European SaaS Provider · Enterprise Software
The company had a strong reliability culture but was fundamentally reactive. Their alerting system fired when thresholds were crossed, by which point customer impact was already occurring. The data to predict incidents earlier existed in their telemetry — gradual drift patterns, correlation signals, deployment anomalies — but the cost of reasoning over all of it meant they relied on statistical sampling that couldn't surface subtle pre-incident signals.
Full telemetry coverage with LLM reasoning configured to detect drift patterns rather than threshold violations. The model analyzed cross-service behavioral baselines and flagged deviations before they reached actionable alert levels.
"The shift from reactive to proactive was something we'd been trying to achieve for years. The data was always there. We just couldn't afford to look at all of it."
Director of Site Reliability Engineering
The team identified a pattern they named 'slow cascade' — a class of incidents where a minor configuration drift in one service would propagate through dependencies over 6-18 hours before becoming visible. At sampled coverage this pattern was completely invisible. At full coverage it became the most reliably detectable incident class in their environment.