1 00:00:00,240 --> 00:00:04,800 Speaker 1: And so when you are looking at the metrics, so like, 2 00:00:04,840 --> 00:00:07,080 Speaker 1: how how does this work? How does like what are 3 00:00:07,120 --> 00:00:10,240 Speaker 1: the things we're measuring to see that? Okay, we're making progress, 4 00:00:10,640 --> 00:00:12,680 Speaker 1: you know, the team is doing we're they're supposed to 5 00:00:12,760 --> 00:00:14,280 Speaker 1: be doing. How do you measure that? 6 00:00:15,120 --> 00:00:17,120 Speaker 2: Yeah, I think there's two folds of the metrics. 7 00:00:17,120 --> 00:00:19,439 Speaker 3: So before we started building this product, I spent a 8 00:00:19,440 --> 00:00:21,840 Speaker 3: lot of time in Europe. I had worked in tech 9 00:00:21,920 --> 00:00:23,720 Speaker 3: my whole life, and so I had a lot of contacts, 10 00:00:23,720 --> 00:00:25,840 Speaker 3: and so I met with a lot of these luxury 11 00:00:25,840 --> 00:00:28,040 Speaker 3: houses and I noticed the commonality and all of the 12 00:00:28,080 --> 00:00:31,400 Speaker 3: answers they had. They needed a product that was personalized 13 00:00:31,400 --> 00:00:35,320 Speaker 3: and needed something that was going to reduced returns. And 14 00:00:35,400 --> 00:00:37,560 Speaker 3: then also on top of that, which is more important 15 00:00:37,600 --> 00:00:39,400 Speaker 3: because they're a business, they need something that's going to 16 00:00:39,400 --> 00:00:42,080 Speaker 3: increase sales, right, And so we wanted to make sure 17 00:00:42,159 --> 00:00:45,400 Speaker 3: that those KPIs were built in to our products. On 18 00:00:45,479 --> 00:00:47,920 Speaker 3: the team side is we need to align with our 19 00:00:48,040 --> 00:00:50,559 Speaker 3: customer goals and so there's a lot of things that 20 00:00:50,600 --> 00:00:53,199 Speaker 3: we need to build, and so it's very important that 21 00:00:53,240 --> 00:00:57,320 Speaker 3: we set milestones because our products, sorry our partners. 22 00:00:57,200 --> 00:00:58,320 Speaker 2: Dictate when they launched. 23 00:00:58,400 --> 00:01:00,960 Speaker 3: Because we're a white level service, right, and so we 24 00:01:01,000 --> 00:01:03,880 Speaker 3: need to make sure that we have the right packages 25 00:01:03,960 --> 00:01:07,600 Speaker 3: for each partner to gear them up to launch as well. 26 00:01:08,640 --> 00:01:11,400 Speaker 1: You said something a moment ago about ninety nine percent accuracy. 27 00:01:11,440 --> 00:01:12,880 Speaker 1: Can you restate that, so I will make sure. My 28 00:01:12,959 --> 00:01:15,679 Speaker 1: next question IS's own point. 29 00:01:16,280 --> 00:01:18,600 Speaker 3: Yes, so it's nine nine percent accuracy. And so how 30 00:01:18,600 --> 00:01:20,640 Speaker 3: it works is there's a lot of math and science 31 00:01:20,760 --> 00:01:24,240 Speaker 3: that's involved into this, and so we've partnered with institutions 32 00:01:24,280 --> 00:01:27,520 Speaker 3: like Carnegie Mellon and MT to help us co develop 33 00:01:27,560 --> 00:01:28,800 Speaker 3: our product, and we also. 34 00:01:28,640 --> 00:01:31,240 Speaker 2: Hire students from there and internships and. 35 00:01:31,200 --> 00:01:34,040 Speaker 3: So basically from a consumer perspective, the way that to 36 00:01:34,160 --> 00:01:36,920 Speaker 3: use our product is it starts with your phone and 37 00:01:36,959 --> 00:01:40,000 Speaker 3: so essentially you take a picture is a full body 38 00:01:40,000 --> 00:01:43,200 Speaker 3: picture of yourself. Right, there's an onboarding process that's saved 39 00:01:43,200 --> 00:01:43,560 Speaker 3: into the. 40 00:01:43,480 --> 00:01:45,360 Speaker 2: Cloud one time. Right. 