Energy
In energy, operational safety comes first, so AI supports operators and does not take control of equipment. Within that limit it can forecast demand and generation, flag equipment faults early and help plan maintenance and grid operations. Operators can check every result.
Challenges
Operational safety comes first
The systems that run plants and grids have to stay protected. AI reads data from them and makes suggestions, and any action on equipment stays with the operators.
Data stays inside the company
Energy infrastructure is critical infrastructure, and its operational data often has to stay on the company's own systems. Models run there too.
Field conditions keep changing
Weather, seasons and the state of equipment change constantly, and some sites have limited connectivity. Models have to keep working in these conditions and be updated as they change.
Typical problems
Demand and generation forecasting
Forecasting consumption and the output of wind and solar plants for the hours and days ahead, to support trading and grid planning.
Equipment maintenance and fault detection
Spotting unusual behavior in sensor data from turbines, transformers and other equipment, and planning maintenance before a fault occurs.
Inspection with imagery
Finding damage and defects on lines, panels and turbines in drone, camera and satellite imagery.
Grid operations and losses
Detecting faults, technical losses and unauthorized consumption in distribution networks from meter and network data.
Planning and optimization
Planning plant operation, maintenance schedules and field crews while balancing cost, capacity and other constraints.
Our research areas
See all research areasExperiments & simulation
Models that combine data with the physical behavior of plants and grids.
Learning from limited data
Learning from rare faults and from data recorded under conditions that keep changing.
Lightweight models
Small models that run on site, close to the equipment.
Interpretability & evaluation
Showing operators what each forecast or warning is based on, and measuring how accurate it is.
Related services
All services- Time series forecastingForecasts of demand, load, capacity and sensor values that show how certain they are.
- Anomaly & fraud detectionSystems that flag the unusual transactions, failures and faults worth a closer look, even with few confirmed examples.
- Computer visionSystems that find, count, measure and inspect objects and events in images and video.
- Geospatial AIMaps, change detection and site analysis from satellite, aerial, drone and location data.
- Decision optimizationSchedules, routes, prices and stock plans that respect your constraints and leave planners in control.
- Edge AI optimizationModels made small and fast enough to run on devices, vehicles and local servers.
Work with us
Work usually starts with one plant, one region or one process, and the system is installed on the company's own infrastructure. Once the results are measured, the scope can extend to other sites.
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
Where does our operational data stay?
On your own infrastructure. Models run there, and the data does not leave the company.
Does the AI control the equipment?
No. The system reads data, makes forecasts and gives warnings. Any action on equipment is taken by your operators.
Does the system need access to our control systems?
It needs to read their data, usually from a copy or from historical records rather than the control systems themselves. How it connects is agreed with your teams at the start.
Where should an energy company start with AI?
Usually with a task that has plenty of data and results that are easy to measure, such as a forecast you already make or a common type of equipment fault.
Who runs the system after handover?
Your own teams. The system is handed over with its documentation, and we can also provide support after that if needed.
From our blog
All articles
Will a proven AI method work on your data?
A proven AI method can still fail on your data and workflow. Testing shows whether published evidence transfers to your specific constraints.
Read article
How to choose an AI model architecture
AI model architecture shapes what a system can learn and where performance plateaus. Prompting or more data cannot fix an architectural mismatch.
Read article
When should you use synthetic data?
Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
Read article