Process industries generate a constant stream of sensor data, most of it unused until something fails. We turn that stream into an early-warning system — built on your process, not a generic template.
Five problems we're set up to solve, and build out from, for a plant or process line.
Predict a quality outcome — strength, purity, defect rate — directly from in-process sensor readings, instead of waiting on a lab test or an end-of-line inspection.
Failures that happen rarely are hard to model with standard classification. We build around the class imbalance directly, so the rare event is what the model is tuned to catch.
Flag equipment drifting toward failure from vibration, temperature or load signals, early enough to schedule maintenance instead of reacting to a breakdown.
Once a process is modeled accurately, the same model can suggest setpoint adjustments — within safe, operator-approved bounds — to hold a target consistently.
Before any of the above works, the data has to be collected, cleaned and time-aligned reliably. We build that pipeline as part of the engagement, not as an afterthought.
Real sensor feeds drop readings and drift out of calibration. We account for that in how the model is validated, not just how it's trained.
A warning that arrives after the batch is already bad isn't a warning. We design for the response time the floor actually needs.
The output has to land where operators already look — a dashboard, an alert, a control-room screen — not a separate tool nobody opens.