Autonomous Application Performance Management
Ensure Business Agility with Continuous Decision-Support Intelligence
annual loss in incidents
per second of app delay
spent on firefighting
Why AdeptDC
We offer an autonomous application performance management solution to reduce toil for software engineers and SREs. Modern software engineering teams have to deal with dynamic data due rapid CI/CD and online transactions. On other hand, the software production environments are becoming increasingly complex with increasing number of micro-services and their intricate inter-dependencies. Current application management approach involves monitoring metrics and detecting failure rule violations. On the other hand, failure resolution often depends on metrics correlations and querying system-scoped event logs and request-scoped distributed traces. These methods are, however, inefficient due to large overhead for managing event logs and distributed tracing, and biases in configuring and tuning failure rules for metrics. At AdeptDC, we believe a better approach would be to use AI-based predictive analytics to detect early incident warnings from metrics data, perform cross-service metrics correlation rankings to gain preventive insights, and finally use distributed traces for request-level root cause analysis and event logs for system-level root cause analysis. We also help SREs to evaluate different resolution strategies with our model-based impact analysis tool. Our home-brew AI algorithm provides high-accuracy predictions for dynamic long-tail and complex periodic data with minimal operating overhead, supported by short training data window and easy declarative tuning.
Continuous Decision-Support Intelligence
Unified Failure Prediction
We eliminate the need for failure rule configuration and tuning with our real-time time-series anomaly detection that works across all metrics (traffic, latency, error rate, saturation) and self-tunes with dynamic time-series distributions. It eliminates operating overhead for rule configurations and false positive handling.
Preventive Mitigation
After identifying failure risks from anomaly detection, we help developers recognize the failure domain with our failure triage, relevancy ranking, and time-series ranking. Then, we help developers identify a suitable mitigation strategy with our model-based impact analysis. We improve upon the traditional incident management workflow on event data with out continuous streaming data analytics.
Smart Tuning
We enable developers pass in the operating context in real-time. Thereby, we eliminate the need for manual hyperparameter tuning and unleash a truly seamless workflow across anomaly detection and failure prevention.