Ellis - AI notes for in-person meetings

Ellis is an AI notetaker for in-person meetings. Record your meeting, get a clean transcript with each speaker identified, then ask anything — what was decided, what you missed, how it went. No laptop. No extra hardware. Just your iPhone (or Apple Watch).

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👋 Hey Product Hunt!

I'm Robin, creator of Ellis — a personal AI notetaker for in-person meetings.

What is Ellis?

Ellis is a simple consumer-first AI notetaker (iPhone and Apple Watch) for in-person meetings. It works anywhere being in the same room matters: coffee meetups, on-site sales meetings, therapy, doctor visits, interviews, or even your teacher-parent conference.

When the meeting ends, Ellis matches your voice against your saved voice profile, gives you a full transcript with an easy way to assign speakers, and writes notes in the format you pick.

Why Ellis? 🤔

Unlike other notetakers that are built for your org, Ellis is built for you as an individual. You can record lots of different in-person conversations, ask questions across them, and find common traits and trends spanning both your professional and personal contexts. Yes — I might want to know how my sales meeting went AND how I navigated a difficult conversation with another caregiver. Two different use cases, but one repository, built for me.

Key features 🤩

  • Getting "who said what" right. Telling speakers apart in a real room is harder than online — every voice hits the same microphone. Ellis solves this with voice enrollment and diarization, plus a fast way to tag yourself and others.

  • Ask anything about a conversation. Pull up what was said, decisions made, or anything you want to revisit from a meeting — just ask.

  • Find it by place. Forgot a name or a detail? Ask by location — "what did we agree on during our walk in Fort Greene?"

  • Private by default. Recordings are automatically deleted once transcription is complete.

Happy to answer questions — and I appreciate the support. 🙏

 Meeting notes that actually capture context is gold. Are you using transcription + summarization or something smarter? Curious if users trust it more for action items vs general notes.

 What makes this better than Evernote?

 what makes it different is the focus on capturing data from conversations.

Recording doctor visits and therapy sessions puts this app in about the most sensitive bucket there is, so deleting the audio right after transcription is a smart move. The transcript still says everything the audio did, though. I saw Supabase in the stack, are transcripts scoped per user at the database level, or does the app layer do the gatekeeping?

 great point! Users are only able to access their own transcripts, which is enforced by the backend (not only in the client-side app). Curious about trying the app in one of these scenarios, or do you have more thoughts here?

The “AI Notes for In-Person Meetings” angle is interesting, especially for teams that still do design reviews, sales conversations, or planning sessions around a table. How does Ellis know who said what in a room? Is speaker separation part of the flow, or is it more focused on capturing clean notes and action items after the conversation?

 so excited that you ask :) So there's a multiple step process.

  1. Users can record their voice during onboarding which works as a "voice enrollment."

  2. When a meeting ends, the app displays several different cards, each with a quote and a "memorable" quote. The card that best matches the voice enrollment gets highlighted as "Best match"

  3. The user can then assign themselves as the speaker, and add names to other attendees

  4. When finalized the notes are updated with a new understanding of who you are in the group.

Recorded a quick team standup on my iPhone and the speaker identification was spot on, even with people overlapping. Asking it what got decided after felt like magic.

 thanks for quick test and feedback! How many people were in the standup?

Runs quietly in the background and nails speaker labels without any setup. Wished I had this for those standup meetings where half my notes end up being nonsense.

 appreciate the review!

The speaker identification works surprisingly well even with overlapping voices in noisy rooms. Love that it runs straight from the watch, no fumbling for the phone mid-conversation.

Used it for a coffee meeting yesterday and the speaker separation was actually solid, even with background noise. Chatting with the transcript afterwards to pull out action items felt weirdly natural.

 appreciate you testing it!

the watch app is such a smart move, honestly didn't expect that and it makes the whole thing feel effortless instead of just another thing to remember to open.

nice, everyone builds for zoom calls and forgets real meetings exist. curious how it does with a noisy room, that's where my notes always fall apart

  give it a try and let me know! What type of noisy rooms are you thinking of? public places?

How well does the speaker identification actually hold up in a noisy room with overlapping talk? Wondering how often I'd need to go back and manually fix who said what.

 you can try it out for free. I purposefully designed a UI that gives you a "best match" on your voice enrollment, and a way for you to pick and label speakers by name if you need to. So the manual "fixing" should be easier

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