Management Summary
Until now, accessing databases and data warehouses at the international financial institution required knowledge of SQL. HMS developed a text-to-SQL solution that translates natural-language questions into SQL queries and displays the results via an interactive interface. For KPI calculations, the LLM can call deterministic functions that deliver precise and reproducible results.
Project summary
International Financial Institution
Industry: Banking
Project Start: 06/2023
Focus: Generative & Agentic AI
Use Case: Data & Analytics Assistants
Project Goal
Enable database queries without requiring SQL knowledge
Key Core Functions
- Translation of quantitative business questions into executable SQL queries
- Modular separation of the chatbot interface and the LLM backend
- Visualization, editing, and export of the query results
Tech Stack
The Starting Point
The company used data from relational databases and data warehouses for KPI calculations, reports, and exploratory analyses. However, direct access was limited to a small group of employees who were proficient in both the data model and SQL.
Quantitative inquiries and ad-hoc analyses therefore required the assistance of individuals with SQL skills. This was particularly true for detailed analyses of management reports, where source data or aggregated KPIs needed to be examined interactively.
Simply expanding existing reports was not sufficient for this purpose. The possible questions were too varied and often arose only during the analysis itself.
What You Can Expect from HMS
With text-to-SQL, the language model, database access, and result validation must function as an integrated system. To achieve this, HMS combines LLM engineering, backend development, and interaction design.
- Implementation of the complete text-to-SQL application
- Modular separation of the user interface, LLM backend, and database connection
- Methodical prompt engineering and systematic verification of queries and results
