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150M judgements.
Surfaced by meaning.

A Language Intelligence deployment for a leading litigation practice. Semantic search across case law, grounded in privileged infrastructure.

Legal-BERTQdrantLLM fine-tuneHITL queueAudit log
CLIENT CONTEXT

A senior litigation practice with case volumes measured in hundreds per week. Research quality is their product. Turnaround time defines their client experience.

THE PROBLEM

Keyword search across 150M+ judgements returned noise. Manual precedent research took senior associates days per case. Every case file is privileged — no cloud AI service was contractually or ethically permissible.

How We Approached It

Four phases.
Each one auditable.

01
Corpus ingestion
150M judgements chunked, normalised, and embedded in a domain-tuned legal embedding model. Stored in an on-premise vector database.
02
Fact-to-precedent
Lawyer submits case facts. The system retrieves semantically closest precedents, with relevance scoring that accounts for jurisdiction, court hierarchy, and recency.
03
Citation validation
Retrieved precedents rendered with full citation trail, headnote summary, and direct document link. Lawyer validates before use.
04
HITL feedback loop
Every lawyer decision (used / not used / partially relevant) feeds back into retrieval tuning. Accuracy improves with every case.
4×
Faster first-pass research
150M
Judgements indexed
100%
Privileged and on-premise

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