Solution
Extracting structured data from messy real-world documents, with confidence thresholds tuned to the cost of being wrong and people kept on judgement.
Back-office processes often receive between a handful and many dozens of documents per case: reports, invoices, photographs, correspondence and schedules. All of it is read by a person and keyed into a system.
Commercial extraction products typically perform well on clean templates and poorly on the photographed, handwritten and scanned material that makes up most real intake — which is exactly the part consuming the time.
The consequence is a process where most of the elapsed time is administration rather than assessment, and where customer satisfaction tracks handling time almost exactly.
We begin with a time-boxed proof of value on a stratified sample of your real documents, deliberately weighted towards the difficult end, with a field-level accuracy target agreed in advance.
The evaluation harness is built before the pipeline. Every subsequent change — model version, prompt, rule, threshold — is measured against an annotated set, and results are visible to the operational team rather than only to engineering.
Confidence thresholds are then tuned per field according to the cost of being wrong, because a reference lookup and a payment instruction do not warrant the same threshold. That single decision determines both the automation rate and the risk profile.
Capabilities
Per-field thresholds decide what is written automatically and what is reviewed.
Every extracted value traceable to the part of the document it came from.
Value shown beside its source, correctable in one action, designed for throughput.
Extractions checked against systems of record before anything is written.
Every human correction captured, growing the evaluation set over time.
Accuracy by document type and field, reported to the operational team.
Outcomes
What this changes for the organisation once it is running. These are the outcomes the design targets, agreed with you as measures before build starts.
Technology
Indicative rather than fixed. The specific choices follow your existing estate, your team's skills and the constraints we find during discovery, and are recorded as architecture decisions before build.
| Area | Typical choices |
|---|---|
| Extraction | Cloud document-understanding services or self-hosted models |
| Language models | Hosted enterprise endpoints, or open weights where residency requires |
| Pipeline | Queue-based processing with structured output validation |
| Review | Purpose-built workspace integrated with the case management system |
A note on these pages. They describe the problem spaces we build for, how we approach them and the architecture we use. They are not client case studies and make no claim about specific engagements or results. Where a client agrees to be named, a full case study will be published in the same place.
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