The ValViser project aims to transform fleet management, starting with waste collection, by developing an AI-powered data interpretation solution leveraging my expertise in heavy machinery optimization and interpretable AI systems. Building on recent breakthroughs in Large Language Models (LLMs), the solution will generate actionable insights from fleet telematics data, addressing utilization challenges. This project builds on my AI research focused on enhancing decision-making through reasoning capabilities, including developing LLM-based knowledge synthesis frameworks and deploying machine learning models. My prior research and industry experience in optimizing heavy vehicle operations, including my doctoral dissertation, led to an 8% reduction in fuel consumption for garbage trucks, reduced mechanism wear, and substantial cost savings. Current fleet management solutions excel in data collection but lack specific recommendations to improve operational efficiency. ValViser will bridge this gap by delivering actionable insights to address challenges like reducing fuel consumption, crew overtime or vehicle downtime. ValViser democratizes advanced data analytics, allowing managers to interact in natural language and make informed decisions without specialized expertise. Waste collection involves balancing fuel efficiency, crew safety, route optimization, and maintenance, which are largely similar across Europe, making ValViser widely applicable. Unlike traditional telematics, my AI-based product will interpret these factors and provide targeted recommendations. By simplifying interaction through natural language processing, the users can efficiently query the system and receive tailored advice.The project targets key cost drivers—fuel, crew, and maintenance—that account for over 80% of fleet expenses (>20000 CHF/mth/truck). ValViser’s subscription model optimizes fuel efficiency, reduces crew overtime, and minimizes maintenance downtime, boosting profitability. Market analysis shows significant potential for industry-wide cost savings and sustainable waste management.Implementation will involve collaborating with industry partners to leverage their infrastructure for seamless integration. Initial proof-of-concept trials will develop an AI interpreter that adapts to existing APIs and a plugin solution for user interfaces to process and visualize vehicle metrics. The goal is to integrate this plugin into at least one partner's platform, enabling users to gain insights from real-time fleet data. Commercialization will scale the solution, targeting the broader European waste management market and related sectors.Through sustainable optimization of waste collection, this project supports SDGs by promoting efficient resource use, reducing CO2 emissions, and enhancing urban safety. ValViser offers a scalable, innovative approach to fleet management, making waste collection more efficient, actionable, and sustainable.