Most of your information isn't in a database — it's trapped in PDFs, emails, forms and photos. We build NLP and computer-vision systems that read it, extract what matters, validate it, and hand your systems clean data they can act on.
Language + vision, structured
NLP · entities
Invoice from Acme Co for $12,400 due Apr 30.
CV · detection
transcription errors on official records
faster turnaround
manual work saved per cycle
what used to take days
What NLP & vision unlock
Four capabilities that turn unstructured text and images into something your systems can finally use.
OCR plus layout and language models that read invoices, forms and contracts and pull out the fields that matter.
Named-entity recognition, classification and routing — turning free text into structured, sortable data.
Detect, count, classify and inspect in images and video — from defect detection to visual search.
Confidence thresholds, rule checks and human-in-the-loop so accuracy is high and mistakes are caught.
How we build it
Accuracy on documents and images comes from pairing the model with validation — not from the model alone.
Connect your documents, images or text streams and handle the real-world mess — bad scans, mixed formats, noise.
OCR, NER, classification or detection turn each item into structured fields with a confidence score.
Rules and thresholds check the output; anything uncertain routes to a human, so errors don't slip through.
Clean, structured data flows into your systems — database, workflow or app — with an audit trail.
Document AI, in production
A county records office produced public notices and records entirely by hand — slow, backlogged, and error-prone on documents where a mistake is a legal problem. We used document AI to extract and validate, with human sign-off on every record.
Hollis County Records Office
County government records office · USA
Saved per cycle
Faster turnaround
Transcription errors
Resident service
“We're a small team doing work residents genuinely depend on. This gave us our hours back without cutting a single corner on accuracy — and people now get records the same day instead of waiting all week.”
Straight answers
They turn unstructured stuff — documents, emails, forms, photos, scans — into structured data your systems can use. Reading invoices, extracting clauses from contracts, classifying support tickets, detecting defects on a line, or pulling fields off a scanned form.
Handling the mess is the work. We combine OCR with layout understanding and validation so the system copes with bad scans, varied formats and noise — and flags what it isn't sure about instead of guessing.
We validate every extracted field against rules and confidence thresholds, route low-confidence items to a human, and measure accuracy on your real documents. On official records we've taken transcription errors to zero by pairing the model with validation and human sign-off.
Yes — it deploys inside your environment with access controls and an audit trail, which matters for regulated, government and healthcare data. Accessibility and compliance can be designed in from the start.
Often not — pretrained vision and language models plus a modest amount of your data go a long way. We assess what's needed in week one and use techniques that minimise labelling effort.
Send us a sample of the documents or images you're processing by hand. We'll show you what an extraction system would do with them.
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