The essential foundation for reliable charger operations
Core alerts powered by Intelligent Detection
Fault, offline, and critical fault detection
Behavior interpretation and pattern analysis
Automatic resets and end-to-end monitoring
A broader automation layer for proactive operations
Automatic ticketing and advanced alerting
Custom integrations and intelligent metrics
Output variations, utilization, and success rates
Move from reactive to proactive stability
Structured insights for data-driven management
KPIs and statistics dashboard
Descriptive analytics and reporting
Scheduled and custom reports
Consistent reporting across your network
Infrastructure models for long-term optimization
CAPEX-focused forecasting
Infrastructure investment planning
Site-level optimization and smart pricing
Hardware replacement and competitive analysis
Reduced Time per Ticket
Reduced Time To Resolution for Faults
Offline Duration Reduced per Case
It is achieved through data consolidation, meaning all information is instantly fetched and curated from multiple sources and large unstructured logs, using reinforcement learning techniques trained on historical data to enable instant identification of root causes and recommended actions.
Key Result: 45% reduction in time per ticket
It is achieved through the use of machine learning models to detect performance deviations relative to predicted patterns, that reveal hidden or indicate future issues.
In a case with a partner, we identified 101 activity deviations on high-frequency chargers in a month.
Within 24hrs of a reset, 86 of these chargers were charging cars again, all of which had been idle for multiple days at the time of reset.
It is achieved through dynamic visualization and benchmarking of charger, configuration, site and network metrics, allowing users to identify patterns, anomalies and improvement opportunities.
In a case we observed that the failure rate of all charging attempts was 32.92% for a group of chargers. Out of 1713 charging attempts, 564 never started, meaning nearly a third of potential consumption was lost.
Following our engine’s automatic recommendation, based on the analytics, a firmware update was applied, reducing the failure rate to only 6.12%.
Since then, just 148 out of 2416 charging attempts have failed, increasing consumption on those chargers by almost 40%.
It is achieved through predictive modeling and scenario analysis that connect operational data with market, utilization and infrastructure trends to guide long-term decisions.
Supported by:
