_spike` fires for Corminster. Multiple senior leaders have left within a compressed window, well beyond normal turnover for a company their size.
**What it tells you.** Executives, as a group, have better information about their own company than any outsider. When several leave at once, they are acting on that information. It does not tell you what is wrong, but it tells you, with unusual reliability, **that something is**, and for a critical vendor that is enough to act on.
**The play.**
1. Raise the vendor's risk rating and log the signal as the documented trigger; your auditors will appreciate a dated, objective source.
2. Ask Corminster directly about continuity: who owns your relationship now, and what changes in the product roadmap.
3. Refresh your exit plan for the service, including data portability and the realistic migration timeline. **The best time to test an exit plan is while you do not need it.**
4. Shorten the review cycle for this vendor from annual to quarterly until the churn stabilizes.
**Automate it.** Run your critical-vendor list through the [People Signals API](/buying-signals/signals-apis/people-signals) monthly, watching `leadership_churn_spike` and the executive-departure signals beneath it. Vendor risk stops depending on annual questionnaires.
**Why this works.** By the time vendor distress reaches a financial statement or a breach notification, your options are expensive. Leadership churn is **the cheap, early version of the same warning**.
## How to read it
Clustered exits signal organizational turmoil.
Know the cause before deciding how to engage.
Feeds restructuring\_signal and risk\_score.
## Outreach playbook
Caution. Understand the cause; instability can mean risk or a reset opportunity.
## Related signals
A C-level executive left the company.
A VP-level leader left the company.
A company is undergoing major restructuring.
A nightly composite score ranking a company's decline and risk.
***
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# Notable Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/notable-hire
A high-profile or highly experienced person joined.
`notable_hire`
People
Person Professional Network profile (followers, experience, prior title).
A high-profile or highly experienced person joined.
## When it fires
`employee_joined` AND the person has ≥ 5,000 followers, OR ≥ 10 years experience, OR a previous title that was C-level or VP at a notable company.
## Magnitude
Moderate to high, depends on the hire's profile.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
High-profile hires often signal serious ambition and bigger budgets. Companies that land well-known or senior talent are usually well-funded and growth-focused, a notable hire is both a credibility marker for the company and a potential influential champion or buyer.
## How to use Notable Hire Signal?
**The setup.** You are the talent partner at a seed-stage venture fund. Half your job is helping portfolio companies hire; the other, quieter half is noticing **where exceptional people choose to work**, because talent flows find great companies before metrics do.
**What you want.** A systematic way to spot the moment a heavyweight operator or researcher joins a small, unknown company, which is often the first public evidence that something interesting is being built.
**The signal fires.** `notable_hire` fires for Nightjar Labs, an eighteen-person company with no website to speak of. The hire: a well-known infrastructure engineer who previously scaled systems at a household-name tech company, with a following to match.
**What it tells you.** People with that profile have infinite options. When they choose an unknown eighteen-person company, they have seen something from the inside that the market has not seen from the outside. **One notable hire is a hint; two is a pattern; three is a company you should already know.**
**The play.**
1. Add Nightjar to your sourcing pipeline with the signal as the documented reason.
2. Map who else has joined recently; a cluster of strong joiners raises the priority immediately.
3. Reach the notable hire through a genuine channel, an intro through a mutual contact beats a cold fund email.
4. If a raise is not on their horizon, stay useful anyway: candidates, customers, a second opinion. **Funds that help before the round exist are the ones that get into it.**
**Automate it.** A weekly [People Signals API](/buying-signals/signals-apis/people-signals) pull on `notable_hire`, filtered to companies under 50 employees, is a deal-sourcing radar that **watches where talent votes with its feet**.
**Why this works.** Financing data tells you where every fund already looked. Talent movement tells you where the best operators are betting their next four years, and that dataset has less competition on it.
## How to read it
Landing notable talent signals serious growth intent.
Notable hires usually require competitive compensation.
High-profile hires often carry weight in decisions.
## Outreach playbook
Quality signal. Notable hires mark ambitious, well-resourced companies.
## Related signals
A person's current company changed to this company.
A new C-level executive joined the company.
A new VP-level leader joined the company.
A company made its first-ever hire for a role function.
***
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# Sales Leader Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/sales-leader-hire
A new sales leader joined the company.
`sales_leader_hire`
People
Person Professional Network profile (title), joined-company attribution.
A new sales leader joined the company.
## When it fires
`employee_joined` AND the title matches sales leadership (VP Sales, CRO, Head of Sales, VP Revenue).
## Magnitude
High, one of the strongest signals for sales-tech sellers.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A new sales leader is one of the highest-value signals for anyone selling sales tools, since they overhaul their stack fast. New CROs and VPs of Sales reset the entire revenue tech stack, CRM, sales intelligence, enablement, often within their first quarter. Feeds pre\_ipo\_signal.
## How to use Sales Leader Hire Signal?
**The setup.** You run a sales-training company, two trainers and you. Your best clients all bought in the same circumstance: a newly arrived sales leader who inherited a team trained by somebody else, on a methodology they do not rate.
**What you want.** A steady feed of **companies that just hired a CRO or VP Sales**, small enough to reach the leader directly, large enough to pay for a full-team program.
**The signal fires.** `sales_leader_hire` fires for Alder & Finch, a 300-person logistics software firm. New CRO, started this month, previously ran revenue at a company twice the size.
**Reading it.** A new sales leader has roughly one quarter to show a plan. The fastest visible lever is **how the team sells: process, messaging, and training**, because pipeline math takes two quarters to move and headcount takes three. Training budgets get unlocked in exactly this window, and they get spent with whoever is already in the room.
**Your move.**
1. Research the CRO's background and infer the methodology they will import.
2. Open with their problem, not your program:
> Most CROs we work with walk into a team trained on someone else's playbook. If you are rebuilding the Alder & Finch sales motion this quarter, I can show you how two similar teams made the switch without losing a month of quota.
3. Offer a diagnostic session, not a course catalog. **New leaders buy clarity about their own team before they buy content.**
4. If the timing is wrong, set a 90-day reminder. The second quarter of a CRO's tenure is your second window, right when the first plan meets reality.
**Put it on autopilot.** The [People Signals API](/buying-signals/signals-apis/people-signals) filtered to `sales_leader_hire` and your company-size range gives you a Monday-morning prospect list that **used to take a week of manual stalking to build**.
**Why it lands.** You are selling change to the one person in the building who was explicitly hired to make some.
## How to read it
New sales leaders rebuild the revenue tech stack fast.
Most stack decisions happen in the first 90 days.
Feeds pre\_ipo\_signal with sales hiring surges.
## Outreach playbook
Top signal for sales-tech. Reach the new leader before they pick vendors.
## Related signals
Sales job postings doubled versus the prior crawl.
A new C-level executive joined the company.
A new VP-level leader joined the company.
A company shows the early pattern of an IPO track.
***
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# Talent Inflow from Company
Source: https://apidoc.cufinder.io/buying-signals/signals/people/talent-inflow-from-company
A company hired multiple people from the same source company.
`talent_inflow_from_company`
People
Person profiles, 90-day window analysis.
A company hired multiple people from the same source company.
