Upload a PDF and define the fields you need. Two AI models handle OCR, extraction, validation, and Q&A — automatically.
A fully automated pipeline from raw document to validated, structured output.
Drop any PDF file. PyMuPDF renders every page into a high-fidelity 200 DPI image — preserving tables, layouts, and scanned content.
Each page image is processed by Gemini 2.0 Flash. It extracts all text and tables into structured Markdown — handling scanned and printed documents.
Define the fields you need with descriptions and types. LLaMA 3.3 70B at temperature 0 returns a strict JSON object matching your schema.
Pydantic validates every field against expected types. If errors exist, add a correction hint and re-extract. Then ask anything about your document.
The Markdown is scanned for pipe-tables. Headers are scored against billing keywords — the best match is extracted instantly with zero LLM calls.
Export your results as JSON. Save your field schemas for reuse across documents. Edit extracted values inline before exporting.
Gemini 2.0 Flash reads images, not raw bytes — handling scanned, rotated, and mixed layouts that traditional parsers miss.
OCR streams progress page-by-page via SSE. See exactly which page is being processed in real time.
Define any fields with keys, descriptions, and types. Import or export schemas as JSON for reuse across documents.
Validation errors and correction hints are injected into the next prompt automatically — no manual prompt engineering required.
Ask anything about the document. Answers are grounded in your document's full Markdown — no hallucinations from outside context.
Pydantic validates every extracted field against string, number, date, and boolean types — flagging mismatches before you export.
Two specialized AI models in separate layers, each optimised for its specific role.
Upload any document and define the fields you need.
No setup, no signup required.