Document & Knowledge Intelligence

Extraction is not verification.

Document AI is often measured by how many fields it extracts. In regulated and trade workflows the harder questions are whether each value can be traced to its source, whether documents agree with one another, and what happens when they do not. A pipeline that cannot show its evidence moves the review burden rather than reducing it.

The design and engineering work

• Extraction and verification specifications for the document types and fields that matter to the decision
• A provenance model linking every value to its document, page and passage
• Cross-document comparison rules and discrepancy classification
• Exception workflows that route findings, with context, to the right reviewer
• Evaluation dataset design and acceptance criteria
• Retrieval over enterprise knowledge that returns citations, not just answers

Illustrative artefacts

• Extraction and verification specification
• Provenance model
• Exception workflow
• Evaluation dataset design

Examples of what an engagement can produce. Scope, deliverables and responsibilities are agreed in writing for each engagement; outcomes are not guaranteed.

Where this applies

Typical applications by sector, not descriptions of completed engagements.

Banking & Financial Services

Review of credit, policy and onboarding documentation where each finding must cite its source.

International Trade, Logistics & Supply Chain

Invoices, transport documents and certificates compared across parties for quantity, party and date consistency.

Cross-Industry Enterprise AI

Contract and policy collections queried with traceable answers.

Boundaries and human review

Automated checks support, and do not replace, the examiners and reviewers responsible for legally or financially significant determinations. Accuracy targets and acceptable error rates are agreed with the client and measured rather than assumed.