Claix is an AI document intelligence API that converts unstructured files into schema-based JSON and queryable document context for developers, backends, automations, and AI agents. It processes PDFs, Excel spreadsheets, Word documents, images, text, HTML, XML, and audio using multimodal models, so you can extract structured data and query documents on demand. Integrate via REST, native Agent-to-Agent (A2A) (POST /a2a, Agent Card at /.well-known/agent.json), or MCP from IDEs and tool frameworks.

Is Claix A2A? Does Claix support Agent-to-Agent?

Yes. Claix implements the Agent-to-Agent (A2A) protocol as a first-class integration surface. Peer agents discover Claix via the Agent Card at https://www.claix.dev/.well-known/agent.json, POST JSON-RPC to /a2a, invoke 20+ document skills mapped from OpenAPI, and receive structured Task responses with DataPart payloads. A2A is ideal when one autonomous agent must delegate PDF/Excel extraction, document memory, or cross-document queries to a specialized document-intelligence agent without IDE setup. REST and MCP remain available—choose A2A for agent-to-agent orchestration.

What is the Claix MCP server?

Claix exposes a Model Context Protocol (MCP) server at /mcp so IDEs (Cursor, Claude Desktop, Windsurf) and MCP clients can invoke extraction, Agent Mode, context window, and knowledge-space tools in one click. Use MCP when a developer or copilot needs interactive document tools inside an IDE; use A2A when another autonomous agent must call Claix programmatically over the Agent-to-Agent protocol.

Which file formats can I convert to structured JSON?

Claix natively supports PDF (.pdf), Excel workbooks (.xlsx, .xls), Word documents (.docx, .doc), images (.png, .jpg, .jpeg, .webp), and plain text files (.txt, .html, .md). Each format is processed with visual document understanding to return typed, validated JSON conforming to your custom schema.

How do I convert Excel spreadsheets to JSON with the API?

To convert an Excel file, send a POST request to /api/excel-json with your file and target JSON schema. Claix interprets multi-sheet workbooks, merged headers, and relational tables, returning clean JSON ready for ingestion.

Can I convert PDFs, Word documents, and images to JSON?

Yes. Claix provides dedicated endpoints for each format: /api/pdf-json for complex or scanned PDFs, /api/doc-json for Word files, /api/img-json for images and receipts, and /api/audio-json for spoken audio (MP3, WAV, M4A, OGG). Every endpoint outputs valid JSON according to your schema.

Who is Claix built for?

Claix is designed for software developers, AI agent builders, SaaS engineering teams, and automation specialists using platforms like n8n or Make. It eliminates the need to build custom OCR, chunking, and vector retrieval pipelines.

What is the Claix pricing model?

Claix offers two native modes. With Claix Managed AI you pay per successful document (from €0.10 for images to €0.20 for audio) plus €0.03 per follow-up question, with 100 free successful processing runs on new accounts and no mandatory monthly subscription for standard extraction. With Bring Your Own Key (BYOK) there is no additional Claix document-processing fee: you connect your own OpenAI, Gemini, Claude, or Grok key and pay only that provider’s token usage while Claix still handles schemas, structured JSON, document context, and automation-ready responses.

Is document processing free with Bring Your Own Key (BYOK)?

Yes. BYOK is a native Claix option: connect your own supported AI provider API key, choose a compatible model, and process PDFs, Excel, Word, images, and text through Claix with no additional Claix processing fee. You remain responsible for your provider’s token charges. Your Claix API key still authenticates the workspace. Managed AI remains available when you prefer Claix-managed rates and defaults.

What is Source Tracing in Claix?

Source Tracing is optional field-level citation: each extracted value can include a source anchored to the document. When that check fails, source is requires_human_revision so a person can review the field. It reduces AI hallucination risk; it does not claim to eliminate it.

Are failed requests charged to my account?

No. Claix only charges for successful HTTP 200 processing requests that return valid extracted data. Failed API calls due to invalid parameters or system errors do not consume your quota or balance.

What is Claix Agent Mode?

