Typeahead suggestions for the open-ended People Search and Company Search filters, plus person and company lookups by name. Send the text the user has typed so far (or nothing - an empty query browses each vocabulary, most popular first for locations, job titles and company keywords, so a picker can open pre-filled; person and company are the exceptions and need at least 2 characters) and one vocabulary type - location, job_title, industry, company_keywords, technology, person or company - and get back up to 25 ready-to-use rows. For location, job_title, industry and company_keywords, each row carries a value you pass as-is to the matching filter (person_location / company_location, person_job_title, company_industry, company_keywords). For person, value feeds person.name (search everyone with that name) and id feeds person.id (pin exactly that one person); each row also carries their current job title and company with its logo. For company, value is the canonical company name and each row carries the id company.id (People Search) accepts, a domain attached to the company (its website when none is attached), logo, industry and employee range - matched by a prefix of the name or of any attached domain. Person and company rows carry saved: true when your workspace has saved them, and saved matches are listed first. A complete email address as the person query finds the person that address is stored on (people your workspace saved or revealed with it come first), and as the company query finds the company by its domain. A LinkedIn profile URL as the person query finds that person. A LinkedIn company URL as the company query finds that company. Every suggestion repeats the requested type, so a mixed list stays self-describing. Location suggestions include the structured parts (city, state, country, ISO country code) and a level; job-title suggestions map abbreviations like "ceo" to their canonical title and tolerate a rephrasing ("VP of Marketing" and "Marketing VP" both suggest "Vice President Marketing"); industry suggestions include the category group; company-keyword suggestions carry a company_count and match a prefix of any word, so "market" finds both "Marketing" and "Digital Marketing". For technology, rows mark technology vs category with kind, and value is the id the matching filter (company_technologies / company_technology_categories) takes. For the closed vocabularies (departments, seniorities, countries, employee ranges and more) fetch the full lists from Enums instead. Free: costs no credits; a person lookup by email address or LinkedIn URL counts against the per-second window of the enrich rate limit (0 units), every other suggestion against no limit. Authenticate with your API key in the x-api-key header.

How are credits spent?

Suggestions are free and consume no credits. Use them as liberally as a typeahead needs - the endpoint is built to be called on every keystroke.

One vocabulary per request

Send the text the user has typed so far and one type; the response is up to 25 ready-to-use filter values, best match first. The query is optional for every vocabulary except person - omit it, or send an empty string, to browse a vocabulary rather than search it: locations return top regions and the most popular places, job titles the most-held titles, industries the alphabetical head of the list, and company keywords the most widely declared specialties, so a picker can open pre-filled. person is the exception: it has no browse mode and always requires a query. Each suggestion's value can be passed as-is to the matching search filter:

• location - suggests places for person_location / company_location on People Search and company_location on Company Search. Each suggestion carries the unified string ("Austin, Texas, United States"), its structured parts (city, state, state_code, country, country_code), and a level of region, country, state or city. Regions such as DACH or EMEA span several countries, so they leave country and country_code null and carry country_codes - their member countries - instead; the full region catalog is GET /v2/enums/regions on Enums. Typing a common alias works too - usa suggests United States, bombay suggests Mumbai - and codes match exactly: gb suggests United Kingdom, tx suggests Texas.

• job_title - suggests canonical titles from our job-title catalog for person_job_title. Abbreviations resolve to the canonical spelling: ceo suggests "Chief Executive Officer", which the search then matches in both spellings.

• person - suggests specific people for person.id / person.name on People Search. Unlike the other vocabularies this one needs at least 2 characters and has no browse mode - "the most popular people" is not a list. Every word you type must appear in the person's name and the last one matches as a prefix, so Dmytro Shu and Shulha Dmy both suggest the same person. Each row carries value (the name, ready for person.name), id (ready for person.id), the person's current job_title, their company with its logo, and linkedin_url. Pass value to search everyone with that name, or id to pin that one person:

  Response

  [
    {
      "value": "Dmytro Shulha",
      "id": "69cfd8b2cfefb668329bab73",
      "job_title": "Chief Executive Officer",
      "company": {
        "id": "66b1f0a2e4b0c8d9f1a2b3c4",
        "name": "Four Ages Software",
        "logo": "https://logos.getprospect.com/4a9f2c1e.jpg"
      },
      "linkedin_url": "https://www.linkedin.com/in/dshulha"
    }
  ]

• industry - suggests accepted company_industry values, each with its category group for building grouped pickers.

