AI agents unifying structured and unstructured data: Transforming support analytics and beyond with Amazon Q Plugins

As organizations seek to derive greater value from their AWS Support data, operational teams are looking for ways to transform raw support cases and health events into actionable insights. While traditional analytics tools can provide basic reporting capabilities, teams need more sophisticated solutions that can understand and process natural language queries about their operational data. Retrieval-Augmented Generation (RAG) architecture forms the foundation for optimizing large language model outputs by referencing authoritative knowledge bases outside of their training data before generating responses. This architecture uses the power of semantic search and information retrieval capabilities to enhance accuracy.

In our previous blog post, Derive meaningful and actionable operational insights from AWS Using Amazon Q Business, we introduced a RAG-based solution using Amazon Q Business. However, while this approach excels at semantic search, it can face challenges with precise numerical analysis and aggregations. In this post, we address these limitations by showing how ...