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Granola AI has become my meeting recorder outside of just Zoom meetings. I recently had a 3-hour meeting with legal for business structuring, and I turned on Granola AI on my phone (with other parties' consent), and it picked up the entire transcript nearly word for word (for over 3 hours!), which I later pasted into ChatGPT to go back and forth on things that I didn't fully grasp in the moment. Step-by-step: 1. I obtained consent from the other participants before recording the in-person legal meeting. 2. I turned on Granola on my phone and used it to capture the full three-hour conversation. 3. I took the resulting transcript and pasted it into ChatGPT. 4. I asked follow-up questions about the business-structuring concepts I had not fully understood in the room. 5. I used the transcript as durable context for learning and clarification after the meeting.

I built a workflow to evaluate whether communication with a legal counseling service was as slow and incomplete as it felt. Email was my primary—and necessary—communication channel with them. I had become increasingly dissatisfied with delayed replies, partial answers, and questions that seemed to remain unresolved. Rather than relying only on memory or frustration, I asked GPT to review the relevant Gmail correspondence and turn it into a structured communication inventory. The workflow identified my outgoing questions, their replies, and whether each question had been fully answered, partially answered, or left open. From that inventory, we could calculate concrete indicators such as the median response time, the longest delay between a question and a substantive reply, and the number of questions that remained unresolved or were only partly addressed. This was useful because long email threads can create a distorted sense of what happened. A few frustrating exchanges can dominate memory, while other delays or omissions disappear into dozens of messages. Structuring the correspondence made the pattern measurable. The analysis was not meant to decide whether the counselors were “good” or “bad.” It was meant to answer narrower questions: How quickly were questions usually answered? Which ones were not answered? Were replies resolving the issues raised, or only responding to part of them? Afterward, we created a clear list of the questions that were still open. I used that list as a set of dossier questions when moving the case to another counselor, turning the analysis into practical continuity rather than just a complaint about the past. In simple terms: Gmail correspondence → question-and-response inventory → response-time and completeness analysis → unresolved-question list → handover to another counselor What I liked about this workflow is that it turned a vague feeling that “this communication is not working” into a documented overview I could actually use. Step-by-step: 1. I gathered the relevant Gmail correspondence with the legal counseling service. 2. I asked GPT to identify my outgoing questions, the counselors’ replies, and the status of each question. 3. I classified each question as fully answered, partially answered, or left open. 4. I calculated indicators including the median response time, the longest delay before a substantive reply, and the number of unresolved or partly addressed questions. 5. I created a clear list of the questions that remained open. 6. I used that list as dossier questions when handing the case over to another counselor.