## When it fires
≥ 3 `employee_joined` events where `meta.previous_company_id` is the same company within a 90-day window.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------------ | ------------------------------------- |
| `meta.source_company_id` | The company being poached from. |
| `meta.poach_count` | The number of hires from that source. |
## Magnitude
High, concentrated poaching reveals strategic intent.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Concentrated hiring from one place reveals strategic intent. When a company pulls three or more people from the same source in a quarter, it's deliberately acquiring a team's expertise, often to build a new capability or replicate a competitor's playbook.
## How to use Talent Inflow from Company Signal?
**Where you sit.** You own a fifteen-person design agency. Millbank Motors, a regional vehicle manufacturer, has been your anchor client for four years, roughly a third of your revenue. The retainer feels safe. Retainers always feel safe, right up until they are not.
**The mission.** Detect the earliest structural sign that a key client is taking your work in-house, early enough to **reposition instead of just absorbing the loss**.
**The signal fires.** `talent_inflow_from_company` fires for Millbank: they have hired three designers in two months, two of them straight out of agencies. Nobody at Millbank has said a word to you about it.
**What it tells you.** Companies do not build internal design teams to leave them idle. The everyday work you handle, the product pages, the campaign adaptations, the brand maintenance, is exactly what in-house teams absorb first. The retainer is not gone, but **its expiration date was just written, and you are the only one who has read it**.
**The play.**
1. Do not confront; confirm. Raise it warmly with your client contact: "Saw the team is growing, congratulations. How should we best work alongside them?"
2. Reposition toward what in-house teams rarely cover: brand strategy, major campaigns, overflow at peak, specialist skills.
3. Propose the new shape before they do, **a smaller retainer you defined beats a cancellation they defined**.
4. Rebalance your pipeline now; use the freed capacity to reduce the revenue concentration that made this scary in the first place.
**Automate it.** Watch your top ten clients through the [People Signals API](/buying-signals/signals-apis/people-signals) on `talent_inflow_from_company`. Client-risk review becomes a monthly glance instead of an annual surprise.
**Why this works.** Agencies usually learn about in-housing from the cancellation call. The hiring pattern announces it months earlier, **while there is still a partnership left to redesign**.
## How to read it
Concentrated hires from one source is deliberate.
Often acquiring a team to stand up a new function.
The poached-from company hints at the strategy.
## Outreach playbook
Strategic intent signal. The source company reveals the capability being built.
## Related signals
A person's current company changed to this company.
An employee left for a known competitor.
A company made its first-ever hire for a role function.
***
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# Talent Outflow to Competitor
Source: https://apidoc.cufinder.io/buying-signals/signals/people/talent-outflow-to-competitor
An employee left for a known competitor.
`talent_outflow_to_competitor`
People
Person profiles, competitor mapping required.
An employee left for a known competitor.
## When it fires
`employee_departed` AND `meta.new_company_id` belongs to a known competitor (requires a competitor mapping).
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------------- | ----------------------------------- |
| `meta.new_company_id` | The competitor the employee joined. |
## Magnitude
Moderate to high, competitive intelligence value.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Talent flowing to a rival is competitive intelligence in itself. When employees leave for a competitor, it can signal the rival is winning the talent war, paying more, or building something that's attracting your target's people, useful market intel even when it isn't a direct buying trigger.
## How to use Talent Outflow to Competitor Signal?
**The scenario.** You lead talent acquisition at a mid-size software company. Exit interviews tell you why people say they left. They do not tell you the pattern: **who is leaving, from which teams, and where they all seem to be going**.
**The goal.** See your own attrition the way a rival's recruiter sees it, as a mapped flow, and intervene where it concentrates before a team quietly hollows out.
**The signal fires.** `talent_outflow_to_competitor` fires on your own company: over recent months, several engineers, notably from the platform team, have moved to Vantablue Systems, your most direct competitor.
**Reading it.** One departure is a career choice. A directional flow to the same competitor means **someone over there is running a deliberate poaching motion**, usually anchored by a former colleague who joined first and is now pulling their old teammates through. The platform team is the beachhead.
**Your move.**
1. Identify the likely anchor: who from the platform team joined Vantablue first, and who did they work closest with.
2. Run stay conversations with the remaining team now, not at review season. **Retention offers made before the resignation cost half as much and work twice as often.**
3. Fix what the flow is telling you; teams rarely leak in one direction unless compensation, tooling, or leadership drifted below market on that specific team.
4. Brief your own recruiters: flows run both ways, and Vantablue has alumni too.
**Automate it.** Point the [People Signals API](/buying-signals/signals-apis/people-signals) at your own company and your named competitors, and review the flow map monthly with HR leadership. Attrition strategy stops being anecdote-driven.
**Why it lands.** Most companies discover a poaching campaign at the fourth resignation. The signal shows you at the second, **while the team still has a core worth keeping**.
## How to read it
Talent flow reveals which rivals are gaining ground.
Needs a per-company or per-industry competitor list.
More about market dynamics than direct intent.
## Outreach playbook
Competitive intelligence. Track which rivals are attracting your targets' talent.
## Related signals
A person left this company for another.
A company hired multiple people from the same source company.
***
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# VP Departure
Source: https://apidoc.cufinder.io/buying-signals/signals/people/vp-departure
A VP-level leader left the company.
`vp_departure`
People
Person Professional Network profile (title), left-company attribution.
A VP-level leader left the company.
## When it fires
`employee_departed` AND the old title was VP-level.
## Magnitude
High.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
The vacancy and the successor both create timing windows. A departing VP leaves a gap that either stalls decisions (until a successor arrives) or opens them (when the successor wants to make changes). Feeds leadership\_churn\_spike and key\_role\_vacancy.
## How to use VP Departure Signal?
**Where you sit.** You run RevOps at a B2B software company, and you own forecast accuracy. Every quarter, a couple of deals slip for the same undiagnosed reason: the executive who sponsored them quietly left the account, and **nobody moved the deal out of commit until it was too late**.
**The mission.** Make sponsor departures a visible, automatic event in your pipeline instead of a post-mortem discovery.
**The signal fires.** `vp_departure` fires for Marbury Tech, a mid-six-figure opportunity your team has forecast to close this quarter. The departed executive is the VP of Operations, the named economic buyer on the deal.
**Reading it.** A departed sponsor does not kill a deal, but it **resets its probability**, and forecasting it at the old number is fiction. Initiatives sponsored by a departed VP get re-litigated by whoever inherits the budget, and inherited initiatives lose more often than they win.
**Your move.**
1. Auto-flag the opportunity and notify the AE and their manager the same day.
2. Downgrade the forecast category until the AE confirms the successor and re-validates budget.
3. Pause sequences aimed at the departed VP's org, so **your automation does not email a ghost**, which reps rarely notice and buyers always do.
4. Create a follow-up task for the successor announcement; a new VP of Operations is also a fresh `vp_hire` opportunity on the same account.
**Put it on autopilot.** Sync your open-opportunity accounts to a weekly [People Signals API](/buying-signals/signals-apis/people-signals) check on `vp_departure`, and wire hits straight into CRM workflow rules. The whole loop runs without a human remembering to check.
**Why it lands.** Most forecast misses are not bad selling, they are stale information. This signal removes one entire class of surprise from your quarter, and **your forecast call stops depending on whether an AE happened to check a profile page**.
## How to read it
A gap can stall or reset functional buying.
A new VP often re-evaluates the stack.
Feeds leadership\_churn\_spike and key\_role\_vacancy.
## Outreach playbook
Track the backfill. Re-engage when a successor is named.
