Prevent recurring outages by identifying clusters of equipment and conditions associated with network failures.
Assign equipment and conditions into a single causal cluster
Key attributes defining each outage pattern (e.g., time-of-day, equipment age, network topology)
Tracking operational operational and network metrics for each cluster
Optimize risk mitigation strategies and empower NOC teams
This describes how far ahead in the future this model can be trained to predict. Ideally, the historical data available for training would be 2-3 times longer than the prediction window.
Reduce troubleshooting time with data-driven root cause insights.
Move from reactive firefighting to proactive pattern prevention.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
