AI Analysis

RectifAI includes AI-assisted analysis in two places:

• workspace reporting
• post-incident drafting

Workspace reporting analysis

In Reports, users can generate an AI-assisted workspace analysis based on the selected time window.

This is designed to help teams answer questions like:

• Which systems are creating the most operational load?
• Which teams are absorbing the most incident volume?
• Are there visible reliability hotspots or repeat patterns?
• What should the team improve next?

The AI output is best used as a planning aid, not as a source of truth.

Post-incident drafting

After an incident, RectifAI can analyse the incident and return a draft containing:

• summary
• root cause
• impact
• action items

That draft can then be used to create a Jira problem record and follow-up tasks.

How to use AI well in RectifAI

• treat AI suggestions as decision support, not automation without review
• review generated summaries before sharing them outside the incident team
• edit problem record drafts so they reflect confirmed facts
• use reports as prompts for discussion, not as fully automated conclusions

Good user expectations

AI is most useful when your workspace already has:

• clear systems
• clear teams
• clean incident records
• consistent incident follow-up

The cleaner the operational data, the more useful the analysis becomes.