Documents that used to need a human typist,
now read, checked and filed automatically
Manual data entry from scans and forms is slow and error-prone in the same predictable ways. We build a pipeline that reads the document with a vision model and validates the result against rules you set. It flags anything it is not confident about instead of guessing.
What used to take a typist now takes a scan
Document processing turns scans, photos of forms, invoices or identity documents into structured, validated data without a person retyping them by hand. It fits any process with a steady flow of documents that currently gets entered manually: visa or application processing, invoice intake, inventory receipts. It is not a replacement for human judgment on documents that genuinely need it. It is built to handle the routine ones reliably and flag the rest.
Four layers between the scan and the record
Extraction runs through a vision model or a dedicated OCR engine, chosen based on your document volume and type. It gets tuned against real samples of what you process, not a generic template. Validation rules check the extracted data against what actually makes sense for your process. A date cannot be in the future. A field must match a known format. That happens before anything gets filed. Every extraction carries a confidence score. Anything below the threshold your team sets routes to a review queue instead of silently filing a bad read. An audit trail links every structured record back to its original document, so a disputed entry can always be checked against the source.
Tuning the threshold before anything goes live
We start with real sample documents from you. Extraction accuracy depends entirely on tuning against your actual document formats, not a generic template. Validation rules get written from how your process actually works, not a guess at what fields probably matter. We test the confidence threshold against a batch of real documents before launch, checking that the review queue catches genuine problems without flooding your team with unnecessary reviews. The structured output connects directly into whatever system should receive it: a CRM, a database, a spreadsheet your team already works from. It is immediately usable, not another export to import by hand.
The error that hides, not the one that shouts
The real risk is a confidently wrong extraction that slips past the confidence threshold. A pipeline that is usually right but occasionally silently wrong is more dangerous than one that is visibly unreliable. That is why we tune the threshold against a real batch of documents, including deliberately ambiguous ones, before launch. It is also why the audit trail matters: linking every record back to its source document is what makes a bad extraction catchable after the fact. Document formats also drift over time, a supplier changes their invoice template, so plan for occasional recalibration rather than treating the pipeline as permanently finished. Keep a sample of rejected or flagged documents on hand for periodic review. Patterns in what gets flagged often reveal a process change worth making upstream, not just a model limitation to tune around.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $2,000 | One document type, validation rules, confidence-based review queue | 3 to 4 weeks |
| Production | from $5,000 | Multiple document types, structured output into your systems, audit trail | 5 to 7 weeks |
| Full control (handover-ready) | from $6,000 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $15 to $60 a month in model and OCR calls, depending on document volume.
What stays yours
You own the extraction pipeline, the validation rules, the audit trail and the full source code, running on your own infrastructure. The handover package documents exactly how extraction and validation work, so your own team can add a new document type later without us.
Related
Pairs with ETL and integrations hub for getting the structured output into every system that needs it. See computer vision product for quality control for the inspection side of vision models. See the AI agents service page and the setup and integrations service page. Real builds: the AI support bots for a visa consulting centre case study and the AI packaging designer with a label validator case study. Still retyping the same kind of document every day? Get in touch and send a few samples.
FAQ
How much does AI document processing cost?
From $2,000 for a single document type (invoices, IDs, or a specific form) with validation rules and a review queue. Multiple document types or a multi-step validation pipeline runs $5,000 to $6,000.
How long does it take?
Three to four weeks for one document type once you share sample documents to tune extraction against. Each additional document type is usually faster to add once the pipeline and review flow exist.
What is the stack?
A vision model, Claude or GPT with vision, or a dedicated OCR engine for high volume, handles extraction. Python and FastAPI handle validation and routing. PostgreSQL holds the audit trail, with a connector into whatever system should receive the structured data.
Who owns the pipeline and the extracted data?
You. The extracted data, the validation rules and the code are yours, running on your own infrastructure. Original documents stay under whatever access controls you already use. We do not add a new place for sensitive data to live.
What happens when the model is not confident in a reading?
Low-confidence extractions get flagged and routed to a human review queue rather than filed as-is. The confidence threshold is something your team sets and can tighten or loosen as the pipeline proves itself.