Executive Summary
A leading global retailer managing vast datasets across multiple business functions needed a more efficient way to retrieve insights from their databases. Analysts and business users relied heavily on technical teams to write, optimize, and execute SQL queries, creating bottlenecks and slowing down decision-making.
To address this, Factspan developed a GenAI-powered chatbot designed to bridge the gap between natural language queries and complex SQL execution. By leveraging LLM-driven schema enrichment, retrieval-augmented generation (RAG), and real-time visualization capabilities, the solution empowered users to ask questions in plain English and receive accurate, optimized SQL queries with data outputs and visualizations. The solution significantly reduced dependency on SQL experts, accelerated data retrieval, and enhanced self-service analytics.
About the Client
A major retail enterprise with thousands of SKUs, customer transactions, and supply chain touchpoints. The company operates on a data-driven strategy but faced challenges in making SQL-driven insights accessible to non-technical teams across merchandising, finance, and operations.
Business Challenge
Users without SQL proficiency struggled to extract insights, leading to frequent dependencies on technical teams. The lack of contextual query handling and visualization support further limited accessibility and decision-making speed.
Our Solution
Factspan developed an AI-powered SQL assistant that enables non-technical users to retrieve insights from databases using natural language. The solution follows a structured approach to ensure accurate SQL generation and execution.
- Schema Understanding: The system processes database schemas and metadata, leveraging semantic search to understand table relationships and field mappings.
- Contextual Querying: When a user submits a query in natural language, LLM-driven schema enrichment adds context, ensuring accurate table and field selection.
- Optimized SQL Generation: The solution applies retrieval-augmented generation (RAG) to generate optimized SQL queries. These queries undergo validation and optimization before execution, ensuring accuracy and efficiency.
- Interactive Visualization: The AI then fetches the results and formats them into structured tables and interactive visualizations for better data interpretation.
- Conversational Flexibility: The system retains multi-turn conversation history, allowing users to refine or expand their queries without starting over.
By following this structured approach, the solution provides a scalable, efficient, and user-friendly method for real-time SQL querying, significantly improving decision-making and reducing dependency on SQL experts.
Business Impact:
- 50% faster data retrieval with AI-assisted SQL generation
- 40% reduction in reliance on SQL experts, empowering business users
- 60% improvement in decision-making speed with real-time insights & visualizations
- 100+ active users leveraging the AI-powered query assistant across the enterprise