Turn documents into data your systems can act on.
Classification and extraction with confidence scoring and human review, so the fields that reach your systems are the ones worth trusting — and the uncertain ones are seen by a person.
The cost of documents nobody can query
Documents are where operational facts go to become unreachable. A contract holds a renewal date, a notice period and a rate. Until someone reads it and types those into a system, none of them exist as data — and the moment the document is superseded, the typed version is wrong.
- People read PDFs and re-type the contents into a system of record.
- Nobody can answer a question that spans documents without opening them one at a time.
- Extraction has been tried, and it silently produced wrong values that reached the ledger.
- Documents are stored, but stored is not the same as searchable.
What we build
A pipeline from arrival to system of record, built around the assumption that extraction will sometimes be uncertain — and that uncertainty must be visible rather than averaged away.
- Ingestion from mail, scanning, portals and shared drives
- Classification by document type, with unknown types quarantined rather than guessed
- Field extraction with a confidence score per field, not per document
- A review queue for anything below the threshold you set
- Publication to the system of record through integration
- A searchable store where the document and its extracted data stay linked
How it runs
Every document takes the same path. Only the confidence decides whether a person is involved.
- 01Ingest
Documents are collected from the channels they already arrive on, with the original preserved unaltered.
- 02Classify
Type is determined first. A document that does not match a known type is held for a human rather than processed as the nearest guess.
- 03Extract with a score
Fields are extracted individually, each carrying its own confidence. A clear invoice number and a smudged total are not treated the same.
- 04Review what is uncertain
Anything under threshold goes to a review queue with the document and the proposed value side by side. Corrections feed back into accuracy.
- 05Publish and index
Confirmed data is written to the system of record, and the document becomes searchable with its extracted fields attached.
What changes once it is running
The practical difference between stored documents and usable ones.
Fields land in the system
Data reaches the system of record through the pipeline rather than through a keyboard.
Uncertainty is handled, not hidden
A low-confidence field is reviewed by a person. It does not quietly become a wrong number in a ledger.
The corpus becomes answerable
Questions that span documents — which contracts renew this quarter, which suppliers changed rates — become queries.
Review effort concentrates
People spend their attention on the documents that genuinely need judgement instead of reading every one.
How an engagement is shaped
Accuracy is established on your documents before anything is connected to a live system.
Sample and prove
We run a representative sample of your real documents, including the poor-quality ones, and report measured per-field accuracy before you commit to a build.
Build and tune
Pipeline, review queue and integration built, with confidence thresholds tuned against your tolerance for review effort versus error.
Operate
Optional managed operation, including accuracy monitoring as document formats drift over time.
Common questions
The things buyers ask before they commit. If yours is not here, it is a good first question for the assessment.
- What accuracy should we expect?
- We do not quote a number before seeing your documents, because accuracy depends on their quality and variety far more than on the model. The sample phase exists to give you a measured figure on your own material.
- Does it handle handwriting and poor scans?
- Partially, and that is exactly what the confidence threshold is for. Poor-quality input produces low-confidence extraction, which routes to review rather than being asserted as fact.
- Does our data leave our environment?
- Only if you choose an architecture where it does. We deploy extraction inside your tenancy or on-premises where confidentiality or regulation requires it. This is a design decision made in assessment, not a default.
- What happens when a supplier changes their invoice layout?
- Extraction confidence drops and the documents route to review, which is the signal. Accuracy monitoring surfaces the drift so the pipeline is adjusted deliberately rather than after a bad posting.
Send us fifty of your worst documents.
Not the clean ones. The faded scans and the odd layouts. That sample tells you more than any demonstration.