Artificial intelligence for industrial processes
Understand before you optimise.
We build models of furnaces, cupolas, presses and thermal lines that combine plant data with the physics of the process and the knowledge of the team that runs it. No civil works, and no changes to your PLC logic.
We call this way of working Industrial Process Intelligence: artificial intelligence that understands the physical root of a process and turns it into decisions you can measure on the P&L.
We are a science team. We model one specific process at one specific plant, and hand the shift recommendations the shift supervisor can question and test before acting on them.
What industrial AI achieves on the plant floor
Three lines of work. Each one starts from a question that today gets answered with experience and the readings on hand.
Spend less energy on the same process
In a melting furnace, burner power, air flow, the load and the moisture content of the feedstock account for much of the consumption per tonne. The model learns that relationship with the physics built in, and proposes the minimum setpoint that holds the outlet temperature. The same applies to cupolas, heat treatment and compressors, and to factoring the price of energy into the production schedule.
Know why rejects are rising before the next pour
When a quality deviation shows up at final inspection, the cause is usually hours earlier, in a combination of variables: feedstock moisture together with line speed, for example. The model links the signals your SCADA already records with the physics of the process, and flags that combination in advance. The alert lands in the CMMS with the variable behind it and the asset it affects.
Choose the production sequence with the cost of energy in view
A production schedule allocates loads, raw materials and changeovers against line capacity, delivery deadlines and the hourly price of energy. The system generates several viable sequences, shows which constraint drives each one, and what it costs (in kWh, changeover hours and lead time) to choose one over another. The planner decides, and that adjustment is logged for next time.
A model that only spots patterns stops working when the feedstock changes
Plant data is scarce, noisy, and depends on the operating regime. A model trained only on historical data works only while the line resembles that history, and fails the moment a new recipe comes in or the season changes. That's why so many pilots stay pilots.
Our models carry the process's thermal and mass balances inside them. When the feedstock changes, the model knows what's still valid.
We work with what the plant already records in SCADA, process history, the CMMS and the ERP. To that we add what the process can and cannot do physically, and what the people who have run that line for years already know, built into the model as rules, rather than waiting for the data to reveal it.
Every recommendation comes with the variables that drove it, the conditions that triggered it, and its confidence level. The shift supervisor can question it.
When the project calls for it, the recommendation is written straight into the control system. If the client prefers a person in the loop, it stays on the shift screen.
The methods, for anyone who wants the detail: causal analysis, physics-informed neural networks, knowledge graphs and neurosymbolic AI. They're explained in how we work.
Pick a process and let's talk about it
45 minutes with the science team about one specific process at your plant. We look at what data you have and tell you whether we see room for improvement, and what it would take to measure it.