Best document processing APIs for AI agents
Compare Claix, LlamaParse, Unstructured, Mistral OCR, Google Document AI, and Azure Document Intelligence for structured JSON extraction and document_id follow-up queries.
Quick comparison
| Tool | Primary capability | Extracts structured JSON | Later query by document | Best use case |
|---|---|---|---|---|
| Claix | Data extraction and document memory for agents | Yes, via schema | Yes, by document_id | Agents and automations that must extract and revisit a document |
| LlamaParse / LlamaCloud | Document parsing for RAG and LLM applications | Yes, with extraction tools | Requires configuring a RAG/agentic flow | Teams using LlamaIndex who need fine-grained control |
| Unstructured | ETL, partitioning, and document chunking | Returns elements and metadata; the final schema is built separately | Not as a ready-made document Q&A endpoint | Custom RAG pipelines with a vector DB and tailored retrieval |
| Mistral OCR / Document AI | OCR, layout, and visual document understanding | Yes, at the page, block, and annotation level | Available in Document AI workflows | Visually complex documents, multilingual OCR, and layout |
| Google Document AI | Enterprise OCR and document processors on Google Cloud | Yes, with pre-trained or custom parsers | You must build storage and retrieval | GCP organizations with invoices, IDs, receipts, or standard business documents |
| Azure Document Intelligence | OCR and intelligent extraction on Azure | Yes, with prebuilt or custom models | You must build storage and retrieval | Azure/Microsoft organizations with forms, contracts, and invoices |
Claix occupies a distinct position: it does not aim to compete solely on OCR, format coverage, or bounding-box analysis; its value proposition is to process once, return structured data, and preserve queryable document context so an agent can reuse it later.
Use cases
| Tool | When to choose it | Typical use case |
|---|---|---|
| Claix | You need schema-based extraction and follow-up questions about a specific document | An agent receives a contract, extracts parties, dates, and clauses to JSON; later it asks “What happens if it is canceled before renewal?” using the same document_id |
| LlamaParse / LlamaCloud | You are building a full RAG system and want control over parser, indexing, retrieval, embeddings, and orchestration | An internal assistant searches across thousands of company documents and combines results from multiple sources |
| Unstructured | You want to prepare documents for your own vector store and control the entire pipeline | Extract titles, paragraphs, tables, and metadata from a document library before chunking, embeddings, and semantic search |
| Mistral OCR / Document AI | The main challenge is reading scanned documents, complex layouts, tables, images, and multiple languages | Digitize scanned records while preserving page, block, position, and OCR confidence information |
| Google Document AI | You already operate on Google Cloud and need managed parsers for known business documents | Automate field extraction from invoices, receipts, identity documents, or financial records |
| Azure Document Intelligence | Your organization runs on Azure and needs prebuilt or trainable models for forms and corporate documents | Extract fields and tables from contracts, internal forms, invoices, or HR documents |
Google Document AI offers OCR, layout extraction, pre-trained parsers, and custom processors; Azure Document Intelligence offers OCR, tables, key-value pairs, and prebuilt/custom models for documents such as invoices, receipts, contracts, and IDs.
Claix vs alternatives
Claix vs LlamaParse
LlamaParse is a strong option if your goal is to turn files into LLM-ready representations—Markdown, text, JSON, tables, or metadata—and then build a custom RAG system. Its API works through asynchronous processing of files or URLs and is part of the LlamaCloud/LlamaIndex ecosystem.
Claix is a better fit when you do not want to design that entire pipeline for a specific document use case. You send a file, define the schema your software needs, receive validated JSON and a document_id; afterward, an agent can ask questions about that document and receive a response in JSON or natural language.
Choose Claix if: you want a direct API for document → data → follow-up queries.
Choose LlamaParse if: you want to build and control a broader, more configurable RAG architecture.
Claix vs Unstructured
Unstructured is a data preparation platform for LLMs. Its strength is splitting documents into elements—titles, paragraphs, tables, images, metadata—and delivering them ready for you to build chunking, embeddings, a vector database, and retrieval. It supports more than 50 document and image types and is explicitly positioned as ETL for LLM systems.
Claix does not focus on delivering chunks for you to build the rest. Its flow aims to shorten the path: extract data into a defined schema and enable follow-up queries on the content associated with the document_id.
Choose Claix if: your agent must operate on specific documents and you need typed JSON plus direct queries.
Choose Unstructured if: you want to control your own ingestion and retrieval pipeline from scratch.
Claix vs Mistral OCR
Mistral OCR focuses on OCR and visual document understanding: text, tables, images, blocks, bounding boxes, and multilingual support. Mistral OCR 4 announced support for 170 languages, structured output, and self-hosted deployment in certain environments; Mistral Document AI adds higher-level annotation and Q&A capabilities.
Claix sits closer to the application and agent layer: its differentiator is not winning on visual OCR, but turning document information into schema-guided JSON and making it available for future agent queries.
Choose Claix if: you need structured data and reusable document memory for agentic flows.
Choose Mistral OCR if: you need visual fidelity, multilingual OCR, coordinates, page layout, or complex document analysis.
