AI-Powered Predictive Maintenance & Energy Analytics
Turn your data into decisions with predictive intelligence.
Galooli Analytics is an AI-powered predictive maintenance engine that processes live and historical data from any connected asset to detect failures before they occur.
It works across hardware from any manufacturer, delivering clear, automated insights with no manual querying and no data science team required.
About the Product
Galooli Analytics continuously processes live and historical data from across your entire network, identifies performance trends and anomalies, and delivers clear, actionable insights automatically. It is hardware-agnostic, pulling data from generators, batteries, solar installations, and any other connected asset regardless of manufacturer.
Galooli Analyst is the built-in AI dashboard suite that analyses energy performance, asset health, and efficiency KPIs without manual querying.
The platform can flag failure signals up to 72 hours before an asset goes down, giving maintenance teams time to act rather than react, reducing maintenance costs by up to 30% compared to reactive approaches.
AI detects patterns and flags issues days before they escalate
Predictive alerts tied to real asset performance data
Automated trend analysis and anomaly detection across all sites
Network-wide benchmarking surfaces optimization opportunities
Our Solutions
Efficiency
Monetizing your site’s performance has never been more efficient
Site Management
Centralizing remote infrastructure for sites looking to optimize operational costs
Asset Security
Maintain the integrity of your sites, by knowing where your assets are, at every given moment
Fleet Management
Know where your fleets go, and what their maintenance needs may be
Asset Visibility
Know the status of your remote assets, at all times
Sustainability
Operational savings that extend beyond the scope of energy usage
Connect With Us
operational cost savings & efficiency?
Predictive maintenance uses live and historical asset data to identify early warning signs of equipment failure before a breakdown occurs.
In energy management, this means monitoring KPIs like voltage, state of charge, temperature, and runtime hours to catch degradation trends and schedule maintenance proactively, rather than responding to failures after the fact.
Galooli Analytics continuously processes real-time data streams from all connected assets and applies AI models to identify patterns that precede failures: voltage irregularities, abnormal temperature trends, performance deviations from baseline.
When a pattern signals risk, the system triggers an alert automatically, typically hours or days before the failure would occur.
Galooli Analyst is the AI-powered reporting and insight layer built into Galooli Analytics.
It processes data from across your asset network and delivers plain-language conclusions — what’s wrong, why, and what to do about it — without requiring manual data queries or specialist