AI Solutions
On-premise AI that gets smarter every shift.
On-premise AI that gets smarter every shift.
Python-based AI that runs locally inside your plant — no cloud, no data leaving your premises. Plugs natively into our MES to predict failures before they happen, eliminate routine operator tasks, and surface insights that stay one step ahead of the line. The longer it runs on your floor, the more it learns about your specific line.
- Locally deployed (Python) — your data never leaves the plant
- Predictive maintenance & live anomaly detection
- Auto-classifies alarms, downtime reasons & defects
- Native MES integration — insights inside the operator UI
The AI that grows with your plant
Smarter every shift. Your data trains your model — and the advantage compounds against any vendor stuck with generic models.
Learns from your data
Every event, alarm, recipe and yield record feeds the model. Six months in, the AI knows your line patterns nobody else does — because nobody else trains on your data.
Learns from your operators
When operators close out alarms, classify defects, or override predictions — the AI captures those decisions as training signal. Your senior operators' judgment scales across every shift.
Adapts to your plant fingerprint
No two plants are alike. Materials, climate, machine wear, even shift habits — the model continuously adapts to your specific floor, not a textbook one.
A widening competitive moat
The longer you run, the further your AI pulls ahead of any cloud vendor's generic model. Your data stays yours; the advantage compounds.
Where our local AI earns its keep on your floor
Eight ways our Python-based, on-premise AI augments operators, engineers and the MES itself.
Predictive Maintenance
Forecast component failure 6–72 hours in advance from vibration, temperature and current signatures.
Live Anomaly Detection
Spot abnormal patterns across SPC, recipe parameters and machine state — before they show up in yield.
Smart Operator Assist
When an alarm fires, AI suggests the most likely fix based on similar past events. Cuts MTTR.
Auto Event Tagging
AI auto-classifies downtime reasons, defect categories and alarm root cause — operators stop typing.
Cross-Station Root Cause
Correlates events across the entire line to surface the actual upstream cause of a downstream defect.
Yield Forecasting
Predicts the final yield of a batch from in-process data, so engineers can intervene mid-run.
Natural Language Reports
"Why did OEE drop on Tuesday?" — get an AI-generated summary with the actual contributing events.
Vision QC Continuous Learning
Defect classifier learns from operator feedback, so accuracy improves week by week.
Built for plants that can't ship data to the cloud
Data sovereignty
Inference happens inside your network. No customer process data ever leaves the plant.
Air-gapped friendly
Runs without internet. Updates pushed via your own IT change-control process.
Millisecond inference
Models run on local GPU/CPU — fast enough to gate a station decision in real time.
Open Python stack
PyTorch, ONNX, OpenCV. No vendor lock-in. Your team can audit and extend the models.
Let's connect every machine on your floor.
Free 30-minute consultation with our engineering team. No cost, no commitment.