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Industrial Process Intelligence

Artificial intelligence
for industrial processes

Understand before you optimize.

We explain why your process consumes, varies or fails, and which lever to pull without putting the part at risk. We work with the data your plant already generates.

Let's talk about your process See how we work
The problem

Your plant runs with more margin than it needs

Machines don't perform the same as they age, and material changes from one batch to the next. To avoid defective parts, the shift raises the setpoint and works with a cushion. That cushion is paid for every day in energy and rejects, and hardly anyone knows how much of it is surplus.

Red-hot steel bar coming out of a forging furnace
Holding furnace · one shift
Shift setpoint
1,180 °C
What the part needs
1,142 °C
Margin paid for in gas
+38 °C
Example figures. In any plant the question is the same: how much of that margin is surplus.
  1. 01What is happening

    You see it in the SCADA and on the line panels.

  2. 02Why it happens

    This is where the margin sits, and nobody in the plant has a way to see it.

  3. 03Which lever to pull

    How much to lower it, when, and with what effect on the part.

Let's talk about your process

Thirty minutes on one specific process. Tell us where your energy or quality is slipping away and we'll tell you whether we can explain it with the data you already record.

What we work on

Three types of process

Pick the one closest to yours.

How far you can lower the setpoint

In a furnace, a boiler or a compressor, the model calculates the minimum power that keeps the part within band with the load and material of that moment. If you plan in batches, it also finds which sequence uses the least energy per tonne.

What it answersWhat power should I give the furnace now, with this load and this material?
Induction furnaces · heat treatment · boilers · compressors · energy planning Energy efficiency with AI

Which combination of variables ends in rejects

When quality drifts, the cause is usually hours earlier: the composition of one batch combined with a line speed, for example. The model finds that combination in the signals you already record and warns you before the next pour.

What it answersWhy did rejects go up this week, and what do I correct for the next one?
Cupolas · material composition · anomalies · root cause of failures · asset degradation Predictive models for industry

Which plan to apply when there are many options

For plants with many ways to operate: the system generates several feasible sequences, says which constraint drives each one and what choosing it costs in energy, hours and lead time. If an incident comes up, it redoes the plan.

What it answersIn what order do I produce this week if today the deadline rules and tomorrow the tariff does?
Sequencing · load planning · resource allocation · terminals and warehouses · scenarios Operational planning with AI
Why it works

The model carries the physics of your process

Energy and mass balances, cause-and-effect relationships between variables, and what people who have spent years on the line know. With that, the model needs little data, stays valid when the raw material or the recipe changes, and every recommendation comes with the variables that explain it. The shift supervisor can question it before applying it.

INDUCTOR POWER100 → 84 kW CHARGING TIMEevery 40 min POURING SEQUENCEweekly plan BATH TEMP.1,468 °C HEAT LOSSESrefractory · inertia IRON MASS6.2 t CONSUMPTION−16 % SAFETY MARGINwithin band PART QUALITYOK
what you can adjustwhat happens inside the furnacewhat you get, explained
100→80 kW

Minimum safe power of a foundry induction furnace, calculated by the model from the furnace's energy balance and real operating data.Savings measured after a year in operation: between 20 and 30 % of consumption, depending on the operating mode.

How we do it
Where we work

From the induction furnace to the port terminal

Companies we have worked withAwards and recognition
How we start

A small case in your plant

One specific part of the process, with the data you already have and a measurable result. If it works, we continue; if not, you find out early.

Book a call
What data do you need to start?
What already exists: SCADA history, CMMS records, ERP or MES orders. No new sensors are installed to get started. You can also start with little history, because the model relies on the process balances and not only on recorded examples.
Do we have to touch the installation or the PLCs?
No. The model reads what the plant records and sends the recommendation to the shift screen or, if the project calls for it, writes it to the control system. PLC logic is not modified.
How is this different from a dashboard?
A dashboard shows what has happened. Here you get what to do, with the variables that weigh on that recommendation and its confidence level, and the model stays valid when the material or the recipe changes.
How does a project start?
With a call about one specific process. Then we review the available data and set a baseline for consumption, rejects or lead time before building anything, so the result can be measured against it.
Which plants is it suited to?
Processes with high energy consumption, variable material or many operating alternatives: foundry, forging, heat treatment, steel, tires, energy and industrial logistics.