Develop an AI-based predictive maintenance system for electric vehicles utilizing LLMs and computer vision to enhance reliability and longevity. By leveraging TensorFlow and YOLO, the goal is to create a system that detects potential failures before they occur, minimizing downtime and repair costs.
Electric vehicle manufacturers and fleet operators seeking to improve vehicle uptime and reduce maintenance costs.
Electric vehicles, while promising for sustainability, face challenges in maintenance predictability. Unexpected failures can lead to high repair costs and downtime, affecting manufacturer trust and user satisfaction.
The target audience recognizes the value of predictive maintenance in reducing operating costs, improving vehicle uptime, and gaining a competitive advantage in the growing EV market.
If left unaddressed, EV manufacturers and operators will continue to face costly repairs, reduced customer satisfaction, and potential loss of market share to competitors offering more reliable solutions.
Current alternatives include traditional scheduled maintenance and basic diagnostic systems that do not predict failures, often resulting in over-maintenance or under-detection of issues.
Our system's unique ability to combine LLMs, computer vision, and predictive analytics on edge AI platforms offers a more accurate and proactive approach to maintenance that existing solutions cannot match.
Our go-to-market strategy involves partnering with EV manufacturers and fleet operators for pilot deployments, showcasing the ROI through case studies, and leveraging industry events for visibility.