Company Search Reference

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.

Minimal Working Example

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

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

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

Parameter Restrictions

The company category does not support the following parameters. Using them returns a 400 error:

Unsupported Parameter	Workaround
`startPublishedDate`	Not available. Use natural language (e.g. “founded after 2020”).
`endPublishedDate`	Not available.
`excludeDomains`	Not available.

Supported Parameters

Parameter	Type	Notes
`query`	string	Natural language. Supports industry, geography, funding, headcount, technology, similarity.
`category`	string	Must be `"company"`.
`type`	string	`"auto"` recommended. `"deep"` and `"deep-reasoning"` also work.
`numResults`	integer	1–100. Default 10.
`contents`	object	`text`, `highlights`, `summary`, all nested under `contents`.

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" }
          ]
        }
      }
    }
  ]
}

Field	Type	Notes
`entities[].id`	string	Stable company entity identifier.
`entities[].type`	string	`"company"`.
`entities[].version`	integer	Entity schema version.
`properties.name`	string \| null	Company name.
`properties.foundedYear`	integer \| null	Year the company was founded.
`properties.description`	string \| null	Short company description.
`properties.workforce`	object \| null	Workforce details. Currently includes `total`, the estimated employee count.
`properties.headquarters`	object \| null	`address`, `city`, `postalCode`, and `country`.
`properties.financials`	object \| null	`revenueAnnual`, `fundingTotal`, and `fundingLatestRound`, all in USD when present.
`properties.financials.fundingLatestRound`	object \| null	`name`, `date`, and `amount` for the most recent funding round.
`properties.webTraffic`	object \| null	`visitsMonthly`, `countryRank`, `avgDurationSeconds`, and monthly `history`.
`properties.webTraffic.history[]`	object	Historical 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.

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"

Common Mistakes

Wrong	Correct
`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"`.

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.

API

Data

MCP

SDKs

Integrations

Admin

Additional Resources

Company Search Reference

Overview

Minimal Working Example

Parameter Restrictions

Supported Parameters

Structured Entity Metadata

Query Patterns

Common Mistakes

Patterns and Gotchas

​Overview

​Minimal Working Example

​Parameter Restrictions

​Supported Parameters

​Structured Entity Metadata

​Query Patterns

​Common Mistakes

​Patterns and Gotchas

Overview

Minimal Working Example

Parameter Restrictions

Supported Parameters

Structured Entity Metadata

Query Patterns

Common Mistakes

Patterns and Gotchas