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Case Study

LLM-based SQL queries in natural language

Overcoming barriers to data access
Initial situation

Restricted data access in companies

Several of our customers stored company data in SQL databases and data warehouses, which were used to calculate KPIs and create reports. However, data access was limited to a small group of experts by experts with SQL knowledge. This prevented a wider group of users from accessing and effectively using the data. This challenge affects not only our customers, but numerous companies.

HMS Solution

LLM-supported system for SQL queries in natural language

HMS developed a tool that uses Large Language Models (LLMs) to enable SQL queries in natural language. This solution allows users to interact with relational databases via a chatbot interface, eliminating the need for SQL knowledge. Users find this solution particularly useful as they can extract valuable insights from complex data sets independently and efficiently. The integration of generative AI technology and a user-friendly chatbot interface represents a significant advance in the accessibility and efficiency of data queries.

Advantages of the tool

  • Democratised data access: Any employee can query data, regardless of technical knowledge
  • Increased efficiency: Simplifies and speeds up the data retrieval process significantly.
  • Improved decision-making: Users gain deeper insights and can make data-based decisions faster.
On the road to success

Technologies

  • Azure Open AI
  • Cloud
  • GPT
  • LLM
  • LangChain
  • MLFlow
  • Python
  • Software & Cloud Architecture
Added value

Accessible and efficient data queries

The HMS tool enables customers to access company data faster and more accurately by converting natural language queries into SQL queries. This leads to a broader use of data within the organisation and supports informed decision-making processes at all levels. Simplified and accelerated data retrieval makes it easier for users without SQL knowledge to gain deeper insights and work more efficiently.

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