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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": {"maxCharacters": 4000}}}'

from exa_py import Exa
exa = Exa(api_key="YOUR_API_KEY")
result = exa.search("fintech companies in Switzerland", category="company", contents={"highlights": {"max_characters": 4000}})

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: { maxCharacters: 4000 } } });

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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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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.
  • 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 structured extraction. Use type: "deep" with outputSchema to extract fields like company name, industry, funding, employee count.