How we built an internal RAG-based search engine for a leading law firm, enabling instant access to millions of legal documents and case-relevant information.
Faster Access
Documents Indexed
Months Build Time
The law firm was drowning in millions of legal documents stored across different systems. Lawyers spent hours manually searching for case-relevant information, leading to inefficient billable time and missed crucial precedents.
We built a sophisticated RAG-based search engine that indexes millions of legal documents, enabling natural language queries and providing contextually relevant results with source citations and legal precedents.
From manual document searches to AI-powered instant access to case-relevant legal information
We analyzed customer behavioral data, usage patterns, service history, and demographic information to identify churn indicators and patterns across different customer segments.
We built advanced ML models using ensemble methods to predict customer churn probability, integrated with automated retention campaign triggers and personalized offer generation systems.
The churn prediction system achieved high accuracy and reduced monthly churn. The company now proactively retains high-value customers with personalized offers before they consider switching.
The RAG-based search engine transformed legal research and document discovery
Dramatically reduced time to find case-relevant information
Comprehensive legal document repository searchable instantly
Highly accurate semantic search results with legal context
Increased lawyer productivity and billable time efficiency
Let's discuss how AI-powered RAG systems can revolutionize your knowledge discovery and research capabilities.
