Next-Gen Predictive Maintenance Platform Using AI & Machine Learning

Our enterprise is seeking to develop a next-generation predictive maintenance platform that integrates state-of-the-art AI and machine learning technologies. The platform will leverage large language models (LLMs), computer vision, and predictive analytics to predict equipment failures before they occur, thereby minimizing unplanned downtimes and optimizing maintenance schedules. The initiative will employ key technologies such as OpenAI API, TensorFlow, PyTorch, and Hugging Face to build a robust, scalable solution capable of processing vast amounts of data from various industrial machinery. The platform will utilize computer vision to detect anomalies in real-time, while predictive analytics will provide actionable insights into machine health. Additionally, the system will integrate AutoML to streamline model updates and improvements. The project requires collaboration with cross-functional teams across engineering, IT, and operations departments, ensuring the solution meets organizational and user requirements. We are targeting a development timeline of 16-24 weeks, with a budget range of $50,000 to $150,000.