AIOps: 70% fewer alerts and incidents fixed 3x faster
IASWITCH implemented alert correlation and anomaly detection to remove 70% of non-actionable alerts and cut mean time to resolution (MTTR) by 3x.
In brief
- Before: alert fatigue, incidents slow to resolve
- After: alerts triaged, grouped per incident and prioritised
- Result: MTTR /3, 70% fewer non-actionable alerts
- Stack: Prometheus, ML, SRE
The challenge
Operations teams suffered from alert fatigue: too many non-actionable notifications drowned real incidents, lengthening resolution time.
The solution
We implemented alert correlation (grouping symptoms of the same incident) and ML-based anomaly detection, rethinking alerts around service level objectives (SLOs) rather than raw thresholds.
The results
70% of non-actionable alerts removed and MTTR cut by 3x. Teams focus on real, high-impact incidents.
What it means for an SME
Less time spent firefighting means more time for the projects that move the business forward, and fewer service interruptions for your customers.
Frequently asked questions
How do you cut MTTR by 3x?
By correlating alerts from the same incident, detecting anomalies and prioritizing by SLO, diagnosis and resolution speed up: by up to 3x on this engagement.
What is alert fatigue and how do you fix it?
Alert fatigue happens when too many non-actionable notifications drown real incidents. It is fixed by removing noise, correlating alerts and alerting by SLO.
Let's talk about the time you could save.
In 20 minutes, we find where AI could save you time or money. Free, no commitment.