Product health monitoring with AI root-cause analysis

On a schedule the scenario checks product metrics, finds anomalies, opens incidents and sends a ready hypothesis of the cause to your messenger, email and knowledge base.

  • Metrics are checked on a schedule
  • Anomalies become incidents
  • AI proposes a cause hypothesis
  • Alerts to messenger, email and knowledge base
Set it up for yourselfScriptera

How it works

Automating Postgres and vk takes no code: a ready-made scenario does the routine for you.

  1. Starts when: daily report trigger
  2. Then: Execute SQL query incident check
  3. Then: sum up
  4. Then: daily report email
  5. Then: Notions database creation
  6. Then: log system final1
  7. Then: log incident
  8. Then: Update notions
  9. Then: slack notification
  10. Then: email alert
  11. Then: log system
  12. Then: daily usage metrics
  13. Then: anomalies
  14. Then: insert incidents
  15. Then: Slack notification
  16. Then: usage health email
  17. Then: log system UH
  18. Then: select open incident
  19. Check: Condition incident
  20. If yes: revenue by country
  21. Check: Merge data
  22. Then: sum up/ hypothesis
  23. Then: root cause summary
  24. Then: root cause summary email
  25. Then: update incident status
  26. Then: log system final
  27. If yes: revenue by plan
  28. Then: Execute the SQL query
  29. Then: anomalies check
  30. Starts when: Trigger RH
  31. Starts when: Trigger CS
  32. Starts when: Trigger UH

You can launch this Postgres + vk integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

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