The short version
A digital twin is a virtual model of a real object, system or place that is kept up to date with data from its physical counterpart. Because it behaves like the real thing and stays in step with it, you can use it to watch what is happening, test changes safely and predict what will happen next.
In one line: a twin is a model with a live connection to reality — and that connection is what makes it useful for decisions.
The three parts of a twin
Every digital twin, whether it mirrors a single machine or a whole city, is built from the same three parts.
1 · Physical
The real system
The network, building, vehicle or machine being mirrored.
2 · Virtual
The model
Geometry plus the physics or logic that makes it behave like the original.
3 · Connection
The data link
Measurements flowing in to keep the model current, and insights flowing back out.
Twin, model or simulation?
The terms are often used interchangeably, but they describe different things. The difference is mostly about behaviour and connection.
| 3D model | Simulation | Digital twin | |
|---|---|---|---|
| Looks like the real thing | Yes | Sometimes | Yes |
| Behaves like it | No | Yes | Yes |
| Mirrors one specific real system | Sometimes | Often generic | Yes |
| Kept current with real data | No | No | Yes |
How detailed should it be?
As detailed as the question needs — and no more. A twin for planning which rooftops get antennas needs accurate building shapes and materials; a twin for checking a factory's throughput may need almost no geometry at all, only the timing of each step.
Starting from the decision you want to make keeps a twin affordable to build and fast to run. Detail can always be added where the first results show it matters.
Where twins are used
- Wireless networks — planning coverage and testing configuration changes before rollout. See our RAN work
- Cities — infrastructure, traffic and connectivity planning.
- Mobility — testing driver-assistance systems in scenarios too rare or risky to stage.
- Industry — monitoring equipment and predicting maintenance.
- AI — generating realistic training data and testing models in closed loop.
Do you need one?
A digital twin is usually worth it when most of these are true:
- Testing on the real system is slow, expensive or risky.
- You will ask the same kind of question repeatedly, not just once.
- Data about the real system exists, or can be collected.
- The system changes over time, so a one-off study would go stale.