How Hathr.AI delivers multi-tenant agentic RAG for regulated US government workloads with Amazon S3 Vectors

This article describes how Hathr.AI, a secure AI platform for regulated industries, migrated vector search from Amazon DocumentDB to Amazon S3 Vectors to support high-churn multi-tenant workloads in AWS GovCloud (US).

• Migrated from DocumentDB to S3 Vectors for per-customer index isolation and compliance boundary enforcement
• Achieved sub-100ms average query latency with zero failed requests since cutover
• Reduced Amazon DocumentDB cluster size to one-quarter of previous peak, lowering costs by 4x
• Implemented agentic RAG using Strands Agents on ECS with Fargate, enabling multi-step retrieval per query
• Used AWS Step Functions and Lambda for document ingestion with per-stage retry policies
• Enforced tenant isolation through per-customer indexes and IAM scoping, not metadata filters alone
• Addressed AWS GovCloud differences: ARN partitions, FIPS endpoints, and compliance program verification

S3 Vectors enabled Hathr.AI to scale vector search for regulated customers while maintaining compliance boundaries and improving query performance for agentic retrieval patterns.