Scale Alert Response: Proactively identify network impacting events

• The Early Signals Are Already There — If You Can See Them

According to Splunk’s 2024 State of Observability report, only 41% of IT teams feel confident in their ability to detect performance issues before users are impacted. Yet the telemetry — metrics, logs, traces — often already holds the clues. The challenge is recognizing anomalies fast enough and at scale, before noise buries the signal.

• AI Knows What “Normal” Looks Like

AIOps platforms constantly learn baseline behavior across systems. When traffic, latency, or error rates deviate, AI surfaces the shift — even if thresholds aren’t breached. One global e-commerce company used AI to detect a 2% increase in checkout latency across edge regions, traced to a misbehaving CDN provider. Issue flagged. Impact avoided.

• From Reactive to Predictive

Traditional alerting waits for something to break. AI-driven observability anticipates — highlighting degradation trends, minor anomalies, or cascading effects across domains. That means earlier interventions, smoother performance, and fewer escalations. One enterprise reported a 48% reduction in critical incident volume after implementing anomaly-based alerting across their stack.

Still waiting for users to tell you something’s broken?

Observeasy helps your team catch issues before they escalate — with AI-powered anomaly detection and proactive signal analysis.

📌 Don’t just monitor. Anticipate.

👉 Book a demo and start spotting trouble before it becomes an outage.