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FieldOps AI — Turning Maintenance Reports into Actionable Work Orders with Multimodal AI Maintenance teams often receive issue reports through scattered text messages, equipment photos, and voice recordings. These reports are frequently incomplete, unstructured, and difficult to interpret, making it time-consuming to identify problems, assess urgency, and create actionable maintenance work orders. FieldOps AI is a multimodal AI-powered maintenance assistant that transforms these fragmented reports into structured, evidence-aware work orders in seconds. Powered by Meta Muse Spark and Muse Voice Transcribe, FieldOps AI enables users to submit maintenance issues through natural-language descriptions, equipment images, and voice recordings. The system automatically transcribes audio, analyzes visual and textual evidence, identifies reported symptoms, suggests priorities, highlights safety concerns, and generates structured work orders with recommended checklists. Unlike traditional ticketing systems, FieldOps AI explicitly separates observed facts from AI inferences, assumptions, and missing information, helping users make more informed decisions while reducing the risk of unsupported conclusions. Every AI-generated work order follows a human-in-the-loop workflow, allowing users to review, edit, and explicitly approve it before proceeding. The application also maintains an auditable history of AI processing and approval decisions. Our goal is simple: reduce manual administrative work, improve maintenance reporting accuracy, and help teams respond to equipment issues faster and more safely. Built as a functional MVP using Next.js, FastAPI, SQLite, and Meta's AI models, FieldOps AI demonstrates how multimodal intelligence can bridge the gap between unstructured real-world reports and actionable operational workflows.