[ LEGAL TECH ]
AI-powered semantic search platform that helps law firms find relevant documents across 1M+ files in under 100 milliseconds.
Contract #2024-0847 - Termination Clause
Section 4.2: Termination clause requires 90-day written notice with cure period for material breach...
Master Services Agreement Template v3
Article 12: Standard termination provisions including force majeure and convenience clauses...
Employment Contract - Senior Associate
Appendix B: Termination for cause definition and precedent review requirements per firm policy...
<100ms
Search response time
1M+
Documents indexed
$220k
Annual savings
85%
Time saved on research
[ THE CHALLENGE ]
A mid-size law firm with 15 years of accumulated documents was losing billable hours to manual searches.
Associates spent 2–3 hours per research task sifting through folder structures and outdated naming conventions to find relevant precedents.
Documents lived across network drives, email attachments, and legacy systems. No unified search meant duplicate work and missed insights.
Traditional search couldn't understand legal concepts. Searching "breach of contract" wouldn't find documents discussing "material default" or "failure to perform."
Client confidentiality required on-premise deployment with role-based access. Public cloud AI solutions were not an option.
[ THE SOLUTION ]
We built LegalSearch as a self-hosted knowledge management platform that uses vector embeddings to understand the meaning behind legal queries, not just keywords. It's one of several platforms delivered through our legal tech development services.
Documents are converted to high-dimensional vectors that capture legal concepts and relationships, enabling conceptual search across the entire corpus.
Automated ingestion pipeline that extracts text, metadata, and structure from any document format while preserving legal context.
Self-hosted deployment with granular permissions ensuring client matters stay confidential and audit-ready.
[ ARCHITECTURE ]
Documents
PDF, DOCX, Email
Processing
OCR + Extraction
Embeddings
Vector Generation
Milvus
Vector Database
Search API
<100ms Response
[ RESULTS ]
Associates now complete research tasks in 20–30 minutes instead of 2–3 hours. At average billing rates, this translates to over $220,000 in recovered capacity annually.
Vector similarity search across 1M+ documents returns relevant results in under 100 milliseconds, even with complex natural language queries.
What previously took hours of manual folder browsing now takes minutes. Semantic understanding surfaces relevant documents that keyword search would miss.
The firm's entire document history — 15 years of contracts, briefs, memos, and correspondence — is now searchable through a single interface.
"Our legal team was spending hours searching through documents. Now queries return results in 100ms even with over 1 million documents. That's $220k+ recovered per year in billable time that was previously lost to manual research."
Managing Partner
Mid-Size Law Firm
[ GET STARTED ]
Let's discuss how AI-powered knowledge management can help your firm recover billable hours and surface insights faster.
