Insurance Knowledge Retrieval Solution
Executive Summary
A Retrieval-Augmented Generation platform built for a major insurance provider, enabling intelligent knowledge retrieval across underwriting guidelines, claims procedures, and policy documents using Amazon Bedrock and OpenSearch to serve 3,000+ insurance professionals with contextually accurate answers.
Business Problem
The insurance organization maintained decades of underwriting manuals, claims processing guidelines, regulatory filings, and policy documentation spread across legacy document management systems, internal wikis, and shared drives. Claims adjusters and underwriters spent an average of 35 minutes per case searching for relevant precedents and guidelines. Inconsistent interpretation of policy language led to a 12% variance in claims decisions across regional offices, creating compliance risk and customer dissatisfaction.
Architecture Overview
The solution implements a RAG architecture with a multi-stage retrieval pipeline optimized for insurance domain terminology. Documents are ingested through a preprocessing layer that handles PDF extraction, table parsing, and semantic chunking tailored to insurance document structures. Embeddings are generated via Amazon Bedrock and stored in Amazon OpenSearch for hybrid vector and keyword search. A query understanding module classifies incoming questions by insurance domain (underwriting, claims, compliance) and routes them to domain-specific retrieval indices. Retrieved context is passed to a large language model for grounded answer generation with citation tracking.
Solution Design
The platform is deployed on AWS using Amazon ECS for containerized microservices orchestration. The ingestion pipeline processes documents from S3, applies format-specific parsers, and generates embeddings through Amazon Bedrock. Processed embeddings and metadata are stored in Amazon OpenSearch with custom index mappings optimized for insurance terminology. The retrieval API runs on ECS with auto-scaling based on query volume. Session context and user interaction history are maintained in Amazon DynamoDB for personalized retrieval and audit trails. Langfuse provides end-to-end observability for prompt performance, retrieval quality metrics, and cost tracking. The entire deployment pipeline is managed through GitHub Actions with automated testing and staged rollouts.
Architecture Diagrams
Technology Stack
Security
Insurance data handling requires strict controls around personally identifiable information (PII) and protected health information (PHI) contained within claims documents. The platform implements document-level access controls mapped to organizational roles, ensuring underwriters only access documents within their authorized lines of business. All data is encrypted at rest using AWS KMS customer-managed keys and in transit via TLS 1.3. PII detection runs during ingestion to redact sensitive policyholder information before embedding generation. Comprehensive audit logging tracks every document access and query for regulatory compliance. The system adheres to insurance industry data handling standards and supports SOC 2 Type II compliance requirements.
Scalability
The platform handles 50,000+ document pages across underwriting manuals, claims guidelines, and regulatory filings. ECS auto-scaling adjusts retrieval service capacity based on query volume, supporting peak loads during catastrophe events when claims volume spikes 5x above baseline. OpenSearch indices are partitioned by insurance line of business with cross-index federation for complex queries spanning multiple domains. DynamoDB on-demand capacity handles variable session loads without provisioning overhead. The ingestion pipeline processes batch document updates nightly with real-time indexing for priority regulatory changes.
Performance Metrics
- Average query-to-answer latency of 2.8 seconds at P95
- Retrieval relevance score improved from 38% to 89% based on adjuster feedback
- 35-minute average search time per case reduced to under 90 seconds
- 12% claims decision variance across offices reduced to 3%
- 3,000+ daily active users across underwriting and claims departments
- 99.7% platform availability over 12-month period
- 40% reduction in escalations due to inconsistent policy interpretation
Challenges
- Insurance documents contain highly specialized terminology and nested clause references that generic chunking strategies fragment incorrectly, requiring custom semantic boundary detection aligned to policy section structures
- Legacy PDF documents with scanned content and complex table layouts required OCR integration with post-processing heuristics to maintain structural fidelity during extraction
- Regulatory updates require immediate reflection in retrieval results while maintaining version history for audit purposes, creating tension between freshness and traceability
- Balancing retrieval precision for specific clause lookups versus recall for broad precedent searches across different user intent patterns
- Ensuring generated answers include proper citations to source documents with paragraph-level granularity for compliance audit trails
Lessons Learned
- Domain-specific chunking aligned to insurance document structures (sections, clauses, endorsements) dramatically outperformed generic fixed-size chunking, improving retrieval precision by 34%
- Langfuse observability was essential for identifying prompt degradation patterns and retrieval quality drift over time, enabling proactive tuning before user-reported issues
- Hybrid search combining vector similarity with BM25 keyword matching proved critical for insurance terminology where exact clause numbers and regulatory references must be matched precisely
- User feedback loops integrated directly into the retrieval interface accelerated quality improvements — adjusters rating answer helpfulness provided high-signal training data for re-ranking models
- Separating retrieval indices by insurance domain (underwriting, claims, compliance) reduced cross-domain noise and improved answer specificity without sacrificing coverage
Business Outcomes
- 35-minute average case research time reduced to 90 seconds, saving an estimated $4.2M annually in operational efficiency
- Claims decision consistency improved from 88% to 97% across regional offices
- Regulatory compliance audit preparation time reduced by 60% through comprehensive citation tracking
- New adjuster onboarding time reduced by 40% with instant access to institutional knowledge and precedent cases
- Customer satisfaction scores for claims resolution improved by 22% due to faster and more consistent outcomes
Questions
FAQ
How does the system handle conflicting guidance across different policy versions?
The platform maintains temporal versioning of all documents with effective date metadata. When conflicting guidance is detected across document versions, the system prioritizes the most recently effective version while surfacing historical context. Users see clear indicators of document currency and can explicitly request historical guidance for retrospective claims analysis.
What is the approach to handling multi-modal content like claims photos and diagrams?
Currently the system focuses on textual content extraction from documents including embedded tables and structured forms. Diagrams and images are indexed with their surrounding context and captions. A future roadmap item includes multi-modal embedding generation for claims photography and engineering diagrams used in property insurance assessments.
How do you measure and maintain retrieval quality over time?
We use a combination of automated metrics including NDCG and MRR evaluated against a curated test set of insurance domain queries, plus production signals from user feedback ratings and answer acceptance patterns. Langfuse dashboards track retrieval quality trends weekly, triggering automated alerts when metrics drift below thresholds. Monthly quality reviews drive embedding model updates and retrieval strategy refinements.
Future Roadmap
Planned enhancements include multi-modal retrieval for claims photography and property assessment documents, automated regulatory change detection with proactive notification to affected teams, integration with underwriting workflow systems for in-context knowledge surfacing, and expansion to support multilingual policy documents for international insurance operations.