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Digital Twin

Forge

The equipment inside the site — and the sensors that feed the models.

The problem

For every AI project the map of which sensor sits on which equipment, under which tag, was rebuilt by hand — weeks of error-prone preparation per model.

The solution

Standardizes the asset hierarchy and a KKS-validated, ML-ready sensor registry — the data contract that feeds every model.

From the interface

Forge / A look at the platform
AAA · sample tagSample
Representative view; data and site names are examples.
REPRESENTATIVE INTERFACE · SYNTHETIC DATA

This mockup illustrates the product workflow; it is not connected to a live system.

How it works

  1. Version the shared part and asset library and track where items are used.
  2. Build the site equipment hierarchy and connect it to the site twin.
  3. Assign KKS tags and machine-learning readiness to each sensor.
  4. Validate and export the registry to feed model training.

Capabilities

  • Versioned part and asset library with usage tracking
  • Site equipment hierarchy
  • KKS-validated sensor registry
  • Machine-learning readiness
  • CSV export
  • Topology, layer and connection management
  • Site-twin association

What it delivers

Does the sensor mapping once, and makes it reusable across every model instead of redoing it each time.

Related modules

Monitoring

Vitals

Catch drift before it becomes downtime.

Digital Twin

Atlas

Every site, in three dimensions.