Intelligent document processing

The input is never clean

The document that decides whether money moves is rarely a tidy form. It is a photo taken in bad light, a voice note recorded on a site, a scan of a page that was already a copy, a spreadsheet somebody restructured last quarter.

Systems that assume clean input fail quietly on the day the input is not clean. We assume the opposite: every field arrives suspect, and has to earn its way into the record.

Capture that rides existing behaviour

The fastest way to lose a document pipeline is to ask the people producing the input to change how they work. So capture uses what they already do — a photo, a voice note, an email attachment — instead of a new app to remember.

What we add is downstream, where it does not cost anyone their day.

Extraction with consequences

Extraction runs against a fixed vocabulary, so a value nobody expected is an exception to review rather than a silent write.

Each extracted field carries a confidence, and confidence decides routing. Above the gate it flows through; below it, a person sees it. Nothing is guessed because the guess looked plausible.

Validation checks the extracted record against the rules that apply to it — the contract, the schedule, the regulation — before it is treated as true.

What comes out

Structured data, the review queue your team works in, and an append-only evidence chain behind every field: who, when, which source document, what changed, who approved it.

That chain is the point. A number nobody can trace back to its source is a number an auditor, a lender or a regulator can refuse.