A genuinely good fit
Pulling structured fields out of unstructured documents is high-volume, previously manual, and immediately checkable by a person — the profile that makes an AI feature safe to ship early.
Extract to a schema, always
Define the exact fields and types you expect and require the output to conform. Free-text extraction cannot be validated; structured output can be, which turns an unverifiable read into a checkable one.
Validate against what you already know
Cross-check extracted values against your own records — does this supplier exist, does the total match the lines, is the date plausible. Most extraction errors fail an arithmetic or reference check instantly.
The point isn’t perfect extraction. It’s knowing exactly which documents to look at yourself.
Route by confidence into a review queue
Documents that pass every check flow through; anything failing one goes to a person with the problem highlighted. That is the difference between a feature that saves time and one that creates a new checking job.
Keep the original, always
Store the source document alongside the extracted data with a link between them. When a number is disputed months later you need the page it came from — the reconstruction requirement in its most literal form.
Bad scans stay bad
Photographs at an angle, faxes, and handwriting degrade accuracy sharply. Measure performance by document source rather than in aggregate, or one bad channel will hide inside an acceptable overall number.