Project Overview
A UK mid-market law firm with 180 fee earners spent an average of 3.2 hours per transaction on document review...
Technology Stack
Compliance & Standards
The Challenge
A UK mid-market law firm with 180 fee earners spent an average of 3.2 hours per transaction on document review — reading Land Registry title registers, mortgage offers, planning consents, and search results to identify relevant clauses, conditions, and risks. The senior partner estimated this represented £2.4M per year in fee earner time that could be better spent on client-facing work. Legal Professional Privilege (LPP) constraints meant client documents could not be sent to any external AI service without client consent. Budget: £120,000.
Our Approach
LPP requirement
- no client document data can leave the firm's infrastructure.
- ClickMasters deployed a self-hosted
LLM stack
- Ollama running Llama 3.1 70B on the firm's on-premises GPU server (Nvidia A100).
- All inference runs locally — no data leaves the firm's network.
UK GDPR
on-premises processing eliminates the Article 28 DPA requirement for the AI model itself.
PDF ingestion
PyMuPDF for structured extraction, then chunking (512 token chunks with 50-token overlap for context continuity).
Embeddings
local Nomic Embed Text model (also on-premises).
Vector store
Chroma DB (self-hosted).
RAG pipeline
LangChain orchestrating retrieval from vector store + Llama 3.1 70B generation.
Output
- structured JSON per document type.
- LegalTech-
Specific Prompting
Document-type-specific prompt templates: Land Registry title register (identify charges, covenants, easements, restrictions), planning search (identify restrictions, conditions, planning history), mortgage offer (identify conditions precedent, terms).
Confidence scoring
each extracted clause includes a confidence score — low-confidence extractions flagged for fee earner review.
SRA Compliance and Auditability
Fee earner must review and approve all AI-generated summaries before use.
Audit trail
every AI summary linked to the source document, extraction timestamp, and approving fee earner.
SRA competency
AI is a tool assisting review, not replacing it — regulatory compliance maintained.
The Results
Deployed at 12 weeks, £108,000 — under budget.
Document review time: 3.2 hours → 42 minutes average (87% reduction).
Fee earner capacity: equivalent of 4.2 additional fee earners without headcount.
LPP: zero client data left the firm's infrastructure.
SRA audit: commended for auditability of AI use.
Estimated annual value: £1.9M fee earner time released for client-facing work.
“The on-premises requirement was non-negotiable for LPP compliance. Every other vendor wanted to send our client documents to a cloud API. ClickMasters built it entirely on our own infrastructure. The 87% reduction in review time is genuinely transformative for our practice economics." — Managing Partner, UK Mid-Market Law Firm (name withheld)”
Project Details
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