AIOps & Observability: less noise, MTTR cut by 3x
IASWITCH sets up observability and AIOps to cut alert noise by 70% and reduce MTTR by 3x, through anomaly detection, alert correlation and automated remediation.
In brief
- End-to-end observability: metrics, logs, traces (OpenTelemetry)
- Anomaly detection and alert correlation
- Automated remediation of recurring incidents
- Measurable reduction in alert noise and MTTR
What is AIOps?
AIOps applies machine learning and automation to IT operations: detecting anomalies, correlating alerts tied to the same incident and automating remediation. The goal is to cut noise and Mean Time To Resolution (MTTR).
Our approach to observability
We instrument your services with OpenTelemetry (metrics, logs, traces) and centralize in Prometheus/Grafana. Alerts are designed around SLOs rather than raw thresholds, which removes non-actionable noise.
Alert correlation and anomaly detection group symptoms of the same incident, and frequent runbooks are automated.
Expected results
On an AIOps engagement, we removed 70% of non-actionable alerts and cut MTTR by 3x. Your teams focus on real incidents.
Frequently asked questions
How do you reduce alert noise in production?
By moving from threshold alerts to SLO-based alerts, correlating alerts from the same incident and removing non-actionable signals. IASWITCH removed 70% of non-actionable alerts on one engagement.
What is MTTR and how do you reduce it?
MTTR (Mean Time To Resolution) is the average time to resolve an incident. We reduce it with better traces, alert correlation and automated remediation — by up to 3x on our engagements.
Which observability tools do you use?
Prometheus and Grafana for metrics and dashboards, OpenTelemetry for unified instrumentation (metrics, logs, traces), and anomaly-detection pipelines for AIOps.
Got an AI project to ship to production?
Let's talk. Tell us about your context and goals, and we'll get back to you quickly.