41 00:01:45,600 --> 00:01:47,600 Speaker 3: What you don't know is when we're taking that picture 42 00:01:47,640 --> 00:01:50,120 Speaker 3: of you, we're actually measuring the depth of field and 43 00:01:50,200 --> 00:01:53,800 Speaker 3: there's all these order sort of contingencies or are that 44 00:01:53,840 --> 00:01:57,280 Speaker 3: are basically applied for us to formulate that. That's the 45 00:01:57,320 --> 00:02:00,240 Speaker 3: first part on the back end of things any of 46 00:02:00,320 --> 00:02:01,320 Speaker 3: AI company. 47 00:02:02,000 --> 00:02:04,760 Speaker 2: One of the things that's probably the most valuable. 48 00:02:04,600 --> 00:02:06,920 Speaker 3: To an AI company is the data. And so the 49 00:02:07,000 --> 00:02:09,000 Speaker 3: question is, well, how do you get that. We have 50 00:02:09,120 --> 00:02:12,600 Speaker 3: vendors all across the world that are basically capturing data 51 00:02:12,600 --> 00:02:16,560 Speaker 3: points of all shapes, all sizes of people, because we 52 00:02:16,600 --> 00:02:18,680 Speaker 3: need to know what to look for, right because as 53 00:02:18,680 --> 00:02:21,080 Speaker 3: you know, there's all saxeu and sizes, all shades, et cetera. 54 00:02:21,880 --> 00:02:24,040 Speaker 3: And then sort of the third component is our partners 55 00:02:24,200 --> 00:02:28,560 Speaker 3: as well, and so we're capturing data on their garments, models, 56 00:02:28,600 --> 00:02:31,680 Speaker 3: et cetera. And so we're able to merge that all together, 57 00:02:31,720 --> 00:02:34,600 Speaker 3: and that's what powers our product now for our sizing product. 58 00:02:35,120 --> 00:02:37,880 Speaker 3: The one sort of additional detail is in the fashion 59 00:02:37,880 --> 00:02:40,480 Speaker 3: world they call it tech packs, and tech packs are 60 00:02:40,520 --> 00:02:43,560 Speaker 3: basically the dimensions of the garments, right, and so we 61 00:02:43,600 --> 00:02:45,120 Speaker 3: have that piece of data as well. So if you 62 00:02:45,160 --> 00:02:47,480 Speaker 3: merge all that together, we're able to with a nine 63 00:02:47,680 --> 00:02:51,120 Speaker 3: percent accuracy, not only tell you your size of the product, 64 00:02:51,320 --> 00:02:53,360 Speaker 3: but also tell you what it would look like and 65 00:02:53,400 --> 00:02:55,200 Speaker 3: how it would feel if you wanted something like a 66 00:02:55,240 --> 00:02:57,240 Speaker 3: tight fit or a loose fit and so forth. 67 00:02:58,040 --> 00:03:00,840 Speaker 1: So it can tell you know, if I'm our size 68 00:03:00,840 --> 00:03:04,840 Speaker 1: eleven and sheet right, so soon I got on our eleven, 69 00:03:04,919 --> 00:03:06,560 Speaker 1: but if I buy some a six, it's not to 70 00:03:06,600 --> 00:03:08,480 Speaker 1: be eleven and a half. So it can tell the 71 00:03:08,560 --> 00:03:09,400 Speaker 1: difference between that. 72 00:03:09,800 --> 00:03:10,040 Speaker 2: Yeah. 73 00:03:10,120 --> 00:03:12,200 Speaker 3: One of the pain points we discovered from talking to 74 00:03:12,240 --> 00:03:14,120 Speaker 3: brands and also through a lot of our case studies 75 00:03:14,200 --> 00:03:18,080 Speaker 3: with consumers is every brand has their own unique size, 76 00:03:18,160 --> 00:03:21,880 Speaker 3: right Bucci where the media medium, Nike wear a large, 77 00:03:21,919 --> 00:03:24,600 Speaker 3: et cetera. And so the critical component to that is 78 00:03:24,680 --> 00:03:28,720 Speaker 3: the tech packs. The tech packs aren't necessarily about the size. 79 00:03:28,720 --> 00:03:31,799 Speaker 3: It's actually the measurements of the garment and it basically 80 00:03:31,840 --> 00:03:34,800 Speaker 3: converts that into data. And so that's how we're able 81 00:03:34,840 --> 00:03:37,640 Speaker 3: to know. We wanted to basically create the world's first 82 00:03:37,680 --> 00:03:41,120 Speaker 3: what we call a universal sizing metric, meaning that from 83 00:03:41,120 --> 00:03:42,400 Speaker 3: a consumer perspective, you. 84 00:03:42,400 --> 00:03:43,880 Speaker 2: Never have to worry about your size again. 85 00:03:44,320 --> 00:03:46,840 Speaker 3: Right, We're telling you every single time, yo, will, this 86 00:03:46,880 --> 00:03:48,800 Speaker 3: is your size in this brand, this is your size 87 00:03:48,840 --> 00:03:50,320 Speaker 3: in that brand, et cetera. 88 00:03:50,560 --> 00:03:54,600 Speaker 1: So yeah, I like that. So I think about you know, 89 00:03:54,840 --> 00:03:58,440 Speaker 1: people who are launching AI companies and most of the 90 00:03:58,480 --> 00:04:01,640 Speaker 1: world just got aware of what was going in in 91 00:04:01,720 --> 00:04:05,160 Speaker 1: AI like eighteen months ago, twenty four months ago, and 92 00:04:06,480 --> 00:04:09,120 Speaker 1: with chat chipbt, you know, because of things like chatchibt, 93 00:04:09,920 --> 00:04:13,080 Speaker 1: how fast like, when did you start building this, When 94 00:04:13,080 --> 00:04:15,640 Speaker 1: did you got to have the idea? And how fast 95 00:04:15,680 --> 00:04:17,560 Speaker 1: to market did this happen? 96 00:04:18,680 --> 00:04:22,560 Speaker 3: Yeah, so I started working on this approximately three years ago. 97 00:04:23,600 --> 00:04:27,920 Speaker 3: It's definitely been a grind. I always loved fashion, so 98 00:04:28,000 --> 00:04:29,040 Speaker 3: I grew up a nerd. 99 00:04:29,120 --> 00:04:30,440 Speaker 2: I grew up an entrepreneur. 100 00:04:30,520 --> 00:04:33,880 Speaker 3: I had two companies that I sold before I was sixteen, 101 00:04:33,920 --> 00:04:36,200 Speaker 3: and then went to work in corporate America and it 102 00:04:36,279 --> 00:04:39,240 Speaker 3: worked at places in like Snapchat, Meta, et cetera. But 103 00:04:39,360 --> 00:04:42,200 Speaker 3: during my time, through this evolution of growth, if you will, 104 00:04:42,440 --> 00:04:45,120 Speaker 3: I was always looking for a way to merge techic fashion. 105 00:04:45,480 --> 00:04:48,560 Speaker 3: There were tools and products out there, the challenge it 106 00:04:48,600 --> 00:04:51,520 Speaker 3: wasn't necessarily up to the par or standard that I 107 00:04:51,640 --> 00:04:55,360 Speaker 3: was looking for. And so before the world started talking. 108 00:04:55,160 --> 00:04:57,320 Speaker 2: About AI and chat and GBT, I was. 109 00:04:57,279 --> 00:05:00,800 Speaker 3: Already sort of in the mix of exploring AI and 110 00:05:00,920 --> 00:05:02,200 Speaker 3: machine learning, etc. 111 00:05:02,480 --> 00:05:04,080 Speaker 2: When Chat GPDBT launched. 