Hey there, Freedom Fighters. My name is Andrew Warner. I'm the founder of Mixergy, where I interview

entrepreneurs about how they built their businesses. If you're listening to me, there's a good chance that you've seen all these AI note takers, not just like on your computer, but every time you join a meeting, there's, I've had meetings where there are multiple note takers in the call with me. Joining me is the founder of one of the originals of these in a company that I see in a lot of my meetings, David Schim, founder of reed.a.I'm curious about how we built it up. And I'm also curious about the competition, how you survive in a world where so many people are getting into the AI space. So let's find out. David, good to have you here. No, I'm excited to be here. And especially, I think you're hitting on the right points to say, what's the differentiation that you can deliver in a world where AI solutions come up every single day. Like, if you go to Product Hunt Today, you'll probably see six meeting note takers that have launched in the past week. You'll see email solutions. You'll see messaging solutions all across the board. I think there's just a lot of noise in the market right now.

And then I even see that notion now will pop up or do you want to record this thing? And then

Chad GPT has now a button to record a meeting and all that. So let me ask you this. Before we even

get started into it, how many people are actually using re-AI now? Yeah. So it's in the millions. And then on a daily basis, we get about 50,000 new accounts created every single day. So that's a run rate of a million plus on a monthly basis, 12 million annually. So we are the fastest growing meeting note taker in the world today. And we have been for the last two years. I had no idea that it was that. And it is very viral. You get into a meeting. You see that there's someone using read AI. You probably get notes afterwards from it by email. You see it in the chat. I totally get it. All right. So let's answer the most difficult question, which is, how does someone survive in a world where everybody seems to be creating all these note takers?

So I think this is the beauty. It's a blessing and a curse when it comes to a yeah. It's very easy to broiled a basic model. So transcription, you can do open source, you can buy some stuff. You can drop into chat, GPT, or Anthropic, and get a summary against it. And those are good. And you'll see there's this kind of table stakes of you should be able to do transcription.

You should be able to do some rate. And then

people start to come in with what's different. And for us, what's really resonated is our ability

to actually measure sentiment and engagement in real time and then apply it to the text. So you and I are

talking right now. Let's say I start going on about our founding and how we raise money and you're kind of like, this is sort of interesting, but then I go for five minutes. You might start looking around and get really distracted. We'll pick that up and we'll actually put that in as the narration layer. So imagine if you read a book and you only read the quotes, you can get a general understanding of what's happening, but that narration layer is that additional context that really puts characters into play that goes in and says, this is compelling versus this isn't interesting. And so for us, it was going in and saying like, hey, can I start to understand are people that are interested when David talks about the weather? Where are you from, Andrew? How's the weekend? You know, what are your plans? Those things are kind of like, 90% of the audience turns out. But when you start talking about AI, application, revenue, people start nodding there and agree. You can actually readjust the summary, not to go in chronological order, but to say what were the most important topics and how should I actually display that

within the summer? I had no idea. I could see that in a sales call, that if I know what I'm

doing in a sales call, because I've got a general rhythm that I go through and an agenda that I

follow, you're telling me I could spot or read could spot the places where people are interested

and highlight that for me and the places where they moved on. And the way that you know it is because you are watching not just the words they use, you're not tracking just that, but you're also tracking people's faces and the way that they're moving their heads. Yeah, so we look at head orientation. So we don't look at your face. We don't do any facial recognition. But we go and we say, if you're looking at the camera straight ahead and you're talking, your camera's there. And then if you look over this way and you go to a fixed position when someone else is talking, your head stays the same spot, we have a model that says that's a second screen. So you're actually paying attention. You're not distracted. But if you start scanning your head back in the forehead, if you go like this a lot, that's going in saying you're not as engaged in the conversation

because you're not going to a fixed point. So we've got these models in place that understand engagement. Like if you go down and you write your notes, people go to the same place because your notepad is right here and you write your notes down. Well, we're able to pick that up as well and say, oh, you're going to a fixed position. So that's one of the things.

But imagine a sales call, you got to sell that up as well and say, oh, you're going to a fixed position. So that's one of the things. But imagine a sales call you have somebody that understands the pitch deck forward and backwards, but they really don't have context around an audience. They might do the whole pitch and say, this is why I work great. This is going to save you money and it only costs this much. Why don't you buy this? And the person says, yeah, that's interesting. Let me get back to you. If I put that into an LLM, it's because it was a fantastic conversation. But if I can actually go

and see the client was not really engaged, they barely spoke in the conversation. When they did

ask a tough question, David went from 150 words per minute to 225 because he was super nervous. So that metadata is not picked up in an LLM. That metadata we pick up to go in and say, David's pacing is 150, but when he's excited or nervous, he goes to 220, that then feeds it in the narration. How do you know that anyone even wants that? It feels like people, they just want notes and that's it. They just want to know, what do they say? Can I go search for the thing that I wasn't paying attention to? They want effective notes. So when you get notes that summarize it in chronological order, those aren't effective notes. So how do you know that what they want is sentiment analysis? Like, how are you able to tell that that's the thing that drives people to come to

read? They actually don't want to see that on the back end. They want the notes to actually take that into account. So when I present you with the summary, they don't care that this is how the algorithm works on the back end. They want to know that this was the most. Yeah, how do you know that that that, so here's the thing that the other thing that gets me. I would have thought that at this point in, in transcriptions and in meeting note takers, that we would have one that's just geared for sales. And I think Gong does that, but it's so expensive. I haven't even used it. And that, that there'd be one for sale, one for sales, one for HR, one for team meetings, one for this, one for that. It doesn't seem like it's broken down that way. And if that was the way it was, then I could totally be with you and realize, hey, you know what? When it comes to sales, you want to know the people are engaged, when it's team meetings, you want to know this. But how are you able to tell that people care without segmenting and having a tight niche that you're

going after? Yeah, I think the big metrics that we look at are retention. So if someone tries

our product, are they actually using the product 7, 14, 30, 60, 90 days after the fact? And for us,

across time, as we've improved the models on a consistent basis, we've seen that retention

...