Unleash Self-Learning Cloud-Native Monitoring

Transform Logs and Metrics into Intelligence for Outage Risk Mitigation

$10K/min revenue loss

due to app degradation

10% bounce rate

per second of app delay

25% of engineering time

spent on incidents

Modern cloud-native infrastructure comes with a large number of dynamic components with complex interdependencies. Due to a large variety of interdependent data sources, it is hard to extract high accuracy insight out of monitoring data. At AdeptDC, we offer an AI software that makes monitoring data learn from itself and assists engineers in making critical decisions around risk optimization.

Time Series Anomaly Detection and Forecasting for Autonomous Risk Assessment

We hypothesize a high-accuracy time series anomaly detection and forecasting framework generates early warning and assists in recognizing the mean time before failures.

Time Series anomaly monitoring for Early Symptom Detection

Time Series Relevance Ranking for Automated Risk Triaging

We hypothesize a high-accuracy relevance ranking that can decipher the importance of a time series within a network of time series assists in automated risk triaging.

Time Series Similarity Ranking for Automated Remediation

We hypothesize a correlation engine that can recognize the similarity distance between time series of unequal size and shape assists in automated remediation, especially for a multi-tiered infrastrucutre such as Kubernetes.

Agentless Monitoring for Continuous Real-Time Analytics

Traditional agent-based monitoring solutions are not portable enough for cloud native monitoring. Instead, software engineers are increasingly embracing CNCF-graduated Prometheus for its easy deployment and metrics exposition, a large number of third-party exporters, active data scraping, multi-dimensional data model, flexible PromQL, single node storage, and built-in alerting/visualization support. However, Prometheus lacks horizontal scalability, multi-tenancy, and distributed storage. We are solving these problems by leveraging Prometheus' remote write API through which we receive data from multiple Prometheus instances to ensure horizontal scalability, and multi-tenancy. We also store metrics for our time series analytics. Unlike other standard open source projects such as M4, Cortex, our monitoring platform is optimized for continuous real-time analytics.