ServicesIndustrial ML

Machine learning that knows the physics of your process.

Process models, setpoint recommendation and predictive quality for manufacturing and heavy industry, built from the plant's own approved records and constrained by the physics of the process.

Who it is forManufacturing and process engineering teams whose expertise lives in a few people's heads, and plants with years of approved recipes and quality records that nobody has turned into a model.

The problem

Industrial data is small, uneven and expensive to collect, and generic machine learning treats it like web data. The result is models that fit the history and violate the physics, which an engineer rejects on sight.

We build models that carry the physics inside: thermodynamic constraints in the loss function, deterministic guards after inference, augmentation that only generates physically plausible samples, and evaluation on a blind hold-out of real records set aside before training starts.

What we deliver

Data readiness assessment and collection guide for the plant
Physics-informed model for the process variable or recipe in question
Web application the plant can run locally, with export to the formats your equipment reads
Validation report on a blind hold-out, against targets agreed in advance
Container image, scripts and infrastructure template for deployment
Knowledge transfer to process and automation engineers

How we work

Data

Inventory the approved records, clean them, and write down what is missing. This is usually most of the work.

Model

Chained or multi-output regression with physical constraints, evaluated by cross-validation during development.

Validate

Blind hold-out, targets from the statement of work, a written report including where the data limited the result.

Deploy

Local application, documentation, handover. Integration with PLC or MES scoped separately if wanted.

What backs it

The method here is the one we use on every industrial engagement: a blind hold-out set aside before training, targets written into the statement of work, and a validation report that says where the data limited the result. A published case study from this practice is in preparation with the client's approval.

Common questions

How much data do we need?

Less than you think if it is approved and consistent. A few hundred good records with the right fields can be enough for a recommendation model; we tell you after the data assessment.

Does the model replace our engineers?

No. It gives them a starting point that is usually close, so qualification takes fewer iterations. Validation and sign-off stay with people.

Can it connect to our line?

The first delivery runs standalone so it can be validated safely. PLC, OPC or MES integration is a separate, scoped step.

Related services

Talk to an engineer about this

Thirty minutes, no slides. Bring the workload and we will tell you what we would do and what it would cost.

Talk to us [email protected]