Document Processing and NLP That People Can Check

It depends on the documents: clean digital PDFs extract far better than faint scans or handwriting. We measure accuracy per field and per document type on a labelled sample of your own documents before you commit, and set confidence thresholds so uncertain values go to a person rather than into your systems.

No. Current document AI and language models handle layout variation without a template per supplier or form. We still add rules where they help — totals that must add up, formats that must match — because checks written in code catch errors a model alone will miss.

Each extracted value carries a confidence score. Below the threshold you choose, the item goes to a review screen showing the source page with the value highlighted. Reviewers correct or approve it, and those corrections become test cases for the next release.

Often, but less reliably than printed text. We test your worst real examples early and design the review step around the document types where handwriting is common, rather than promising that everything will be read automatically.

Document processing turns each document into structured data — fields, categories, summaries — that feeds another system. Retrieval-augmented generation (RAG) answers people’s questions across a whole collection. They often work together, since extracted fields make retrieval more precise; if question answering is the main need, our RAG and knowledge search page is the better starting point.

Yes. For each value we store the source document, page and position, the model or rule that produced it, its confidence score, and any correction with who made it and when. That record is what lets you answer an auditor or customer who asks where a figure came from.

Yes, with the right design: data minimisation, redaction of fields that are not needed, processing in a UK or EU region or in your own cloud where required, access controls and retention limits. We document the data flow for your DPIA; the lawful basis and sign-off stay with your DPO.

Anything with an API or a dependable import route: accounting packages, ERPs, CRMs, case and claims management systems, or your own database. Where there is no API, we agree a file-based hand-off and monitor that it runs.

A discovery sprint from £2,000 tests extraction on a sample of your documents and produces a fixed quote for the pipeline. Running costs — OCR and model fees per page — are paid to the provider, and we estimate them up front from your volumes.

A pipeline for one family of documents is usually measured in weeks, delivered in two-week sprints with a demo at the end of each. Before it replaces anything, it runs alongside your manual process so the thresholds are set on real results.