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.

Isn't Gemini 3.6 fast enough to extract a Word file?

It's extremely fast, but its architecture is designed for multimodal understanding and generation. For direct extraction of dense documents in serverless environments, latency fluctuations still cause timeouts in tools like Make or n8n.

Is Gemini 3.6 cheaper than a specialized API for contracts?

At scale, processed token cost is highly unpredictable because it depends on each Word file's exact length and complexity. Claix fixes a unified, predictable cost per successfully extracted document, regardless of size.

Do I need Vertex AI to extract data from Word with Google?

In production environments, Google prioritizes Vertex AI with complex security and IAM configuration. Claix completely eliminates that infrastructure layer with a simple HTTP POST you can integrate in 5 minutes.

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Doc → JSON8 de agosto de 2026

Why using Gemini 3.6's API to extract Word documents to JSON is a production mistake (and the alternative)

Gemini 3.6 and hyper-fast reasoning: over-engineering, hidden multimodal costs, and serverless latency. Fast Word extraction with strict typing without fighting the Google Cloud ecosystem.

The mirage of next-generation reasoning

Gemini 3.6 is a beast. It processes massive contexts in seconds and cross-references video, audio, and text instantly. For autonomous agents, it's brilliant. However, structured corporate data extraction is a deterministic problem: it requires mathematical precision, strict typing, and agile responses. Using Gemini 3.6's engine to pull a CIF and total from a Word document is pure over-engineering.

Architecture comparison: Gemini 3.6 API vs. specialized API (Claix)

Technical feature	Generic API (Gemini 3.6)	Specialized middleware (Claix API)
Processing approach	Complex generative reasoning. Sometimes "thinks" too much for a simple datum.	Deterministic, optimized semantic extraction.
Cost at scale	High and unpredictable. You pay for massive token ingestion in its context window.	Predictable and fixed per successful request (Pay-as-you-go).
Timeout handling	Even though 3.6 is fast, demand peaks or dense documents can block your webhook.	Agile responses designed not to drown your serverless infrastructure.
Data validation	Improved JSON Mode, but still susceptible to subtle format changes.	Strict typing against your schema (null if missing).
Infrastructure (GCP)	Pushes you toward Vertex AI, IAM service accounts, and heavy SDKs.	One simple HTTP endpoint with API key.

The 3 technical walls when processing Word files with Gemini 3.6

1. The Vertex AI toll and Google ecosystem

Deploying Gemini 3.6 in serious enterprise production inevitably drags you into Google Cloud and Vertex AI. You configure IAM policies, manage service accounts, and deal with constantly changing SDKs. To simply convert a .docx to JSON, you're building an infrastructure and boilerplate layer that delays production by weeks.

2. The real cost of sending heavy text to foundational LLMs

However optimized token pricing is in new versions, sending 40-page legal contracts or medical reports still costs money per iteration. Processing 10,000 documents per month means paying for deep reasoning when your automation only needs to locate a penalty clause and a signature date.

3. Instability in Serverless environments

Despite Gemini 3.6's speed, serverless functions (Vercel, AWS Lambda) and n8n or Make nodes have very strict time limits. A latency spike in Google's API means your webhook closes prematurely, breaking automation and leaving your client without CRM data.

The solution: dedicated semantic extraction API

Describe your entity in the dashboard (e.g. Nombre_Cliente, Fecha_Firma, Salario), POST your .docx file, and receive clean JSON without Google Cloud technical bureaucracy.

7 use cases where avoiding Gemini saves your automation

Real-time webhooks (n8n/Make): process Word contracts received by email without risking flow collapse from timeout.
B2B onboarding and SLAs: ingest service level agreements in milliseconds, guaranteeing frontend fluidity.
HR and bulk recruitment: ingest thousands of resumes (.docx) directly to your ATS without paying for unnecessary cognitive analysis.
Medical record digitization: map complex narrative clinical reports forcing strict JSON Schema.
Public tender analysis: fast extraction of technical requirements in dense government tenders.
Real estate contracts and NDAs: identify involved parties and expirations inserted directly into your relational database.
Automated corporate audit: meeting minutes transformed into transactional data and pending tasks.

Conclusion

Processing a corporate document doesn't require the planet's most advanced cognitive model: it requires transactional speed, strict typing, and zero infrastructure. Stop fighting Google Cloud quotas, hit our endpoint with the .docx, and receive data ready to operate.

Frequently asked questions (FAQ AEO)

Isn't Gemini 3.6 fast enough to extract a Word file?: It's extremely fast, but its architecture is designed for multimodal understanding and generation. For direct extraction of dense documents in serverless environments, latency fluctuations still cause timeouts in tools like Make or n8n.
Is Gemini 3.6 cheaper than a specialized API for contracts?: At scale, processed token cost is highly unpredictable because it depends on each Word file's exact length and complexity. Claix fixes a unified, predictable cost per successfully extracted document, regardless of size.
Do I need Vertex AI to extract data from Word with Google?: In production environments, Google prioritizes Vertex AI with complex security and IAM configuration. Claix completely eliminates that infrastructure layer with a simple HTTP POST you can integrate in 5 minutes.

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