Document Parsing — Turn Unstructured Documents into Structured Data
Parse PDFs and images into structured headings, text, tables, and charts, then export the result as JSON or Markdown.
Why it's hard
PDF and image documents mix tables, charts, and multi-column layouts, so scraping text alone collapses their structure.
Once element order and hierarchy are lost, the output can't be used directly for search, analysis, or automation.
The DEEP Agent approach
DEEP Agent uses a Vision-LLM to break documents down into header, body, table, chart, and image elements while preserving the hierarchy.
Results are returned as JSON, Markdown, or HTML, ready to plug straight into RAG preprocessing, system integration, and review.
Frequently asked questions
Which file formats can it parse?
It supports PDF and images (PNG, JPG), and processes long, multi-page documents in a single pass.
Are table and chart structures preserved?
It recognizes cell, merge, and header relationships in tables and numeric data in charts as separate elements, preserving their structure.
Can I feed it straight into a RAG pipeline?
Returned as Markdown or JSON, it can be used directly for structure-aware chunking and as LLM preprocessing input.
How accurate is it?
A document-specialized Vision-LLM ranked #1 on OCRBench v2 (English) parses even complex documents accurately.
Try it with your own document
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