Every energy system wastes more than it admits. Buildings over-cool empty rooms, factories run furnaces on schedules set years ago, and grids shed renewable power on windy nights because they cannot predict what comes next. AI exists to close that gap - and it is turning out to be the fastest, cheapest decarbonisation tool we have.
Our energy practice spans three layers: individual assets such as buildings and plants, the distribution networks that carry power, and the markets that set its price. The through-line is the same: forecast accurately, optimise continuously, and act inside safe limits.
The challenge
Traditional energy management runs on rules of thumb and worst-case assumptions. A building is cooled for the hottest expected day, not the actual one. A plant keeps spare boilers hot in case demand spikes. A grid holds fossil-fuel plants on standby to cover the variability of solar and wind.
Those assumptions keep the lights on, but they cost money and carbon. The opportunity is to replace the worst case with the likely case, updated continuously from real data.
Our approach
We begin with a digital model of the asset or network, calibrated against its real behaviour. From there, forecasting models predict demand, price, weather and renewable output at the horizons that matter - minutes ahead for control, days ahead for scheduling.
- Buildings. Occupancy, weather and tariff signals drive heating, cooling and lighting in real time, trimming load without touching comfort.
- Industry. Production schedules bend toward the hours when power is cheapest and cleanest, without missing a deadline.
- Grids. Demand is shifted, batteries are dispatched and outages are anticipated before they cascade.
The technology
At the heart of it are time-series transformers for forecasting and reinforcement-learning controllers for action. A building controller learns the thermal behaviour of a specific structure - how quickly it heats, how it responds to sun on its glass - and then finds the cheapest way to keep people comfortable. A grid controller treats thousands of flexible loads as one coordinated resource.
Because energy mistakes are expensive and sometimes dangerous, every controller operates inside hard constraints: minimum comfort, maximum ramp rates, equipment limits. The AI optimises within the fence, never outside it.
A demand forecast accurate fifteen minutes ahead is worth more than a perfect forecast next month.
Cooling the cloud
Data centres are among the world's hungriest buildings, and cooling is the largest controllable cost. At scale, machine-learning controllers have cut cooling energy by double-digit percentages by predicting how heat moves through the facility and adjusting chillers and airflow before temperatures rise - all without raising server temperatures beyond their limits. It is a clear example of a problem where a good model beats a good engineer, simply because it can consider more variables at once.
What it delivers
Energy is the rare domain where savings show up directly on the balance sheet. The same optimisation that reduces carbon also reduces the bill, which makes AI unusually easy to justify.
Representative outcomes
- 10-30% lower energy use in optimised buildings and cooling systems.
- Lower peak demand and smaller bills through load shifting.
- More renewable energy absorbed instead of curtailed.
- Fewer carbon tonnes for the same production output.
- Longer equipment life from gentler, data-driven operation.
A global picture
Energy markets differ sharply by region, and so does the work.
- Europe leads on demand response and building efficiency, driven by high prices and steep climate targets.
- The United States sees rapid growth in grid-scale batteries and AI-driven trading of renewable power.
- China applies AI to vast industrial loads and the world's largest build-out of solar and wind.
- The Gulf uses AI to optimise cooling in extreme heat - the single biggest load in the region.
- Africa and South Asia leapfrog to smart metering and AI-managed microgrids where the grid is thin.
The human side
Efficiency projects fail when they ignore the people inside the building. A controller that saves energy by making offices uncomfortable will simply be switched off. We design with operators and occupants from the start, so automation earns its place rather than fighting for it.
What is next
The frontier is coordination across whole regions: fleets of buildings, vehicles and batteries trading energy with each other to smooth the demand curve. As electrification spreads, the grid's flexibility - not just its generation - becomes the most valuable asset, and AI is what makes that flexibility usable.