Open Source The PDF extraction engine for the AI era
The PDF Engine for RAG Pipelines
Feed your LLMs clean structured data. EdgeParse extracts headings, tables, lists, and reading order from any PDF — in milliseconds, with zero ML dependencies. Built in Rust.
0+ pages/sec
0% accuracy
0 ML dependencies
0 SDK languages
Works with
Python Node.js Rust CLI
Contact the EdgeParse Team
Reach out if you are evaluating EdgeParse for production, planning a self-hosted deployment, or need help integrating PDF extraction into your AI pipeline. If you are still comparing options, start with the documentation, the live demo, or the enterprise overview before opening a deeper conversation.
Choose Your Contact Route
Use a verified channel so your message reaches the right team quickly.
Email the team Best for product questions, architecture reviews, and implementation support. mailto
Open a GitHub discussion Best for community questions, feature ideas, and public technical conversations. community
Talk to Elitizon Best for enterprise engagements, custom delivery, and partnership discussions. enterprise
Typical response window: 1 to 2 business days.
Helpful starting points
Getting started docs Install EdgeParse, run your first extraction, and learn the core concepts. Live demo Try EdgeParse directly in your browser — no install, no server required. Enterprise overview Self-hosted deployment, data sovereignty, priority support, and custom integrations. Docker deployment Run EdgeParse in containers with pre-built images for production pipelines.