Digital twins and predictive intelligence, built from the physics of your operation.
First-principles digital twins, forecasts with the reasoning attached, computer vision and autonomous systems for plants, fleets and cities, with the cause and the confidence shown next to every number.
- 01Physics first
- We build each model from the thermodynamics and mechanics of the equipment and calibrate it on your data. When the process drifts, we adjust the parameters and the model stays valid.
- 02Reasoning attached
- We attach the mechanism and the confidence to every forecast. A reliability engineer needs that before opening the machine, and an incident review asks for it afterwards.
- 03One model of the asset
- Component to equipment to line to facility, reconciled across SAP, Maximo, historians and GIS, so the twin, the forecast and the register use the same asset identity.
01Capabilities
What we do
Four capabilities that share one model of the asset.
- Physics-based digital twins
Component to equipment to line to facility, in one calibrated model.
- Governing equations first, operating data for calibration
- Multi-level models from a single separator to a full process train
- What-if runs for higher load, deferred maintenance or intervention, with the consequences quantified
- Forecasting with the reasoning shown
Mechanism and confidence on every forecast.
- Degradation forecasts with the mechanism stated
- A calibrated confidence interval on every forecast
- Anomaly detection with an attributed cause
- Audit trails that stand up in an incident review
- Industrial computer vision
Measurement where no sensor exists.
- Quality inspection and defect detection on the line
- PPE, exclusion-zone and near-miss monitoring
- Spill, emission and visual anomaly detection
- Progress tracking on construction and port sites
- Data reconciliation and automation
One identity per asset across every system that names it.
- Entity resolution across SAP PM, Maximo, historians, GIS and spreadsheets
- Provenance on every reconciled value, with ambiguous cases routed to a person
- Workflow automation with a person in the loop for the decisions that need one
- Control and robotics on ROS 2 where the case warrants it
- 01Reconciled asset register with provenance on every field
- 02Calibrated model per asset class, with the equations documented
- 03Back-test report against your own history
- 04Forecasts in your maintenance system or a dashboard, with the reasoning shown
- 05Runbook and training for your reliability team
02Common problems
Problems we solve.
01The predictive-maintenance model worked until the process changed.
A model fitted to history contains no physics of the machine. Ours start from the physics, so when the process moves we recalibrate and keep the model.
02Nobody approves a shutdown on a score with no reason.
We ship the mechanism and the confidence interval with the number, in your maintenance system or on a dashboard.
03Five systems, five names for the same pump.
Entity resolution across every system that names an asset, with provenance on each value and the ambiguous cases routed to a person.
04No sensor where the problem is.
Industrial computer vision for quality on the line, exclusion zones and near misses, spills and emissions, and progress on a site.
Tell us your pain points.