An Untangled System: Capture in Real Time, Refine with AI

Move faster without losing context: capture in real time, then refine with AI.

At 8:00 AM we kicked off a revenue conversation: should we charge a stakeholder group we don’t currently serve with premium offerings? Sales, customer success, engineering, product, and our CEO joined. The product manager and I owned the follow-up.

I captured the meeting in Granola (AI meeting recorder/notes) and asked it to outline the options we discussed. After the meeting, the PM and I huddled with Granola again. There, we pruned the list to the candidates worth sizing.

Next, I dropped the raw transcript and notes into ChatGPT with a clear prompt for a decision document. I moved the draft into Google Docs for light edits. While editing, I shared it with my Claude project (rich org context) to stress‑test and strengthen the argument. A few edits later, we had a solid draft by 10:30. Theme: preserve the room’s context while accelerating the write‑up. Let tools assist; let humans decide.

The capture→refine pattern matters more than the tools, though each shines at specific tasks. For example, I’ve found ChatGPT (GPT‑5) produces decision documents that are shorter and to the point compared to Claude.

This workflow lets the room set context together while AI assembles the draft. In the old model, one notetaker drove the doc—often thin or skewed. This pass preserved the group’s nuance, not just my own.

This is the pattern that worked for me:

• Capture once (Granola)
• Prune with a partner (Granola)
• Draft and organize (ChatGPT)
• Edit (human)
• Context‑aware critique (Claude project)

Result: 150 minutes from kickoff to a shareable decision draft with clear options, trade‑offs, and next steps.

How do you trade off between tools?

-Kate