Paper in.
Structured data out.

Extraction accuracy on clean sample documents is close to meaningless. Real documents arrive creased, photographed at an angle in bad light, faxed at some point in their history, and formatted differently by every supplier who sends them. The pipeline has to be built for that population rather than the one in the vendor demo.

Which makes confidence scoring the actual feature. A pipeline that extracts everything and tells you nothing about its own certainty forces a human to check all of it, which is the manual process you were replacing. A pipeline that flags the fifteen per cent it is unsure about and routes them to a review queue removes most of the work and keeps the errors visible.

Validation catches the rest. Line items should sum to the total, a supplier should be one you recognise, a price should not be forty times last month's. These checks catch extraction errors that look perfectly plausible in isolation, and they are cheap to implement compared to the reconciliation they prevent.

Kitchen Stocker runs this over supplier invoices and delivery notes in production, feeding a stock ledger and food-cost reporting — so the review queue, the confidence thresholds and the validation rules come from operating one, not from designing one.

What you get

• Extraction into a structured schema you define
• Per-field confidence scoring rather than a single document score
• Review queue for low-confidence documents
• Validation rules against totals, suppliers and expected ranges
• Integration into the system that consumes the data

Not included

• Model inference costs, billed at provider rate
• Training bespoke foundation models