Automated product health monitor with anomaly detection & AI root cause analysis
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 32 nodes and runs on a schedule (every day at 08:00) (plus 3 other triggers). Along the way it connects Postgres, OpenAI, Slack and Gmail and 1 more.
How it runs
Trigger: On a schedule (every day at 08:00) (plus 3 other triggers).
- Execute the SQL query (Postgres): run a query
- select open incident (Postgres): run a query
- daily usage metrics (Postgres): run a query
- Execute SQL query incident check (Postgres): run a query
- anomalies check (Code): runs custom JavaScript code
- Condition incident (If): checks a condition and branches into true / false
- anomalies (Code): runs custom JavaScript code
- sum up (Code): runs custom JavaScript code
- log incident (Postgres): run a query
- revenue by country (Postgres): run a query
…plus 18 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (9)
- Postgres
n8n-nodes-base.postgres - Code
n8n-nodes-base.code - OpenAI
@n8n/n8n-nodes-langchain.openAi - Slack
n8n-nodes-base.slack - Schedule Trigger
n8n-nodes-base.scheduleTrigger - Gmail
n8n-nodes-base.gmail - Notion
n8n-nodes-base.notion - If
n8n-nodes-base.if - Merge
n8n-nodes-base.merge
From the author
Description This workflow transforms raw SaaS metrics into a fully automated Product Health Monitoring & Incident Management system. It checks key revenue and usage metrics every day (such as churn MRR and feature adoption), detects anomalies using a…