112 00:05:04,440 --> 00:05:07,600 Speaker 3: In many ways we benefited from that because it educated 113 00:05:07,640 --> 00:05:10,159 Speaker 3: the world on what this AI thing is and we 114 00:05:10,240 --> 00:05:12,919 Speaker 3: became a part of that conversation because the name of 115 00:05:12,920 --> 00:05:15,400 Speaker 3: the company Spree AI, and so when people are searching, 116 00:05:15,680 --> 00:05:17,039 Speaker 3: we come up until we get a lot of that 117 00:05:17,240 --> 00:05:21,039 Speaker 3: SEO and so in many ways, it was very meaningful 118 00:05:21,640 --> 00:05:22,200 Speaker 3: to us. 119 00:05:22,839 --> 00:05:25,440 Speaker 1: So when you went to start raising money, was there 120 00:05:25,480 --> 00:05:28,680 Speaker 1: like already an MVP in place, or was the convergence 121 00:05:28,680 --> 00:05:33,040 Speaker 1: of the idea just situated such that investors are like, yeah, 122 00:05:33,040 --> 00:05:35,360 Speaker 1: he's billy AI, that's throw money at it. 123 00:05:35,680 --> 00:05:38,320 Speaker 3: Yeah, it was the convergence of the idea. I think, 124 00:05:38,520 --> 00:05:41,920 Speaker 3: you know, entrepreneurs, one of the challenging things is raising money, 125 00:05:41,960 --> 00:05:44,400 Speaker 3: amongst many things. I think specifically for me, I was 126 00:05:44,520 --> 00:05:47,440 Speaker 3: very fortunate, bless that I had a reputation because I 127 00:05:47,480 --> 00:05:50,320 Speaker 3: already had startups before that I sold, and I already 128 00:05:50,360 --> 00:05:52,880 Speaker 3: had worked in tech until in many ways, the investors were, 129 00:05:53,279 --> 00:05:55,840 Speaker 3: for lack of better words, investing in me. It wasn't 130 00:05:55,839 --> 00:05:58,720 Speaker 3: necessarily about the product. And I know that's not usually 131 00:05:58,760 --> 00:06:01,640 Speaker 3: the case for entrepreneurs, and so I wouldn't say it 132 00:06:01,680 --> 00:06:03,880 Speaker 3: was easy for me to raise money, but it wasn't 133 00:06:03,920 --> 00:06:06,960 Speaker 3: as difficult as maybe someone that's starting from scratch to 134 00:06:07,520 --> 00:06:11,760 Speaker 3: you know, raise money and then you know, start their journey. 135 00:06:12,560 --> 00:06:16,720 Speaker 1: Yeah. So how do you then, because you've exited twice, 136 00:06:17,080 --> 00:06:19,599 Speaker 1: you know, kudos to you for that, how do you 137 00:06:19,760 --> 00:06:22,360 Speaker 1: personally define generational wealth? 138 00:06:23,440 --> 00:06:28,080 Speaker 3: Yeah, it's it's so funny that you mentioned that for me, 139 00:06:28,839 --> 00:06:33,480 Speaker 3: I've never in my entire life been basically motivated by opportunity. 140 00:06:33,480 --> 00:06:37,720 Speaker 3: It's sorry, buy money, not opportunity. Yeah, yeah, sorry, I've 141 00:06:37,720 --> 00:06:40,520 Speaker 3: never been motivated by money. It's been mostly about opportunity 142 00:06:40,520 --> 00:06:43,479 Speaker 3: and obviously money falls with that. And so, you know, 143 00:06:43,600 --> 00:06:47,120 Speaker 3: I'm first generation Nigerian. My parents grew up poor. I 144 00:06:47,120 --> 00:06:50,160 Speaker 3: didn't come for money, and so you know, my parents 145 00:06:50,160 --> 00:06:52,480 Speaker 3: had to sacrifice a lot to come to this country 146 00:06:53,080 --> 00:06:56,160 Speaker 3: and basically create a means for us to you know, 147 00:06:56,320 --> 00:06:59,279 Speaker 3: live and be where we are to today. And so, 148 00:06:59,520 --> 00:07:01,440 Speaker 3: as I said, to begin through this journey, it became 149 00:07:01,560 --> 00:07:04,560 Speaker 3: very apparent to me that you know, oftentimes when we 150 00:07:04,600 --> 00:07:09,880 Speaker 3: think of success, especially in our community, sometimes it's comparable 151 00:07:09,920 --> 00:07:12,920 Speaker 3: to things that we see in our eyes all the time, 152 00:07:13,040 --> 00:07:16,560 Speaker 3: like sports and things of that nature, right, and so, 153 00:07:16,600 --> 