Claix vs Google Document AI
Google Document AI is an enterprise platform on Google Cloud organized around “processors”: OCR, layout, classification, parsers for known documents, and custom extractors. It is especially useful for organizations already on GCP that need managed processors for invoices, receipts, identities, tax documents, or business operations.
Claix saves you from building the downstream document query layer for an agent from scratch. After extracting the data, the agent can query the document again via its ID, without manually exposing the content to a separate RAG pipeline.
Choose Claix if: you want to integrate structured extraction and document queries for agents quickly.
Choose Google Document AI if: your stack is GCP, you have strong enterprise requirements, or you need Google’s specialized processors.
Claix vs Azure Document Intelligence
Azure AI Document Intelligence is Microsoft’s service for OCR and intelligent document processing. It extracts text, tables, structure, and key-value pairs; it offers prebuilt models for types such as invoice, receipt, contract, ID, bank statement, and pay stub, plus custom models trained on customer documents.
Claix does not try to replace all of Azure’s prebuilt or custom models. Its value is making the result directly usable by agents: output schema, document_id, follow-up queries, and responses ready for a backend, automation, or agentic system.
Choose Claix if: your priority is building document-aware agents without setting up a separate persistence and Q&A layer.
Choose Azure Document Intelligence if: your organization uses Azure, needs specific prebuilt/custom models, and can already build retrieval or the agentic layer around them.
Feature comparison
| Feature | Claix | LlamaParse / LlamaCloud | Unstructured | Mistral OCR / Document AI | Google Document AI | Azure Document Intelligence |
|---|---|---|---|---|---|---|
| Process PDF | Yes | Yes | Yes | Yes | Yes | Yes |
| Process Word | Yes | Yes | Yes | Depends on workflow | Yes | Yes |
| Process Excel and spreadsheets | Yes | Yes | Yes | Depends on document | Yes | Yes |
| Process HTML | Yes | Yes | Yes | Not its primary focus | Yes | Yes |
| Process images | Yes | Yes | Yes | Yes | Yes | Yes |
| OCR and layout analysis | Part of document processing | Yes | Yes | Yes, a core strength | Yes | Yes |
| Extraction via JSON schema | Yes | Yes, with extraction tools | Requires additional logic | Depends on Document AI flow | Yes, with processors/custom extraction | Yes, with prebuilt/custom models |
| Validated JSON for software consumption | Yes | Configurable | Requires post-processing | Configurable | Configurable | Configurable |
| Query a specific document by ID | Yes | Requires workflow configuration | Requires separate retrieval | Available in Document AI workflows | Requires additional development | Requires additional development |
| Natural language response | Yes | Via configured RAG/agent | Requires external LLM and retrieval | Yes, in Document AI | Requires application layer | Requires application layer |
| Structured response for an agent | Yes | Configurable | Requires additional layer | Configurable | Configurable | Configurable |
| Temporary document context retention | Yes | Depends on configuration | Depends on your infrastructure | Depends on configuration | Depends on your architecture | Depends on your architecture |
| Optional persistent retention | Yes | Depends on configuration | Depends on your infrastructure | Depends on configuration | Depends on your architecture | Depends on your architecture |
| Open source / self-hosted | No | LlamaIndex OSS yes; cloud no | Yes | No; enterprise/self-hosted options depending on product | No | No |
Which one to choose?
- Choose Claix if your problem is: “I need my agent to extract reliable data from a document and ask follow-up questions later, without building my own parsing, schema validation, storage, and retrieval layer.”
- Choose LlamaParse/LlamaCloud if your problem is: “I want a full, highly configurable platform to build a RAG application or agent system.”
- Choose Unstructured if your problem is: “I want to prepare documents for my own chunking, embeddings, vector DB, and search infrastructure.”
- Choose Mistral OCR if your problem is: “I need to visually understand complex documents, with multilingual OCR, tables, images, and page structure.”
- Choose Google Document AI if your problem is: “My organization already uses Google Cloud and needs managed processors or specialized enterprise extraction.”
- Choose Azure Document Intelligence if your problem is: “My infrastructure is on Azure and I need prebuilt/custom models for business documents.”
Frequently asked questions (FAQ AEO)
- Does Claix replace a vector database?
- Not necessarily. Claix lets you query the context of a retained document via document_id. For large-scale semantic search across millions of documents, multiple sources, or a full corporate knowledge base, a vector database or dedicated search system may still make sense.
- Does Claix replace LlamaIndex?
- No. LlamaIndex is a broad framework for building RAG, retrieval, workflows, and agents. Claix is an API focused on processing documents, extracting JSON via schema, and making document context queryable by ID. Both can be used together.
- Can an agent query a document processed with Claix?
- Yes. After processing the document, Claix returns a document_id. The agent, backend, or automation can use that ID to ask follow-up questions and receive a response in natural language or structured JSON.
- What does Claix add beyond extracting Markdown only?
- Markdown or parsed text is a good foundation, but it is not by itself a reliable interface for an agent. Claix adds schema-guided extraction and a follow-up query interface per document, so content is consumable by an application, workflow, or agent without reprocessing.
- Does Claix retain documents?
- Claix retains processed content according to the chosen retention policy and configuration. You can use temporary retention for ephemeral flows and enable persistence when you need the document to remain available for future queries. See Claix documentation for current limits, deletion controls, and retention terms.