7 Live Metrics Powering HEX64 AI Augmented NOC Real Data from Operation
Discover how HEX64 enhances uptime, reduces MTTR, and optimizes service quality using real-time metrics in our AI-driven NOC. Learn what sets our operations apart—and why these metrics are redefining modern network monitoring.
Why Metrics Are the Backbone of Our NOC Services
At HEX64, our Network Operations Center (NOC) goes beyond traditional monitoring. We integrate AIOps, automation, and real-time analytics to ensure faster incident response, predictive alerting, and 24x7 service excellence.
This post explores 7 critical metrics that we track every hour, helping us deliver on our SLA-driven support, and improve network reliability for clients across industries.
1. Noise Reduction Rate (NRR)
We measure how effectively our system filters out irrelevant or redundant alerts through AI-based correlation and noise suppression.
Example: From 12,000 alerts generated in one month, less than 15% reached technician queues.
2. Mean Time to Resolution (MTTR)
MTTR is a core KPI for HEX64’s SLA-backed operations. Our integrated ticketing and automation systems allow us to minimize time-to-resolution dramatically.
Our MTTR is consistently under 60 minutes for 80%+ of incidents.
3. Auto-Resolution Ratio
Tracks the percentage of tickets resolved through self-healing scripts, bots, or predefined runbooks—without human intervention.
Frequent fixes: restarting failed services, clearing stuck sessions, DNS flushes.
4. SLA Threat Prediction Score
Our AI model calculates the likelihood of any open ticket breaching its SLA. It triggers early escalations or automated interventions to stay compliant.
This predictive layer supports our guaranteed response and resolution timelines.
5. Escalation Avoidance Index
Shows how many issues are resolved at Level 1 support vs. those escalated to L2/L3 teams. It reflects both knowledge base maturity and triage efficiency.
Over 70% of alerts are resolved at L1 at HEX64—reducing client impact and overhead.
6. Hourly Alert Heatmap
Visualizing alert trends by time, client, and asset class allows us to forecast issues, redistribute team focus, and optimize support coverage.
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