AI data extraction
Pulling the fields out of a document is the easy part. We organise what comes out, compare it with the question you asked and run it through a check that corrects its own mistakes. What reaches your systems is data to work with, not values to recheck one by one.
{"vendor": "ACME S.r.l.","invoice_no": "2026-0412","total": 1240.00,"currency": "EUR","confidence": 0.98}
Takes anything
No templates, no zones to map, no setup for each format. Layout, tables, handwriting and photographs are interpreted in context: if the same document changes its layout, there is nothing to reconfigure.
How it works
You upload files in any format. Layout, tables, handwriting and images are read with no need for templates.
The models read the content in light of what you asked and pick out the fields and relationships needed to answer, not every field on the page.
The data is mapped to the schema you define, keeping the relationships between values: typed JSON, tables or records ready for the database.
Before it leaves, the result is compared with the source and corrected. Then it goes where it is needed, as JSON, CSV or records, via API or export.
Verification & correction
Producing structured output is the easy half. The other half is knowing whether the values are right, and that is what decides whether you can use the result without checking it again. So the work does not end when the fields are filled.
A second pass goes back to the original document and compares each extracted value with the passage it came from. A value that is plausible but wrong in context is exactly the error that one-shot extraction does not see.
What does not add up is extracted again and rechecked, not just marked as doubtful. Passing an error along with a low score means leaving the job of fixing it to you.
The genuinely ambiguous cases come back marked, with the reason. That way a person reviews the few real exceptions, instead of spot-checking the whole batch to find them.
What you get
Define the schema and get consistent, typed JSON every time, ready to store, index or hand to an automation.
No zone mapping to maintain. The models read the document the way a person would and adapt to the layout by themselves.
Complex tables, nested items and multi-page documents come out with their structure intact.
Every field has its own confidence score: you accept the sure cases automatically and review only the rest.
The whole pipeline runs on your servers with open models. Confidential documents stay in the company.
A simple API brings extraction into the workflows and integrations you already have, with little glue code.
Use cases
Totals, VAT, line items and supplier details, straight into the accounting system.
Parties, dates, clauses and obligations extracted in seconds, even from long agreements.
Paper and PDF forms turned into records, with nothing retyped by hand.
Structured reports generated from raw data. It is an automation we also use ourselves, in-house.
