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Document to Markdown for AI Agents and RAG
Turn any document into agent-ready Markdown for RAG, retrieval and reasoning—without building a separate document pipeline for every format.
Claix converts PDFs, spreadsheets, documents, images, audio, text, HTML and XML into one structured Markdown layer the agent can read, retrieve and use before it acts.
The problem
The problem with traditional RAG
Agents need context to reason and act on real information. Adding documents usually means a different pipeline for every format: parse PDFs, interpret spreadsheets, process images, transcribe audio, clean HTML, chunk content, generate embeddings and maintain a vector store.
That stack can work, but it forces a long list of decisions before the agent can use a file. The agent does not only need similar text. It needs context that is readable, consistent and reusable.
File → Parsing → Cleanup → Chunking → Embeddings → Vector database → Retrieval → Context → Agent answer
- Which parser to use for each format.
- How to extract tables, read images and transcribe audio.
- How to chunk without losing headings and structure.
- Which embedding model to use, and how to index and update it.
- How to return evidence and handle incomplete documents.
- How to join files that arrived in different formats.
One layer
One common layer for every format
Claix turns heterogeneous files into one representation. The agent works across formats without a separate flow for each file type.
Any document becomes one structured context format, then any agent runtime, any retrieval strategy and any next action.
PDF
Excel / CSV
Document
Image
Audio
TXT / HTML / XML
↓
Structured Markdown
↓
RAG, retrieval or agentFormats
Supported formats
Each route is documented on its own page. They share the same idea: format-specific input, Markdown output, agent context.
Excel and CSV → Markdown
Turn spreadsheets into Markdown tables and keep headers, rows and column relationships. Server-side parsing, no schema_id. POST https://claix.dev/api/excel-markdown.
- Leads
- Inventories
- Catalogs
- Orders and metrics
PDF → Markdown
Turn PDFs into readable, structured Markdown for retrieval and reasoning.
- Contracts
- Reports
- Invoices
- Technical docs
Document → Markdown
Turn office documents into Markdown the agent can query and reuse.
- Procedures
- Proposals
- Case files
Image → Markdown
Turn visible image content into structured Markdown.
- Document photos
- Screenshots
- Forms
- Scanned tables
TXT / HTML / XML → Markdown
Normalize text and structured markup into one format for agents and RAG pipelines.
- Web pages
- Exports
- Feeds
- Technical content
Audio → Markdown
Turn speech into Markdown the agent can search, analyze and use as context.
- Meetings
- Calls
- Voice notes
- Support
Why Markdown
Why Markdown for agents
Markdown sits between the original file and the context the agent needs. It can keep titles, sections, lists, tables, order, and the relationship between headings and values.
Unstructured text such as “Supplier Example Corp invoice total 2480.50 due date October 15” is harder to use than a headed invoice with a table of total, due date and purchase order.
# Invoice ## Supplier Example Corp ## Financial details | Field | Value | | Invoice total | 2,480.50 EUR | | Due date | 2026-10-15 | | Purchase order | PO-2048 |
- The agent can tell what each value means.
- It can retrieve a section, compare blocks and pass context to the next tool.
- It can show the relevant source or section.
Two outputs
Markdown does not always replace JSON
Markdown is for agent context. JSON is for structured action. Document → Markdown for retrieval and reasoning → the agent interprets the context → JSON to execute an action.
| JSON for defined fields | Markdown for retrieval and reasoning |
|---|---|
| Strict schemas, validation and function calling | RAG and open questions |
| Create records and update a database | Explore a file without a schema |
| Send data to a CRM or ERP | Keep structure across formats |
| Deterministic workflows | Readable context for tables, sections and transcripts |
One extraction layer
RAG without a different pipeline per format
Without a shared layer you end up with a PDF pipeline, a spreadsheet pipeline, an OCR pipeline and a transcription pipeline, each with its own chunking and embeddings.
With Claix those inputs go through one extraction layer and come out as structured Markdown for agentic RAG. Formats still differ in content and quality. The agent receives a common representation after extraction.
PDF + Excel + Image + Audio + HTML
↓
Claix extraction layer
↓
Structured Markdown
↓
Agentic RAGAgentic RAG
What structured Markdown changes
The agent receives a task, decides which documents it needs, reads Markdown, checks whether the evidence is enough, calls another tool, compares files and escalates when it is uncertain.
One conceptual interface
Input format changes. Agent context stays consistent.
Less format-specific code
You can try the workflow before writing a different document integration for every format.
Less cleanup inside the prompt
The agent does not have to interpret a binary file, a headerless table or a messy transcript.
Easier human inspection
Markdown is readable while you log, debug and show results.
Room to grow
Start without a strict schema. Add JSON, a retriever, MCP, n8n or your own backend later.