00:07:19,680 Speaker 3: but we don't necessarily translate that to tech at least yet. 154 00:07:19,720 --> 00:07:22,720 Speaker 3: I know it's getting texts becoming more mainstream. And what 155 00:07:22,760 --> 00:07:25,320 Speaker 3: I mean by that is when you think about someone 156 00:07:25,360 --> 00:07:28,560 Speaker 3: like Lebron James or Michael or Michael Jordan, one of 157 00:07:28,600 --> 00:07:30,760 Speaker 3: the things that comes to mind is this person is 158 00:07:30,800 --> 00:07:33,800 Speaker 3: a unicorn. They're kind of a one of a kind 159 00:07:34,080 --> 00:07:36,240 Speaker 3: and they're basically about to put on for their family 160 00:07:36,400 --> 00:07:39,160 Speaker 3: and friends and so forth. And so I started to 161 00:07:39,200 --> 00:07:41,720 Speaker 3: carry that same mindset with me, and I realized how 162 00:07:41,760 --> 00:07:44,600 Speaker 3: fortunate I was because a lot of the technology that 163 00:07:44,680 --> 00:07:46,040 Speaker 3: I learned was all self taught. 164 00:07:46,120 --> 00:07:48,200 Speaker 2: Right, If you think about the complexities. 165 00:07:47,680 --> 00:07:50,080 Speaker 3: Of technology, it's not an easy thing to get into, 166 00:07:50,240 --> 00:07:53,080 Speaker 3: just like arguably it's not easy to get into the NBA. 167 00:07:53,680 --> 00:07:55,480 Speaker 3: And so in many ways, this is like a once 168 00:07:55,480 --> 00:07:59,960 Speaker 3: in a lifetime opportunity. And so generational wealth means to me, 169 00:08:00,600 --> 00:08:03,520 Speaker 3: it's not about me, it's about everyone else around me, 170 00:08:03,600 --> 00:08:06,440 Speaker 3: plus people that aren't here yet, So my kids, my 171 00:08:06,560 --> 00:08:10,000 Speaker 3: kids kids, setting up a foundation that they don't have 172 00:08:10,040 --> 00:08:13,280 Speaker 3: to work as hard as I did, and being able 173 00:08:13,320 --> 00:08:16,320 Speaker 3: to have a stable company to where you know, maybe 174 00:08:16,320 --> 00:08:17,880 Speaker 3: you have a son one day and you're like, yo, 175 00:08:17,960 --> 00:08:20,680 Speaker 3: he needs an internship. I'm like, I got you right, 176 00:08:20,720 --> 00:08:23,880 Speaker 3: Being able to like form those connections and help elevate 177 00:08:24,240 --> 00:08:25,760 Speaker 3: other people that are around. 178 00:08:25,520 --> 00:08:26,119 Speaker 2: Me as well. 179 00:08:27,200 --> 00:08:30,000 Speaker 1: Yeah, something you just said there is like I'm going 180 00:08:30,040 --> 00:08:32,080 Speaker 1: to do my best to formulate the question out of this, 181 00:08:32,120 --> 00:08:34,040 Speaker 1: but I'm hoping you just kind of get what I'm 182 00:08:34,080 --> 00:08:37,280 Speaker 1: trying to say. So you just said something that. I 183 00:08:37,320 --> 00:08:41,840 Speaker 1: also heard another recent interview Todday or youreende frough Campus 184 00:08:42,040 --> 00:08:45,199 Speaker 1: just said also in that you're not necessarily thinking about 185 00:08:45,200 --> 00:08:47,960 Speaker 1: the money opportunity when you're going to build something because 186 00:08:48,000 --> 00:08:51,400 Speaker 1: the money that's part that part is a given when 187 00:08:51,440 --> 00:08:54,600 Speaker 1: you've achieved the mission. And I think, like so often 188 00:08:54,720 --> 00:08:58,240 Speaker 1: we get money focused by because we're taught to. You know, 189 00:08:58,240 --> 00:09:01,040 Speaker 1: when you when you talk about you know total you 190 00:09:01,080 --> 00:09:03,839 Speaker 1: know market opportunity, you know TAM, you know the total 191 00:09:03,960 --> 00:09:07,000 Speaker 1: addressable market, the you know all those sorts of things. 