A graph showing how machine learning and predictive analytics can help building operators

Predictive Maintenance


Maintenance schedules are often insufficient.

  • Detect issues before they lead to equipment failure
  • Extend equipment life and avoid costly capital expenditures
  • Prioritize equipment adjustments with more confidence
  • Keep operations running smoothly and efficiently

Intelligent Notifications


Operators, engineers and managers are notified immediately when an equipment failure is detected or when Enertiv's predictive analytics engine uncovers an issue with machinery or integrated systems.

Reports going to building operations managers

Enhanced Communication Pathways


  • Receive regular reports on notifications that are being triggered across your portfolio
  • Be confident that building staff is aware of issues
  • See how quickly your teams are responding
  • Know exactly when major issues are fixed

Display of an equipment schedule and related consumption

Equipment Scheduling


  • Machine-learning algorithms determine a schedule based on operating hours and usage
  • Identify which systems are running off schedule
  • Determine how much scheduling inefficiencies are costing you
  • Calculate how much can be saved by making the appropriate adjustments.
Highlights of which equipment services are contributing to peak demand

Peak Demand


  • Identify when a building reaches its highest level of demand
  • Pinpoint exactly which pieces of equipment are contributing most to your peak
  • Know when you are approaching a new peak
  • Get actionable insights on precisely what to do to reduce spikes
Enertiv can integrate with sensors for gas, water, steam, occupancy, temperature, and much more

Third-Party Meters & Sensors


  • Incorporate existing water, gas and steam meters into analysis
  • Pull occupancy, temperature and air quality sensor data to add context to insights
  • Enertiv integrates with nearly every third-party meter and sensor on the market