## Related signals
A new VP-level leader joined the company.
A C-level executive left the company.
A senior role stayed unfilled for 60 days.
Multiple senior leaders departed in a short window.
***
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# VP Hire
Source: https://apidoc.cufinder.io/buying-signals/signals/people/vp-hire
A new VP-level leader joined the company.
`vp_hire`
People
Person Professional Network profile (title), joined-company attribution.
A new VP-level leader joined the company.
## When it fires
`employee_joined` AND the new title contains VP, Vice President, or Head of.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| --------------- | -------------------------- |
| `meta.function` | The function the VP leads. |
## Magnitude
High, VPs typically own functional budgets.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
VP hires frequently own budget for their function. A new VP of a function often gets a budget and a mandate to build, making them an ideal buyer, especially when the function maps to your product. Feeds executive\_team\_buildout.
## How to use VP Hire Signal?
**The scenario.** You are the new-business director at a mid-size creative agency. Agencies rarely lose accounts to better work; they lose them to **new marketing leadership with old agency relationships**. Which means the same door revolves in your favor somewhere else.
**The goal.** Be the first agency in the conversation whenever a company in your target verticals hires a new VP of Marketing.
**The signal fires.** `vp_hire` fires for Hollowell Foods, a regional CPG brand you have admired and never cracked. The new VP of Marketing arrives from a much bigger brand with a reputation for bold campaigns.
**What it tells you.** Incumbent agencies serve the strategy of the person who hired them. A new VP brings a new strategy, and **agency reviews follow new marketing VPs the way audits follow new CFOs**. The review will happen with or without you; the only question is who is on the list when it does.
**The play.**
1. Study the VP's last three campaigns before you write a word. Your outreach should prove you did.
2. Send a point of view, not a credentials deck: two specific ideas for Hollowell's brand under its new leadership, one safe, one brave.
3. Ask for twenty minutes, and **position yourself for the review, not the pitch**: even landing on the consideration list is the win at this stage.
4. Track every VP-hire outreach in one pipeline view so you learn your real conversion window. For most agencies it is day 30 to day 100 of the VP's tenure.
**Automate it.** A weekly [People Signals API](/buying-signals/signals-apis/people-signals) pull on `vp_hire`, filtered to your verticals, replaces the trade-press scanning your team half-does when things are slow.
**The payoff.** The revolving door that costs agencies their oldest accounts becomes **the system that wins your newest ones**.
## How to read it
VPs own and deploy budget for their area.
New VPs are equipped to evaluate and buy tools.
meta.function tells you if it maps to your product.
## Outreach playbook
High-priority when the function matches. Reach out in their first quarter.
## Related signals
A VP-level leader left the company.
A new C-level executive joined the company.
A new sales leader joined the company.
A new engineering leader joined the company.
A company rapidly built out its leadership team.
***
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# Affiliated Pages Count Change
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/affiliated-pages-count-change
The net count of affiliated pages shifted notably.
`affiliated_pages_count_change`
Structure
Company Professional Network page (affiliated pages).
The net count of affiliated pages shifted notably.
## When it fires
Net count of affiliated pages changes by ≥ 2 in a single crawl.
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Sizable affiliate changes hint at structural reorganization. A net swing of two or more affiliated pages suggests the company is restructuring its entity relationships, adding regional pages, merging units, or reorganizing its presence.
## How to use Affiliated Pages Count Change Signal?
**The scenario.** You run GTM at a legal-entity-management platform; your product keeps corporate structures, subsidiaries, filings, ownership records, from becoming a compliance liability. Your buyer is any general counsel whose org chart changes faster than their spreadsheets, and your discovery problem is finding **which companies' structures are actually churning**.
**The goal.** Rank prospects by structural volatility, because a company whose corporate family reshuffles constantly is a company whose entity-management pain is compounding monthly.
**The signal fires.** `affiliated_pages_count_change` fires for Vantora Holdings, and not for the first time: their count of affiliated pages has moved three times in a year, up, up, then down. Acquisitions, new ventures, a divestiture, the exact churn pattern your product exists for.
**What it tells you.** A single change in affiliated-page count is an event; repeated changes are a lifestyle. Vantora's legal team is managing a moving target: every added entity brings registrations, officers, and filing calendars, every removed one brings wind-down obligations, and somewhere a paralegal maintains the spreadsheet that holds it together. Structural churn compounds, **each change multiplies against every existing entity's requirements**, which means their pain grows quadratically while their tooling stays flat.
**The play.**
1. Score your prospect universe by fire-frequency of this signal, volatility, not size, is your best predictor of urgency.
2. Approach the GC with their own churn as the hook: three structural changes in a year means the entity map is out of date the moment anyone prints it.
3. Demo with their public structure pre-loaded, **watching their own subsidiaries appear in your product does more than any slide**.
4. Sell the audit risk to finance in parallel; missed filings from stale records carry fines with the CFO's name on them.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `affiliated_pages_count_change`, aggregated quarterly, produces a volatility-ranked prospect list no firmographic filter could build.
**Why it lands.** Every company technically needs entity management. The ones whose structure moved three times last year **know they need it**, and this signal finds exactly them.
## How to read it
Affiliate changes reflect entity reorganization.
Adds can mean new regional or product pages.
The signal measures net change, not individual adds.
## Outreach playbook
Reorganization context. Useful alongside structure and location signals.
## Related signals
A new Professional Network showcase page was linked.
A new subsidiary appeared in the company's list.
A new office location appeared.
***
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# Company Type Change
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/company-type-change
The company's entity type changed.
`company_type_change`
Structure
Company Professional Network page (company type field).
The company's entity type changed.
## When it fires
`company_type` field value changes (for example, Privately Held → Public Company).
## Magnitude
High, entity-type shifts are rarely minor.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Entity-type shifts are rarely minor. Moving from Privately Held to Public Company is an IPO; other transitions reflect acquisitions or restructurings. This signal underpins the ipo\_signal composite.
## How to use Company Type Change Signal?
**The scenario.** You handle business development at a mid-market accounting firm. Your best new engagements do not come from companies shopping for accountants, they come from companies whose **legal structure just changed underneath their existing accountant**, creating work the incumbent was never scoped for.
**The goal.** Detect entity-type changes among regional businesses, because each one drags a tail of tax elections, filing obligations, and advisory questions that must be answered on a deadline.
**The signal fires.** `company_type_change` fires for Weller Bros. Construction, a 150-person contractor two counties over: their company type has changed from partnership to privately held corporation.
**What it tells you.** Nobody converts a partnership to a corporation casually. The usual drivers are bringing in outside investment, preparing for a sale, succession planning between generations, or liability restructuring after a scare. Every one of those drivers means **new tax elections with clocks attached**, S-corp elections have filing windows, basis calculations need rebuilding, and the owners' personal tax pictures just changed shape. A generalist bookkeeper who served the partnership fine is often out of depth the day the conversion closes.
**The play.**
1. Lead with the deadline, not the relationship: converted entities face time-boxed elections, and missing them costs real money.
2. Write to the owners with one concrete, checkable item: the election window that applies to their conversion date, and what choosing wrongly costs a company their size.
3. Offer a post-conversion review as a fixed-fee engagement, **an audit of the decisions their conversion just forced**, which either validates their current advisor or replaces them.
4. Ask, gently, why they converted; the answer is your roadmap to the next engagement, sale prep means valuation work, investment means audit readiness.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `company_type_change` in your region surfaces conversions while their election windows are still open.