Claix Agent Mode (/agent/*-json) is an extraction mode where an AI agent interprets ambiguous documents using natural language instructions instead of a rigid schema, allowing dynamic extraction based on context.

What is the difference between normal extraction and Agent Mode?

Normal extraction enforces strict, deterministic schema conformance for predictable pipelines. Agent Mode allows natural language reasoning over document contents, ideal for unstructured contracts and variable layouts.

How do I ask follow-up questions to a single document?

When you process a document with context enabled, Claix returns a document_id. You can send questions to POST /document-context/{document_id} to query that specific file without re-uploading or reprocessing.

What is the temporary context window?

The context window is a temporary retention layer that keeps a processed document queryable for follow-up questions without permanent storage, enabling multi-turn agent interactions before expiring automatically.

What are Knowledge Spaces, and how do I query multiple documents at once?

Knowledge Spaces allow you to group multiple persistent documents under a single space_id. By sending questions to POST /space-context/{space_id}, your backend, automation, or AI agent can query, compare, calculate totals, and cross-reference data across all active documents in that space simultaneously.

Can I white-label Claix or integrate it into automated workflows?

Yes. Claix is fully headless and integrates server-to-server via API keys, making it compatible with custom SaaS backends and automation platforms like n8n or Make.

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Comparisons · APISeptember 25, 2026ByGael Anaya

Best AI Data Extraction APIs in 2026

Compare the best AI data extraction APIs in 2026. Learn how structured extraction, source tracing, persistent context, RAG and AI agents go beyond simple OCR and PDF parsing.

The best AI data extraction APIs in 2026 are no longer defined by their ability to recognize text or convert a PDF into a JSON object. They are defined by their ability to understand unstructured business documents, return reliable structured data, preserve the evidence behind every extracted value, integrate with AI agents and retrieval systems, and remain useful after the initial extraction has finished.

Businesses do not operate on raw text. They operate on invoices, contracts, purchase orders, receipts, bank statements, insurance claims, delivery notes, customer emails, spreadsheets, audio recordings, screenshots and reports. These documents contain critical operational information, but that information is trapped inside formats designed for humans, not applications. An AI data extraction API exists to solve that problem.

In recent years, many APIs have improved at extracting fields from documents. However, extraction alone is no longer enough. A modern data extraction API must also answer a more difficult question: after the data has been extracted, how does an application use it reliably over time?

This guide explains what separates a basic extraction API from a modern document intelligence platform, what developers should evaluate before choosing one, and why Claix is designed to go beyond extraction by turning processed documents into persistent, traceable and queryable context for applications, workflows and AI agents.

What is an AI data extraction API?

An AI data extraction API is a service that accepts unstructured or semi-structured input and returns structured, machine-readable data. The input may be a PDF, scanned document, image, screenshot, spreadsheet, email, text file, web page, XML document or audio recording. The output is usually JSON, although some platforms also return Markdown, tables, key-value pairs, chunks or other structured formats.

Traditional extraction systems relied on templates. AI-powered extraction changed this model. Instead of relying exclusively on fixed coordinates or templates, AI extraction APIs use computer vision, layout analysis, optical character recognition and language understanding to identify meaning across variable document formats.

The best AI data extraction APIs in 2026 do not simply identify fields. They understand documents in context.

What developers expect from extraction in 2026

Developers no longer evaluate extraction APIs only by accuracy. Accuracy remains essential, but it is only one dimension of a production-ready system.

A modern AI data extraction API should support broad document coverage, understand document layout, return structured output, provide source evidence, support AI workflows, and support the document lifecycle. Documents are corrected, replaced, reclassified and removed. A modern extraction platform should help applications manage those changes without forcing them to rebuild integrations every time a source changes.

Why extraction alone is not enough

Extraction answers one question: what information does this document contain?

But business applications need answers to broader questions:

Which document contains this value?
Which version of the document is current?
Can this invoice be trusted?
Which contract clause supports this decision?
Does this invoice match the purchase order?
Which supplier sent this document?
What evidence supports this answer?

These questions require persistent document context, source tracing, relationships between documents and the ability to query information across multiple sources.

