Handles many input formats (PDF, images, DOCX, PPTX, XLSX, HTML, EPUB) and outputs Markdown, JSON, HTML, or chunks sized for RAG
Preserves structure: tables, equations as LaTeX, code blocks, reading order, and image extraction, with headers/footers removed
Selective OCR keeps it fast by only invoking the vision model on bad or scanned pages, with a CPU-only no-OCR path available
Optional --use_llm hybrid mode works with Gemini, Claude, OpenAI-compatible, Azure, Vertex, OpenRouter, or local Ollama models
Apache-2.0 code with CLI, Python API, batch processing, and a simple API server
Proprietary Aurora LLM trained on millions of transactional documents
Supports 276 languages plus handwriting with continuous learning
Strong ergonomic validation UI for human-in-the-loop review
Unlimited seats on all plans
Certified integrations with SAP and Coupa, plus NetSuite, Workday, and more
Cons
Model weights are not freely commercial: a modified OpenRAIL-M license caps free use at under $5M funding/revenue and requires paid licensing above that
Best accuracy needs a GPU plus a local inference server (vLLM via Docker, or llama.cpp on CPU/Apple Silicon), adding setup
Very complex layouts with nested tables and forms may still convert poorly
The built-in API server is explicitly basic and not intended for large-scale production use
Entry pricing starts around $18,000/year, out of reach for small teams
Quote-only pricing with a one-year minimum contract
No free-forever plan; only a 14-day trial
No self-hosted/on-prem option (cloud-native only)
Optimized for transactional documents, less suited to arbitrary types
Comparison generated from each tool's listing. Add or remove tools above to change it.