**Why it lands.** Companies rarely switch accountants out of dissatisfaction. They switch **when the work outgrows the incumbent overnight**, and a type change is exactly that night.
## How to read it
Type changes mark major corporate milestones.
Private → Public is the basis of ipo\_signal.
Public status changes scrutiny and spending.
## Outreach playbook
Major event. Private → Public means new budget and compliance tooling needs.
## Related signals
The company changed between nonprofit and for-profit status.
The company became or stopped being a subsidiary.
A company went from private to public.
***
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# Founded Year Added
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/founded-year-added
A previously missing founded year was populated.
`founded_year_added`
Structure
Company Professional Network page (founded year field).
A previously missing founded year was populated.
## When it fires
`founded_year` was null or missing and is now populated.
## Magnitude
Low, usually a data completeness update.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Often a data cleanup, but worth flagging. Adding a founded year is typically profile maintenance rather than a strategic event, low intensity, but it can indicate someone is actively grooming the company page.
## How to use Founded Year Added Signal?
**The scenario.** You are a platform associate at a venture fund, responsible for keeping the sourcing engine fed. Brand-new companies are the hardest to systematically find: too young for databases, too quiet for press, often too stealthy for even a proper website. But they do one thing early, **they fill in their public profiles**, field by field, as they come out of hiding.
**The goal.** Use profile-completion events as a discovery layer for young companies entering the visible world.
**The signal fires.** `founded_year_added` fires for Mosaic Grid, an energy-software company: a founded year has just appeared on a profile that previously did not list one. The year added: last year. This is not an old company tidying its page; this is **a young company assembling its public identity in real time**.
**What it tells you.** A recently founded company adding its founding year is typically moving from stealth toward market: hiring publicly, talking to customers, preparing to be findable. That transition window is the best sourcing moment a fund gets, the company is discoverable but not yet crowded, and a thoughtful early conversation still stands out. Combined with a young founding year, this small signal works as a tripwire for exactly the cohort your partners want to see early.
**The play.**
1. Filter hard: the signal alone is housekeeping; the signal plus a founding year within the last two years is a lead.
2. Enrich the company immediately, team, sector, hiring pattern, and score it against the fund's theses.
3. If it fits, reach the founder while the company is still under-networked, referencing their actual space rather than a template.
4. If it is too early even for you, **put it on a six-month re-check, young companies compound fast**, and your note-to-self beats a competitor's cold call later.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `founded_year_added`, filtered to recent years and your sectors, is a stealth-exit detector running quietly alongside your other sourcing.
**Why this works.** Every fund sees companies once they raise. The edge is the window between **becoming visible and becoming obvious**, and profile-completion events mark its opening.
## How to read it
Usually profile completion, not a strategic move.
Suggests active page maintenance, sometimes pre-event.
Rarely actionable alone.
## Outreach playbook
Low priority. Note only as evidence of active page maintenance.
## Related signals
The founded year value changed.
A previously empty tagline is now populated.
***
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# Founded Year Change
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/founded-year-change
The founded year value changed.
`founded_year_change`
Structure
Company Professional Network page (founded year field).
The founded year value changed.
## When it fires
`founded_year` value changes. Usually a data correction; emitted with confidence 0.5 and requires confirmation.
## Magnitude
Low, treated as low-confidence by default.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is usually a data correction, so we set lower confidence and require confirmation. A changed founding year rarely reflects a real event, occasionally it surfaces a merger that reset the founding date, but most cases are corrections.
## How to use Founded Year Change Signal?
**The setup.** You are an analyst at a due-diligence firm; lenders and acquirers pay you to find the discrepancies that glossy data rooms omit. Your craft runs on a simple principle: **facts that change without explanation are threads worth pulling**, and few facts should change less often than the year a company was founded.
**What you want.** Automated flags on identity-level inconsistencies across the companies you monitor for clients, because inconsistency is where diligence findings live.
**The signal fires.** `founded_year_change` fires for Calloway Freight, a trucking company your client is considering acquiring: the founded year on their profile has moved from 1998 to 2016.
**Reading it.** There are innocent explanations and interesting ones, and diligence exists to tell them apart. Innocent: a data correction, or marketing aligning the date to a rebrand. Interesting: the 2016 date marks a restructuring the seller prefers not to discuss, a bankruptcy and asset repurchase, a legal entity swap that voided old liabilities, or a "continuity" story stitched over an ownership break. **A company that moved its own birthday has a reason**, and your client is paying you to know it.
**The play.**
1. Pull the corporate registry history for both dates; the 2016 entity's formation documents usually answer the question in an afternoon.
2. Check what else changed around the same period, name, type, parent signals on the same profile, to reconstruct the event.
3. Put the finding in the report either way, **a resolved discrepancy builds client trust as much as a damning one**, and note which representations in the data room the answer touches.
4. Add the pattern to your monitoring playbook; founding-date drift correlates with restructuring history across portfolios, not just this deal.
**Automate it.** Run client watchlists through the [Company Signals API](/buying-signals/signals-apis/company-signals) with `founded_year_change` as a standing tripwire; it fires rarely, and almost never boringly.
**Why it lands.** Diligence is the business of noticing. This signal notices **a category of change designed not to be noticed**.
## How to read it
Most founding-year changes are data fixes.
Emitted at 0.5 confidence, requires confirmation.
Occasionally reflects a reset founding date post-merger.
## Outreach playbook
Low priority. Confirm before acting; usually a correction.
## Related signals
A previously missing founded year was populated.
Two companies merged under a common new parent.
***
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# Nonprofit to For-Profit
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/nonprofit-to-for-profit
The company changed between nonprofit and for-profit status.
`nonprofit_to_for_profit`
Structure
Company Professional Network page (company type field).
The company changed between nonprofit and for-profit status.
## When it fires
`company_type` changes between Nonprofit and any for-profit category (either direction).
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ---------------- | ------------------------------------------------------------ |
| `meta.direction` | The direction of the change (toward or away from nonprofit). |
## Magnitude
High, a fundamental model change.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
A model change like this reshapes the entire buying dynamic. Switching between nonprofit and for-profit changes funding sources, budget structures, procurement rules, and decision-making, requiring a completely different sales approach.
## How to use Nonprofit to For-Profit Signal?
**The setup.** You are a commercial banker covering education and healthcare businesses. Your quietest, best prospects are organizations at a structural rebirth, and few rebirths are as total as **a nonprofit converting to a for-profit company**. Everything about how that organization handles money is about to be rebuilt.
**What you want.** To find conversions early, because the converted organization needs a full commercial banking relationship, credit, treasury, merchant services, where a nonprofit banking setup used to be, and the first banker in the door usually wins all of it.
**The signal fires.** `nonprofit_to_for_profit` fires for Open Meadow Learning, a tutoring and curriculum organization: their profile has shifted from nonprofit to for-profit status.
**Reading it.** This conversion is rare and never accidental; it typically follows an acquisition, an investor-backed restructuring, or a leadership decision to scale commercially. The practical consequences are immediate: donation-based revenue becomes sales revenue, grant reporting becomes investor reporting, and the organization suddenly qualifies for, and needs, **commercial credit products it was never eligible for as a nonprofit**. Meanwhile their existing bank still has them coded as a charity.
