Launching today
Ellis
AI notes for in-person meetings
175 followers
AI notes for in-person meetings
175 followers
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).
Love that you're focusing on in-person meetings
Most AI note takers are optimized for virtual calls. Being able to capture discussions with just an iPhone or Apple Watch feels really convenient. Curious, how well does Ellis handle noisier environments like conferences or busy cafés?
Ellis
@worksforme well, and getting better. Using AssemblyAI under the hood. Curious about your conference use-case. Have you tried other notetakers in the past?
How well does the speaker identification hold up when a few people are talking over each other, like in a fast brainstorming session?
Ellis
@sercanpcke Give it a try! Brainstorming sessions are a perfect use-case
How well does the speaker identification actually hold up when people talk over each other or move around the room, especially in louder spaces like cafes or open offices?
Ellis
@sabanl51707 So I tested cafes and open offices. That is my main testing ground and it works quite well. But give it a try yourself
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?
Ellis
@vollos 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?
How well does it handle meetings with more than like 6 people talking over each other, especially in a noisy cafe setting?
Ellis
@birsen338162233 Give it a try. But talking over each other will be difficult in any situation.
How well does the speaker identification actually work in a noisy room with multiple people talking over each other? Curious how it handles real-world chaos versus a quiet conference room.
Ellis
@boran925180 So real-world chaos is, of course, going to be harder. But typically those are not the most productive meetings nor where you would want to extract valuable information from. Give it a try
Everyone solved Zoom notes, nobody solved the conference-room whiteboard session. In-person is the harder and more valuable problem. How does it handle multiple speakers without per-person mics?
Ellis
@medal411 There are multiple steps under the hood.
Voice enrollment, which captures voice attributes
Diarization using assembly AI? And finding a best match to your voice
A UI that lets you easily pick and assign speakers
@robingreenwood Thanks for breaking that down — enrollment capturing voice attributes + diarization, then best-match, makes sense. The speaker-assignment UI is the part most tools get wrong, so smart to make that easy. Congrats on the launch!
Ellis
@medal411 appreciate it!