Industrial AI and process modelling
Models that obey your process physics, and tell you how sure they are.
The problem we solve
Your line produces different results on different days with the same inputs, and nobody can say why. Purely data-driven models break when conditions change, and they give one number with no sense of risk. Unplanned downtime costs the world's 500 largest companies US$1.4 trillion a year, and the average facility loses 27 hours a month (Siemens, 2024).
Features and deliverables
- Predictive maintenance models for critical assets
- Process optimisation: recommended set-points for quality, yield and throughput
- Quality and yield analytics that link defects to process variables
- Energy and waste analytics (steam, power, scrap, rework)
- Forecasts with stated uncertainty, such as prediction intervals from conformal methods
- Model documentation: data used, assumptions, limits and validation results
Benefits and business impact
- Physics constraints mean the model needs less data and stays within physically possible values
- Operators see how confident each forecast is, so they know when to act and when to check
- Less unplanned downtime, scrap and energy per tonne, with targets agreed with you and measured against your own baseline