This topic covers building and operating industrial predictive maintenance (PdM) AI systems—using Claude-based models and competing LLMs—by integrating sensor and time‑series data into robust, production-grade data and model stacks. No related articles were provided; the overview synthesizes the listed tool descriptions and prevailing industry trends to explain how orchestration, MCP servers, storage and database connectors, and memory services accelerate PdM workflows. PdM systems require continuous ingestion, transformation, and explainable inference across edge and cloud. Data Pipeline Orchestration (Dagster) and event-driven integration platforms (Pipedream) automate streaming and batch workflows. MCP (Model Context Protocol) servers and database connectors—DBHub, Neon, Supabase, MotherDuck/DuckDB, and the MCP Toolbox for Databases—let LLMs and agents safely query and act on operational databases, perform analytics, and persist results. Memory and semantic search layers such as cognee-mcp and mcp-memory-service provide graph‑RAG and hybrid semantic memory for agents to maintain context, accelerate root‑cause analysis, and retain incident histories. Key trends relevant in late 2025 include hybrid edge/cloud deployments to reduce latency and bandwidth, tighter integrations between LLMs and transactional/analytical stores for explainable alerts, and a move toward standardized MCP-based connectors to improve security and developer velocity. Storage management and data catalog/lineage practices become essential to trace model inputs and meet compliance. Chat API integrations enable human-in-the-loop troubleshooting and report generation from model outputs. Together, these components form an operational PdM architecture: orchestrated pipelines feed curated data to models, MCP-enabled connectors expose authoritative sources, memory services retain operational context, and analytics platforms enable long‑term trend analysis—supporting reliable, auditable predictive maintenance workflows without overreliance on marketing claims.