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Apolla AI
Engineering

Alert triage and root cause

Aggregates monitoring alerts and logs, attributes them to likely root causes with recommended actions, and shortens time to diagnosis.

Sample input and output

Sample input

(37 alerts received within 3 minutes starting at 2:14 a.m.)

Sample output

Grouped into 1 incident: elevated 5xx errors on the payment gateway. Most likely root cause is the gateway configuration release at 1:58 that lowered the connection pool limit (high confidence). Recommend rolling back the configuration; the relevant runbook and 2 similar past incidents are attached.

Details

A single incident often comes with dozens of alerts from different systems. This template connects to monitoring, logging and change management systems, groups alerts within the same time window into one incident, infers the likely root cause from recent releases and configuration changes, and recommends actions with the relevant runbook.

Each attribution comes with an evidence chain: which metrics went abnormal, which log line errored, which change matches in time. Engineers can confirm or push back directly, and their feedback flows back into the rule base.

  • Multi-source alert aggregation and de-duplication
  • Root cause inference using change records
  • Traceable evidence chain
  • Linked runbooks and similar past incidents

How close is this template to your use case?

Most customers go live after changing 20–40% of a template. Book a workshop and we will assess it together.