AIOps incident triage
in under 60 seconds.

A monitoring system detects threshold breaches and queues alerts for manual investigation. This agent correlates alerts from multiple tools into one incident, classifies severity with ML, enriches with runbook and topology context, routes to the right on-call team, and triggers automated remediation for known patterns, autonomously.

The engine applies four methods simultaneously: temporal correlation (alerts within a configurable time window), topological correlation (CMDB service dependencies), semantic correlation (NLP similarity on alert titles and resource identifiers), and ML-based pattern correlation (trained on your historical incident data).

Remediation runs against a pre-approved playbook library your SRE team defines — each specifying the trigger pattern, confidence threshold, action sequence, environment scope, and blast radius limit.

Observability: Datadog, Splunk, New Relic, Dynatrace, CloudWatch, Google Cloud Monitoring, Azure Monitor, Prometheus, Nagios, and Zabbix.

Multi-owner incidents generate a coordinated record with a primary incident commander (from the most critical affected service owner) and explicit responder assignments per team.

At close, the agent generates a structured postmortem pre-populated from the incident record: full chronology with timestamps, affected services and customer impact, root cause analysis, contributing factors, and recommended action items.

A standard implementation runs 5–7 weeks. Week 1: monitoring tool connector configuration — API credentials and webhook endpoints for each observability platform and initial CMDB/service topology ingestion.

MTTR reduction: Forrester's AIOps Total Economic Impact studies show 50–75% MTTR reduction in production deployments. For a team averaging 4.5-hour MTTR on major incidents, that represents a reduction to 1–2.25 hours — at $5,600 per minute of downtime, each major incident resolved 2 hours faster saves $672,000 in downtime cost.