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.
EdgeParse for Enterprise
Production-grade PDF extraction for teams that need deployment control, data isolation, reliable operations, and a path from prototype to internal platform.
Self-Hosted
Deploy on your own infrastructure. Air-gapped environments, private clouds, or on-premises — EdgeParse runs wherever you need it.
- Docker & Kubernetes ready
- No external API calls
- No data leaves your network
High Performance
Process thousands of PDFs per minute with constant, predictable resource usage. Rust-native speed, zero ML overhead.
- 40+ pages/second per core
- 10–100× faster than Python alternatives
- Zero ML & GPU dependencies
Multi-Format Output
Structured JSON, Markdown, HTML, or plain text — integrate directly into your RAG pipeline, document workflow, or data lake.
- JSON with bounding boxes
- Table-aware Markdown
- Clean HTML with headings
Taking PDF Extraction Into Production?
EdgeParse can support internal platforms, customer-facing document pipelines, and regulated deployments that need more than a prototype stack.
Enterprise Security
Support for regulated environments that need full data sovereignty, air-gapped deployment, and auditable processing pipelines.
Priority Support
Work directly with the team on architecture reviews, rollout plans, troubleshooting, and production issues.
Custom Integrations
Integrate into internal systems, custom deployment patterns, and proprietary workflows — Python, Node.js, Rust, CLI, or WebAssembly.