Convert Microsoft Word documents (.docx, .doc, .odt, .rtf) into clean, structured GitHub-Flavored Markdown. Preserves heading hierarchies, bullet lists, footnotes, and data tables.
Supported formats: .docx, .doc, .odt, .rtf • Up to 25MB
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Knowledge base
Binary `.docx` files cannot be diffed, merged, or reviewed effectively in Git or GitHub pull requests.
Converting Word documents into Markdown allows your team to manage technical documentation, specifications, and PRDs directly alongside your codebase with full change tracking.
AnyDoc parses the underlying OpenXML structure to preserve H1-H6 heading tags, bold/italic text, strikethroughs, code blocks, numbered sequences, and complex data tables.
Footnotes and endnotes are seamlessly serialized into Markdown reference links.
Extracting text from DOCX files using standard zip extractors loses layout context and table cell alignment.
Clean Markdown preserves structural boundaries, enabling embedding models and LLM agents to accurately retrieve relevant passages.
Binary containers like PDF, DOCX, and XLSX carry massive visual formatting, font definitions, and XML schema metadata that consume valuable context window tokens. Markdown strips away this visual bloat while preserving semantic headings, bullet lists, and tables—reducing token count by up to 85% and significantly speeding up AI inference.
Powered by Firecrawl AnyDoc, the conversion engine calculates the physical geometry of spreadsheet grids and document tables, translating them into GitHub-Flavored Markdown (GFM) pipe tables (| Col 1 | Col 2 |). This preserves column alignment and relational meaning without data scrambling.
AnyDoc extracts text directly from digital and vector PDFs with an embedded text layer in single-digit milliseconds. If your PDF consists of scanned pages or camera photos without a text layer, optical character recognition (OCR) is required.
No. Document conversions are processed entirely in-memory and the resulting Markdown is streamed immediately back to your browser. Your files are never written to disk, stored in databases, or used for AI training.
You can convert files up to 25MB in size directly through the web interface. For larger enterprise workloads, batch document processing is available via the SpeedVitals API.
