RAG System for Insurance Documentation
Mid-Sized Insurance Broker
-75%
Research Time
From 2.5h to 38min per day
94%
Response Accuracy
Previously 67%
340%
ROI
Payback in 4 months
45,000+
Documents Processed
Policies, reports, guidelines
Challenge
Employees spent 2-3 hours daily manually searching through fragmented document repositories. Policies, claims reports, and compliance documents were scattered across multiple systems.
Outcome
A custom RAG system with semantic search reduced research time by 75% and significantly improved the accuracy of client advisory.
Starting Point
A mid-sized insurance broker with over 200 employees had a concrete problem: advisors spent an average of 2.5 hours a day searching for documents they needed to do their work.
Policies, claims reports, compliance documents, and internal guidelines were scattered across SharePoint, a legacy DMS, and local network drives. Full-text search returned too many irrelevant results, and a lot of institutional knowledge lived in people's heads rather than in any searchable system.
Solution
We built a custom RAG system (Retrieval-Augmented Generation) with four components:
- Document pipeline: automated processing and chunking of PDFs, Word documents, and emails
- Vector database: semantic indexing of all 45,000+ documents with domain-specific embeddings
- Chat interface: natural language queries with source citations and confidence scores
- Access control: role-based permissions aligned with existing compliance requirements
Result
The system went live in 8 weeks. Research time dropped by 75%, response accuracy climbed from 67% to 94%, and the project hit 340% ROI within 4 months.
AI Agent & RAG Developer
AI Agent & RAG Developer with 10+ years of software engineering experience. Specialized in intelligent AI solutions for enterprises in the DACH & Nordic region.