NarrateAI provides on-demand conversational BI for SMGS leaders using Amazon Bedrock AgentCore

NarrateAI gives SMGS (Sales, Marketing and Global Services) leaders immediate, conversational access to business performance data, eliminating the delays and manual work that previously separated executives and field teams from operational insights. Accessible through the Amazon Quick conversational interface, the assistant returns context — aware, persona — based answers so leaders can act without reconciling dashboards or waiting for bespoke reports. The system uses a two-layer architecture: an automated batch narrative generation layer produces role-tailored narratives, and a real-time interaction layer serves those narratives and answers natural — language queries. This split lets heavy data processing run offline while preserving fast, interactive responses for end users, keeping conversational latency low and outputs targeted to specific roles.

AWS built the solution to address known BI constraints — time-intensive manual preparation for reviews, fragmented metrics spread across systems, and dashboards that require specialist knowledge. The conversational approach unifies views across hierarchies and datasets, reduces dependence on reporting teams and accelerates decision — making by delivering ready — to-use narratives rather than raw charts or queries. Amazon Bedrock AgentCore provided the orchestration and runtime for NarrateAI, sparing teams from building custom orchestration infrastructure. AgentCore offered serverless execution, built — in authentication, memory management, integration with foundation models, native Amazon CloudWatch observability and automated session management. Those capabilities helped compress deployment timelines from months to weeks while preserving production — grade security and monitoring.

The batch narrative pipeline is a three — stage process. First, configuration — driven, parameterized SQL templates extract structured data from Amazon Redshift with role-and permission — aware filtering and multi — level breakdowns. Second, AWS Lambda transforms the extracted rows into structured JSON using section — type logic — objects, arrays, breakdowns and containers — with explicit field mappings and hierarchical organization. Third, the system applies hierarchical, domain — aware chunking for large datasets and stores each user’s narrative as a text file for fast retrieval.

Implementation also includes specialized AI agents for intelligent routing and validation that ensure queries reach the correct data sources and that responses meet quality checks before delivery. For builders, the practical patterns are clear: separate heavy batch processing from fast real-time responses; use parameterized SQL and Lambda — based transforms to keep narratives auditable; employ template rendering (for example, Jinja) for readable output; and leverage AgentCore for serverless orchestration and integrated observability so deployments can iterate quickly and securely.