**The play.**
1. Move within the quarter; banking decisions post-conversion happen fast because payroll and payments cannot wait.
2. Approach the executive director or new CFO with the transition framed as the agenda: a conversion this size usually needs working capital, new merchant accounts, and treasury built for commercial cash flow, and you have walked converted organizations through the sequence before.
3. Bring the checklist, **converted nonprofits do not know what they now qualify for**, and the banker who explains it becomes the banker of record.
4. Loop in your wealth-management colleagues; conversions often enrich founders and board members, and the personal relationship reinforces the commercial one.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `nonprofit_to_for_profit` fires rarely, which is precisely why watching it costs nothing and missing it costs the whole relationship.
**Why it lands.** Banks compete brutally for established companies. A newly converted organization is **a commercial client with no commercial incumbent**, the rarest thing in banking.
## How to read it
The entire economic model of the org shifts.
Buying processes and approvals change fundamentally.
meta.direction tells you which way it went.
## Outreach playbook
Re-qualify entirely. The buying process and budget structure have changed.
## Related signals
The company's entity type changed.
The company became or stopped being a subsidiary.
***
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# Structure signals
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/overview
Signals tracking the legal and organizational shape of a company, parents, subsidiaries, and entity types.
Signals tracking the legal and organizational shape of a company, parents, subsidiaries, and entity types.
The structure category contains **13 signals**. Each links to a full reference page with the exact trigger condition, stored fields, magnitude, and an outreach playbook.
The company's entity type changed.
The company changed between nonprofit and for-profit status.
The company became or stopped being a subsidiary.
A previously missing founded year was populated.
The founded year value changed.
The parent company changed to a different one.
A parent company was set where there was none.
A parent company relationship was cleared.
A new subsidiary appeared in the company's list.
A subsidiary was removed from the company's list.
A new Professional Network showcase page was linked.
A Professional Network showcase page was unlinked.
The net count of affiliated pages shifted notably.
# Parent Company Added
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/parent-company-added
A parent company was set where there was none.
`parent_company_added`
Structure
Company Professional Network page (parent relationship).
A parent company was set where there was none.
## When it fires
`parent_id` was null and is now populated. Strong acquisition signal.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| -------------------- | ------------------------------------ |
| `meta.new_parent_id` | The new parent company's identifier. |
## Magnitude
High, one of the clearest acquisition markers.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
This is a strong acquisition signal. A company gaining a parent for the first time has almost certainly been acquired, the new parent is stored so you can map the relationship. It's a core input to acquired\_signal.
## How to use Parent Company Added Signal?
**The scenario.** You are an employee-benefits broker for mid-size employers. Renewals are sticky, employers hate re-shopping benefits, so your growth depends on catching accounts at the rare moments when the incumbent arrangement breaks on its own. The most reliable such moment: **the company gets acquired, and its benefits program collides with its new parent's**.
**The goal.** Find newly acquired companies in your region during the benefits-transition window, when decisions about plan consolidation are open and advice is scarce.
**The signal fires.** `parent_company_added` fires for Fernhill Clinics, a 300-employee physiotherapy group: a parent company has appeared on their profile for the first time. They have just been acquired by a larger health-services group.
**Reading it.** Post-acquisition, benefits go one of three ways: absorption into the parent's plans, continuation as-is, or a negotiated hybrid. Which path wins is usually decided within two renewal cycles, by an HR team **navigating the decision for the first time in their careers**, under pressure from a parent whose plans may fit their workforce badly. Physiotherapists and clinic staff have very different needs from the parent's corporate employees, and someone has to argue that case with data.
**The play.**
1. Reach Fernhill's HR leader before the first joint renewal, with the transition itself as the agenda: what typically happens to benefits after an acquisition, and the three decision paths ahead of them.
2. Offer a comparison analysis, their current plans against the parent's, priced against their actual employee census. **You are arming them for an internal negotiation they did not know was coming.**
3. Stay neutral on the outcome; whichever path wins, the analysis makes you the trusted advisor, and advisors write the next placement.
4. Work the pattern upstream too: the acquiring group's other recent acquisitions face the same collision.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `parent_company_added` in your region and size band is a feed of accounts whose incumbent broker relationship just became negotiable.
**Why it lands.** You almost never beat an incumbent broker on price or friendship. You beat them **when the account's world changes and they are slow to notice**.
## How to read it
Gaining a parent for the first time means being acquired.
meta.new\_parent\_id lets you understand the new owner.
Core component of acquired\_signal.
## Outreach playbook
Strong acquisition signal. Map the parent; the deal may release or freeze budget.
## Related signals
The parent company changed to a different one.
A parent company relationship was cleared.
The company became or stopped being a subsidiary.
A company was acquired.
***
Surface `parent_company_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Parent Company Change
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/parent-company-change
The parent company changed to a different one.
`parent_company_change`
Structure
Company Professional Network page (parent relationship).
The parent company changed to a different one.
## When it fires
`parent_id` already existed but is now a different company.
## Magnitude
High, ownership transfers are major.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
That's a re-acquisition or ownership transfer. When an existing parent is replaced by a new one, the company has been sold again or moved between portfolio entities, a significant change in who controls strategy and budget.
## How to use Parent Company Change Signal?
**Where you sit.** You are a partner at an IT integration consultancy. Your projects exist because of a corporate ritual: a company changes hands, and two incompatible technology estates must become one. The work is lucrative and time-boxed, and the mandate goes to whoever is **credibly in the room during the first hundred days of new ownership**.
**The mission.** Track ownership transitions among mid-market companies in your sectors, and arrive during the planning window, after the deal closes but before the integration roadmap is fixed.
**The signal fires.** `parent_company_change` fires for Brightwater Labs, a 400-person diagnostics company: their parent has changed from one healthcare group to another. A change of parent, not a first acquisition, meaning Brightwater has been through integration before, and is about to go through it again in the opposite direction.
**Reading it.** A parent swap is two migrations wearing one trench coat: unwinding the systems inherited from the old parent while adopting the new parent's stack. Identity, email, ERP, security tooling, all of it moves twice. The new parent's internal IT team owns strategy but rarely has capacity for execution, and **the gap between the integration deadline and internal capacity is precisely the consultancy market**.
**The play.**
1. Research the new parent's known stack; your pitch should name the actual migration, not "integration services".
2. Approach the new parent's IT leadership, not Brightwater's, budget authority moved with the ownership.
3. Lead with the double-migration insight: companies changing parents carry legacy from the previous integration, and cleaning both layers at once is cheaper than sequentially.
4. Propose a discovery assessment scoped in weeks, **the integration roadmap is being drafted right now, and the drafter tends to win the execution**.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `parent_company_change` across your sectors delivers a handful of qualified transitions a quarter, each worth six or seven figures in mandate value.
**Why it lands.** Integration budgets are approved in the deal model before you ever call. You are not creating spend, **you are showing up where it is already allocated**.
## How to read it
The company moved to a new owner.
Common in PE portfolio shuffles.
A new parent may reset vendor relationships.
## Outreach playbook
Significant. Map the new parent and reassess budget authority.
## Related signals
A parent company was set where there was none.
A parent company relationship was cleared.
The company became or stopped being a subsidiary.
A company was acquired.
***
Surface `parent_company_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Parent Company Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/parent-company-removed
A parent company relationship was cleared.
`parent_company_removed`
Structure
Company Professional Network page (parent relationship).
A parent company relationship was cleared.
