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Connected leads, clients, matters, milestones, tasks, contracts, and receivables in one operating model.
Case study · Delivery evidence
Legal AI that returns every conclusion to its evidence
A legal operations and dossier-analysis system designed around document coverage, page-level citations, matter permissions, and accountable lawyer review.
By Onyx Devs Lab · Published · Updated
The operating challenge
Law-firm operations and large case files created two different information problems: fragmented business status and high-risk evidence retrieval across very large dossiers. A fluent answer was not enough; lawyers needed coverage, provenance, and explicit review.
Designed intervention
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Tracked ingestion by file, page, and text chunk so missing or failed material remained visible.
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Combined keyword and vector retrieval with file, page, source text, and confidence coordinates.
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Applied matter-level access, audit trails, exception queues, and lawyer approval before formal output.
Validation snapshot
These figures separate delivered scope from pilot validation. Each metric retains its measurement definition so product coverage is not mistaken for business impact.
operating chains unified
Law-firm operations and large-dossier analysis
evidence coordinates
File, page, source text, and confidence
citation-location accuracy
Lawyer sample matched citations to the correct material and page
priority-material recall
Lawyer-labelled key facts entered the candidate set
case initial-review cycle
Effective work time to the first issue list; 18.5 h before
Cross-scenario architecture guide
Compare the retail decision layer, accounting multi-agent controls, and legal-AI provenance in one public guide while retaining each case’s evidence boundary.
Evidence boundary
The validation figures come from a controlled project sample and lawyer-labelled reference set. They demonstrate the tested workflow, not legal accuracy for every jurisdiction or matter.