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Overview

Endpoint: POST https://api.exa.ai/search with "category": "company" What it searches: 50M+ company pages including LinkedIn company profiles, official websites, and Crunchbase-style data. Semantic search over industry, funding stage, headcount, geography, and technology attributes. Natural language queries return relevance-ranked company results. For creating lists or enriching over many companies at scale, use Websets.

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Minimal Working Example

curl -X POST "https://api.exa.ai/search" \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{"query": "fintech companies in Switzerland", "category": "company", "contents": {"highlights": true}}'

from exa_py import Exa
exa = Exa(api_key="YOUR_API_KEY")
result = exa.search("fintech companies in Switzerland", category="company", contents={"highlights": True})

import Exa from "exa-js";
const exa = new Exa("YOUR_API_KEY");
const result = await exa.search("fintech companies in Switzerland", { category: "company", contents: { highlights: true } });

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Parameter Restrictions

The company category does not support the following parameters. Using them returns a 400 error:
Unsupported ParameterWorkaround
startPublishedDateNot available. Use natural language (e.g. “founded after 2020”).
endPublishedDateNot available.
excludeDomainsNot available.

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Supported Parameters

ParameterTypeNotes
querystringNatural language. Supports industry, geography, funding, headcount, technology, similarity.
categorystringMust be "company".
typestring"auto" recommended. "deep" and "deep-reasoning" also work.
numResultsinteger1–100. Default 10.
contentsobjecttext, highlights, summary, all nested under contents.

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Structured Entity Metadata

Company Search returns structured company metadata in entities for result rows that resolve to a company. Each company entity has type: "company", a stable id, a schema version, and a properties object with company profile fields. 
{
  "title": "Example AI",
  "url": "https://www.example.ai",
  "entities": [
    {
      "id": "company_...",
      "type": "company",
      "version": 1,
      "properties": {
        "name": "Example AI",
        "foundedYear": 2021,
        "description": "AI infrastructure company for enterprise search.",
        "workforce": { "total": 120 },
        "headquarters": {
          "address": "123 Market Street",
          "city": "San Francisco",
          "postalCode": "94105",
          "country": "United States"
        },
        "financials": {
          "revenueAnnual": null,
          "fundingTotal": 42000000,
          "fundingLatestRound": {
            "name": "Series B",
            "date": "2025-03-15",
            "amount": 30000000
          }
        },
        "webTraffic": {
          "visitsMonthly": 250000,
          "countryRank": 12000,
          "avgDurationSeconds": 180,
          "history": [
            { "value": 250000, "dateFrom": "2026-04", "dateTo": "2026-04" }
          ]
        }
      }
    }
  ]
}
FieldTypeNotes
entities[].idstringStable company entity identifier.
entities[].typestring"company".
entities[].versionintegerEntity schema version.
properties.namestring | nullCompany name.
properties.foundedYearinteger | nullYear the company was founded.
properties.descriptionstring | nullShort company description.
properties.workforceobject | nullWorkforce details. Currently includes total, the estimated employee count.
properties.headquartersobject | nulladdress, city, postalCode, and country.
properties.financialsobject | nullrevenueAnnual, fundingTotal, and fundingLatestRound, all in USD when present.
properties.financials.fundingLatestRoundobject | nullname, date, and amount for the most recent funding round.
properties.webTrafficobject | nullvisitsMonthly, countryRank, avgDurationSeconds, and monthly history.
properties.webTraffic.history[]objectHistorical monthly visits with value, dateFrom, and dateTo.
Top-level property keys are present on company entities. Treat individual values and nested objects defensively because sources can vary in the information they include.

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Query Patterns

Named lookup: 
"Sakana AI company"
"Tell me about exa.ai"
Attribute filtering: 
"fintech companies in Switzerland"
"Japanese AI companies founded in 2023"
Funding queries: 
"agtech companies in the US that have raised series A"
"startups that raised 30M to 80M"
Composite queries: 
"Israeli security companies founded after 2015"
"German enterprise SaaS companies with more than 500 employees"
Semantic / similarity: 
"Companies like Bell Labs"
"Companies working on making space travel cheaper"
"competitors of Notion"

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Common Mistakes

WrongCorrect
excludeDomains: [...] with category: "company"Remove excludeDomains. Not supported for company. Returns 400.
startPublishedDate: "2023-01-01" with category: "company"Remove date filters. Use natural language like “founded after 2023” in query.
Missing category: "company"Without category, the search runs against the general web index. Always include "category": "company".

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Patterns and Gotchas

  • Always set category: "company". Without it, you search the general web index and won’t get company-specific results.
  • Natural language handles what filters can’t. Since date/text/exclude filters aren’t supported, put all constraints in your query: “Series A fintech companies in Europe with 50-200 employees founded after 2020”.
  • Use highlights for agent workflows. Company pages are long. Highlights extract key details (industry, funding, headcount) efficiently.
  • Use entities for typed metadata. Read founded year, workforce, headquarters, financials, and web traffic from results[].entities[].properties; use text or highlights for supporting snippets.
  • Similarity queries work well. “Companies like X” and “competitors of X” leverage semantic understanding of the company index.
  • Python SDK uses snake_case. numResults → num_results, maxCharacters → max_characters.
  • Combine with deep search for custom enrichment. Use type: "deep" with outputSchema when you need fields outside the built-in company entity schema.