This white paper shows how generative AI can improve predictive maintenance by connecting machine data, maintenance history, technical knowledge, and operational resources. Its four-stage framework begins with machine prioritization using RAG and structured and unstructured data, followed by failure prediction with sensor streams and machine-learning models. LLMs then generate repair plans, work orders, and resource requirements before combining service notes and other information into technician guidance. MongoDB Atlas provides the unified data foundation, stream processing, aggregation, and contextual data needed across these stages. The paper argues that effective predictive maintenance can improve productivity and reliability while reducing downtime and maintenance costs, with AI exten

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