SQL in the AI chat: direct data access with structured, more readable answers

Babylon introduces SQL query execution directly inside the AI chat, improving data access and making business information analysis more immediate. The update lets users query databases naturally while keeping control and clarity in the result.

In an enterprise AI context, access to data is often tied to separate tools and specific technical skills. This slows down processes and limits the use of data in everyday work. Integrating SQL into the AI chat reduces this distance and makes information more accessible within operational workflows.

An effective AI chat must let users query data without switching tools.

With this update, SQL queries can be run directly in chat and answers can be returned in two modes: a concise mode for quick reading and a complete mode that returns the extended query result. This makes it possible to adapt the output to the context, both for a quick check and for deeper analysis.

The system also uses table metadata to improve the understanding of requests. This allows the AI chat to interpret queries more accurately and work with data more consistently, reducing errors and ambiguity. Users do not need to know the database structure in detail: the system can use the available information to build more relevant answers.

This approach is especially useful where data analysis must be fast and accessible. Reducing technical complexity lets more business roles use data directly, without relying on intermediate steps or separate tools.

More readable answers make data analysis faster and more usable.

The additional system rules introduced further improve answer quality. The AI chat behavior is guided more precisely, ensuring consistency in query execution and result delivery. This helps make the interaction more stable and reliable even in complex scenarios.

The combination of SQL execution, metadata usage and flexible answer modes creates a concrete benefit: users can move directly from question to data, reducing time and operational steps. This improves access speed and makes data more effective within business processes.

The impact is especially clear in daily activities such as checking performance, verifying operational figures or analyzing indicators. The AI chat becomes a central tool for querying data and supporting operational decisions based on updated, structured information.

Direct data access and clear answers are essential for faster operational decisions.

This update reduces the risk of errors caused by manual interpretation and improves analysis quality. The ability to obtain structured answers immediately makes it possible to work with data with greater confidence and continuity, even in dynamic contexts.