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Documentation Index

Fetch the complete documentation index at: https://docs.cala.ai/llms.txt

Use this file to discover all available pages before exploring further.

Returns a tabular JSON rows plus matching entities from Cala’s knowledge base. Use knowledge_query (POST /v1/knowledge/query) when you need structured data ready for programmatic use rather than a natural-language answer. Input can be a Cala QL expression or a natural-language question — both produce the same structured response shape.
  • Navigate relationships between entities using dot notation
  • Filter results by applying conditions directly in the path
  • Access nested attributes across multiple entity relationships
  • Get structured, typed responses ready for your applications

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Query language syntax

Build queries using dot notation and operators. This syntax works on both knowledge_query and knowledge_search.
OperatorMeaningExample
.Navigate relationships and access propertiesOpenAI.founded.year
=Filter by Exact matchstartups.location=Spain
!=Excludestartups.location!=US
>Greater thanstartups.funding>10M
<Less thanstartups.funding<50M
>=Greater than or equalstartups.funding>=50M
<=Less than or equalstartups.funding<=50M
Chain filters with dots: startups.location=Spain.funding>10M.funding<50M

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Example

Startups in Spain with funding between €10M and €50M: 
{"input": "startups.location=Spain.funding>10M.funding<50M"}
Every response includes two parts:

Structured Results

Typed, filterable data — not paragraphs of text. Each result is a structured object with consistent fields.
{
  "results": [
    {
      "company": "Amenitiz",
      "sector": "Hospitality SaaS",
      "funding_amount": "€38.9M",
      "round_type": "Series B",
      "year": 2025
    },
    {
      "company": "H2SITE",
      "sector": "Hydrogen Energy",
      "funding_amount": "€36M ($37.2M)",
      "round_type": "Series B",
      "year": 2025
    },
    {
      "company": "Fracttal",
      "sector": "AI Maintenance",
      "funding_amount": "€29.8M ($35M)",
      "round_type": "Series B",
      "year": 2026
    },
    {
      "company": "Heura",
      "sector": "Plant-based Foods",
      "funding_amount": "€40M",
      "round_type": "Total (multiple rounds)",
      "year": 2023
    },
    {
      "company": "Housfy",
      "sector": "Real Estate",
      "funding_amount": "€30M",
      "round_type": "Series B",
      "year": 2022
    },
    {
      "company": "Highlight Therapeutics",
      "sector": "Biotech",
      "funding_amount": "€37.6M",
      "round_type": "Venture Round",
      "year": 2024
    },
    {
      "company": "011h",
      "sector": "Construction Tech",
      "funding_amount": "€40.4M",
      "round_type": "Venture Round",
      "year": 2024
    },
    {
      "company": "Kreios Space",
      "sector": "Satellite Tech",
      "funding_amount": "€10.6M",
      "round_type": "Seed",
      "year": 2025
    },
    {
      "company": "Internxt",
      "sector": "Security/Storage",
      "funding_amount": "€10.9M",
      "round_type": "Seed",
      "year": 2020
    },
    {
      "company": "Nomad Solar Energy",
      "sector": "Solar Energy",
      "funding_amount": "€15M",
      "round_type": null,
      "year": null
    },
    {
      "company": "Universal DX",
      "sector": "Healthcare",
      "funding_amount": "€20M",
      "round_type": null,
      "year": null
    },
    {
      "company": "Maisa AI",
      "sector": "AI",
      "funding_amount": "€30M",
      "round_type": null,
      "year": null
    },
    {
      "company": "Reveni",
      "sector": null,
      "funding_amount": "€17.3M",
      "round_type": null,
      "year": null
    }
  ]
}
13 startups returned — each with company name, sector, funding amount, round type, and year. This is structured data, not text to parse.
Every company in the results is automatically linked to a Cala entity with a unique UUID and type.
{
  "entities": [
    {"id": "e2ce8098-dea6-4fdd-b226-c33923b5efe1", "name": "Amenitiz", "entity_type": "Company"},
    {"id": "b2171d05-18d4-4b1b-8953-208ce66be8d6", "name": "H2SITE", "entity_type": "Company"},
    {"id": "714be9ad-cce1-46e5-b0d5-a01f71f26d01", "name": "Fracttal", "entity_type": "Company"},
    {"id": "3d47d7b1-8f83-4be3-be42-b9d8712c1d4c", "name": "Heura", "entity_type": "Company"},
    {"id": "3349e982-694e-4923-9af7-480af3efcedc", "name": "Housfy", "entity_type": "Company"},
    {"id": "b5c7a256-79b6-43d9-84fb-24c4403cc098", "name": "Highlight Therapeutics", "entity_type": "Company"},
    {"id": "63a254cb-40bc-49a2-9922-7ae806398c38", "name": "011h", "entity_type": "Company"},
    {"id": "73f3603f-624e-457a-b5ad-f192792cc15b", "name": "Kreios Space", "entity_type": "Company"},
    {"id": "9beb50ad-d4dc-4ef8-bd59-7a26bbcbb413", "name": "Internxt", "entity_type": "Company"},
    {"id": "877fc47b-b23a-4cc9-b929-50d2a629fd38", "name": "Nomad Solar Energy", "entity_type": "Company"},
    {"id": "fd163be0-ab87-4218-b0a6-48a5cc46c711", "name": "Universal DX", "entity_type": "Company"},
    {"id": "42285452-6100-473b-a8bb-85c731e3e1e5", "name": "Maisa AI", "entity_type": "Company"},
    {"id": "ff47dfd6-97de-401a-82ae-edc3adf35694", "name": "Reveni", "entity_type": "Company"}
  ]
}
Use any entity UUID to get the full profile: POST /v1/entities/e2ce8098-dea6-4fdd-b226-c33923b5efe1 returns everything Cala knows about Amenitiz — founders, executives, HQ, employee count, and more.

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Natural-language input also works

You can also send a plain question — the response is still structured rows, not prose: 
{"input": "What are the most promising climate tech startups in Southern Europe?"}

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More query examples

These use the Cala QL syntax. Natural-language questions also work with this endpoint. 
{"input": "OpenAI.founded.year"}
{"input": "ibex35.companies.employee_count>2000"}
{"input": "companies.founder.incorporation>2020.previous_job=Google"}