I built an AI meeting-intelligence workflow that turns my meetings, emails, chats, and handwritten notes into one living action tracker. Every action stays linked to its original source, and the workflow automatically generates a prep sheet for each meeting. Two problems drove me to build it. First, provenance: action lists are often disconnected from their sources. A line such as “follow up with vendor on pricing” is much less useful when I can’t open the Teams message or email it came from to review the details or reply. The context disappears as soon as the action is recorded. Second, I take most of my notes by hand because I think better on paper. Those notes used to be a dead end: they never entered any system, so the follow-ups disappeared. The workflow runs on a morning schedule or on demand before a meeting. I use Claude in Cowork with MCP connectors for email, chat, and calendar. It pulls recent messages and upcoming meetings while capturing the deep link or ID for each source item, so every action can be traced back to the exact email or Teams thread. For handwritten notes, I photograph the pages into a OneDrive folder and tag them. The workflow uses OCR and vision to extract the text and action items. Quick shorthand tags—`action`, `prep`, and `done`—on messages and photos tell it what to track or close. A Python layer then deduplicates the actions, assigns each one a stable ID, applies the tags, and updates the master tracker while preserving the source links. The result is one self-maintaining action list. I can click any item to return to its original artifact to reference the details or reply, and my paper notes finally flow into the same system. To recreate it, connect an AI assistant to your mail, chat, and calendar through MCP and store each source link with its action. Add a OneDrive photo drop with OCR for handwritten notes, use a simple tag vocabulary, and separate the responsibilities: let the AI handle capture and OCR, while plain code handles deduplication, IDs, and status updates. Step-by-step: 1. I scheduled the workflow to run each morning and added an option to run it on demand before a meeting. 2. I connected Claude in Cowork to my email, chat, and calendar through MCP so it could gather recent messages and upcoming meetings. 3. I captured the deep link or ID for every source item, allowing each action to trace back to the exact email or Teams thread. 4. I photographed handwritten notes into a tagged OneDrive folder and used OCR and vision to extract their text and action items. 5. I used the shorthand tags `action`, `prep`, and `done` on messages and photos to indicate what to track or close. 6. I used a Python layer to deduplicate actions, assign stable IDs, apply tags, and update the master tracker without removing source links. 7. I used the resulting tracker to return to the original artifact for each action and to bring paper-note follow-ups into the same system.