What Is a Digital Twin and How Does It Work?
A digital twin is a virtual representation of a physical object, process, system or person.
Unlike a static digital model, a digital twin can use information from its real-world counterpart to reflect current conditions, simulate behaviour, test possible changes and support better decision-making.
Digital twins were initially associated mainly with engineering, manufacturing and infrastructure. The term now also covers virtual representations of business processes, customer journeys and people.
For example, a manufacturer might create a digital twin of a machine to monitor its performance. A business could also create a human digital twin that combines an AI avatar, voice clone and multilingual video technology.
This guide explains what a digital twin is, how digital twins work, what technology powers them and how they are created.
TLDR: What is a digital twin?
A digital twin is a digital representation of a real object, system, process or person.
It is created using data from the real-world counterpart and can be used to:
- Monitor current conditions
- Simulate different scenarios
- Analyse performance
- Predict possible outcomes
- Identify problems
- Test changes before applying them
- Improve communication or content production
A digital twin usually involves five core elements:
- A real-world object, system, process or person
- Data collected from that real-world subject
- A digital model that represents it
- A connection between the physical and digital versions
- Software that analyses, displays or acts on the information
Some digital twins update continuously using real-time data, while others are updated at intervals or created for a particular task.
Digital twin explained simply
The easiest way to understand a digital twin is to imagine a living digital version of something that exists in the real world.
For example, consider a wind turbine.
Sensors on the physical turbine might collect information about:
- Temperature
- Rotation speed
- Energy output
- Vibration
- Weather conditions
- Component performance
This information is sent to a digital model of the turbine.
Engineers can then use the digital twin to understand how the turbine is performing, identify unusual behaviour and test what might happen under different conditions.
A human digital twin works differently but follows a related principle.
Instead of collecting sensor data from a machine, the process may collect:
- Video recordings
- Photographs
- Voice recordings
- Facial movements
- Speech patterns
- Approved scripts
- Language preferences
These inputs can be used to create a digital version of the person for videos, presentations, training or multilingual communication.
Nertia creates high-fidelity human digital twins that combine visual avatars, voice modelling and video dubbing.
How does a digital twin work?
A digital twin works by connecting information from a real-world subject to a digital representation.
The exact process varies depending on what is being represented, but most digital twins follow six stages.
1. Define the real-world subject
The process begins by identifying what the digital twin will represent.
This could be:
- An individual machine
- A complete production line
- A building
- A vehicle
- A supply chain
- A business process
- A customer journey
- A person
The organisation must also define what it wants the digital twin to achieve.
For example, the goal might be to predict equipment failure, reduce energy consumption, test a workflow or create video content without repeated filming.
2. Collect relevant data
Data is gathered from the real-world subject.
For physical digital twins, this may include:
- Sensor readings
- Equipment logs
- Location data
- Temperature readings
- Performance data
- Maintenance records
- Environmental conditions
For process digital twins, the data may come from:
- CRM systems
- Workflow platforms
- Analytics tools
- Transaction records
- Customer interactions
- Operational reports
For human digital twins, the inputs might include:
- Video footage
- Voice recordings
- Photographs
- Facial expressions
- Mannerisms
- Speech patterns
- Approved knowledge or scripts
The data selected should relate directly to the purpose of the digital twin. Collecting more information does not automatically produce a better model if that information is inaccurate or irrelevant.
3. Build the digital model
The collected information is used to create a digital representation.
Depending on the use case, this representation might be:
- A three-dimensional model
- A dashboard
- A mathematical simulation
- A software-based process model
- A visual AI avatar
- A voice model
- A combination of several technologies
The model must represent the characteristics that are important to the intended outcome.
A digital twin of a machine might focus on component behaviour and performance. A digital twin of a person might focus on appearance, voice and presentation style.
4. Connect the model to data
The digital representation is connected to information about its real-world counterpart.
This connection may operate:
- In real time
- At scheduled intervals
- When new data becomes available
- Through manual updates
- During a specific project or simulation
A real-time connection is useful when a business needs to monitor changing conditions continuously.
However, not every digital twin requires a live data connection. A human digital twin used to produce approved videos, for example, may be updated when new scripts or recordings are supplied.
5. Analyse, simulate or generate outputs
Once the digital model is established, it can be used to perform tasks.
