How Precisely transforms user experience with AI agents using Amazon S3 Vectors

This article describes how Precisely leverages Amazon S3 Vectors to enhance its Data Integrity Suite with conversational AI agents for natural language search and intelligent recommendations.

• S3 Vectors enables tenant-aware indexing with thousands of independent indexes in a single bucket, ensuring data isolation and linear cost scaling
• Conversational search uses LLMs to interpret natural language queries and generate vector searches with metadata filters for precise asset discovery
• Intelligent recommendations proactively suggest relevant data quality rules and metadata curation based on user context and conversation flow
• Vector ingestion pipeline processes catalog changes via Amazon MSK and generates embeddings using Amazon Titan Text Embeddings V2
• Query path routes user requests through conversational agents that leverage S3 Vectors similarity search with access control metadata filtering
• Benchmark testing shows hundreds of queries per second with low hundreds of milliseconds latency on 50 million vector indexes
• Cost modeling for 87 tenants with 80 million assets projects approximately $1,000 monthly cost, with query processing dominating expenses

S3 Vectors provides a cost-efficient, scalable foundation for conversational search platforms by combining natural language processing with precise vector retrieval and consistent performance.