Extraction stays separate from reasoning
Claix turns the file into structured context. The agent decides what it means and what to do next.
Persistence
Context retention and multimodal Knowledge Spaces
When the endpoint allows it, send window_context=true and a valid window_time. Allowed minutes are 5, 10, 15, 30, 45, 60, 90, 120, 180, 240, 360, 480, 720, 1440, or infinity. The response then includes document_id. infinity requires Persistent Mode on the account. space_id attaches the kept document to a Knowledge Space.
A space can hold a contract PDF, an inventory spreadsheet, a delivery-note photo, a meeting recording and an HTML note. The agent queries that collection instead of one isolated file.
Before you index
Markdown versus chunks and embeddings
Chunking and embeddings remain valid later. Claix gives you structured context before you commit to a custom chunking, embedding or vector-search architecture.
| Detached fragments | Structured Markdown |
|---|---|
| Example Corp / Spain / certificate / missing | Headings, tables and lists stay together |
| Structure is rebuilt after indexing | Structure is kept before you index |
| You design chunking per format first | You can test the agent before a complex chunk strategy |
| Embeddings, indexes and reprocessing from day one | You can add your own vector store later |
Architectures
Ways to use the Markdown
1. Straight to the agent
File → Claix → Markdown → agent context. Fits prototypes, small files and one-off questions.
2. A retrievable document
File → Markdown → Knowledge Space or your retriever → agent. Fits collections and repeated questions.
3. Markdown and JSON together
Markdown for reading, then a JSON schema and a tool call into CRM, ERP or a task system.
4. Multimodal agentic RAG
PDF, Excel, image, audio and HTML through one Markdown layer. The agent chooses the relevant context and calls business tools.
Use cases
Multiformat workflows
Finance agent
Invoice.pdf, Expense.xlsx and Receipt.jpg become Markdown context for reconciliation, approval or review.
Legal document agent
Contract.pdf, Amendment.docx and Email.html become context for dates, obligations and a review task.
Customer onboarding
Application.pdf, ID-card.jpg and Company.csv become context to complete, reject or review a case.
Support and operations
Emails, screenshots, invoices, inventories, delivery notes and meeting audio share one readable context.
Research agent
Report.pdf, Dataset.xlsx and Interview.mp3 become Markdown context for findings, comparison or a report.
Pricing
Pay for the format you process
Use the extraction layer you need and pay according to the input format. Prices are in euros, per processed file.
| Audio | €0.20 |
|---|---|
| Image | €0.10 |
| €0.10 | |
| Excel / CSV | €0.05 |
| Document | €0.05 |
| Text | €0.05 |
Markdown conversion is €0.05 per file for text, documents and spreadsheets. Image and PDF processing is €0.10 per file. Audio processing is €0.20 per file. The price does not depend on pages, minutes or credits beyond that per-file rate.
Scope
What this does not promise
Claix gives you a structured Markdown layer before you commit to a custom chunking, embedding or vector-search architecture.
It does not claim that Markdown removes every RAG problem, that chunking is never useful, that no internal system uses embeddings, that every file will be interpreted perfectly, or that Markdown always replaces JSON or a vector database.
Frequently asked questions
- What is document-to-Markdown extraction for AI agents?
- It converts PDFs, spreadsheets, documents, images, audio and text-based files into structured Markdown that an AI agent can use for context, retrieval, reasoning and tool calls.
- Why use Markdown in RAG?
- Markdown preserves headings, sections, tables and relationships in a readable format. Agents get structured context before the content goes into a retriever or a reasoning workflow.
- Is Markdown better than JSON for AI agents?
- It depends on the task. Markdown is usually better for reading, retrieval and exploration. JSON is usually better for validation, function calls and writing defined fields into another system.
- Does Claix replace vector databases?
- Claix can reduce the need to build and operate a vector database for initial or simpler document-agent workflows. It is not a universal replacement for every retrieval architecture.
- Does Claix remove the need for chunking?
- It lets you start with structured Markdown before custom chunking. For large collections you may still index or segment the output later.
- Can I use the Markdown with LangChain or MCP?
- Yes. Pass the Markdown to a LangChain agent, retriever or chain, or let an MCP tool call Claix and expose the document context during the workflow.
- Can different formats share one Knowledge Space?
- Yes, when the documents are kept and associated with the same space. Agents can work across PDFs, spreadsheets, images, audio and other supported content.
- Is the Markdown generated by a model?
- It depends on the endpoint. Spreadsheet conversion is built on the server without a model call. Each endpoint page states when a model is involved.
Build agentic RAG on structured context—not raw files.
Convert PDFs, spreadsheets, documents, images, audio and text into structured Markdown for RAG, retrieval and agentic workflows—without building a separate extraction pipeline for every format.