Analytics Assistant (NL-to-SQL)
The Challenge
The client's analytics team was the bottleneck for the entire organization. Business users across clinical and operational departments had to submit SQL query requests, wait days for results, and often go back-and-forth to refine requirements. Non-technical stakeholders had zero direct access to the data warehouse — creating delays in decisions that affected patient care and operational efficiency.
Solution & Approach
We engineered an intelligent assistant that lets anyone ask data questions in plain English. The system uses AWS Bedrock foundation models for natural-language understanding, with a schema-aware query planner that maps user intent to the correct tables, columns, and join paths. Generated SQL is validated against allowed schemas before execution — preventing unsafe or expensive queries. Deployed on ECS for elastic scaling, it handles concurrent users with sub-second query generation.
- Schema-aware query planning — Maps natural language to the correct warehouse tables, columns, and join paths automatically.
- Guardrails & validation — Generated SQL is validated against allowed schemas before execution, preventing unsafe or expensive queries.
- Conversational context — Supports follow-up questions that refine or drill into previous results without starting over.
- Knowledge transfer — The client’s engineering team was trained to maintain, monitor, and extend the system independently.
Results & Impact
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