Ellis: AI Notes for In-Person Meetings
Ellis is a personal AI notetaker for in-person meetings, available on iPhone and Apple Watch. It records meetings, provides transcripts with speaker identification, and allows you to ask questions about the conversation. Unlike enterprise tools, Ellis is built for individuals, covering both professional and personal contexts, with privacy by default.
Ellis: AI Notes for In-Person Meetings | Product Hunt
Ellis
Launching today
AI Notes for In-Person Meetings
23 followers
AI Notes for In-Person Meetings
23 followers
Visit website
AI notetakers
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).
Overview
Reviews
Alternatives
Built with
Team
More
Free
Launch tags:Productivity•Artificial Intelligence•Audio
Launch Team / Built With
Subscribe
SocialX
Promoted
Maker
📌
👋 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. 🙏
Report
2mo ago
how well does it pick up multiple people talking over each other in a louder room, and what happens if someone joins the meeting late mid recording?
Report
37m ago
Maker
@nevzatozuldqgl great questions. Give it a try! I'm testing different scenarios everyday, but I haven't tested super noisy yet.
I'm using a combination of AssmeblyAI for speaker diarization, Pyannote for speaker embeddings (the user records a short snippet of themselves in onboarding to keep their voice as reference), and as well as a UI for the user to explicitly select themselves from speakers in the room.
As far as someone joining later it, that speaker should be added as an additional attendee of the meeting.
What's your use-case?
Report
26m ago
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.
Report
20m ago
Maker
@yusufgbelbkvy thanks for quick test and feedback! How many people were in the standup?
Report
8m ago