Tailored for production environments, the PODO® AI Framework offers a unified data environment supporting diverse analytic applications, both current and future. This asset data-centric approach is crucial for achieving digital transformation in manufacturing.
Our platform seamlessly connects to data from any source—IoT sensors, equipment, control systems, and historian databases—delivering a real-time 360-degree view of production health and performance.
Leveraging advanced AI algorithms, our solutions continuously assess the health of each monitored asset 24/7 to provide you with the most up-to-date and accurate health status in real-time. No more digging through charts and trying to make sense of disparate sensor data!
Identify equipment performance issues with AI-driven, self-optimizing detection models. The system's Remaining Useful Life (RUL) feature delivers early warnings of potential problems, helping to prevent catastrophic downtime and optimizing the maintenance schedule with a predictive approach.
Our cloud-enabled platform delivers relevant insights through user-friendly dashboards, from the CEO to the factory floor. Whether you're an executive seeking plantwide status or a maintenance engineer troubleshooting issues, every detail of your facility is at your fingertips.
Our solution includes an embedded work order feature as well as the ability to connect to enterprise maintenance systems, to allow engineers to seamlessly schedule and trace maintenance/repair activities for a closed-loop predictive maintenance workflow. This vastly increases service efficiency and extends the life of your assets.
Predictive maintenance becomes more valuable when equipment risk is connected to operating context and the maintenance workflow that follows. By combining real-time asset health, historical behavior, Remaining Useful Life insights, and maintenance history, AI-powered asset performance management helps engineering teams prioritize the issues that matter most and plan intervention before unplanned downtime occurs.
Follow one developing motor issue from live detection through connected evidence, likely cause and a maintenance-ready response.
In a focused walkthrough, we’ll use one real asset question to show how Aidentyx connects signals, operating context and maintenance history—then turns the evidence into a review-ready response.
