Specialty chemicals and refining
- Predict temperature runaway 15-30 min ahead
- Simulate yield impact before catalyst change
- Reduce unplanned shutdowns from thermal exceedances
How AMI Labs builds your machine twin
Three stages: connect your historian data, train a causal world model specific to your machine, then query predicted responses before any engineer touches the line.
Request a pilot- Data sources
- OPC-UA and Historian
- No new hardware
- Required
- Deployment
- On-premise only
01
Connect your historians
AMI Labs connects to your existing OPC-UA server or SCADA historian. No new hardware. No sensor replacement. The connector reads your existing tag list and retrieves the full multi-year signal history for every channel you designate.
- OPC-UA DA/HDA and Wonderware, OSIsoft PI, Ignition historians
- Configurable tag selection: start with the 10 channels that matter most
- On-premise deployment, data stays inside your network boundary
- Initial history pull takes 30-90 minutes depending on volume
02
The machine learns itself
The training run discovers the causal structure of your machine: which actuators drive which responses, at what lag, under what operating conditions. The result is not a curve fit. It is a dynamic model that represents the machine's actual physical behavior.
- Causal inference over signal pairs, not naive correlation
- Separate sub-models for each operating regime (startup, steady-state, high-load)
- Model validation against held-out test windows before deployment
- Retraining trigger fires automatically when drift is detected
03
Predict before you commit
Enter a proposed setpoint change. The world model runs a forward simulation and shows you the expected response trajectory, uncertainty bounds, and any predicted exceedances, before the change goes live.
Equipment World Model
Live Prediction
Equipment
Compressor C-101
OPC-UA
Channel
TT-1042
90d history
Proposed change
Suction pressure setpoint
+0.4 bar
Horizon
30 min
Discharge temp delta
+2.3 C
SAFE
Vibration amplitude
+0.8 mm/s
MONITOR
Motor current draw
-1.2 A
SAFE
Model confidence
87%
Works across industrial sectors
The world model approach applies to any continuous or batch process where control variables drive measurable responses in physical channels.
Compressors, turbines and pumps
- Predict vibration amplitude shift before setpoint moves
- Detect bearing degradation from learned baseline signatures
- Optimize inter-stage pressure ratios with simulation
Reactors and fermentation
- Model feed-rate to yield relationships per batch grade
- Predict end-of-batch quality before the batch ends
- Reduce rejects from undocumented recipe drift