AI on the plant floor that earns its keep

The technical work is mostly data engineering: aligning time-series signals with quality outcomes, labeling events — failures, defects, changeovers — accurately enough to train on, and building features from raw signals, like spectral content from vibration or load profiles from current. Models range from anomaly detection on healthy-state baselines, useful when failure examples are scarce, to supervised models where history is rich, to fine-tuned vision models for defect classes. Deployment is industrial-grade: models run at the edge or on-prem where latency and IT policy demand, with versioning, drift monitoring, and a defined fallback when confidence drops.

Production success means the model's outputs change behavior and the change pays: maintenance scheduled from predictions with fewer surprise stops, defect detection with escape and false-reject rates measured against the manual baseline, energy or yield gains traceable to recommended setpoints. Every model ships with monitoring, because plants change — new products, new materials — and a model nobody watches degrades silently.

Scoping is a data-readiness review with a walk through the plant. We look at what is already logged and at what resolution, whether failures and defects are recorded with accurate timestamps, how labels are captured, and where the process data and quality outcomes can be joined. Just as important, we sit with the maintenance planner or process engineer who will act on the model's output and design what that output looks like — a ranked work list, an HMI indicator, a setpoint recommendation with a reason. If the data cannot support the decision yet, the scope becomes instrumenting and logging until it can, with a defined checkpoint.

Model outputs advise; they do not control safety functions, and setpoint recommendations are bounded by limits your process engineers set, typically running in advisory mode first. What usually goes wrong is drift: a new material, a rebuilt machine, or a seasonal shift changes the signal and the model's confidence quietly decays. We design against it with monitoring on input distributions and prediction rates, alerts when either moves, and a retraining procedure your team can run. Handover includes the data pipeline code, the trained model with its evaluation record, the monitoring dashboards, and documentation explaining the model's limits in plain language for the people relying on it.