A digital twin used to be a mirror — a live model that showed you what was happening. Add AI and it becomes something more useful: a model that tells you what's about to happen, and what to do about it.
A digital twin is a virtual replica of a physical asset or system, continuously fed live sensor data so it mirrors the real thing's state. Adding AI changes its job: instead of only reflecting the present, an AI-powered twin predicts future behaviour, flags anomalies early, recommends or automates optimal actions, and runs what-if scenarios. In energy, that means predictive maintenance, fewer surprises, and continuous optimisation of assets that are expensive to get wrong.
“Digital twin” is one of those terms that gets stretched until it means everything and nothing. Stripped back, it’s simple: a digital twin is a virtual replica of a physical thing — an asset, a process, or a whole system — kept in sync with reality by a stream of live data. The value is that you can observe, test and simulate against the twin without touching (or risking) the real asset.
The live data feed. A static 3D model or a one-off simulation is not a twin. A twin is continuously updated from sensors on the real asset, so it reflects the current state — temperature, load, wear, output — not a snapshot from commissioning day. That connection to reality is the whole point.
A plain twin is descriptive: it tells you what is. AI makes it predictive and prescriptive:
A descriptive twin answers “what is happening?” An AI-powered twin answers “what will happen, and what should we do about it?” — which is the question operators actually need answered.
Anywhere an asset is expensive, hard to inspect, or costly to get wrong: predictive maintenance of generation and grid equipment; optimisation of battery storage cycling and degradation; tuning of building or process energy use; and de-risking changes to complex systems by testing them virtually first. The common thread is high stakes and rich sensor data — exactly the conditions where guessing is expensive.
A good twin is a full-stack problem: reliable sensing and data acquisition at the edge, a clean and trustworthy data pipeline, a model that genuinely fits the physics and the operation, and an interface (often programmatic) that lets the twin’s outputs flow into real decisions. Weakness in any layer undermines the rest — a twin fed bad data is worse than no twin, because it’s confidently wrong.
This is our core territory — hardware to cloud, models to decisions. It builds on the same foundations as AI in the energy sector and anomaly detection in operational data, with the trust considerations we raise in black-box decision making.
Related capability · AI & Mathematics →
From sensing to models to decisions, we build digital twins on data you can trust. Let's talk about your assets.