Document Intelligence Platform

The problem

Critical knowledge lives in documents: contracts, expert reports, filings, decisions. They are long, inconsistently structured and occasionally contradict each other. Anyone who wants to base decisions on them needs reliable, structured data rather than a summary – and must be able to say where every single value came from.

The approach

A language model on its own produces plausible answers, not certainty. The platform therefore treats the model as one component among several:

• Layout understanding: pages, sections, tables and footnotes are recognised as structure before anything is extracted.
• AI extraction: the model proposes values – each with its location in the original document.
• Deterministic rules: domain rules check what the model delivered: formats, totals, dependencies, plausibility.
• Structured knowledge: values land in a domain model, not in a blob of text.
• Conflict detection: when two documents or two passages disagree, the conflict is surfaced instead of silently resolved.
• Human clarification: what the machine cannot decide with confidence goes to a domain expert as a concrete question – with both sources side by side.

The outcome

Data you can trust: every value with its provenance, every uncertainty named, every human decision recorded. Results are available through an API and flow into existing systems.

The hard part is not the extraction – it is the domain model and the handling of contradictions. That is where the work is.