Depending on the type of twin, these might include:
- Monitoring current performance
- Detecting unusual activity
- Predicting maintenance needs
- Testing changes
- Comparing possible outcomes
- Identifying inefficiencies
- Producing video or audio
- Presenting information
- Supporting customer interactions
Artificial intelligence may be used to identify patterns, generate content or make predictions.
6. Apply the findings
The final stage is using the digital twin’s outputs to improve the real-world subject or process.
For example:
- A maintenance team repairs a machine before it fails
- A building manager adjusts energy usage
- A logistics company changes a delivery route
- A business redesigns an inefficient workflow
- A marketing team creates translated videos using a human digital twin
- A website presents customers with more accessible information
The digital twin becomes valuable when its outputs lead to a useful action, decision or improvement.
The digital twin process at a glance
| Stage | What happens | Example |
|---|---|---|
| Define | The real-world subject and objective are identified | Monitor a production machine |
| Collect | Relevant information is gathered | Temperature and vibration data |
| Model | A digital representation is created | A virtual model of the machine |
| Connect | Data is transferred to the model | Live sensor updates |
| Analyse | The model identifies patterns or simulates outcomes | Detects unusual vibration |
| Act | The business applies the insight | Schedules maintenance |
What makes a digital twin different from a digital model?
A digital model is a digital representation of an object or system, but it does not necessarily exchange information with the real-world version.
A digital twin is generally more dynamic.
| Feature | Digital model | Digital twin |
|---|---|---|
| Represents a real subject | Yes | Yes |
| Uses real-world data | Sometimes | Usually |
| Updates when conditions change | Not necessarily | Often |
| Supports monitoring | Limited | Common |
| Supports simulation | Sometimes | Common |
| Connected to the real-world counterpart | Usually no | Usually yes |
| Can support predictions | Limited | Possible with analytics or AI |
A three-dimensional model of a building is not automatically a digital twin.
It becomes closer to a digital twin when it receives information about the building, such as occupancy, temperature, energy use or maintenance status, and uses that information to reflect changing conditions.
Digital twin vs simulation
A simulation is used to model how something might behave under a particular set of conditions.
A digital twin can include simulation, but it may also connect to current or historical information about a specific real-world subject.
| Digital twin | Simulation |
|---|---|
| Represents a particular real-world object, process or person | May represent a general or hypothetical system |
| Often connected to real data | Can operate using assumed data |
| May update as the real subject changes | Usually runs for a defined scenario |
| Can support ongoing monitoring | Commonly used for individual tests |
| May contain several simulation models | Is often one component of a wider digital twin |
For example, a simulation could test how a generic engine performs under high temperatures.
A digital twin could use information from one specific engine to test how that engine may perform under the same conditions.
Digital twin vs AI
A digital twin is not the same as artificial intelligence.
The digital twin is the digital representation of the real-world subject. AI is one of the technologies that may help the digital twin analyse information, recognise patterns, make predictions or generate outputs.
A digital twin does not always require AI.
A basic digital twin might simply display live data from a machine. A more advanced version might use machine learning to predict when a component is likely to fail.
For a human digital twin, AI may be used to:
- Generate speech from written text
- Recreate a person’s voice
- Produce facial movements
- Translate content
- Synchronise speech and video
- Answer approved questions
- Generate presenter-led content
The distinction can be summarised as follows:
- Digital twin: what is being represented
- Artificial intelligence: how the system may analyse, predict, interact or generate
- Data: the information used to build or update the representation
- Software: the platform through which the model is managed and used
Types of digital twins
Digital twins can be grouped in several ways. One common approach is to categorise them according to the scale or subject they represent.
Component digital twin
A component twin represents an individual part within a larger asset.
Examples include:
- A motor
- A turbine blade
- A battery
- A pump
- A vehicle component
It can help teams understand how that specific part performs and when it may require attention.
Asset digital twin
An asset twin represents a complete physical asset made up of several components.
Examples include:
- A vehicle
- A wind turbine
- A production machine
- A piece of medical equipment
The twin can show how the components interact and how the asset performs as a whole.
System digital twin
A system twin represents a collection of connected assets or processes.
Examples include:
- A factory
- An energy network
- A transport system
- A building
- A warehouse
It provides a wider view of how several parts work together.
