An AI Solutions Engineer at Prescott Data designed an enterprise-grade RAG system using AWS Bedrock, Pinecone, and Neo4j to solve complex document search challenges for TradeMark Africa. The solution addresses difficult-to-navigate policy documents by combining vector similarity search with knowledge graph reasoning. The architecture utilizes AWS Textract for multi-column PDF extraction, token-aware chunking to respect AWS Bedrock context limits, and a dual retrieval strategy merging Pinecone vectors with Neo4j graph relationships. DynamoDB manages session history for conversational context, while DeepSeek-R1 generates responses.

This approach delivers 87% retrieval accuracy and 2.3-second response times, significantly improving employee access to internal data. It demonstrates how hybrid retrieval systems outperform pure vector search, particularly for entities requiring precise terminology and relationship mapping. The deployment uses AWS Lambda and React, proving cost-effective at approximately $75/month.