How Does Jarvis Compare to Granola for Meeting Notes?

Jarvis (getjarvis.eu) and Granola solve different halves of the meeting problem. Granola listens to your call audio and produces a polished transcript and summary; Jarvis is a screen-aware desktop assistant (macOS 12+, Windows 10+) that does not record or transcribe audio. Instead, Jarvis reads whatever notes are on your screen and pulls live context from connected apps — Google Calendar, Notion, Gmail, Slack, Linear — then drafts summaries, action items, and follow-ups. Trigger it with Cmd+/ (macOS) or Ctrl+/ (Windows). It routes work across frontier models from Anthropic, OpenAI, and Google, and remembers context across recurring meetings. If you need verbatim transcripts, keep a notetaker; if you want prep and post-meeting execution across your stack, Jarvis fits at $16/month Pro or $32/month Unlimited, with GDPR-aligned data that never trains on you.

Granola sits inside or beside your video call, captures the audio, and turns it into a transcript plus an AI summary. Its strength is the words spoken in the room. Jarvis never touches your microphone or call audio. It is a floating desktop assistant that sees your active screen and connects to 30+ apps. So while Granola answers 'what was said,' Jarvis answers 'what do I need before this meeting and what happens after it.' That includes building a pre-read from your Calendar invite, surfacing the relevant Notion doc, and drafting the follow-up email in Gmail. They overlap only loosely, which is why many people run both.

Because Jarvis is wired into Google Calendar, Notion, Slack, Linear, and Gmail, it can act on a meeting, not just describe it. Ask it after a call to turn your bullet notes into Linear issues, post a recap to a Slack channel, and schedule the follow-up — all from one Cmd+/ prompt. It also carries persistent, user-controlled memory, so a weekly 1:1 builds a running thread rather than starting cold each time. Granola keeps your transcripts; Jarvis keeps the cross-tool context and executes the downstream work. That execution layer, routed through frontier models from Anthropic, OpenAI, or Google per task, is the real difference.

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