Process digital twin
A process twin represents a workflow or sequence of activities.
Examples include:
- A manufacturing process
- A customer onboarding journey
- A supply chain
- A sales process
- A logistics operation
It can help a business identify delays, bottlenecks and opportunities for improvement.
Customer digital twin
A customer digital twin represents aspects of customer behaviour, requirements or interactions.
It may use information from:
- Website activity
- Purchase history
- Service interactions
- Preferences
- Customer journeys
Businesses should use customer data responsibly and ensure their collection and processing practices comply with relevant privacy requirements.
Human digital twin
A human digital twin is a digital representation of a person.
The term can describe different technologies depending on the context.
In healthcare or scientific research, it may refer to a digital model of biological or physiological information.
In business communication, a human digital twin may combine:
- A video avatar
- A photo avatar
- A cloned voice
- Facial movements
- Speech patterns
- Multilingual dubbing
- Approved knowledge
- Interactive AI
This type of digital twin can be used for videos, training, presentations and communication without requiring the person to record every output manually.
Types of digital twins compared
| Type | Represents | Common purpose |
|---|---|---|
| Component twin | One individual part | Monitor component performance |
| Asset twin | A complete physical asset | Understand overall asset behaviour |
| System twin | Connected assets or environments | Optimise a wider system |
| Process twin | A workflow or operation | Identify inefficiencies |
| Customer twin | Customer activity or behaviour | Improve customer experiences |
| Human twin | A person’s appearance, voice or data | Communication, modelling or content |
What technology powers digital twins?
Digital twins are not usually created using one technology alone. They combine several systems depending on the intended purpose.
Sensors and connected devices
Sensors collect information from physical objects and environments.
They may measure:
- Temperature
- Pressure
- Movement
- Location
- Speed
- Energy consumption
- Sound
- Vibration
- Humidity
Internet of Things devices can transfer this information from the physical subject to the digital system.
Data platforms
Digital twins may use information from databases, analytics tools, CRM systems, operational platforms and cloud services.
These systems help collect, organise and make information available to the model.
Cloud computing
Cloud infrastructure can provide the storage and processing capacity required to manage large or frequently changing data sets.
It can also make the digital twin accessible to different teams and locations.
Edge computing
Some information may be processed near the physical object rather than being sent to a distant cloud server first.
This can be helpful when a system needs a fast response or has limited connectivity.
Artificial intelligence and machine learning
AI and machine learning can help digital twins:
- Recognise patterns
- Identify anomalies
- Forecast possible outcomes
- Recommend actions
- Generate content
- Process language
- Adapt to new information
AI is particularly important for predictive digital twins and interactive human digital twins.
Three-dimensional modelling
Three-dimensional models can represent physical objects, buildings and environments visually.
These models may be connected to performance or environmental data to show how the physical subject is changing.
Simulation software
Simulation tools allow teams to test possible events or changes without immediately affecting the real-world subject.
For example, a company might test how a production line responds to increased demand before changing the live operation.
APIs and system integrations
Application programming interfaces allow the digital twin to exchange information with other software.
A digital twin might connect to:
- Analytics platforms
- CRM systems
- Maintenance software
- Websites
- Mobile applications
- Customer support platforms
- Business dashboards
Visual and voice AI
Human digital twins use specialised technologies to represent a person’s appearance and speech.
These may include:
- AI avatar generation
- Facial tracking
- Lip synchronisation
- Voice cloning
- Text-to-speech generation
- Language translation
- Video dubbing
Nertia’s digital twin service brings visual avatar production, authentic voice modelling and multilingual video together within a managed workflow.
Do digital twins always use real-time data?
No. Not every digital twin operates in real time.
Digital twins can use:
- Live data
- Frequently updated data
- Historical data
- Manually supplied information
- Recorded visual or audio data
- A combination of several sources
The update frequency should match the purpose of the twin.
A digital twin used to monitor critical machinery may require continuous updates.
A human digital twin used to create pre-approved videos may only need new information when a script, visual model or voice setting is changed.
The defining feature is the meaningful relationship between the digital representation and its real-world counterpart, not simply whether the system receives data every second.
How is a digital twin created?
The creation process depends on the complexity and intended use of the twin.