## When it fires
`parent_id` was populated and is now null. Spin-off or divestiture signal.
## Magnitude
High, spin-offs are major events.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Pointing to a spin-off or divestiture. A company losing its parent has become independent, often through a spin-off or divestiture, which means it now controls its own budget and will rebuild its vendor stack.
## How to use Parent Company Removed Signal?
**The setup.** You are an AE at a mid-market ERP vendor. Your hardest competitor is not another vendor, it is inertia: companies already run on something. Which makes one buyer uniquely valuable, **the company that just lost its systems**: a carve-out, spun off from its parent, walking away from the ERP, payroll, and IT infrastructure it used to borrow.
**What you want.** To catch divested companies inside their transition-services window, the contractual period, typically 12 to 24 months, during which they may still use the former parent's systems before they must stand on their own.
**The signal fires.** `parent_company_removed` fires for Sable Point Energy, a 600-person services firm: their parent company has been removed. They have been spun off, likely to private equity, and are now independent for the first time in a decade.
**Reading it.** A carve-out is a company-shaped hole where infrastructure used to be. Everything Sable Point ran on, ERP, finance systems, procurement, probably belonged to the parent, and the transition-services agreement burning down in their data room is **a countdown clock that ends in either a signed replacement or an operational crisis**. There is no "keep the incumbent" option. Someone is winning this deal; the only question is who arrives while the evaluation is forming.
**The play.**
1. Move fast on discovery: find the TSA timeline, which any operations leader there will discuss because it dominates their planning.
2. Pitch the deadline, not the product: standing up independent ERP inside a TSA window is a known, brutal project, and you have run it for other carve-outs.
3. Bring reference customers who were themselves carve-outs, **this buyer trusts scar tissue over feature lists**.
4. Engage the PE sponsor's operating partner too; carve-out system decisions are often steered from the sponsor, not the company.
**Automate it.** The [Company Signals API](/buying-signals/signals-apis/company-signals) on `parent_company_removed` is your carve-out radar; every fire is an account with a mandatory purchase and a deadline.
**Why it lands.** ERP deals usually die to "not now". Carve-outs are the one segment **where "not now" is not on the menu**.
## How to read it
Losing a parent means newfound autonomy.
Newly independent companies re-evaluate every vendor.
Often a deliberate divestiture from the former parent.
## Outreach playbook
Prime greenfield opportunity. Newly independent companies rebuild their stack.
## Related signals
A parent company was set where there was none.
The parent company changed to a different one.
The company became or stopped being a subsidiary.
***
Surface `parent_company_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Showcase Page Added
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/showcase-page-added
A new Professional Network showcase page was linked.
`showcase_page_added`
Structure
Company Professional Network page (showcase page links).
A new Professional Network showcase page was linked.
## When it fires
A new showcase page is linked.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------ | ------------------------------------- |
| `meta.showcase_id` | The added showcase page's identifier. |
## Magnitude
Moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
New showcase pages often accompany product launches or new business units. A showcase page is how companies spotlight a specific product or brand, adding one signals a new offering or division worth investigating.
## How to use Showcase Page Added Signal?
**The setup.** You lead partnerships at a business-intelligence platform. Your integration partnerships work best when they attach to a partner's product early, ideally at launch, when their team wants ecosystem proof and their marketing wants co-announcements. The trouble is knowing **which companies are about to launch something**, since launches are secret until they are not.
**What you want.** A structural early indicator of new product lines and sub-brands at companies in your ecosystem.
**The signal fires.** `showcase_page_added` fires for Helix Manufacturing, an industrial-equipment maker in your customer base: a new showcase page called "Helix Insight" has appeared. The name and the early follower pattern say software, a data product from a hardware company.
**Reading it.** Companies create showcase pages when a product line becomes strategic enough to deserve its own audience, usually a quarter or two before a serious market push. A hardware company standing up a software brand is mid-transformation, and its new product team is **assembling launch assets, integrations, and credibility right now**. An analytics integration partner arriving at this moment is not a cold pitch; it is a launch resource.
**The play.**
1. Scout the page and recent hires to size the initiative, a showcase page plus product-management postings means the launch is funded.
2. Approach their product lead with launch value, not partnership boilerplate: an integration live on day one, a co-marketing slot, your platform's distribution behind their announcement.
3. Time the ask to their calendar, **partnerships close easiest when they make someone else's launch look bigger**.
4. Watch showcase pages across your whole customer base; every addition is a product strategy telling on itself.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) pull on `showcase_page_added` across customers and targets surfaces pre-launch motion consistently, no briefing deck required.
**Why it lands.** After launch, you are one of fifty integration requests. Before launch, **you are the partner who believed early**, and teams remember which one you were.
## How to read it
Showcase pages often mark a new product.
Can signal a distinct division spinning up.
Check the showcase page to learn what's new.
## Outreach playbook
Spot new products and units. Investigate meta.showcase\_id for the offering.
## Related signals
A Professional Network showcase page was unlinked.
A new specialty was added.
A tracked strategic keyword appeared in the description.
***
Surface `showcase_page_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Showcase Page Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/showcase-page-removed
A Professional Network showcase page was unlinked.
`showcase_page_removed`
Structure
Company Professional Network page (showcase page links).
A Professional Network showcase page was unlinked.
## When it fires
A showcase page is unlinked.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| ------------------ | --------------------------------------- |
| `meta.showcase_id` | The removed showcase page's identifier. |
## Magnitude
Low to moderate.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Removing one can signal a sunset product or reorganization. Unlinking a showcase page often means a product was discontinued or a business unit folded, a quiet indicator of portfolio change.
## How to use Showcase Page Removed Signal?
**Where you sit.** You are a product marketing manager at a customer-experience platform, and you own competitive displacement. Your best campaigns have always targeted users of dying products, tools that still have customers but no longer have a future. The hard part is proving the "dying" part **before the vendor admits it**.
**The mission.** Detect product sunsets at competitors early, from structural evidence rather than roadmap rumors.
**The signal fires.** `showcase_page_removed` fires for Argo CX, a competitor: the showcase page for their "Argo Engage" product line, active for years, has been deleted. No sunset announcement exists. Their pricing page still lists the product.
**Reading it.** Companies do not delete the public home of a healthy product. A removed showcase page means marketing investment has formally stopped, the audience it gathered has been abandoned, and internally the product is somewhere on the spectrum between maintenance mode and scheduled shutdown. The customers on Argo Engage are now **paying for a product whose own vendor has stopped promoting it**, and they will find out the hard way unless someone tells them sooner.
**The play.**
1. Corroborate before campaigning: check for stopped release notes, disappearing job posts, and quiet doc staleness on the product line.
2. Build the displacement campaign around risk, not features, an evaluation guide for teams on aging CX platforms, sidestepping the legal risk of naming their internals.
3. Arm sales with the observable fact, **"their vendor deleted the product's own page in March" is verifiable and devastating**, in a way roadmap FUD never is.
4. Target Argo Engage's known customers first; review sites and integration marketplaces list them publicly.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) watch on `showcase_page_removed` across competitors makes product retreats visible the week they happen.
**Why it lands.** Displacement campaigns fail when the incumbent seems fine. This signal finds the moment **the incumbent stopped seeming fine to itself**.
## How to read it
Removal can mean a discontinued offering.
May reflect folding a unit into the main brand.
Subtle but useful portfolio-change context.
