We help industrial organizations prevent equipment failures before they happen using AI-powered predictive maintenance—enhanced with intelligent troubleshooting recommendations to accelerate issue resolution and reduce unplanned downtime.
Unplanned equipment failures are one of the biggest drivers of lost production, increased costs, and operational inefficiencies. Traditional maintenance approaches —whether reactive or time-based— fail to capture early warning signals hidden in operational data.
At nilmuda, we build AI-driven predictive maintenance solutions that continuously monitor equipment health, detect anomalies, and predict failures before they occur.
Our approach combines machine learning, real-time sensor data, and historical maintenance records to deliver actionable insights directly to operations and maintenance teams.
We go beyond prediction.
Our solution provides AI-powered troubleshooting recommendations—leveraging maintenance logs, manuals, and historical data to suggest likely root causes and corrective actions. This enables faster diagnosis, reduced downtime, and more efficient maintenance workflows.
In Drax's wood pelleting operation in Louisiana, USA, we deployed predictive monitoring on drag chain conveyors, a critical component of the production line.
👉 Result: ~65% reduction in unplanned downtime, significantly improving production stability and throughput.
Our predictive maintenance solutions are designed for asset-intensive environments, including: