Data centres
Independent advice on where machine learning genuinely moves the numbers in data-centre operations and construction — and where it’s noise. We start from your facility and your evidence, not from a model looking for a problem.
What AI is actually doing in data centres
Machine-learning control of cooling plant has cut energy use in hyperscale facilities, and reinforcement-learning controllers can hold thermal targets while trimming consumption. It is real and proven — but it depends on dense, trustworthy telemetry and carefully bounded control, with safe fallback to conventional operation.
Anomaly detection on equipment telemetry — chillers, pumps, switchgear — can surface developing faults before they become outages. The value is in the catch rate and the false-alarm rate, which is an evidence question, not a slogan.
Better forecasting and response can lower the cost and carbon of the power you buy, particularly where on-site generation or flexible load is in play.
On a build, computer-vision progress tracking compares site reality against the programme and flags slippage early — useful precisely because schedule risk on data-centre construction is expensive.
How we approach it
We diagnose first. Before anyone talks about models, we work out where the measurable opportunity actually is for your facility or your build, what it would take to do it safely, and — just as often — where AI isn’t the right answer and something simpler is. The scope stays narrow and evidence-led. You get a clear read on what’s worth doing, not a platform you didn’t ask for.
The evidence
Or describe your own problem and see the closest evidence we have.