## Outreach playbook
Low priority. Context for portfolio changes and product sunsets.
## Related signals
A new Professional Network showcase page was linked.
A specialty was dropped from the list.
***
Surface `showcase_page_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Subsidiary Added
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/subsidiary-added
A new subsidiary appeared in the company's list.
`subsidiary_added`
Structure
Company Professional Network page (subsidiaries list).
A new subsidiary appeared in the company's list.
## When it fires
A new entry appears in this company's subsidiaries list.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| -------------------- | ---------------------------------- |
| `meta.subsidiary_id` | The added subsidiary's identifier. |
## Magnitude
High, signals acquisition from the buyer's side.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Adding subsidiaries signals acquisition activity from the buyer's side. When a company gains a subsidiary, it just acquired or established a new entity, this is the acquirer's view of an acquisition and indicates an active, well-funded buyer.
## How to use Subsidiary Added Signal?
**Where you sit.** You lead strategy at a B2B software company in a consolidating market. Your fiercest competitor, Ketch Systems, has private-equity backing and a habit of buying capabilities instead of building them. Their acquisitions never get press releases until the integration is done, **by which point your sales team has been losing deals to the new bundle for a quarter without knowing why**.
**The mission.** Detect competitor acquisitions at the structural level, when the subsidiary appears, not when the announcement drops.
**The signal fires.** `subsidiary_added` fires for Ketch Systems: a new subsidiary, a small analytics vendor called Parundel, now sits under their corporate structure. No announcement anywhere. Parundel's product happens to fill the exact reporting gap your reps exploit in every competitive deal.
**Reading it.** An unannounced subsidiary is a strategy document you were not supposed to read yet. Ketch buying Parundel means the reporting-gap objection your battlecards lean on has a countdown on it, integrations typically take two to four quarters, and that your roadmap team is now racing a clock it did not know existed. Found early, this is **a two-quarter head start on a competitive shift**; found late, it is a string of mysterious losses.
**The play.**
1. Verify quietly: Parundel's team pages, mutual customers, and hiring posts will confirm the integration's pace.
2. Update battlecards in two phases, **today's truth (not integrated, two roadmaps, uncertain support) and next year's risk**, so reps push deals to close before the bundle is real.
3. Brief product leadership with the timeline; build-versus-buy responses need the full runway.
4. Mine the seam: acquisitions wobble the acquired company's customers and staff, and Parundel's unhappy customers are your easiest wins this year.
**Automate it.** A weekly [Company Signals API](/buying-signals/signals-apis/company-signals) watch on `subsidiary_added` for every named competitor turns corporate-structure changes into an early-warning system.
**Why this works.** Competitors announce what they want you to react to. Structure changes reveal **what they hoped you would miss**.
## How to read it
This company is the one doing the acquiring.
Active acquirers have capital and ambition.
meta.subsidiary\_id identifies the new entity.
## Outreach playbook
Identifies active acquirers. Strong fit for tools that support integration.
## Related signals
A subsidiary was removed from the company's list.
A parent company was set where there was none.
A company was acquired.
***
Surface `subsidiary_added` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Subsidiary Removed
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/subsidiary-removed
A subsidiary was removed from the company's list.
`subsidiary_removed`
Structure
Company Professional Network page (subsidiaries list).
A subsidiary was removed from the company's list.
## When it fires
A subsidiary is removed from this company's subsidiaries list.
## Stored fields
This signal persists the following metadata you can read downstream:
| Field | Description |
| -------------------- | ------------------------------------ |
| `meta.subsidiary_id` | The removed subsidiary's identifier. |
## Magnitude
Moderate to high.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Divestitures and restructuring show up here. Removing a subsidiary means the company sold or wound down an entity, a restructuring or portfolio-optimization move that can free up or redirect budget.
## How to use Subsidiary Removed Signal?
**The scenario.** You are an associate at a lower-mid-market private equity fund. The deals your partners love most never hit an auction: orphaned business units, divisions a corporate parent has stopped loving but not yet formally marketed. The eternal problem is finding them **after the internal decision to divest, but before the bankers are hired**.
**The goal.** Use corporate-structure changes as a divestiture radar, catching parents in active portfolio-pruning mode.
**The signal fires.** `subsidiary_removed` fires for Rowanwood Group, an industrial holding company: one of their subsidiaries, a specialty-coatings business, no longer appears in their structure. Sold quietly, wound down, or reclassified, the signal does not say. The follow-up is your job.
**Reading it.** However this particular exit happened, it tells you something durable: **Rowanwood is actively pruning its portfolio**, and pruning is rarely a single cut. Holding companies that divest one unit are usually mid-review on the whole portfolio, which means somewhere in their remaining structure sit one or two more units being quietly evaluated. A fund that opens a relationship now, before the next unit reaches a banker, buys itself a proprietary look.
**The play.**
1. Reconstruct the event: registry filings and the removed unit's own profile usually reveal whether it was a sale, and to whom, within an hour.
2. Map Rowanwood's remaining subsidiaries and rank them against your fund's thesis; the misfits, small units in non-core sectors, are the likely next candidates.
3. Approach corporate development directly, with specificity as credibility: your fund buys exactly this kind of unit at this size, and you would welcome a conversation whenever the portfolio review reaches the next one.
4. Track every holding company that fires this signal, **serial pruners are the gift that keeps giving**, and a quarterly call cadence with two of them outperforms a hundred banker books.
**Automate it.** A monthly [Company Signals API](/buying-signals/signals-apis/company-signals) sweep on `subsidiary_removed` across holding companies in your sectors is proprietary deal flow for the cost of a query.
**Why it lands.** Auctioned deals are priced by competition. This signal finds sellers **before the competition is invited**.
## How to read it
Removing a subsidiary means selling or closing it.
Often part of a strategic refocus.
meta.subsidiary\_id shows what was divested.
## Outreach playbook
Restructuring indicator. Note the divested entity for context.
## Related signals
A new subsidiary appeared in the company's list.
A parent company relationship was cleared.
A company is undergoing major restructuring.
***
Surface `subsidiary_removed` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Subsidiary Status Change
Source: https://apidoc.cufinder.io/buying-signals/signals/structure/subsidiary-status-change
The company became or stopped being a subsidiary.
`subsidiary_status_change`
Structure
Company Professional Network page (company type field).
The company became or stopped being a subsidiary.
## When it fires
`company_type` changes to or from Subsidiary.
## Magnitude
High, ties closely to M\&A activity.
For the full bucket definitions used across percentage-based signals, see [Magnitude buckets](/buying-signals/concepts/magnitude-buckets).
## Why it matters
Becoming or shedding subsidiary status usually ties to acquisition activity. A company becoming a subsidiary was likely acquired; one shedding the status was spun off, both reshape who controls budget and vendor decisions.
## How to use Subsidiary Status Change Signal?
**Where you sit.** You are an enterprise AE at a supply-chain software company, two stages deep into a promising deal with Tarn Optics, a precision-lens manufacturer. Legal review is scheduled, security questionnaire returned, champion enthusiastic. The deal feels like it is yours to lose. Deals like that get lost anyway, and usually **the killer comes from outside the room**.
**The mission.** Know immediately when an active account's corporate status changes, because ownership changes rewrite the rules of any purchase in flight.
