It’s an AIOps approach that ingests events from across your stack, uses machine learning to correlate them into meaningful incidents, detects anomalies, pinpoints root cause, and automates response — replacing manual, alert-by-alert firefighting.

The correlation engine deduplicates events and groups related signals by topology and time, while noise-reduction suppresses flapping and honors maintenance windows — so thousands of alerts become a handful of real incidents.

It maps each event to the assets and services it belongs to, then links events that are related in the topology or in time into a single incident with enriched context — so responders see one story, not scattered alerts.

Smart RCA reconstructs the causal chain across layers, ranks the most probable root causes with supporting evidence, and suggests a fix — so engineers start from a likely cause instead of hunting across tools.

Every automated action runs inside configurable guardrails, with approval workflows for sensitive changes, closed-loop verification, and a full audit trail — so you choose exactly how much to automate.

It ingests from common monitoring, logging, and cloud tools and pushes to ITSM, on-call, and collaboration platforms — via native integrations, OpenTelemetry, and open APIs — fitting the workflows teams already use.