Can your OCR tell an invoice number from a phone number? We build the layer that does.
Plain OCR would just hand that text back. Document AI understands the document, extracts the fields that matter, validates them, and puts structured records into the system that needs them.
With an audit trail per document, and running on private models so confidential paperwork stays inside your boundary.
- Scope: complex structured PDFs and real-time images shot on a phone — a food sample label, a nameplate, a handwritten form.
- Custom AI agents built around your document types and fields, not a generic template.
- Up to 98% accuracy, with exceptions routed to a person instead of silently guessed.
- Database updates pushed straight into your structured database via webhooks.
- Image-to-text conversion as the first step — OCR is where we start, not where we stop.
OCR reads the text. Document AI does the job.
OCR (optical character recognition) turns an image into raw text — it doesn't know an invoice number from a phone number. Document AI is what we build on top of it: it understands the document's structure, extracts the fields that matter, validates them against your other systems, and hands off a structured record instead of a wall of text someone still has to read.
Text on the page, no structure. Someone still finds the invoice number, checks the total, and types it into the right system.
The invoice number, total and line items land in your system already matched and validated, exceptions flagged for a person.
Paperwork that is really a data-entry queue
Instrument output and result sheets read into validated records, with the source image kept against every figure.
Line items, totals and tax matched against the order and the goods received, with exceptions routed to a person.
Submitted paperwork turned into records, including the handwritten fields nobody wants to key.
Key terms, dates and obligations extracted into a register you can query, with the clause it came from.
Delivery notes, customs paperwork and certificates reconciled against what was ordered and what arrived.
A pipeline that is right 90% of the time creates a review job. We design for the accuracy your process can actually accept, and route the rest to a human.
Extraction is easy. Trusting it is the work.
Every field carries a confidence score and a link back to the place on the page it came from. Low-confidence values go to review rather than into your system unnoticed. Validation rules catch what the model cannot know — totals that do not add up, dates out of range, values a lab would never report.
You decide the threshold. We show you what it costs at each level.
- 01Ingest
Email, scanner, folder, portal or API — documents arrive the way they already arrive.
- 02Read & extract
Layout understood per document type, fields and tables pulled with position retained.
- 03Validate
Business rules, cross-checks against your own records, confidence scoring per field.
- 04Review what needs it
A review screen showing the value next to the original image, so a check takes seconds.
- 05Into your system, with a trail
Structured records written where they belong, every figure traceable to its source page. How we deploy privately →
One engagement, in detail
Test results that stopped being retyped
- Context
- A food testing laboratory in Europe. Results are client-confidential and the lab works to accreditation requirements, so every reported figure has to be traceable.
- The problem
- Results arrived as images and were keyed into the lab system by hand. It was slow, it introduced transcription errors into reported data, and it put a person between the instrument and the record.
- What we built
- Automated reading of the result images into structured, validated records — field-level confidence, range checks against expected values, and the source image retained against each figure. Anything the pipeline is unsure about goes to a reviewer with the image alongside.
- Deployment
- Privately hosted models, no test data or client identifier leaving the approved environment, with an audit trail per document for accreditation.
- Capabilities
- Image to structured dataValidation rulesConfidence scoringHuman reviewPer-document audit trail
Next to this
Send us the document your team keys in by hand.
A discovery session is a working conversation, not a demo. Bring a handful of real examples, including the bad scans.