From SQL to Small Talk with Amazon Bedrock

What if a CEO could ask "What was our hold percentage by channel last quarter?" in plain language — in Serbian or English — and get an accurate answer in seconds, without writing a line of SQL or waiting on a data team?

This is Echo: a 100% AWS-native conversational analytics platform that lets business users query a Redshift data warehouse using natural language. In this session, I'll walk through the real architecture and engineering decisions behind building production-grade text-to-SQL with Amazon Bedrock Agents.

We'll cover:
Architecture — how the pieces fit together to turn a plain-language question into a trustworthy answer, and the design decisions that matter most along the way.
The Knowledge Base — why it sits at the heart of the system, and how the way you structure and feed it drives everything downstream.
Response quality — what it takes to get accurate, reliable answers, how to measure quality rather than guess at it, and the optimizations that move the needle.

Attendees will leave with a concrete blueprint for building trustworthy, domain-specific conversational analytics on AWS — including the architectural rules, eval discipline, and cost optimizations that separate a demo from a deployable product.

Key Takeaways:
- How to architect a Bedrock Agent for natural-language querying of a data warehouse.
- Why the knowledge base — not prompt engineering — is the real lever, and how to measure quality.
- Practical cost and accuracy optimizations from a real production build.

Transcript