Tractian, Brazil — Enhancing Asset Reliability and Operational Efficiency
Mission
Prevent unplanned downtime and optimize industrial operations by giving manufacturers real-time visibility into machine health, predictive maintenance, and operational reliability.
Context
Industrial machinery downtime costs companies millions annually in lost production, emergency repairs, and inefficiencies. Legacy maintenance systems are reactive, siloed, and offer limited predictive insights. Manufacturers need centralized, real-time monitoring to reduce failures, improve asset reliability, and maximize operational ROI.
AI Asset
Tractian delivers an AI-powered Industrial Copilot platform that combines Condition Monitoring, CMMS (Computerized Maintenance Management System), and Operational Intelligence to:
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Predict machine failures before they happen
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Monitor real-time performance across assets
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Streamline maintenance workflows and task management
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Optimize operational efficiency with actionable analytics
Proof
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Thousands of machines monitored globally in real-time across manufacturing sites
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Up to 80% reduction in unplanned downtime reported by enterprise customers
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Predictive alerts prevent up to 50% of emergency maintenance events
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100% real-time visibility of machine health across plants
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Trusted by Air Liquide, Kraft Heinz, Whirlpool, CSX
These metrics reflect enterprise adoption and measurable operational impact, not pilot experiments.
Outcomes
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Minimized downtime and maintenance costs
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Increased asset reliability and operational efficiency
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Data-driven decision-making for plant managers and engineers
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Scalable insights across multiple sites and global operations
Website
Reference Videos
1️⃣ Condition Monitoring & Predictive Maintenance Overview
https://www.youtube.com/watch?v=XtxXd2v_u2M
Demonstrates Tractian’s temperature and condition‑based maintenance platform in real‑time operations.
2️⃣ Smart Trac Real‑Time Equipment Monitoring Demo
https://www.youtube.com/watch?v=eKpkcq9ViZU
Shows Tractian’s Smart Trac condition monitoring system detecting anomalies and predicting machine failures.
