Illustrative solution pattern
Document intake and extraction
Structured data extracted from inbound documents, with confidence surfaced and a person confirming before anything is committed.
The challenge
The problem this solves
High volumes of semi-structured documents arrive by email and portal — invoices, referrals, applications, consignment paperwork — and someone has to read each one and type its contents into a system.
Existing process
The limitation being removed
Manual re-keying is slow, error-prone and impossible to audit after the fact. Backlogs build up at predictable points in the month, and errors are found downstream where they cost the most to correct.
The solution
What was built
An intake pipeline that classifies each document, extracts the fields the receiving process needs, validates them against expected formats and reference data, and presents the result for confirmation with per-field confidence shown. High-confidence, low-risk records can be configured to pass automatically; everything else goes to a reviewer with the uncertain fields highlighted.
Implementation approach
- Define the target schema per document type, with the receiving system's rules as the authority
- Build classification and extraction, evaluated against a labelled sample of your real documents
- Add validation and reference-data matching so implausible values are caught before review
- Build the reviewer interface, showing confidence per field and the location it was read from
- Set automation thresholds per field and per document type, agreed with the process owner
- Run in parallel with the manual process until measured accuracy justifies switching over
Technologies used
- Document classification and structured extraction models
- Schema validation with typed field definitions
- Reference-data matching and duplicate detection
- Reviewer interface showing per-field confidence and source location
- Durable job queue with retry and dead-letter handling
Services provided
Applicable sectors
- Healthcare
- Logistics & transport
- Professional services
- Workplace safety & compliance
Security and governance
Controls designed into the solution
Decided before implementation. Every one of these is an architectural choice, which is why they cannot be added afterwards without a rebuild.
- Confidence surfaced per field; low-confidence values always require confirmation
- Automation thresholds configured per document type and reviewed, not set globally
- Complete audit trail: source document, extracted values, reviewer, and any correction made
- Personal data minimised to the fields the process requires, with retention agreed up front
- Inbound documents treated as untrusted input, with injection containment applied
- Failed extractions routed to a person rather than silently dropped
Outcome
Expected outcome (design intent, not a measured result)
These are design expectations for this pattern, not measurements from a delivered engagement. We would agree how to measure them with you before building.
- Staff review structured output instead of reading and re-keying whole documents
- Errors are caught at intake by validation rather than downstream by reconciliation
- Every record has an auditable link back to the document it came from
- Backlog peaks flatten, because throughput no longer depends on available reading time
Related
Other solution patterns
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An assistant that answers staff questions from your approved documents, cites its sources, and declines when the corpus does not cover the question.
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Submissions checked against a written rule set, with every finding citing its clause and a competent person confirming the outcome.
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Inbound requests classified, enriched and routed, with straightforward cases resolved from approved guidance and the rest escalated with context.
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AI agents, assistants and automated workflows that carry out real work inside your systems, with human approval at the points that matter.
Cloud & Infrastructure
Cloud architecture, migration and system integration — designed for the cost, resilience and compliance position you actually need.
Could this work for you?
Tell us how your situation differs from this example. Where a material uncertainty remains, a bounded proof of concept with a pass threshold agreed in advance is usually the cheapest way to find out.
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