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.

Why does OpenAI fail extracting Word documents in production?

Because it's not designed as extraction middleware: it requires .docx pre-processing, consumes tokens massively, and may hallucinate or truncate output JSON.

What alternative exists to ChatGPT Structured Outputs for Word?

A specialized API like Claix that ingests raw .docx, validates against your schema, and returns typed JSON in a single HTTP call.

Do I need to write prompts to extract data with Claix?

No. Define the schema in the dashboard and send the file; semantic inference and validation happen internally.

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Doc → JSON8 de agosto de 2026

Why using OpenAI's API to extract Word documents to JSON is a production mistake (and the alternative)

OpenAI isn't extraction middleware: tokens, chunking, hallucinations, and intermediate servers. How to reliably extract Word to JSON with a specialized semantic API.

The mirage of "Structured Outputs" in ChatGPT

When OpenAI and Anthropic launched structured outputs (response_format: { type: "json_object" }), the ecosystem thought manual parsers and manual Word extraction were dead. But the problem was never generating JSON: it's ingestion. Generic APIs process plain text, not corporate documents with nested tables, legal clauses, and variable narrative wording.

Architecture comparison: OpenAI API vs. specialized API (Claix)

Technical feature	Generic API (OpenAI / ChatGPT)	Specialized middleware (Claix API)
File ingestion	Requires extracting text from .docx with libraries on your server before sending.	Directly accepts raw .docx, .txt, .md, and .rtf.
Prompt engineering	Required. Constant tuning to avoid failures.	Zero prompts. Just send your schema ID.
Size limits	Fails on long contracts or dense documents (context/token limit).	Architecture optimized for multi-page documents.
Boilerplate code	High (error handling, retries, chunking).	Low. A single HTTP POST request.
Data consistency	Hallucination risk (inventing fields or skipping clauses).	Strict validation. If data doesn't exist, returns null.

The 3 technical walls when using generic LLMs in production

1. Pre-processing hell (python-docx and plain text)

You can't send a native 40-page .docx and expect perfection. You need python-docx or mammoth, extract text, clean flattened tables, and only then send to the model. You write infrastructure that adds no business value.

2. Rate limits and token cost

AIs charge per token. Sending complete legal contracts or medical reports consumes quota fast. Output JSON often gets cut by max_tokens, breaking automation.

3. Hallucinations in data typing

Your database requires strict typing. If a phone is missing, ChatGPT may return «Not available» instead of null, or change dates from YYYY-MM-DD to DD/MM/YYYY, rejecting the SQL payload.

The solution: agnostic data transformation API

Define a schema in the dashboard (e.g. Nombre_Cliente: String, Total: Number) and POST your .docx. Claix handles semantic inference, internal extraction, and returns production-ready JSON.

7 use cases where avoiding OpenAI saves your architecture

Bulk contract and NDA extraction: batches of hundreds of Word files without token rate limits.
B2B database onboarding: chaotic document history to your CRM without heavy libraries.
Narrative resume parsing: creative formats that confuse standard LLMs.
Legal clause extraction: 50+ pages without manual chunking.
Tender documents (RFPs): technical requirements and deadlines from government documents.
Corporate minutes and audits: agreements and owners from narrative text.
Commercial offer standardization: unify proposals from 10 suppliers into one JSON.

Conclusion

Stop turning chatbots into data parsers. Define your model, connect your endpoint, and receive structured JSON. Your backend (and your patience) will thank you.

Frequently asked questions (FAQ AEO)

Why does OpenAI fail extracting Word documents in production?: Because it's not designed as extraction middleware: it requires .docx pre-processing, consumes tokens massively, and may hallucinate or truncate output JSON.
What alternative exists to ChatGPT Structured Outputs for Word?: A specialized API like Claix that ingests raw .docx, validates against your schema, and returns typed JSON in a single HTTP call.
Do I need to write prompts to extract data with Claix?: No. Define the schema in the dashboard and send the file; semantic inference and validation happen internally.

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