A typical project follows these steps.
Step 1: Establish the objective
The organisation should identify the problem it wants the digital twin to solve.
Examples include:
- Reducing equipment downtime
- Improving a workflow
- Monitoring energy consumption
- Testing operational changes
- Scaling presenter-led content
- Producing multilingual videos
A clear objective helps determine what data and technology are actually required.
Step 2: Define the required level of detail
Not every digital twin needs to recreate every feature of the real-world subject.
A machine twin designed to track energy consumption may not need a highly detailed visual model.
A human digital twin designed for professional marketing videos, however, may require accurate appearance, voice and facial delivery.
Step 3: Gather suitable data
The project team collects the information needed to build the model.
The data should be:
- Relevant
- Accurate
- Consistent
- Secure
- Collected with appropriate permission
- Suitable for the intended use
Step 4: Create the model
Specialist software is used to produce the digital representation.
This may involve:
- Engineering models
- Three-dimensional modelling
- Process mapping
- Data modelling
- AI model training
- Avatar creation
- Voice modelling
Step 5: Connect relevant systems
The digital twin is connected to the sources and platforms it needs.
This may include sensors, databases, dashboards, websites or content-production systems.
Step 6: Test the twin
The team checks whether the model accurately represents the important characteristics of the real-world subject.
Testing may assess:
- Data accuracy
- Model behaviour
- Predictions
- Visual quality
- Voice quality
- Response speed
- Integration reliability
- Security
Step 7: Deploy and monitor
The digital twin is introduced into the intended environment.
Its performance should be monitored and refined as new data, requirements and use cases emerge.
Example: How a machine digital twin works
Imagine a company wants to reduce unexpected breakdowns in a production machine.
The process might work as follows:
- Sensors collect temperature, vibration and output data.
- The data is sent to a digital representation of the machine.
- The system compares current readings with expected operating patterns.
- Analytics identify unusual changes.
- The digital twin estimates whether a component may be deteriorating.
- The maintenance team reviews the information.
- The component is inspected or replaced before a major failure occurs.
The digital twin does not physically repair the machine. It gives the team information that helps them decide when and how to act.
Example: How a human digital twin works
Imagine a company founder regularly records product explainers and company updates.
Creating a human digital twin might involve:
- Recording the founder on camera
- Capturing facial expressions and presentation style
- Recording their voice in a controlled environment
- Creating a visual avatar
- Creating an approved voice model
- Writing and reviewing a new script
- Generating a video using the avatar and voice
- Reviewing the final output before publication
- Adapting the video into additional languages when needed
The digital twin can then help the founder produce repeatable content without attending every recording session.
This process should involve clear consent, controlled access and an approval system governing how the person’s appearance and voice may be used.
Digital twin vs AI avatar
An AI avatar is a visual representation of a person or character.
A human digital twin can be broader.
It may combine an AI avatar with:
- A voice model
- Mannerisms
- Language capabilities
- Approved knowledge
- Interactive features
- Content-generation workflows
| AI avatar | Human digital twin |
|---|---|
| Primarily represents appearance | Represents multiple aspects of a person |
| May use a generic voice | Can use an approved clone of the person’s voice |
| Often used for visual content | Can support content, training and communication |
| May not use personal knowledge | Can be connected to approved information |
| Can be fictional | Usually relates to a particular person |
Not every AI avatar is a digital twin. The term is more appropriate when the digital representation is meaningfully connected to a real person and reproduces several aspects of their identity or behaviour.
Digital twin vs chatbot
A digital twin represents something that exists in the real world.
A chatbot is a conversational interface designed to receive questions and provide responses.
| Digital twin | AI chatbot |
|---|---|
| Represents an object, process, system or person | Manages conversations |
| May be visual, operational or data-based | Usually text or voice based |
| Can support simulation and monitoring | Primarily supports communication |
| Does not always interact with users | Designed for interaction |
| May incorporate a chatbot | Can operate without a digital twin |
The two technologies can work together.
For example, an AI chatbot could interpret a website visitor’s question, while a human digital twin presents the answer through a visual avatar and voice.
Benefits of digital twins
Better visibility
A digital twin can bring information from several sources into one digital representation, helping teams understand the current condition of an object or process.
