This article presents a comprehensive engineering practice for building a data analysis robot that transforms natural language queries into SQL and then into visual charts. The pipeline covers NL2SQL model integration, query execution against databases, and dynamic chart rendering. Key engineering decisions include handling ambiguous queries, optimizing SQL generation for performance, and designing a flexible visualization layer. This approach is highly relevant for teams building internal analytics tools or AI-powered BI platforms, as it demonstrates a production-ready architecture that bridges natural language understanding with data visualization. The article provides insights into the trade-offs between accuracy and latency, and how to structure the system for maintainability and scalability.
A practical engineering walkthrough of a full NL2SQL-to-visualization pipeline for data analysis robots.