Building RAG-powered agents doesn’t have to come with sky-high vector database costs. In this session, learn how to create an
Amazon Bedrock Agent backed by Amazon S3 Vectors, a new low-cost vector storage option designed to analyze research
papers efficiently and affordably. We’ll walk through:

1. Using a custom parsing workflow with AWS Lambda to process research papers

2. Setting up a Bedrock Knowledge Base using S3 Vectors for scalable semantic retrieval

3. Building a Bedrock Agent that can reason over parsed research content

4. Managing retrieval permissions with metadata filtering for fine-grained access control

Whether you’re building research assistants, internal copilots, or large document-analysis pipelines, this talk shows how to
extract insights at scale without the cost of traditional vector databases.

By the end of this talk, attendees will understand how to design a low-cost retrieval pipeline, know how to combine Bedrock
Agents with S3 Vectors effectively, and be equipped to build their own research analysis assistant on AWS.