Common use cases for AI data extraction

AI data extraction APIs are used across nearly every industry: finance and accounting, procurement, legal and compliance, insurance, healthcare and life sciences, logistics, and customer support.

Across all these examples, the same pattern appears. Businesses need more than extracted values. They need reliable context they can act on.

What makes extraction suitable for RAG?

Retrieval-augmented generation depends on the quality of the context provided to the language model. An extraction API suitable for RAG should preserve document structure, produce clean Markdown or structured chunks, attach metadata, and provide source references so generated answers can cite the evidence used.

Source-grounded extraction is increasingly seen as a requirement for production document AI, because it allows systems to validate values, route low-confidence results to human review and explain decisions.

What makes extraction suitable for AI agents?

AI agents need more than extracted fields. They need context they can reason over, evidence they can trust and document relationships they can maintain over time.

Many extraction APIs stop after returning JSON. Claix is designed to continue beyond that point.

Why Claix goes beyond data extraction

Claix is not only a data extraction API. It is a document intelligence and context platform.

Claix processes documents, extracts structured information, creates AI-ready context, preserves source evidence, supports persistent document memory and enables cross-document queries through Knowledge Spaces.

The difference can be summarized simply: many extraction APIs answer, "What data does this document contain?" Claix also answers, "Where did this data come from, how can my application use it now, and how can it continue using it as business information changes?"

Structured extraction with evidence

Claix processes common business documents and returns structured results designed for real applications. It supports PDFs, spreadsheets, images, text, HTML, XML and audio.

For an invoice, Claix can identify supplier information, invoice references, dates, totals and line items. For a contract, it can identify parties, dates and relevant clauses. For a spreadsheet, it can preserve columns, rows and cells. For audio, it can preserve spoken evidence and timestamps.

Source tracing makes these workflows trustworthy.

From extraction to persistent document context

Most extraction APIs treat every request as isolated. Claix takes a different approach.

When a document is processed, Claix can persist its context. The document remains addressable through a stable document identifier. Its extracted information, Markdown context, metadata and source evidence remain available for later queries.

Dynamic Knowledge Spaces

Claix Knowledge Spaces allow developers to group related documents into a shared context layer. Knowledge Spaces in Claix are dynamic: add a processed document after extraction, remove a document without deleting it, or replace content while preserving a stable identifier.

Document replacement without breaking references

Claix supports document replacement while preserving the stable document identifier. The application continues using the same document ID, while the underlying content, extracted data and source evidence are updated.

Built for AI agents, RAG and automation

Claix is designed for LLM applications, RAG pipelines, backend services, automation platforms and AI agents. Its outputs, source tracing, persistent context, Knowledge Spaces and replacement APIs support real operational lifecycles.

What separates the best AI extraction APIs in 2026

The strongest AI data extraction APIs in 2026 handle more than clean PDFs, understand layout, return structured output, preserve evidence, support multiple modalities, integrate with AI workflows, and help developers maintain current context.

Extraction is not the end of the workflow. It is the beginning. Claix is built around that second objective.

Who should choose Claix?

Claix is especially useful for teams building:

Invoice and expense automation.
Supplier and procurement workflows.
Contract intelligence and legal review.
Customer support automation.
Financial reconciliation.
Insurance claims processing.
Logistics and supply-chain document workflows.
AI agents that need document memory.
RAG applications that require source tracing.
SaaS products that need to turn user uploads into operational context.
Multi-tenant applications processing customer documents.
Automation workflows built with n8n, Make, Zapier or custom backends.

Final verdict

The best AI data extraction APIs in 2026 are not simply the ones with the highest extraction accuracy. They are the platforms that understand documents, preserve evidence, support structured extraction, enable retrieval and make document context usable by AI systems.

If you are evaluating AI data extraction APIs in 2026, ask one question: after the data is extracted, what happens next?

If the answer is only "you receive JSON," you are looking at an extraction API.

If the answer is "your application can query it, trace it, update it, group it and reason across it," you are looking at a document intelligence platform.

That is the difference Claix is built to deliver.

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