Visionaize is pushing its AI-powered digital twin platform at power utilities that lose hours hunting through disconnected systems to diagnose one failing asset. The industrial AI company says the software covers generation, transmission, and distribution operators.
Utility infrastructure is sprawling and geographically-scattered, and the information about it sits in SCADA, GIS, AMI, enterprise asset management systems, maintenance applications, engineering drawings, and technical documents. Visionaize builds an interactive 2D/3D digital twin and ties each physical asset to all the records that describe it.
Sumanta Basu, Head of Innovation at Visionaize, said: “Utilities already have enormous amounts of data. The challenge is transforming fragmented information into actionable intelligence.
“Visionaize connects operational, engineering, and enterprise information around the physical asset and adds AI-driven intelligence, enabling utilities to improve reliability, maintenance efficiency, workforce productivity, and capital decision-making.”
The company cites industry benchmarks for what such programmes can target. These include a 10-20 percent cut in asset management and maintenance costs, and a 15-30 percent drop in unplanned outages and downtime. They also include a 10-15 percent improvement in capital investment effectiveness, and savings of up to 15 percent on selected field and overhead maintenance, mainly through fewer site visits, inspections, and truck rolls.
Visionaize is explicit that these are not customer results. The figures are benchmarks and potential outcomes drawn from comparable digital transformation work. Real returns depend on infrastructure, use cases, deployment scope, operating maturity, and baseline performance.
SCADA, GIS, and AMI data sources
The platform can ingest data from DCS, SCADA, and APC systems, along with GIS and geospatial platforms. It also draws on AMI and smart-meter infrastructure, IoT and condition-monitoring sensors, and ERP and asset management software. Engineering drawings, specifications, maintenance histories, inspection records, and equipment manuals complete the list.
The practical upshot is that a technician no longer opens a string of applications to investigate one transformer—they open the twin and find the relevant material attached to the equipment itself.
Visionaize also argues the approach improves safety. Engineers can review a facility remotely, locate equipment, pull up documents, and plan maintenance before anyone travels to site. That cuts unnecessary field visits and reduces exposure to high-voltage and other hazardous environments.
The company highlights four stages of digital maturity: Visualize, Contextualize, Predict, and Act. The first gives users a 2D/3D view of the infrastructure. The second links equipment to operational data, engineering documents, maintenance records, and enterprise information. Predictive analytics then flags abnormalities, deterioration, and developing risks so staff can prioritise maintenance and respond before faults escalate.
Listed applications include transformer condition monitoring, switchgear and circuit-breaker health assessment, substation equipment monitoring, and transmission asset inspection. Predictive maintenance, anomaly detection, fault investigation, and root-cause analysis complete the set.
Catching problems earlier should mean less emergency maintenance, better asset availability, and shorter outage response times.
Contextualised data architecture for utility AI agents
Visionaize’s argument is that an AI system reading a lone sensor value or a single document knows too little—it must also know which asset the data belongs to, where that asset sits, and how it connects to neighbouring infrastructure. In addition, it needs the governing documentation and the asset’s operating history.
The company says its architecture supplies that context by linking OT, IT, and engineering information around each physical asset.
Vikas Agrawal, CEO of Visionaize, explains: “The next generation of digital twins will do much more than show operators what infrastructure looks like. They will help understand what assets are doing, identify emerging risks, explain potential causes, and support the next best action.
“That is where digital twin technology begins to deliver significant operational and financial value.”
The platform’s listed use cases span generation, transmission, substations, and distribution networks. They cover asset condition monitoring, grid and substation visualisation, remote inspection, AI-assisted troubleshooting, maintenance planning, engineering information retrieval, asset lifecycle management, and operational decision support.
Visionaize says grids are absorbing renewable generation, distributed energy resources, intelligent substations, and advanced metering. Its goal is to make the digital twin the operational interface between the physical grid and engineering information, operational systems, enterprise data, and AI.
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