192 00:09:07,000 --> 00:09:10,439 Speaker 1: You're thinking about dollar signs. And what I hear you 193 00:09:10,520 --> 00:09:14,800 Speaker 1: say is like I'm thinking about, is the opportunity big enough? 194 00:09:14,920 --> 00:09:15,000 Speaker 2: It? 195 00:09:15,000 --> 00:09:17,280 Speaker 1: Can I make a big enough dit in the world 196 00:09:17,760 --> 00:09:19,520 Speaker 1: to where if I do that, then the money is 197 00:09:19,559 --> 00:09:22,400 Speaker 1: like a given? Does it make sense what I'm trying 198 00:09:22,400 --> 00:09:24,560 Speaker 1: to say, Like we're trying to get like some money focused. 199 00:09:24,679 --> 00:09:26,440 Speaker 1: We talk to be money focused in one way, but 200 00:09:26,480 --> 00:09:29,120 Speaker 1: you're saying, let's just focus on the concept and be 201 00:09:29,200 --> 00:09:29,760 Speaker 1: big enough. 202 00:09:30,280 --> 00:09:32,960 Speaker 3: Yeah, exactly, I get exactly to say. I think though, 203 00:09:33,000 --> 00:09:37,040 Speaker 3: to play Devil's advocate. Where it becomes challenging is when 204 00:09:37,040 --> 00:09:39,560 Speaker 3: you don't come from money and you're trying to survive 205 00:09:39,679 --> 00:09:42,360 Speaker 3: until money becomes a priority because you're like, yo, I 206 00:09:42,400 --> 00:09:45,880 Speaker 3: gotta eat right, and so I think that becomes challenging. 207 00:09:45,920 --> 00:09:48,240 Speaker 3: But I think naturally for me, the way that I 208 00:09:48,480 --> 00:09:51,559 Speaker 3: was raised is, you know, we weren't rich or anything 209 00:09:51,600 --> 00:09:53,800 Speaker 3: like that. Like I said, we you know, my parents 210 00:09:53,800 --> 00:09:56,640 Speaker 3: got good jobs later on in their careers. When we 211 00:09:56,679 --> 00:09:58,040 Speaker 3: were growing up, you know, we used to share the 212 00:09:58,120 --> 00:10:00,720 Speaker 3: same room things of that nature. There's you know, there's 213 00:10:00,760 --> 00:10:03,960 Speaker 3: four of us between our brothers and sisters. And I 214 00:10:04,080 --> 00:10:06,679 Speaker 3: just realized what was really important was for me to 215 00:10:06,760 --> 00:10:10,000 Speaker 3: leave my impact on the world and create something that's 216 00:10:10,040 --> 00:10:13,120 Speaker 3: so meaningful that it would last forever. 217 00:10:13,559 --> 00:10:13,760 Speaker 2: Right. 218 00:10:14,280 --> 00:10:16,240 Speaker 3: And then if you think about it from that notion, 219 00:10:16,400 --> 00:10:19,079 Speaker 3: anything that is meaningful in the world, the money does 220 00:10:19,120 --> 00:10:22,080 Speaker 3: come from that. It is associated with that, right. And 221 00:10:22,120 --> 00:10:24,560 Speaker 3: so I just felt like, you know, I'm a religious person. 222 00:10:24,600 --> 00:10:25,680 Speaker 3: I know not everybody is. 223 00:10:25,679 --> 00:10:26,240 Speaker 2: That I had this. 224 00:10:26,240 --> 00:10:30,280 Speaker 3: God given talent and it was my duty to get 225 00:10:30,320 --> 00:10:32,920 Speaker 3: back to the world these skill sets and if it 226 00:10:32,920 --> 00:10:35,640 Speaker 3: brings money, which it has, that's great, Right, then I 227 00:10:35,640 --> 00:10:38,720 Speaker 3: can use that to provide for my family and friends 228 00:10:38,720 --> 00:10:41,280 Speaker 3: and the people that aren't here yet that I haven't created. 229 00:10:41,440 --> 00:10:41,560 Speaker 2: So