> ## 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. # 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: ```json theme={null} {"input": "startups.location=Spain.funding>10M.funding<50M"} ``` Every response includes two parts: Typed, filterable data — not paragraphs of text. Each result is a structured object with consistent fields. ```json theme={null} { "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. ```json theme={null} { "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: ```json theme={null} {"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. ```json theme={null} {"input": "OpenAI.founded.year"} {"input": "ibex35.companies.employee_count>2000"} {"input": "companies.founder.incorporation>2020.previous_job=Google"} ```