In the evolving landscape of enterprise data management, automating the creation of ETL pipelines emerges as a crucial objective. This master's thesis delves into employing state-of-the-art Artificial Intelligence techniques to streamline the integration and transformation of enterprise data, aiming to minimize the manual effort in developing data processing workflows. In partnership with Mediamente Consulting Srl, the study focuses on designing and implementing a system that efficiently addresses user requests within the ETL framework, leveraging cutting-edge technology. To this end, a tailored algorithm was designed to process user requests, employing sophisticated data representation techniques to encapsulate the semantic nuances and contextual cues embedded in these queries.