IT Operations: Resolve Incidents in Minutes Instead of Hours

An IT service provider with 80 employees manages the critical IT infrastructure of 40 mid-sized business clients — servers, networks, backups, cloud connectivity. When an incident strikes, hundreds of log entries, monitoring alerts, and support tickets arrive within minutes. On-call technicians had to wade through this flood of data manually to find the root cause. Every hour of downtime costs clients an average of €12,000.

The Noise Problem

Not every alert is a real incident. In hindsight, 70% of monitoring warnings are noise — thresholds set too low, known behaviour, or issues already auto-resolved. The remaining 30% are real, but the root cause took 45–90 minutes of manual investigation to identify.

AI-Powered Incident Copilot with SoverIQ

SoverIQ Stack was connected to the provider’s monitoring system, ticketing platform, and log aggregators — all on-premises. When an alert fires, the system automatically analyses:

• Logs from affected systems over the past 4 hours
• Correlation with recent deployments and configuration changes
• Similar historical incidents from the ticket database
• Dependency graph: which other systems could be affected?

The output is a structured incident brief for the on-call technician — with the three most likely root-cause hypotheses, suggested first diagnostic steps, and references to similar resolved incidents.

Results

Mean Time to Identify (MTTI) dropped from 67 to 11 minutes. False alarms that woke technicians at night fell by 58%. Client SLA fulfilment rate rose from 91% to 98%. On-call burden for technicians decreased measurably — a decisive factor in staff retention.