Build a Custom Digital Twin Dashboard Using AI in Minutes
Describe your Digital Twin Dashboard in plain English and create a production-ready build, from design and development to deployment, in minutes. No code needed.
Trusted by Builders Worldwide
Active Builders Worldwide
Apps Successfully Created
Countries Globally Reached
Emergent Key Features for Building a Digital Twin Dashboard
A live model beside real behaviour, with deviation and calibration tracked so the twin stays honest.
Predicted Beside Measured
Model output and real readings are shown together with deviation quantified, so the twin's credibility is visible rather than assumed.
Deviation as an Early Signal
Drift between model and asset is surfaced and ranked, which often indicates a developing fault before any individual reading looks abnormal.
Interactive Simulation
Inputs can be changed and effects recalculated, allowing a setpoint or configuration change to be tested before it is applied physically.
Calibration Accuracy Tracked
Prediction accuracy is monitored over time and by operating condition, so a model degrading quietly does not keep informing decisions.
Gradual against Sudden Drift
Slow divergence is distinguished from a step change, because the first suggests wear and the second suggests an event.
Historical Deviation Retained
Past divergence is stored with what followed, building a record of which deviation patterns preceded real faults.
Digital Twin Dashboard Use Cases You Can Build in Minutes
Keep the Model Honest against Reality
Predicted against measured values for each modelled output over time, deviation in absolute and percentage terms, periods where deviation exceeded tolerance, the input conditions during those periods and current agreement status.
window.awbMockup = { modelAgainstActual: "Build a digital twin dashboard showing predicted against measured values for each modelled output over time, deviation in absolute and percentage terms, periods where deviation exceeded tolerance, the input conditions during those periods, and a current status showing whether the twin and the asset agree.", simulation: "Build a simulation dashboard where an engineer adjusts inputs such as setpoints, load, feed rate or ambient conditions and sees the predicted effect on output, efficiency, throughput and any constraint being approached, comparing several saved scenarios side by side with the current operating point marked.", deviationDetection: "Build a twin deviation dashboard highlighting where measured behaviour has drifted from prediction, ranking deviations by magnitude and duration, showing which modelled component the drift is associated with, deviations that developed gradually against those appearing suddenly, and previous deviations that preceded a known fault.", modelCalibration: "Build a model calibration dashboard showing prediction accuracy per output over time, whether accuracy is degrading, the operating conditions where the model performs worst, parameters most recently recalibrated with the resulting change in accuracy, and the data periods used for each calibration."};
Test a Change before It Reaches the Asset
Adjust inputs such as setpoints, load, feed rate or ambient conditions and see the predicted effect on output, efficiency, throughput and any constraint approached, comparing saved scenarios with the current operating point marked.
window.awbMockup = { modelAgainstActual: "Build a digital twin dashboard showing predicted against measured values for each modelled output over time, deviation in absolute and percentage terms, periods where deviation exceeded tolerance, the input conditions during those periods, and a current status showing whether the twin and the asset agree.", simulation: "Build a simulation dashboard where an engineer adjusts inputs such as setpoints, load, feed rate or ambient conditions and sees the predicted effect on output, efficiency, throughput and any constraint being approached, comparing several saved scenarios side by side with the current operating point marked.", deviationDetection: "Build a twin deviation dashboard highlighting where measured behaviour has drifted from prediction, ranking deviations by magnitude and duration, showing which modelled component the drift is associated with, deviations that developed gradually against those appearing suddenly, and previous deviations that preceded a known fault.", modelCalibration: "Build a model calibration dashboard showing prediction accuracy per output over time, whether accuracy is degrading, the operating conditions where the model performs worst, parameters most recently recalibrated with the resulting change in accuracy, and the data periods used for each calibration."};
Read Drift as an Early Fault Signal
Where measured behaviour has drifted from prediction, deviations ranked by magnitude and duration, the modelled component associated with each, gradual against sudden divergence and previous deviations that preceded a known fault.
