Real-time Anomaly Detection
Identifies ENERGY_PIT, KWH_DECREASE, POWER_LOSS and 12+ more anomaly types instantly as sessions stream in via OCPP.
From raw OCPP data to actionable insights — the full observability stack in one platform.
Identifies ENERGY_PIT, KWH_DECREASE, POWER_LOSS and 12+ more anomaly types instantly as sessions stream in via OCPP.
Every anomaly receives a 0.0–1.0 risk score computed by our ML model, enabling priority-based triage at scale.
Full OCPP 1.6 / 2.0 protocol coverage. Track heartbeats, authorize transactions, and verify status notifications.
Manage multiple clients or sites under one platform. Isolated data, role-based access, and per-org dashboards.
Route detected anomalies to technicians automatically. Track status from OPEN → IN_REVIEW → RESOLVED with audit trail.
Visualize kWh delivery, session duration trends, and fleet-wide efficiency metrics with interactive time-series charts.
Three steps from raw OCPP data to a fully monitored charging network.
Point your charge point management system at our endpoint. Supports OCPP 1.6 and 2.0 over WebSocket — no agent install required.
Our model processes every session event as it streams in. Anomalies are identified, classified, and risk-scored within 2 seconds.
Detected events are routed to the right team member. Track status, add notes, and close the loop — all from one dashboard.
Join operators already protecting their charging infrastructure with AI-powered anomaly detection.
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