A system at 95 percent field accuracy can still send every document to a reviewer, because one uncertain field stops the record
Every field carries a score and the thresholds are yours, so you choose how much risk to accept per document type
Start conservative, loosen it as the data comes in
Template-based OCR needs a new template every time a supplier redesigns an invoice
Modern extraction reads structure and context, so a new format is usually just another document
We still test on your worst examples before making promises
Extracted values are checked against your rules, your master data and internal arithmetic
A mistyped total is caught immediately rather than reconciled three weeks later
Garbage that posts cleanly is worse than garbage that stops
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No, and this is where modern extraction clearly beats template-based OCR. It reads layout and context rather than fixed coordinates, so a new supplier format does not require configuration. Very poor scans remain hard for any system.
Field accuracy on typical invoice and form data is high, but the number to negotiate on is straight through rate, which depends on your thresholds and document mix. We measure it on your documents during a pilot rather than quoting a benchmark.
Printed handwriting on structured forms, usually yes. Free-flowing cursive on a poor scan, often not. We test on your worst documents before committing, because the average case is never the one that causes trouble.
In your ERP, accounting system, CRM or a database, through an integration built as part of the project. Every field keeps a link to the source document and page, which matters the first time someone disputes a number.
Extraction can run in your own environment with open-weight models. Accuracy is close on most document types, and for regulated material it is often the only workable option.