• company_keywords - suggests company specialties for the company_keywords filter on both People Search and Company Search. Each suggestion carries a company_count - how many companies declare that specialty - and rows come back most-declared first, so the head of the list is the vocabulary that will actually return results. Only specialties declared by enough companies to be useful as a filter are suggested; one-off text a single company wrote about itself is not a keyword:

  Response

  [
    { "value": "Email Marketing", "company_count": 57393 },
    { "value": "Email Deliverability", "company_count": 539 },
    { "value": "Email Automation", "company_count": 1216 }
  ]

For location, job_title, industry and company_keywords, matching is case- and diacritic-insensitive, and the query can hit the start of a value or an inner word - market suggests both "Marketing Manager" and "Digital Marketing Manager". person is case-insensitive too, but diacritics are significant there, exactly as they are for person.name on People Search: Jose Alv and José Álv suggest different people.

Closed vocabularies

Suggestions cover the open-ended fields only. The closed lists - departments, seniorities, countries, employee ranges, company types, business types, revenue streams and keyword sources (the list of fields company_keywords can search, not the keywords themselves) - are small and fixed; fetch them once from Enums and filter client-side.

Body parameters

• typeenumrequired

  Which vocabulary to suggest from. location = unified location strings for person_location / company_location, with structured parts and a level (region / country / state / city; regions like EMEA or APAC also carry country_codes - their member countries); job_title = catalog titles for person_job_title (typing an abbreviation like "ceo" suggests its canonical title); industry = accepted company_industry values, each with its category group; company_keywords = company specialties for the company_keywords filter on both People Search and Company Search, each row carrying a company_count, ordered by popularity when browsing and by relevance when searching; person = people matching the typed name, each carrying the id person.id accepts, their current job title, and their company with its logo; company = companies matching the typed name or domain prefix, each carrying the id company.id (People Search) accepts, a domain attached to the company (its website when none is attached), logo, industry and employee range - value is the canonical name company.name accepts; technology = web technologies and technology categories for the company_technologies / company_technology_categories filters (Company Search and People Search): categories first, then technologies, each by how many companies use it; kind says which filter value (the id) goes to.

  Allowed values: location job_title industry company_keywords person company technology

  Example

  {
    "type": "location"
  }

• querystringoptionalmax length: 100

  The text typed so far, matched case-insensitively. Location, job_title, industry and company_keywords also ignore diacritics; person and company do not, so type "Nestlé", not "Nestle". Job-title and industry suggestions also survive a rephrasing: connector words and punctuation are ignored and the words may come in any order, so "VP of Marketing", "VP, Marketing" and "Marketing VP" all suggest "Vice President Marketing" - the same spellings person_job_title treats as that title. Location codes match exactly too: "gb" suggests United Kingdom, "tx" suggests Texas. Omit (or send empty) for browse mode, so a picker can open pre-filled: locations return top regions and the most popular places (the full region catalog lives at GET /v2/enums/regions), job titles the most-held titles, industries the alphabetical head of the list. Max 100 chars. Company-keyword suggestions match a prefix of any word, so "market" finds both "Marketing" and "Digital Marketing". person and company are the exceptions to browse mode: they need at least 2 characters and answer an empty list below that.

  Example

  {
    "type": "location",
    "query": "aust"
  }

• limitintegeroptionalmin: 1max: 25default: 10

  Maximum suggestions to return.

Responses

• 200 The suggestions, best first; [] when nothing matches (still a 200).
• 400 Invalid request (VALIDATION_ERROR; payload.code says which kind of mistake).
• 401 Missing, unknown or inactive credentials (UNAUTHORIZED).
• 429 Over the per-second window of the enrich rate limit (RATE_LIMIT), which only person lookups by email address or LinkedIn URL count against; wait Retry-After seconds. Sent only where rate limits are enforced.
• 500 Unexpected server error.

For error handling see Errors. Response objects are described in Person object and Company object.