AI can run the calculations. It can't tell you the sensor's drifting, the tag's mis-scaled, or the model's answering a question nobody asked. That takes someone who's actually stood on the plant floor.
We build the monitoring your process actually needs. Not a generic dashboard you have to fit your process into.
Built from real plant-floor work. Numbers that looked right at every checkpoint and weren't: a scaling error a control loop was quietly hiding, a truncated data type, a variance default that flattered a Cpk. Every engine here exists because of a problem like that.
A number on a dashboard carries authority whether it deserves it or not. We check first, and show uncertainty instead of hiding it.
Every statistical method has a blind spot. Influence scoring combines four, and shows you when they disagree, because that disagreement is information.
Analytics run read-only against your own data first. Only a proven track record earns the right to write back to the PLC, and if validation fails, the system holds.
The case studies cover every one of these problems in depth.
Read the case studies