**The signal fires.** `subsidiary_status_change` fires for Tarn Optics: they are now marked as a subsidiary of Vela Group, an industrial holding company. Your champion has not mentioned it. There is a decent chance your champion does not fully grasp what it means yet either.
**Reading it.** When a company becomes a subsidiary, its purchasing autonomy is the first thing renegotiated. Vela Group almost certainly has procurement thresholds, preferred-vendor lists, and integration freezes that now apply to Tarn. Your deal, scoped and priced for an independent company, **may now need approval from people you have never met, against standards you have never seen**. Pretending otherwise is how six-month deals become eighteen-month deals.
**The play.**
1. Raise it with your champion directly and early: how does the Vela relationship change approvals for a purchase this size?
2. Re-map the committee assuming a parent-company gatekeeper exists; ask for the introduction before you need it.
3. Check whether Vela's other subsidiaries use a competitor, **parent-level standards can kill you or crown you**, and you want to know which before legal review.
4. If a freeze is coming, compress: a signable proposal before integration formalizes beats a perfect proposal after.
**Automate it.** Sync every open-opportunity account to a weekly [Company Signals API](/buying-signals/signals-apis/company-signals) check on `subsidiary_status_change`, wired to alert the deal owner the day it fires.
**Why it lands.** You cannot stop an acquisition from complicating your deal. You can be **the only vendor in the account who adapted to it a quarter early**.
## How to read it
Becoming a subsidiary usually means being acquired.
Leaving subsidiary status signals independence.
The parent may now control or release purchasing.
## Outreach playbook
M\&A indicator. Determine whether budget authority moved to or from a parent.
## Related signals
A parent company was set where there was none.
A parent company relationship was cleared.
The company's entity type changed.
A company was acquired.
***
Surface `subsidiary_status_change` across millions of companies, enriched with verified emails, phones, and full firmographics. Start free, no credit card required.
# Setup Guide for ChatGPT Web
Source: https://apidoc.cufinder.io/mcp/chatgpt-web
Connect CUFinder MCP with ChatGPT in minutes. Access 1B+ contacts and 85M+ companies for lead generation and data enrichment directly in your AI conversations.
## Note on ChatGPT support
Full support for MCP client access in connectors and tools is currently in **beta** in ChatGPT. At the moment, it's only available in **Developer mode** for **Pro and Plus accounts on the web.**
While it's in beta, we recommend being as **explicit and precise** as possible in your prompts. For example:
* Mention the **CUFinder MCP connector** directly.
* Specify the **tool or endpoint** to use.
* Indicate which **fields to pull** and how to **sort the results.**
* Excplicitly ask ChatGPT **not to use the web search.**
For the latest details, see the official ChatGPT Developer Mode docs
## Setup guide
Follow these steps to add CUFinder MCP server to ChatGPT Web:
1. Go to Settings > Apps > Advanced settings
![mcp-11]()
2. Enable the "Developer mode".
![mcp-12]()
3. Click the "Create app" button.
![mcp-13]()
4. Fill in the new Connector details:
* MCP Server URL: [https://mcp.cufinder.io/mcp](https://mcp.cufinder.io/mcp)
* Authentication: OAuth
Select the option “I trust this application” and click the "Create" button.
![mcp-14]()
5. In the opened page, enter your CUFinder dashboard username and password, then click Connect.
6. Then, click Yes to confirm that Chatgpt is requesting access to your CUFinder account.
![mcp-15]()
7. Check that the apps in ChatGPT is successfully connected.
![mcp-16]()
8. Activate the CUFinder connector in the chat before starting the query:
* Click on the "+" sign → "More" → "CUFinder”
![mcp-17]()
# Setup Guide for Claude Web
Source: https://apidoc.cufinder.io/mcp/claude-web
Learn to connect CUFinder MCP with Claude Web in minutes. Access 1B+ contacts and 85M+ companies for lead generation directly in your AI conversations.
## Installation
1. Click on your profile icon (often your initials) in the lower-left corner and choose "Settings" from the menu.
2. In the Settings, select Connectors and click "Add custom connector."
![mcp-3]()
3. Enter the connector's name ‘CUFinder’ and URL [https://mcp.cufinder.io/mcp](https://mcp.cufinder.io/mcp) and click "Add".
![mcp-4]()
4. The CUFinder MCP connector will appear in the list of connectors. Click "Connect".
![mcp-5]()
5. In the opened page, enter your CUFinder dashboard username and password, then click Connect.
6. Then, click Yes to confirm that Claude is requesting access to your CUFinder account.
![mcp-6]()
7. Once connected, you will see a CUFinder MCP server was added to your connectors.
![mcp-7]()
## Using CUFinder Connectors
1. Enable the connector in the chat settings dropdown
![mcp-8]()
2. Add a query, for example: "give me top 10 software companies in california. write results in excel mode”
![mcp-9]()
3. Note that in Claude Connectors Settings , if the connector is set to “Always ask permission”, you will be prompted to grant permission before any query can return an answer. You can choose “Allow unsupervised” to grant the connector access automatically. This ensures smooth and uninterrupted use of the Ahrefs tools without repeated permission prompts.
![mcp-10]()
# What is CUFinder MCP
Source: https://apidoc.cufinder.io/mcp/introduction
**CUFinder MCP** lets AI agents securely access the CUFinder API and enrich their answers with real B2B data. MCP (Model Context Protocol) is a standard that allows AI tools to connect with third-party APIs in a secure, consistent way.
In our case, it connects AI tools with CUFinder, enabling things like finding verified business emails, enriching company profiles, discovering decision-makers, and accessing real-time B2B intelligence directly within your AI workflows, like this:
![mcp-1]()
With the CUFinder MCP server, your AI assistant can tap into over 1 billion enriched people profiles and 85 million company records, refreshed daily, without leaving your conversation. Whether you're prospecting for leads, validating contact information, or building targeted outreach lists, the MCP integration brings CUFinder directly to your AI environment.
## Ways to use Ahrefs MCP
CUFinder provides the following option for running the MCP server:
**Streamable HTTP** (recommended): [https://mcp.cufinder.io/mcp](https://mcp.cufinder.io/mcp)
Most popular AI tools support connecting to the remote MCP directly. If you run into issues, check out our step-by-step setup guides:
* Claude Web / Desktop / Extension
* Claude Mobile
* Claude Code
* ChatGPT Web
## API limits and usage
The remote MCP server is available on **paid plans starting from Growth.**
* Each plan has a **maximum number of rows per request** that an AI agent can access.
* Each plan also comes with a **limited amount of monthly API units.** The higher your plan tier, the more units are included.
* When a third-party app makes an API call on your behalf, it consumes **API units** from your monthly allowance, [The same way as direct API usage.](/apis/usage-limits)
You can track your current usage in **Account settings** → API Dashboard.
![mcp-2]()
See the CUFinder pricing page for details on plan limits.
## Keys and authorization
When you connect CUFinder MCP to an AI agent, you'll need your CUFinder API key to authenticate requests and access the platform's B2B data enrichment capabilities.
## How to get your API key:
* Log in to your
CUFinder dashboard
* Navigate to **Account Settings**
* Click on **API Dashboard**
* Copy your unique API key
* Keep your API key confidential. Never share it publicly or commit it to version control.
* Credits are consumed only when services successfully return results.
* Monitor your usage in the API Dashboard to track credit consumption.
* Need more credits? Explore
CUFinder's pricing plans starting from a free tier with 50 credits/month.