IoT Integration

Description
The power of prediction begins at the point of data collection.
VIRA™ Predict integrates with a wide range of IoT-enabled field devices to capture the real-time telemetry needed for deviation detection, procedural validation, and governance alerting. Voltage sensors, PPE wearables, badge access logs, LOTO state sensors, and test equipment telemetry all feed into the platform’s AI and Digital Twin engines — ensuring every risk signal is captured and contextualized.
By aggregating data from both human- and machine-generated inputs, VIRA™ Predict enables a complete situational awareness loop — transforming fragmented site data into a cohesive governance framework for high-risk electrical activities.
Supports ingestion from voltage monitors, relay panels, RFID badge readers, breaker status sensors, and environmental sensors. These IoT inputs serve as the foundation for evaluating whether field conditions match procedural expectations.
Tracks proximity, personnel movement, and PPE compliance using integrated wearables and access logs. This enables validation of who performed the work, when, and under what safety conditions.
Synchronizes task execution data (MOPs, LOTO steps, energized actions) with telemetry time stamps to detect timing mismatches or unauthorized activity. This supports forensic reconstruction and real-time alerts.
Designed with open integration logic to support future sensor types and 3rd-party safety devices. VIRA™ Predict ensures long-term compatibility and scalability across IoT ecosystems in hyperscale construction environments.

Get Answers to Common Questions
From data fidelity to device compatibility, this section addresses how VIRA™ Predict uses IoT telemetry to fuel its AI safety engine and Digital Twin validation logic.
VIRA™ Predict supports voltage sensors, RFID badge systems, wearable safety trackers, isolation point sensors, cameras, and more. If the device outputs telemetry or task data, it can be ingested and contextualized.
IoT devices provide the real-time, ground-level data needed to compare field activity against the procedural expectations set by the Digital Twin. The AI engine uses this data to detect skipped steps, premature energization, or unverified actions.
Yes. VIRA™ Predict is built with a modular API framework designed to integrate with existing IoT infrastructure — including hardware from third-party vendors commonly used in commissioning, access control, and safety enforcement.
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