Manufacturing
On a production line, AI can catch defects, spot the signs of a breakdown before it happens, adjust process settings and help plan production, using the cameras, sensors and machine data the plant already has. It runs inside the plant and has to stay reliable when products, materials or suppliers change.
Challenges
Data stays inside the plant
Production data and machine networks are usually kept apart from outside systems, so the models have to run on the plant's own hardware.
Systems work with existing equipment
AI has to read data from the machines and systems already in place, and show its results on the screens operators and engineers already use.
Results hold up when production changes
New products, materials and suppliers change what normal looks like on a line. Models have to be checked and updated when that happens, without starting over.
Typical problems
Quality control
Checking parts, surfaces and assemblies with cameras at line speed, and flagging defects for operators to review.
Predictive maintenance
Tracking vibration, temperature, current and other machine signals to spot the signs of a breakdown before it stops the line.
Process optimization
Learning how settings such as temperature, pressure and speed affect quality and scrap, and suggesting settings for engineers to try.
Production and demand planning
Forecasting demand from orders and the plant's own records, and planning production, materials and capacity from it.
Energy and resource efficiency
Tracking energy, water and raw material use across the plant, and finding where consumption can be reduced.
Our research areas
See all research areasLearning from limited data
Learning from many good parts and few defects, and updating models when a new product or material arrives.
Lightweight models
Running inspection and monitoring models on hardware inside the plant, fast enough to keep up with the line.
Experiments & simulation
Combining the physics of the process with plant data to model how settings affect quality and consumption.
Interpretability & evaluation
Measuring how often alarms and suggestions are right, and showing the data behind each one.
Related services
All services- Computer visionSystems that find, count, measure and inspect objects and events in images and video.
- Anomaly & fraud detectionSystems that flag the unusual transactions, failures and faults worth a closer look, even with few confirmed examples.
- Decision optimizationSchedules, routes, prices and stock plans that respect your constraints and leave planners in control.
- Time series forecastingForecasts of demand, load, capacity and sensor values that show how certain they are.
- Edge AI optimizationModels made small and fast enough to run on devices, vehicles and local servers.
- LLM deploymentLanguage models running on your own servers or private cloud, sized and secured for your workload.
Work with us
Work usually starts with one line or one machine group, and the system runs on the plant's own hardware. Once the results are measured, the scope can extend to other lines and plants.
R&D projects
For problems with no proven solution yet. We test approaches on your data and work toward a prototype in agreed milestones. At each milestone we tell you plainly what looks feasible and what doesn't.
Software development & consulting
For problems with known solutions. We build production software on proven methods, or advise your team on architecture, tooling and model choice.
Funded research
Joint R&D projects backed by national and international research funding, with industry and academic partners.
Corporate training
Training programs tailored to your team. Topics include everyday work with AI assistants, product development, integration and strategy.
Frequently asked questions
Does production data leave the plant?
It doesn't have to. Models run on hardware inside your plant, and production data stays there.
Can AI work with the machines and systems we already have?
Usually, yes. It can read the data your machines and systems already record, and show results on the screens your teams already use.
Can it be tested without disrupting production?
Yes. The first phase runs on past data, before anything touches the line.
Where should a manufacturer start with AI?
Usually with a problem whose cost is clear and whose results are easy to measure, such as a common defect type or a machine that stops often.
What happens when a product or material changes?
The model is checked against the new conditions and updated if needed. After handover your engineers can do this themselves, or we can support you.
From our blog
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Why AI projects stall after the prototype
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Don't hire a genius for a calculator's job: when not to use an LLM
An LLM can be the wrong tool for a narrow business task. Deterministic software may be faster, cheaper, and more reliable when the rules are clear.
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