Autonomous Application Performance Management

Ensure Business Agility by Mitigating Kubernetes Downtime

$10K/min revenue loss

due to app degradation

10% bounce rate

per second of app delay

25% of engineering time

spent on incidents

Our contextual AI software mitigates cascading failure risks with minimal operational overhead.

Distributed systems like Kubernetes are inherently prone to cascading failures. Our contextual AI software reduces monitoring and troubleshooting overhead for cascading failures. It relieves engineers from health rule maintenance, alarm fatigue, and brute-force debugging. Our AI software uses unsupervised anomaly detection for streaming data, dynamic alarm ranking, and time-series correlation ranking for causality detection.

Unified Anomaly Monitoring to Minimize Monitoring Overhead

Traditional health rule-based failure detection is increasingly losing efficacy in detecting cascading failure risks in distributed systems with thousands of metrics. Our unsupervised real-time anomaly detection algorithm eliminates the need for health rules. Our algorithm can handle irregular spikes and complex seasonalities. It can accept high-level user feedback for rapid anomaly baseline tuning.

Dynamic Alarm Ranking to Minimize Alarm Fatigue

A cascading failure, by its nature, produces an alarm flood. Our dynamic ranking algorithm sorts alarms based on underlying anomaly concurrencies to assist software engineers cope with alarm fatigue. Our algorithm is designed to scale for 10,000+ alarms with near real-time responsiveness. It can accept high-level user feedback for rapid tuning of ranking.

Time-Series Correlation Ranking to Minimize Troubleshooting Overhead

Asynchronous event flow in a distributed system makes root cause analysis hard. Our time-series correlation ranking assists software engineers in root cause analysis by rapidly identifying the underlying causal factors. Our algorithm is designed to handle related operational issues such as phase shift, scale variance, and spuriousness. Moreover, it can accept high-level user feedback for rapid tuning of ranking.

Demo