Identify network segments and services at risk of performance degradation before SLA violations occur.
Service-level and segment-level propensity scores for QoS decline or SLA breach, including risk deciles and centiles
Identify key indicators of service degradation (e.g., bandwidth utilization, latency trends, packet loss patterns, traffic spikes, infrastructure capacity)
Performance assessment including predicted SLA violation reduction, customer satisfaction improvements and ROI impact
Optimize network capacity planning, proactive interventions and procurement
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.
Accurately predict and prevent SLA violations before they occur.
Maintain service excellence with continuous performance monitoring.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
