Documents whose data extracts itself.
Invoices, acts, waybills, contracts and requests arrive by mail in every format, and then someone retypes them into a system. We build a loop that recognizes the document, extracts the fields, validates them and passes them into your system — with a human approving wherever the cost of an error is high.
Recognition and parsing
A scan, photo or PDF becomes structured text. Tables and stamps are a separate difficulty — that's where most off-the-shelf solutions break.
Field extraction
Exactly the fields you need are pulled from the document: counterparty, amounts, dates, numbers, line items. The rules are described for your document flow, not for an average template.
Validation and human approval
Totals add up, details are valid, a duplicate doesn't pass twice. Anything the system isn't sure about goes to a human — with the spot in the document highlighted.
Handover to your system
The result lands in your accounting system or storage with no intermediate spreadsheet. Otherwise manual entry just moves one step later.
Do you work with 1C?
1C is not our stack, and it's more honest to say so up front. We build the processing loop and hand data over in an agreed format or via API — the 1C side is usually covered by your accounting contractor.
What accuracy can we expect?
It depends on scan quality and format variety. An honest answer comes after a test on your own sample of documents — before that, any percentage would be invented.
Is a human needed in the process?
Where an error costs money — yes, on approval. The point isn't removing the human but having them verify instead of retyping.
What if the documents are confidential?
For sensitive documents we use self-hosted models or a setup where data never leaves your perimeter.
What RAG actually is and when a business needs it
RAG means the model answers from your documents rather than from the internet, with a visible source under every claim. What it is made of, where quality is lost in chunking and updates, how to measure that it works and when you do not need it.
Chunking documents for RAG: why chunks matter more than the model
A bad RAG answer almost always comes from chunking rather than from the model. How to split policies, contracts, scanned PDFs and support tickets, what breaks on a corpus of tens of thousands of long documents, and how to tell that chunking is the culprit.
Checklist: are your documents ready for RAG
PDF, 2 pages. No sign-up, no email — just the file.