Physics-informed AI, applied to real plants

We build AI solutions for industrial automation and advanced control — grounded in the physics of the process, validated against how plants actually operate.

The problem

Classical control does exactly what it was designed to do — and no more. It handles the conditions it was tuned for, but leaves performance on the table when processes drift, feedstocks change or constraints tighten. Pure machine learning promises to close that gap, yet in industrial settings it routinely fails the entry test: it needs data plants don't have, extrapolates poorly outside what it has seen, and offers little transparency to the engineers who are accountable for safe operation.

The result is a familiar stalemate: promising pilots, few deployments. The missing ingredient is not more data or bigger models — it is structure.

Our approach: physics-informed AI

Physics-informed AI embeds what is already known about a process — conservation laws, reaction kinetics, thermodynamic constraints, equipment models — directly into the learning problem. The data no longer has to teach the model how the world works; it only has to refine the parts first principles leave open.

In practice, that yields models that:

• need substantially less data, because physics carries most of the signal;
• extrapolate credibly beyond historical operating envelopes, because the physics constrains the predictions;
• remain interpretable — parameters and states retain engineering meaning, so your team can audit what the model believes;
• behave predictably enough to sit inside closed-loop control, not just dashboards.

Where it applies

Our work targets industrial automation and advanced control systems. Typical application areas include:

• Advanced process control (APC) — learning-augmented, model-based control for demanding multivariable loops;
• Hybrid process modeling — digital models combining first principles with data, for simulation, design and what-if analysis;
• Soft sensors — physics-constrained estimation of qualities and states that are expensive or slow to measure;
• Process optimization — pushing setpoints toward economic optima while respecting physical and safety constraints.

Every engagement starts from your process, not from a product: we assess whether physics-informed methods genuinely fit the problem before anything is built.