Nobody should readforty thousand documents
AI document processing that extracts, checks and files what your business receives, and answers questions from what it already holds. Every claim cited to source, every uncertain case routed to a person.
Every business runs on documents. Invoices, contracts, applications, filings, reports. Someone opens each one, finds the handful of fields that matter, and retypes them somewhere else, and that someone is usually a person you hired to do something harder. Reading, extracting and re-keying is exactly what AI now does well. The catch is that a model with no checking will confidently misread a contract, and nobody notices until it costs money. The difference between useful and dangerous is whether anyone can prove what it got right.
A pipeline, not a prompt.
Ingest whatever arrives
PDFs, scans, spreadsheets, filings, feeds, email attachments. Real documents defeat single-method pipelines, so ingestion runs as a waterfall: the structured route first, then fallbacks for the sources that fight back. The system takes what your business actually receives, not an idealised version of it.
Extract into structure
Extraction returns named fields, not a paragraph of prose. Structured output between every stage means each step hands the next something checkable: a date that parses, an amount that sums. That joinery is what makes the rest of the pipeline testable.
Validate before it counts
Every extracted record is checked against your business rules before anything downstream trusts it. When validation fails, the specific errors go back into the loop for a retry, with a cap on attempts. If the information isn't in the document, the system says so rather than guessing.
Route by confidence
High-confidence output completes on its own. Uncertain cases route to a person with the document and the doubt highlighted. Corrections are stored and fed back, so accuracy climbs with use. Your team reviews the exceptions, and only the exceptions.
Dozens of sources.
Nobody reading.
A confidential client needed signal pulled from large volumes of unstructured public data: government publications, regulatory filings, industry reports and news feeds, spread across dozens of sources. Their team was scanning it all by hand, and by the time something was spotted it was often too late to act.
We built the full pipeline: automated ingestion that collects and structures every source type, natural language processing that extracts entities and relevance, models that flag patterns and emerging trends, and an executive dashboard presenting it cleanly. It runs autonomously on scheduled pipelines.
The pattern transfers: documents in, structured intelligence out, on a schedule, without a person reading any of it.
What changes for your team
The reading leaves the week
The opening and re-keying disappears from your team's day. What stays is the judgement: reviewing the cases the system flags, which is the part a person was always needed for.
Answers with citations
Ask questions of your own document store and get answers that point back to the source passage. Our research engine does this across a corpus of forty thousand specialist documents, and the same grounding applies at any size.
Accuracy you can audit
Every run is scored against a ground-truth test set built with your team. Confidence scores and stored corrections mean you can see what the system got right, what it flagged, and how the accuracy is trending.
Documents are where it starts
AI Consulting
Document processing is one shape of applied AI. The full picture covers scoping, evals and the route to production for whatever your business repeats.
Learn moreWorkflow Automation
Documents are usually one step in a longer process. When the extraction feeds a quote, a report or a decision, automating the whole workflow is the bigger win.
Learn moreDrowning in paperwork
your team shouldn't be reading?
Tell us what arrives, what gets extracted, and where it has to end up. We'll tell you what's feasible and what it would take, with real numbers.