Problem
Users reported issues and feature requests without enough page context, screenshots, visible errors, or workflow detail. That made follow-up slower and left builders guessing at the real problem.
Case Study
Microsoft Graph and OpenAI automation
An anonymized workflow example that connected CRM feedback, Microsoft context, OpenAI-assisted triage, and Notion follow-up.
Approach
Built a structured feedback workflow that captured page links, user descriptions, visible errors, and screenshots, then routed the request into Notion with AI-assisted triage context.
Stack
Salesforce feedback entry points, Microsoft Graph, SharePoint, OneDrive, Outlook context, OpenAI API, Notion task routing, APIs, webhooks, permissions, OAuth, and secrets-aware implementation.
Timeline
Phased rollout through sandbox testing, feedback review, documentation, and controlled deployment into the working process.
What Changed
- User feedback included the context needed to understand and reproduce issues.
- Notion became a structured triage layer instead of a loose list of vague requests.
- AI-assisted summaries helped reduce ambiguity between user testing and deployment iteration.
Why It Matters
Feedback loops are operations infrastructure. Good triage captures what happened, where it happened, and what the user was trying to do.
Anonymized project example