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No credit card requiredTables and formulas preservedDocuments not used for training
RAG document ingestion

PDF to Markdown for RAG—fix the source before retrieval

Raw PDF extraction breaks hierarchy and tables. Clean Markdown gives splitters natural semantic boundaries and retrievers better context.

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Heading-based chunks · Tables intact · LangChain-ready

The 25–40% figure describes documented gains from cleaner structured sources, not a guarantee for every retrieval system.
curlbash
curl -X POST https://blazedocs.io/api/v1/convert \
  -H "Authorization: Bearer $BLAZEDOCS_API_KEY" \
  -F "file=@document.pdf"
“Finally a PDF converter that outputs clean Markdown for my RAG pipeline.”

Alex T. · Software engineer

25–40%
potential accuracy lift from better structure
Before and after

Make the source usable before the next workflow

Before
Raw PDF chunks
Fixed token cuts split headings from content, flatten tables, and mix reading order across columns.
After
Semantic Markdown chunks

Headings become boundaries and tables remain connected to the sections that explain them.

Heading metadataTable contextSection boundariesSource citations
## Risk factors

| Factor | Score |
|---|---:|
| Liquidity | 0.82 |
Why BlazeDocs

Structure you can use—not another text dump

Chunk by meaning
Markdown headings create natural semantic boundaries instead of arbitrary fixed-token cuts.
Keep retrieval context
Tables, lists, formulas, and hierarchy stay attached to the content your retriever embeds.
Agent-ready interfaces
Use REST, CLI, MCP, or the BlazeDocs agent skill with LangChain, LlamaIndex, and OpenAI tools.
Product walkthrough

See why clean Markdown retrieves better

Compare broken raw chunks with semantic sections and intact tables.

Developer quickstart

Start with one copy-pasteable request

The examples use the production API response shape and work with the tools already in your stack.

curlbash
curl -X POST https://blazedocs.io/api/v1/convert \
  -H "Authorization: Bearer $BLAZEDOCS_API_KEY" \
  -F "file=@document.pdf"
Pricing

Improve retrieval before buying more model tokens

Test representative PDFs free, then process the corpus through API, CLI, or batch workflows.

Starter includes 500 pages/month

Start freeView all plans
FAQ

Questions before you try it?

Answers about accuracy, pricing, security, and this workflow.

How accurate is BlazeDocs OCR?+

BlazeDocs uses Mistral AI OCR and preserves reading order, headings, tables, formulas, and lists. Clean text PDFs are near-perfect; difficult scans typically exceed 95% character accuracy.

How does BlazeDocs pricing work?+

The free plan includes three uploads per month with the first five pages of each file. Paid plans start at $9.99 per month for 500 pages, with API plans for production volume.

Are uploaded documents private?+

Documents are processed securely and are not used to train models. PDFs are handled in memory rather than permanently stored; review the security and privacy pages for current controls.

Why not chunk raw extracted PDF text?+

Raw text commonly loses headings, table relationships, and reading order. Markdown provides stronger semantic boundaries and more useful metadata for retrieval.

Does this work with LangChain and LlamaIndex?+

Yes. Use the API or CLI to produce Markdown, then pass it to Markdown-aware splitters, loaders, embedding pipelines, and vector stores.

Ready to make this document usable?

Try the workflow with your own document before deciding. No credit card required.

Build a better RAG sourceView RAG benchmark

Explore related BlazeDocs workflows

PDF APIPDF for AIRAG guideAI agent workflowsAPI documentationPricingParser benchmarksPDF to Markdown guides