This paper addresses the challenge of increasing hardware complexity, long development cycles and high costs associated with integrating multiple systems. The research explores the potential of Large Language Models (LLMs) when applied as chatbots to revolutionize the design and development of automotive electrical hardware systems, encompassing areas such as convenience features, safety systems, advanced lighting, vehicle body control and modular electronic control units. A key focus is on how LLMs can automate cost-reduction design tasks, including design optimization, requirements verification and component validation, ultimately driving down expenses without compromising performance or reliability. Furthermore, the research investigates how LLMs can assist in decision-making by providing data-driven insights that inform critical design choices and facilitate enhanced team collaboration, leading to improved productivity through innovative tools and streamlined workflows. In that sense, the project’s scope includes creating a Data Warehouse with relevant design, features offering, testing and quality feedback data to train the LLMs. A research tool based on LLMs will be developed to offer optimization recommendations, such as identifying oversizing, suggesting cost reduction and hardware modularization. The tool will be seamlessly integrated into the cost-reduction engineering teams’ workflow, promoting agility and modernization. The expectation is that this research will drive innovation, enhance competitiveness and promote sustainability within the automotive sector, leading to accelerated time-to-market for new vehicle models, improved engineering efficiency in the development of electrical hardware systems and a strengthened market position for automotive OEMs. Ultimately, the goal is to create more efficient, safer and feature-rich automotive experiences while simultaneously optimizing cost-effectiveness in the design and production of advanced electrical systems.