From difficult documents to trusted, structured data.
We combine OCR, document understanding, validation logic and private LLMs to extract text, tables and fields from complex documents and images — then turn them into structured data your systems can use.
Low-confidence values are validated, corrected or re-checked before they are accepted, with source-level traceability and private deployment for sensitive documents.
- 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 turns an image into raw text — it does not 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.
Paperwork that is really a data-entry queue
Nutrition tables, ingredient lists, allergen statements, claims, barcodes and product information extracted from labels and packaging — including complex layouts, rotated regions and phone-captured images.
Instrument output, certificates and result sheets converted into validated records, with the source image retained against every reported value.
Line items, totals, taxes and supplier details extracted and cross-checked against orders and received goods, with exceptions routed for review.
Submitted paperwork turned into structured records, including handwritten fields, checkboxes and mixed printed content.
Inspection reports, certificates, specifications and audit documents converted into structured, traceable data for downstream workflows.
Key terms, dates, parties and obligations extracted into structured records, with every value traceable to the source clause.
Delivery notes, shipping documents, customs paperwork and certificates reconciled against what was ordered and what arrived.
Every document type above started as a one-off. If yours has its own layout, language or field set, we build a custom extraction agent around it rather than force-fitting a generic template.
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
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.