The travel industry relies heavily on large volumes of data, requiring efficient methods to extract insights from big and heterogeneous datasets. This thesis presents a novel approach to link Large Language Models (LLMs) with SQL databases to enable natural language querying, facilitating more intuitive and accessible data interactions for all user profiles. The research is conducted in collaboration with Amadeus IT Group and focuses on developing a co-pilot tool that interprets user queries, formulates corresponding SQL queries, and returns their results while assisting the user. The core challenge addressed in this work is the inherent ambiguity present in natural language queries.