Schema
Specify field types, required values, normalisation, and cross-field validation.
Versioned record contractEnterprise AI · Document intelligence
Turn documents into reviewable records without losing the original evidence.
Document intelligence combines ingestion, classification, extraction, validation, and exception handling. We design for provenance and reviewer efficiency so a structured field can always be traced back to the submitted artifact.
- Unit
- A document case with source, version, and processing history
- Review
- Field-level evidence and exception-focused queues
- Proof
- Representative layouts, quality bands, and failure cases
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01 · Information contract
A document workflow needs a field contract before it needs extraction logic.
We define document classes, required fields, validation rules, evidence locations, retention, downstream consumers, and the conditions that force manual review.
Provenance
Retain document identity, page or region reference, processing version, and reviewer action.
Field-level lineage exampleException policy
Route unsupported, incomplete, ambiguous, or poor-quality inputs to a defined owner.
Exception taxonomy and queue rules03
02 · Processing path
Each stage should produce a result that can be inspected and retried independently.
The pipeline records file checks, classification, extraction, deterministic validation, cross-source checks, review, and downstream acceptance as distinct states.
Receive
Validate format, integrity, source identity, duplicates, and handling policy before processing.
Ingestion receipt and rejection reasonInterpret
Classify and extract into a schema while retaining field-level source references.
Structured candidate with provenanceAccept
Apply validation and reviewer policy before publishing to the system of record.
Acceptance decision and audit event04
03 · Evaluation
Aggregate accuracy can hide the field that creates operational risk.
Evaluation separates document detection, field extraction, normalisation, validation, and exception routing. Results are segmented by representative layout and quality conditions.
Field results
Score material fields independently with tolerance rules agreed by downstream owners.
Field-level confusion and error reportCase coverage
Include scans, rotations, handwriting where in scope, missing pages, and template variation.
Representative corpus registerReview results
Measure reviewer corrections, handling time, and unresolved exception patterns.
Reviewer study and correction log05
04 · Reviewer experience
A reviewer should see the field, its evidence, and the applicable rule together.
The review surface prioritises exceptions, highlights source regions, explains validation failures, preserves corrections, and keeps batch state visible.
Illustrative reviewer cockpit: extracted records stay connected to source evidence and exceptions.
Evidence in context
The original page remains available beside the proposed structured value.
Reasoned queue
Priority reflects consequence, confidence-relevant signals, age, and service commitments.
Correction capture
Reviewer changes become traceable quality evidence, subject to retention policy.
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05 · Handover
Document populations change; the operating model must expect it.
Handover covers corpus ownership, exception operations, schema versioning, downstream compatibility, quality review, and release tests for new document classes or layouts.
Corpus stewardship
Representative samples are versioned, access-controlled, and reviewed for coverage.
Schema governance
Record changes include consumer impact, migration handling, and acceptance evidence.
Exception learning
Recurring corrections become backlog items rather than silent manual work.
The next step is a bounded working session: one workflow, its evidence sources, the decision owner, and the conditions under which the system must stop or hand over.
Assess a document workflow