Documents read at machine speed, held to human standards
Most of your operation's knowledge arrives as documents — contracts, invoices, claims, filings — and people re-type it. We build document intelligence that extracts, classifies and reviews at machine speed, with accuracy measured on your documents and humans in the loop exactly where confidence drops.
What we point it at
Four document families where the payback is fastest — each with its own accuracy bar and review flow.
Contracts & legal documents
Clause extraction, playbook deviation flags and first-pass review — grounded in your firm's own precedents.
Invoices & financial documents
Line items, totals and references extracted and matched against orders and ledgers — exceptions queued for people.
Claims & case files
Multi-document files assembled, summarised and triaged — the reviewer starts at the decision, not page one.
Compliance filings & KYC
Identity documents, registers and forms checked and cross-referenced, with a full audit trail per decision.
From sample documents to production pipeline
Accuracy is measured before it's promised — on your files, including the badly scanned ones.
Feasibility sprint on your real documents
Two weeks, your actual files. You get measured accuracy per field and a written go/no-go before committing to the build.
Build the evaluation harness first
A scored test set from your documents becomes the contract the system must keep — every change is measured against it.
Confidence-routed review
High-confidence documents flow straight through; low-confidence ones route to your team with the model's reasoning attached.
Deploy, monitor, improve
Accuracy tracked continuously in production; drift alerts fire before quality problems reach your customers.
What ships with every pipeline
The model is the visible 10% — this is the rest.
Common questions
What accuracy can we expect?
We measure before we promise: the feasibility sprint runs on your real documents and reports accuracy per field. Then confidence thresholds decide what flows automatically and what a person sees — accuracy becomes a dial you control, not a gamble.
Our documents are scans, photos and faxes. Still workable?
Usually — and that's precisely what the sprint tests. Modern OCR plus layout-aware models handle most of it; where quality is genuinely too poor, the system routes to humans instead of guessing.
Where does our data go?
Wherever your sensitivity requires: frontier APIs under data-processing agreements, EU-hosted models, your own VPC, or fully self-hosted open weights. No training on your data without written consent.
What does it cost to run at volume?
The proposal prices per-document run costs next to the build cost — model, hosting and monitoring included — so payback is calculated on the total, not the flattering half.
A law firm first-passes every contract through its own review model
Lawyers review flagged clauses instead of reading page one to signature — delivered on the signed estimate.
Read the case study →More AI solutions
Copilots for teams →
Sales, support and ops assistants grounded in your data — with citations and permissions.
Agentic workflows →
Multi-step processes executed end-to-end — with human checkpoints and full audit logs.
Forecasting & decisions →
Demand, pricing and risk models embedded where the decision is actually made.
The full practice →
Everything we do in AI solutions.
Bring a folder of real documents
The feasibility sprint will tell you in two weeks whether this works on your files — with numbers.