How do your meetings become something your AI can read?

Last updated 2026-08-18 · Overview — no new claims; each statement links to the entry that carries the primary sources.

Most of what you know about your work gets said on calls and then evaporates. A transcription tool catches it, and that changes what your AI can do for you.

Transcripts are for your AI

The rule I give on calls: transcribe every meeting. Not for you to read — the information is already in your brain, maybe, but not in the AI’s brain. An assistant that has read your last three months of calls doesn’t need you to recap strategy, past decisions, or who said what; it retrieves the meeting and works from it. Transcripts are the richest context that already exists about your work, at zero writing cost.

Know what your tool actually captures

Tools differ more in defaults than in transcription quality. Granola has no auto-join: it transcribes only meetings you click into, joins no bot, keeps a transcript rather than a recording, and notes stay private by default — workspace admins can’t read them (Granola auto-transcribe). Fireflies inverts both defaults: it auto-joins every meeting with a conference link and shares recaps with teammates. On a shared account that means colleagues can read your one-to-ones — the realization that flips people. So the habit to build with Granola is simply clicking into the note when the meeting reminder fires.

Clean the garble

Raw transcripts mangle names, acronyms, and tool names. Two fixes that stack (Transcript cleanup): paste your team’s vocabulary into the tool’s custom-vocabulary setting before the call, and run an LLM pass after — I feed each Granola transcript to a Claude prompt that infers garbled words from context, and it usually does a pretty good job.

Give your agent the files

Agents reach Granola four ways, all read-only: the hosted connector, a Business-gated API, the desktop app’s local cache, and a once-daily CSV export — there is no one-button markdown export (Granola agent access). What I set up on calls: a one-off pull of the transcripts into a local folder as markdown files. Local files the agent certainly reads and searches exhaustively; the app holds the archive, your folder holds the working context. And always download first, then build — people who skip that step blame the AI for hallucinating what it never received.

Steer the transcript while it runs

Once a call is being transcribed, it becomes an input you can write to by talking. Say the important thing out loud — a decision, a problem you want help with — and it lands in the record. After the call, feed the transcript back to the AI and have it turn what was explained into an instruction manual; I do this all the time, and sending someone the transcript hands them the technique itself.

Know who else can see them

Two layers. First, the transcription tool’s sharing defaults, above. Second, the AI account you feed transcripts into: on a Team plan the organization owns the data and its Primary Owner can export your conversations, though teammates see nothing and Anthropic doesn’t train on it (Team account data); a personal account has no processor contract covering the other people on your calls (GDPR personal accounts). What I advise: work transcripts on the work account, personal ones on your own — and keep genuinely confidential call folders separate, where they can’t bleed into other work.

Go deeper

• Granola auto-transcribe — what Granola captures, and how its defaults invert Fireflies’
• Granola agent access — the four read-only routes from app to agent
• Transcript cleanup — custom vocabulary before the call, an LLM pass after
• Team account data · GDPR personal accounts — who owns the transcripts you feed in