Off-the-shelf convenience functions might answer basic questions—but not the ones that keep engineers up at night. At ei3, we believe that successful predictive maintenance solutions require more than just advanced AI tools. They need data scientists who deeply understand both the algorithms and the engineering behind the machines. That's why we've built more than just a powerful AI toolbox—we've developed a proven methodology to create real-world value from industrial data.
Over years of experience working with manufacturers around the world, we've refined a systematic approach that reduces risk, shortens development time, and delivers measurable ROI.
We start by analyzing the available data to build a mathematical model of the machine or process—often referred to as a "digital twin." This model is created using:
Once we have the model, we monitor for deviations from expected behavior. These anomalies are then enriched with semantic information through a process known as "tagging."
This critical step combines:
This allows us to accurately identify whether deviations indicate normal variation, early signs of failure, or long-term wear and tear.
Finally, the model and its tags are transformed into a real-time predictive algorithm. This algorithm runs continuously:
Siri, Alexa, and all others, step aside. Our system delivers the answers that engineers and operators actually need: advance warning before things go wrong.
Want to see how it works in a real production environment? Contact us for a demo and discover the results AI can deliver on the factory floor.