INDUSTRIAL AI

AI FOR MANUFACTURING

AI that understands the process, not just the data

Most industrial AI pilots fail for the same two reasons: the data was never good enough to learn from, and the model had no understanding of the physics it was supposed to predict. We start from the other end. We build the data foundation first, and we model the process before we model the statistics — because an engineer will only act on a prediction they can understand and check.

Fifteen years of process modelling

We have spent fifteen years building validated numerical implementations of international engineering standards — thermodynamics, heat and mass transfer, fluid properties — used in production by engineers worldwide. We come to industrial AI from the physics, not from a data science course.

We build the data foundation ourselves

Our MES collects process data directly from the machines, second by second, structured and timestamped. That is the foundation industrial AI needs and most plants do not yet have. We are not guessing what your data looks like — we build the systems that produce it.

AI needs data that reflects reality

A model is only as good as what it learns from. Shift reports and spreadsheets are not a training set. Before any model is worth building, the plant needs continuous, machine-level data with the process context attached — which is exactly what an MES layer produces. For most plants, that is the honest first step toward AI, and it delivers value on its own.

From plant floor data to a model an engineer can trust Four stages: machine data collected from the floor, structured into a process data foundation, used to train a physics-informed model, and returned to operators as an actionable recommendation that can be checked. 1  COLLECT 2  STRUCTURE 3  MODEL 4  ACT Machine data Second-by-second from the PLCs Process data Contextualised, labelled, validated Physics-informed Constrained by what the process can do Operator action Explainable and checkable Outcomes feed back - the model is re-validated against what actually happened

Most plants begin at stage one or two. We are equally happy to start there.

Where AI earns its place on a production floor

Specific problems, specific model classes, measurable against a KPI you already track. We will tell you plainly when a problem does not need AI to solve it.

Predictive Maintenance

Detecting the drift that precedes a failure — in vibration, load, temperature or cycle behaviour — early enough to schedule the intervention instead of reacting to a breakdown mid-shift.

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Visual Quality Inspection

Computer vision for surface and dimensional defects that are tedious, inconsistent or simply too fast for manual inspection — with the defect record written straight into the quality history.

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Process Optimisation

Setpoint advisory grounded in the physics of the process — suggesting operating conditions that hold quality while reducing energy, scrap or cycle time, with the reasoning visible to the engineer.

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Forecasting & Demand Planning

Production, throughput and demand forecasting built on your own history rather than a generic template — so planning decisions rest on how your plant actually behaves.

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Digital Twin & Simulation

A numerical model of the process you can run experiments against — testing a change before committing a line to it. This is where our fifteen years of simulation work applies most directly.

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AI Readiness Assessment

A short, practical review of what data you have, what it would support, and what would need to change first. Often the most useful place to start — and sometimes the answer is that AI is not the right tool yet.

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How we approach an AI project

1

1. ASSESS

We look at the data you actually have and the problem you actually want solved, and say honestly whether a model would help — or whether better measurement would get you further, faster.

2

2. PILOT

One problem, one line, a success criterion agreed in advance and measured against a KPI you already track. Fixed scope, so the pilot either proves itself or it does not.

3

3. DEPLOY & VALIDATE

A model that runs in production needs watching. We deploy it into the systems your operators already use and keep re-validating it against what actually happens on the floor.

Fair questions about industrial AI

Most pilots fail on data, not on algorithms — the history was too sparse, too inconsistent, or missing the process context needed to learn anything. We assess that honestly before proposing a model, and we will tell you if the first step is measurement rather than machine learning.
Not necessarily, but you do need reliable, continuous, machine-level data with process context attached. An MES layer is the most common way to get it, and it pays for itself in visibility long before any model is trained.
That is the design constraint we work to. Models are built to respect the physics of the process and to expose their reasoning, so a recommendation can be checked against engineering judgement rather than taken on faith. A prediction nobody acts on has no value.
It stays on your infrastructure, inside the EU — on-premise or in an EU-hosted private cloud. Models are trained and run on your own systems. Your production data is not sent to a third-party service.
Start with an honest assessment

IS YOUR PLANT READY FOR AI?

Tell us what you are trying to improve and what data you have. We will give you a straight answer about what is realistic — including if the answer is not yet.

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