# Proactive Incident Prevention

Sedai detects resource risks before thresholds breach and autonomously remediates — no pages, no tickets. Shift from firefighting to prevention.

## Fix Problems Before Users Find Them

Static alerting thresholds fire too late, too often, or both, leaving your team in permanent reaction mode. Sedai analyzes real-time resource trends to identify what's about to go wrong, and autonomously intervenes before it becomes an incident. Shift from firefighting to prevention without adding headcount.

## From Reactive Alerts to Autonomous Prevention

Sedai doesn't wait for thresholds to breach. It monitors the trajectory of your workloads in real time and acts before a trend becomes an outage.

### Establish What Normal Looks Like

Sedai monitors your workload metrics over time to build a precise baseline of normal behavior, so it knows the difference between high utilization and a genuine risk.

### Predict What's About to Go Wrong

Regression models analyze the direction of resource usage, not just the current level. Sedai flags a memory trend climbing from 40% to 80% in minutes, even if it hasn't crossed a threshold yet.

### Intervene Before Users Are Impacted

When Sedai confirms a risk, it autonomously remediates — increasing memory limits, expanding CPU headroom, or scaling capacity — without waiting for a human to respond.

“By having Sedai in place, we’re not just saving money, we’re preventing would-be customer problems before they become an issue.”

— Matt Duren, VP of Engineering // KnowBe4

## Key Capabilities

Sedai covers the full spectrum of availability risks — from resource exhaustion to application-level failures — with autonomous remediation at every layer.

### OOM Prevention

Detects upward memory trends in Kubernetes pods and Lambda functions and increases allocation before an out-of-memory event occurs.

### CPU Throttling Prevention

Identifies containers approaching their CPU limits and expands headroom before performance degrades.

### Memory Leak Mitigation

Provides autonomous temporary relief for memory leaks — keeping services available while your team diagnoses the root cause, without the pressure of an active incident.

### Trend-Based Alerting

Replaces noisy static thresholds with trajectory-aware signals — fewer false positives, and early warning on the risks that actually matter.

## The Case for Autonomous Action

## Resources

## Stop Responding to Incidents.

Start Preventing Them.