The first extraction engine with a built-in human-in-the-loop Review Inbox. 99.9% accuracy on the easy fields, zero surprises on the hard ones.
Trusted by document-heavy teams at
Three steps to automate an entire document pipeline — from inbox to your warehouse.
Forward an email, drop a PDF, or hit our API. We accept anything from scans to native digital docs.
An LLM-native engine reads the document against your schema and returns JSON with per-field confidence.
Low-confidence fields land in the Review Inbox. Everything else streams straight to your stack.
99% accuracy still means 1 in 100 invoices wrong. Try the interactive demo — approve, edit, and watch corrections become training signal.
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No two documents are identical. Parse.me handles edge cases that break rules-based parsers.
Works across thousands of templates without per-format rule mapping.
Native support for 40+ languages including CJK and Arabic scripts.
Vision models trained on noisy, rotated and handwritten documents.
Nested rows, merged cells, multi-page tables — extracted cleanly.
Describe the fields you want. No training data required.
Encryption in transit and at rest. Strict no-train data policy by default.
Start free. Pay for what you process.
For prototyping and small pipelines.
For growing automation teams.
Dedicated instances and SLA.
OCR returns characters. Parse.me returns structured JSON keyed to your schema, with per-field confidence and a built-in human review path.
Anything below your threshold is routed to the Review Inbox. A teammate approves or corrects it in seconds, and the correction becomes a training signal.
No. The default policy is strict no-train. Enterprise customers can opt into private fine-tuning on a dedicated instance.
Most teams ship their first production pipeline in under an afternoon. No model training, no labeling, no rules.
Spin up your first extraction pipeline in minutes. Your finance team will thank you.