Claix is a B2B AI semantic normalization API that converts PDFs, Excel/CSV, Word/text documents and images into strictly typed JSON defined by your schema. Official site: https://www.claix.dev/en

What file formats does the Claix API support?

Claix supports Excel (.xlsx) and CSV, JSON to Excel export, PDF (text or scanned), .docx/.txt/.md/.rtf documents, and .jpeg/.jpg/.png/.webp/.heic/.heif images. Each format uses a dedicated endpoint and schema type.

How do I convert a PDF to JSON with Claix?

Create a pdf-json schema, obtain an API key, and send a multipart POST to https://www.claix.dev/api/pdf-json with file and schema_id fields and the x-api-key header. Documentation: https://www.claix.dev/en/documentation/pdf-to-json

What is Claix pricing?

Pay-as-you-go. The first 15 successful calls (HTTP 200) are free. Excel↔JSON is billed by variable volume (€0.25–€1.00). PDF→JSON €0.35, Doc→JSON €0.20, Img→JSON €0.10 per successful request. 4xx/5xx errors are not billed.

Does Claix offer an embeddable widget?

Yes. Claix provides a free unlimited iframe widget (https://www.claix.dev/widget/{id}), customizable and production-ready. JSON is delivered to the owner's HTTPS webhook; the API key is never exposed in the browser.

Where is the OpenAPI specification?

The OpenAPI 3 spec is at https://www.claix.dev/openapi.yaml. AI context summary: https://www.claix.dev/llms.txt and full knowledge base: https://www.claix.dev/llms-full.txt

Is Claix compatible with n8n and Make?

Yes. Use generic HTTP multipart or the n8n-nodes-claix community node. Guide: https://www.claix.dev/en/blog/integrar-claix-en-n8n

Is Claix GDPR compliant?

Claix offers EU-oriented processing, a public DPA at https://www.claix.dev/en/dpa, zero file retention for model training, and no training on customer documents.

PDF-to-JSON · Agent mode

PDF to JSON with Agent mode

Endpoint

POSThttps://www.claix.dev/agent/pdf-json

Agent mode active

This endpoint runs structured extraction from the main schema first, then a reasoning phase with agent_definition. The response includes data[] (extraction) and agent_data (typed inference). The schema must have is_agent_mode enabled.

This endpoint accepts a PDF file and returns a single JSON object with the data extracted from the document, following exactly the structure you define via a schema. It works with selectable-text PDFs and scanned PDFs, because analysis is performed by an AI model with native document understanding, not a plain-text extractor.

It is intended for server-to-serverintegrations (backends, scripts, n8n/Zapier/Make). It must not be called from an end user's browser because it requires a secret API key.

Unlike Excel/CSV, the full PDF is treated as a single data source and the response contains exactly one record in data — ideal for invoices, contracts, forms, certificates, or reports.

1. Authentication

Every request must include your API key. It is a personal server credential and should be handled with the same care as a database password.

Option A — Dedicated header (recommended):

x-api-key: <YOUR_API_KEY>

Option B — Standard Authorization header:

Authorization: Bearer <YOUR_API_KEY>

Either one is sufficient. If you send both, x-api-key takes priority.

Before processing the PDF, the system validates that:

The API key exists and is active.
The associated account is active (not suspended).

If validation fails, the request is rejected with 401 without processing the file.

2. Request format

Method: POST · Content-Type: multipart/form-data (required)

Field	Type	Required	Description
file	Binary file	Yes	The PDF to analyze. Must be the file itself, not a path or URL.
schema_id	Text (UUID)	Yes	Schema previously created in your account, of type PDF → JSON.

Field names must be exactly file and schema_id. Aliases such as pdf, documento, or upload are not supported.

The schema_id must correspond to a schema of type PDF → JSON. If you send one of Excel → JSON or another type, you will receive 400.

File requirements:

Format: .pdf only (validated by MIME type and extension).
Cannot be empty (0 bytes).
Maximum size: 15 MB (413 error if exceeded).
Compatible with text and scanned PDFs.

3. How to build the request

Have your API key and the correct schema_id ready.
Build a POST request to the endpoint URL.
Add the authentication header.
Send multipart/form-data with file (PDF) and schema_id.
Check the HTTP status code: only 200 indicates success.

4. Request examples

See the panel on the right for examples in cURL, JavaScript, Node.js, Python, PHP, and n8n.

5. Successful response format

200 OK · Content-Type: application/json

{
  "success": true,
  "schema_utilizado": "Contratos",
  "total_registros": 1,
  "data": [
    {
      "puesto a ocupar": "Ing. Software Principal (Backend)",
      "Nombre contratante": "Tech Solutions S.L.",
      "fecha del contrato": "8 de Agosto de 2026",
      "persona contratada": "Gael Anaya"
    }
  ],
  "agent_data": {
    "salario": 55000,
    "es_parcial": false,
    "fecha_contrato": "después del 20/07/2026",
    "resumen_contrato": "Contrato indefinido: Gael Anaya como Backend en Tech Solutions S.L. Jornada completa, 55.000 €/año."
  }
}

Field	Type	Description
success	boolean	Always true when HTTP is 200.
schema_utilizado	string	Name of the schema used for extraction.
total_registros	number	Number of records in data.
data	array	Objects extracted from the main schema (same as extraction mode).
agent_data	object	Typed Agent Mode answers per agent_definition (booleans, numbers, strings).

6. Error codes

{
  "error": "Descripción legible del problema.",
  "detalle": "Información técnica adicional (solo presente en algunos casos)."
}

400 — Missing file or schema_id, invalid multipart, not a PDF, empty file, corrupt file, or schema of the wrong type.

401 — Authentication failed.

404 — schema_id does not exist or does not belong to your account.

413 — PDF exceeds 15 MB.

422 — PDF read but no extractable data according to the schema.

502 — AI service failure (transient).

405 — Method other than POST. · 500 — Internal error.

7. Code summary

Code	Category	Retry?
200	Success	—
400	Client error (malformed file or data)	No — fix the request first
401	Authentication error	No — fix credentials first
404	Resource not found	No — fix schema_id first
405	Incorrect HTTP method	No — fix the method first
413	PDF too large	No — reduce file size first
422	No extractable data	No — review PDF/schema first
500	Internal server error	Yes, with caution
502	AI service failure	Yes, recommended with backoff

8. Best practices

Validate the HTTP status code before reading data[0].
Check the PDF size on your client before sending it.
Remember: data always has exactly one element (one document = one object).
Retry automatically only on 500 and 502, never on 400, 401, 404, 413, or 422.
Clear descriptions on schema properties improve accuracy on documents with non-standard layouts.
Do not include your API key in frontend code or public repositories.