Executive Summary
Enterprises dealing with large databases often struggle to extract insights efficiently, requiring technical expertise to write complex SQL queries. A global organization faced challenges in enabling business users to query databases without SQL knowledge, leading to bottlenecks in decision-making.
Factspan implemented an advanced Text-to-SQL solution, leveraging LLMs, vector databases, and intelligent query decomposition to enable seamless natural language querying. By integrating database metadata, table relationships, and semantic search, the solution empowered users to extract meaningful insights in real time without SQL expertise.
About the Client
The client is a multinational enterprise managing vast datasets across multiple departments. They needed a self-service analytics solution to enable business teams to query databases in natural language while ensuring accuracy in SQL execution.
Business Challenge
Business users relied heavily on data teams to generate SQL queries, causing delays in decision-making. Manually identifying relevant tables and relationships made querying inefficient, leading to incomplete or inaccurate insights. A scalable, automated solution was needed to simplify data access while ensuring accuracy.
Our Solution
Factspan developed a Text-to-SQL system that allows users to query databases using natural language while ensuring accuracy and efficiency.
The solution first processes database metadata and schema, enhancing table relationships and business metrics using LLMs and vector databases. When a user asks a question, semantic search and retrieval-augmented generation (RAG) identify the relevant tables and generate an optimized SQL query.
Before execution, the query is validated and refined to ensure correctness and efficiency. The system then retrieves the required data, formatting results into interactive tables and visual insights. Additionally, context retention allows for multi-turn interactions, enabling users to refine their queries without starting over.
By following this structured approach, the solution significantly reduces the time needed to extract insights, enhances data accessibility, and minimizes dependency on SQL experts.
Business Impact:
- 60% faster insights by eliminating manual SQL writing.
- 40% reduction in dependency on data teams for report generation.
- 35% increase in query accuracy with automated SQL validation.
- Scalable framework supporting multiple databases and functions.
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