window.awbMockup = { modelAgainstActual: "Build a digital twin dashboard showing predicted against measured values for each modelled output over time, deviation in absolute and percentage terms, periods where deviation exceeded tolerance, the input conditions during those periods, and a current status showing whether the twin and the asset agree.", simulation: "Build a simulation dashboard where an engineer adjusts inputs such as setpoints, load, feed rate or ambient conditions and sees the predicted effect on output, efficiency, throughput and any constraint being approached, comparing several saved scenarios side by side with the current operating point marked.", deviationDetection: "Build a twin deviation dashboard highlighting where measured behaviour has drifted from prediction, ranking deviations by magnitude and duration, showing which modelled component the drift is associated with, deviations that developed gradually against those appearing suddenly, and previous deviations that preceded a known fault.", modelCalibration: "Build a model calibration dashboard showing prediction accuracy per output over time, whether accuracy is degrading, the operating conditions where the model performs worst, parameters most recently recalibrated with the resulting change in accuracy, and the data periods used for each calibration."};
Notice a Model Degrading Quietly
Prediction accuracy per output over time, whether accuracy is declining, the operating conditions where the model performs worst, parameters most recently recalibrated with the resulting change and the data used for each calibration.
Notice a Model Degrading Quietly
Prediction accuracy per output over time, whether accuracy is declining, the operating conditions where the model performs worst, parameters most recently recalibrated with the resulting change and the data used for each calibration.
Build a custom digital twin dashboard in 4 simple steps
Run a live model of your asset or process beside its real behaviour, so deviation is detected and changes are tested before they reach the physical system.
Start with the asset or process you are representing, the inputs it responds to, the outputs you want predicted and the tolerance within which model and reality should agree. Emergent builds the model structure and the comparison together.
Link sensor feeds, control setpoints and process data so the twin receives the same inputs as the physical system. Emergent runs the model against live inputs and stores both predicted and actual values, which is what makes deviation measurable.
Ask to add an input, model another output, change a tolerance band or alter the calibration window, and the comparison and simulation views rebuild around the new model.
Publish one link where engineers see live deviation and can adjust model inputs to simulate a change. A proposed setpoint or configuration is evaluated against the model before it is applied to the real system.
More things you can build
with Emergent
AI Website Builder
Build and publish responsive websites with AI — from landing pages to complete websites.
AI App Builder
Create and launch mobile apps with AI, without the complexity of traditional app development.
AI Web App Builder
Build and ship fully functional web apps with custom interfaces, logic, and workflows.
AI Dashboard Builder
Create interactive dashboards to visualize data, track KPIs, and manage business operations.
AI SaaS Builder
Build complete SaaS products with user accounts, subscriptions, dashboards, and core product workflows.
AI CRM Builder
Build custom CRMs for managing contacts, leads, pipelines, campaigns, and automated workflows.
AI Agent Builder
Create AI agents that can understand tasks, use tools, automate workflows, and work across your business.
Build Your Own
Start with any idea and build exactly what you need with AI — no templates or limits.
Why choose Emergent?
Most tools give you a demo you have to rebuild. Emergent gives you a product that is ready to run.
| Comparison | Other Tools |  |
|---|---|---|
| Built for | Demos and MVPs | Products you keep growing |
| What you get | Front end shell only | Full stack, wired end to end |
| Backend and database | Set it up yourself | Built and connected for you |
| Customization | Surface level styling | Deep workflow control |
| Integrations and APIs | Manual glue work | Connected from a prompt |
| Code ownership | Locked to the platform | Clean code you can export |
| Time to launch | Weeks of patching and setup | Live the same day |
Pick the Pricing Plan That Works for You
Choose the plan that fits your building ambitions. From weekend projects to enterprise applications, we've got you covered.
No credit card required*
Find the Right Dashboard Builder
for Your Business
Frequently Asked Questions
Your Questions, Answered
Don't change this element unless you know what you are doing
on Emergent today
