This thesis presents the design and implementation of a large language model (LLM)–based system for the automotive domain, aimed at improving access to proprietary knowledge. Companies in this sector may struggle with large and complex datasets that are typically accessible only to technical experts. The proposed system enables non-technical users to retrieve relevant information through natural language queries, simplifying access to complex data sources. The architecture is built around a hierarchical network of LLM agents, each dedicated to a specific function. In the current implementation, a Vehicle Basket Agent and a User Preferences Agent are developed to demonstrate this approach. Particular emphasis is placed on modularity, ensuring that the system can be extended with additional agents as new requirements emerge.