Structured Answer

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

Query language syntax

Build queries using dot notation and operators. This syntax works on both knowledge_query and knowledge_search.

Operator	Meaning	Example
`.`	Navigate relationships and access properties	`OpenAI.founded.year`
`=`	Filter by Exact match	`startups.location=Spain`
`!=`	Exclude	`startups.location!=US`
`>`	Greater than	`startups.funding>10M`
`<`	Less than	`startups.funding<50M`
`>=`	Greater than or equal	`startups.funding>=50M`
`<=`	Less than or equal	`startups.funding<=50M`

Chain filters with dots: startups.location=Spain.funding>10M.funding<50M

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.

Entities

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.

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?"}

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"}

Getting Started

Features

Integrations

API Reference

Query language syntax

Example

Natural-language input also works

More query examples

​Query language syntax

​Example

​Natural-language input also works

​More query examples

Query language syntax

Example

Natural-language input also works

More query examples