Document processing
A pattern for extracting structure from unstructured documents at volume: invoices, applications, claims, contracts and identity documents. It replaces manual reading with classification and field extraction, while keeping a human review path for the cases where the machine is not confident.
When this pattern is the right one.
Most back offices run on documents that were designed for people to read, not for systems to interpret. Volume is high, the layouts change without notice, and a single misread field can hold up an entire process chain downstream.
Documents arrive as scans and photographs with inconsistent quality and orientation
The same logical field sits in a different place on every template
High-volume intake makes manual keying the single largest cost in the process
Errors are found late, often downstream, after work has already proceeded on them
Nobody can quantify how much of the backlog was processed correctly
How the work is done.
We treat extraction as a measured problem rather than a demo. Each field is defined with its type, its acceptable values and the confidence below which a human must see it. Documents are classified first so that extraction is driven by a known layout family, then parsed, validated against arithmetic and business rules, and only then written to the system of record. Every extraction keeps a link back to its source region so a reviewer can verify a decision in seconds.
Layout classification before field extraction so each template has its own parser path
Field-level confidence with a defined threshold for human review
Source-region mapping so every extracted value remains traceable to the document
Arithmetic and business-rule validation applied before data reaches the ledger
Review queues ordered by confidence and by downstream value at stake
Evaluation set built from real documents and re-run on every model or template change
What this is usually built with.
Chosen per problem rather than run as one stack for everything. The list below is representative, not a commitment.
The outcome this pattern is chosen for.
This pattern is designed to support high-volume intake without accepting silent errors. Its purpose is to move the effort from reading documents to checking the ones that need judgement.
Removes routine re-keying from the intake path
Surfaces low-confidence cases for review instead of passing them through as fact
Makes every extracted value traceable to its source document
Provides a measurable baseline for extraction quality before further automation is added
Allows historical backlog processing under the same rules as new intake
The parts that tend to go wrong.
Written down because they are the same parts that go wrong everywhere, and knowing them up front is cheaper than discovering them halfway through.
Template drift is the usual failure mode; a supplier changes a form and accuracy falls without any error being raised
Confidence scores are not probabilities and cannot be compared across fields without calibration against real examples
Retraining on unreviewed output reinforces the model’s own mistakes
Bulk back-processing without sampling and reconciliation produces large volumes of unverifiable data
PII in source documents requires retention and deletion rules decided before ingestion, not afterwards
These are solution patterns, not client case studies. Each one describes a reusable engineering approach and the problem shape it addresses. No client is named and no result is claimed.
Nothing here reports a measured outcome. The outcome sections describe what each pattern is designed to support, and what a team should expect to be able to do once it is in place.
Named references, implementation detail and engagement history can be discussed directly on request, subject to client confidentiality.
This page is structured so that verified case studies can be added alongside these patterns without changing the underlying format.
Recognise your situation in this one?
Patterns are general. The engineering decisions are not, and they are worth discussing against your actual constraints.