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Enterprise RAG Platform for Global Insurance Firm

Designed and built a production-grade Retrieval-Augmented Generation platform with enterprise security, hybrid data connectors, and multi-tenant guardrails.

RAGAWSSecurity Insurance
72%
Faster Information Retrieval
45%
Reduction in Support Load
99.9%
Platform Uptime
40+
Enterprise Data Connectors
10K+
Daily Active Queries

Business Problem

Enterprise data was scattered across multiple systems — docs, wikis, tickets, databases, and SaaS apps. Employees struggled to find accurate, up-to-date information: existing search was keyword-based, slow, and not context-aware. The goal was a secure, accurate, and scalable AI assistant that could understand enterprise data and provide trustworthy answers with citations.

Architecture Overview

A secure, modular, and observable RAG platform with hybrid data ingestion, advanced retrieval, guardrails, and LLM orchestration — deployed in a private VPC with encryption at rest and in transit.

  1. 1 Data connectors + document processing
  2. 2 Chunking + metadata extraction
  3. 3 Storage & indexing (S3, OpenSearch, DynamoDB)
  4. 4 Query understanding + hybrid retrieval
  5. 5 Re-ranking + context building
  6. 6 LLM with guardrails → cited answers

All components are deployed with private networking, scoped IAM roles, and encryption at rest and in transit.

Key Design Decisions

  • Hybrid retrieval with keyword + vector search. Dense retrieval captures meaning; sparse retrieval preserves identifiers and exact domain language — fused for higher recall and precision.
  • Re-ranking with a cross-encoder model. A reranking stage over fused candidates improves answer relevance for roughly 100ms of latency.
  • PII detection and redaction before the LLM. Sensitive fields are masked before context leaves the trust boundary — toward providers and into logs.
  • Tenant isolation with data partitions and scoped IAM roles. Permissions are enforced in the retrieval layer, before ranking, so unauthorized content never reaches the model.
  • Streaming responses with citations. Every answer carries source citations for transparency; low evidence coverage produces an explicit no-answer.

Technology Stack

AWS BedrockOpenSearchLangChainFastAPIDynamoDBPythonTypeScript

My Role

  • Architected the end-to-end RAG platform.
  • Designed data ingestion pipelines and indexing strategy.
  • Implemented retrieval, re-ranking, and guardrails.
  • Built scalable backend services and APIs.
  • Integrated observability, monitoring, and alerts.
  • Collaborated with the security team for compliance.

Measurable Impact

  • 72% Faster Information Retrieval
  • 45% Reduction in Support Load
  • 99.9% Platform Uptime
  • 40+ Enterprise Data Connectors
  • 10K+ Daily Active Queries

Challenges & How I Solved Them

  • Heterogeneous data: Built robust parsers and metadata extractors for diverse formats — PDFs, wikis, tickets, tables.
  • Retrieval quality: Combined hybrid search with re-ranking and query expansion to lift accuracy on exact terminology.
  • Data security: Implemented PII redaction and strict access controls at chunk level.
  • Latency optimization: Caching, async pipelines, and optimized indexing kept interactive latency within budget.

Lessons Learned

  • Quality data ingestion is the foundation of accurate AI responses.
  • Hybrid search consistently outperforms single-modality retrieval.
  • Guardrails and observability are critical for enterprise trust and adoption.
  • Iterative evaluation with real users drives meaningful improvements.