Model the physical world accurately,then visualise it
The value of a digital twin is not the 3D scene but whether the model matches the site, the data is live and the conclusions guide operations. We start from asset inventory and data ingestion and deliver a twin that stays in sync with the site.
Models from the site, not from imagination
Built from drawings, point clouds and asset registers; geometry, topology and attributes map one-to-one to the site and are maintained as the site changes.
Live data, one definition
SCADA, PLC, IoT and business systems feed a unified time-series and semantic model; every metric traces back to its source tag and timestamp.
Built for operations, not demos
Alert location, condition replay, predictive maintenance and emergency drills in one interface, producing actionable recommendations with a recorded basis.
Digital twin capabilities
Six capability areas from modelling to operations, delivered individually or as a complete twin platform.
3D modelling and scene building
BIM, point-cloud and manual modelling of plants, lines, data centres and campuses, with lightweighting and LOD tiers for web and control-room displays.
Data ingestion and semantic modelling
OPC UA, Modbus, MQTT and business database ingestion; a unified asset model and metric definitions mapping tags to equipment to process.
Live monitoring and alert linkage
Key metrics mapped to the model in real time; threshold and rule alerts located in 3D space and linked to video, work orders and emergency plans.
Simulation and prediction
Condition replay from historical data, capacity and energy simulation, equipment life and failure prediction for scheduling and maintenance.
Platform and integration
Two-way integration with MES, EAM, SCADA and the security operations platform; permission model tied to enterprise identity.
Operations and evolution
Ongoing maintenance of models and data, metric iteration and scenario expansion under annual terms, keeping the twin consistent with the site.
A dashboard displays; a twin operates. The difference is whether the model matches the site, whether data arrives in real time, and whether conclusions feed back into operations. Acceptance requires that a change on site is detected and handled in the model.
Customer story
Reallysec Empowers Siemens Digital Industries, Leading the Intelligent Future
With visualisation and analytics in place, Siemens DI gained full-path visibility into product master data flows, plus anomaly self-healing.