From connectivity to
autonomous agents.
Six capabilities span classical control, digital twins, optimisation and supervised AI — built on control theory and shown the way an engineer would draw them.
Predictive & adaptive control
The controller supervises the regulatory PID layer — setpoint, error, move, measure — and never replaces it. MPC, adaptive PID, extremum seeking and fuzzy logic all live here.
Model-predictive control
Optimise over a horizon, apply the first move, then re-plan on the next cycle — respecting every constraint as it goes.
Digital twins
A synchronised virtual replica runs beside the asset, estimating health and margins that are rarely instrumented directly.
Real-time optimisation
The economic optimum sits on a constraint. The platform drives there and holds it, where classical control backs off.
Hybrid AI
First-principles physics fused with a learned residual — accurate and trustworthy, and re-identified as new data arrives.
Autonomous agents
Agents perceive the plant, reason with the platform’s own tools, and propose moves — with the operator approving each one.
What's implemented —
and what's still to validate.
We label this honestly. Almost everything here is built and runs inside the platform. What it has not yet had is validation on live plant data — and that is exactly what a first pilot delivers.
“Implemented” means built and running inside the platform — not yet validated on live plant data. That independent testing and tuning is the core of a first pilot.
Let’s run a proof of value.
One well-instrumented unit, a read-only pilot, and a quantified benefit estimate — with a reusable playbook for what comes next.
Book a conversation →