Document Intelligence & Multimodal AI

Document intelligence extracts and reasons across text, layout, tables, images and handwriting while retaining links to page-level evidence and downstream workflow controls.

Matchpoint approaches AI as an operating capability with accountable owners, explicit decision gates, measurable acceptance criteria, documented architecture and a practical path from discovery to production.

Complex documents encode meaning through layout as well as language. Tables, columns, headers, footnotes, handwriting, diagrams, signatures and neighbouring fields can change the interpretation of the text. We model this structure explicitly instead of relying on a flattened text stream.

The solution design may combine OCR, layout models, vision-language models, deterministic extraction, document taxonomies and evidence-linked review. The required output can range from structured fields and reconciliations to document comparison, classification, summarisation and question answering across a corpus.

The acceptance suite represents the document variation found in production: different templates, scans, tables, handwriting, missing pages, low-quality images, conflicting fields and long documents. Accuracy, coverage, evidence location, confidence and exception routing are measured separately so users know which results can flow through and which need review.