Safer testing
Businesses can explore potential changes digitally before applying them to live equipment or operations.
Predictive insights
Advanced digital twins can use historical and current data to identify patterns and estimate future outcomes.
Reduced downtime
Identifying unusual equipment behaviour earlier may help maintenance teams act before a complete failure occurs.
Improved efficiency
Digital twins can reveal bottlenecks, wasted energy, unnecessary steps and underperforming components.
More consistent communication
Human digital twins can deliver approved messages across videos, presentations and languages.
Greater scalability
A digital representation can help a business monitor more assets, test more scenarios or create more content without increasing physical resources at the same rate.
Limitations and challenges
Data quality
A digital twin is only as useful as the information used to build and update it.
Incomplete, outdated or inaccurate data can produce unreliable outputs.
Technical complexity
Large operational digital twins can require specialist software, integrations, infrastructure and ongoing management.
Implementation cost
Costs vary significantly depending on the subject, model complexity, data requirements and level of integration.
A human avatar used for video production will have very different requirements from a real-time digital twin of a factory.
Privacy
Digital twins may process personal, operational or commercially sensitive information.
Organisations must understand what information is being collected, why it is needed and who can access it.
Cybersecurity
A connected digital twin may create additional routes through which business systems or sensitive data could be accessed.
Security should be considered throughout the project rather than added only after deployment.
Model accuracy
A digital representation cannot perfectly reproduce every aspect of the real world.
Businesses should understand the twin’s assumptions and limitations before relying on its outputs.
Consent and identity protection
Human digital twins should only be created with the informed permission of the person being represented.
The organisation should define:
- Who owns the digital assets
- Who can generate content
- Which uses are permitted
- How the model is secured
- How approval can be withdrawn
- What happens when the working relationship ends
How much does a digital twin cost?
There is no single standard price for a digital twin.
The cost depends on factors including:
- What is being represented
- The amount of data required
- Whether live data is needed
- The complexity of the model
- The number of integrations
- The required accuracy
- The visual detail
- The need for AI or predictive capabilities
- Ongoing storage and maintenance
- Security and compliance requirements
An industrial digital twin connected to thousands of sensors may require substantial infrastructure and specialist development.
A human digital twin for video, voice and multilingual communication may be more accessible because it uses a narrower and more defined production workflow.
Businesses should begin with a clear use case and assess the likely value before investing in a large-scale implementation.
Where are digital twins used?
Digital twins are used across sectors including:
- Manufacturing
- Energy
- Transport
- Construction
- Property
- Healthcare
- Retail
- Logistics
- Software
- Education
- Media
- Professional services
Their purpose differs across each industry.
For a detailed breakdown of practical applications, read our guide to digital twins for business, use cases and real-world applications.
How digital twins can be used on websites
A digital twin can form part of a wider digital customer experience.
For example, a website could include:
- A video avatar introducing the business
- A digital spokesperson explaining services
- Multilingual video content
- Product demonstrations
- Employee training resources
- An AI chatbot answering visitor questions
- Personalised onboarding content
The technology should support a clear user journey rather than being added only for novelty.
Nertia’s website design and development service can help businesses organise video, written content, chatbots and interactive tools within a clear, conversion-focused website.
The future of digital twin technology
Digital twins are likely to become more accessible as AI, connected devices, cloud infrastructure and no-code tools continue to develop.
Future digital twins may offer:
- Faster real-time updates
- More accurate simulations
- Better predictive capabilities
- More natural digital humans
- Real-time multilingual communication
- Deeper integration with business software
- Easier creation and management
- Stronger identity and access controls
However, more advanced technology will not remove the need for clear objectives, accurate data and human oversight.
The most valuable digital twins will be those built to solve a specific problem rather than those created simply because the technology is available.
Create a digital twin built around your goals
A digital twin can help your business monitor systems, test ideas, create content or communicate more consistently.
For human digital twins, quality and control are particularly important. Your digital presence should accurately reflect how you look, sound and communicate while giving you clear oversight of where it is used.
Nertia creates bespoke digital twins using high-fidelity visual avatars, authentic voice modelling and multilingual video production.
Whether you need a digital spokesperson, a scalable content workflow or guidance on how a human digital twin could support your business, we can help you plan and create the right solution.