Turning a filing cabinet into structured, checkable data
We built a pipeline that reads dense, inconsistent documents in a regulated domain and extracts the fields that matter, with a confidence score and a citation back to the source page on every value.
Document parsingStructured extractionValidation rulesHuman review
The challenge
The documents arrived as scans, exports, and decades-old templates, no two laid out the same. Staff read them line by line to pull out the same handful of facts, and a transcription error downstream could become a compliance problem. They needed extraction they could defend, not a black box that was usually right.
What we built
- A layout-aware parsing stage that handles scans, tables, and multi-column pages before any extraction runs, so the model reads structure, not soup.
- Field extraction with a confidence score and a bounding box on every value, linked back to the exact page and region it came from.
- A validation layer that checks extracted fields against domain rules and flags anything that doesn't reconcile for human review.
- A review queue that routes low-confidence and rule-failing documents to a person, and learns from the corrections.
The outcome
- Every extracted value carries a source citation a reviewer can verify in one click.
- Documents that took an hour to process by hand are prepared in under a minute, then checked.
- Confident, validated fields pass straight through; only the genuinely ambiguous ones reach a human.
Common questions
Have a problem shaped like this?
If this looks like the kind of system you need, let's talk through it. First call is always free.