1 00:00:03,640 --> 00:00:06,640 Speaker 1: Cure and welcome to the Business of Tech powered by 2 00:00:06,800 --> 00:00:10,440 Speaker 1: two Degrees Business, where we dive into the latest trends 3 00:00:10,440 --> 00:00:13,680 Speaker 1: and stories shaping the world of technology and business here 4 00:00:13,720 --> 00:00:17,520 Speaker 1: in New Zealand and beyond. I'm Peter Griffin, and today 5 00:00:17,600 --> 00:00:22,240 Speaker 1: we're spotlighting a homegrown AI success story that's quietly making 6 00:00:22,320 --> 00:00:26,160 Speaker 1: waves on the global stage. We'll get into the podcast shortly, 7 00:00:26,239 --> 00:00:30,319 Speaker 1: but first a new and occasional feature I'm adding to 8 00:00:30,360 --> 00:00:34,360 Speaker 1: the podcast where I put a single question to someone, 9 00:00:34,800 --> 00:00:37,440 Speaker 1: someone who may have been a past guest on the show, 10 00:00:37,880 --> 00:00:40,240 Speaker 1: or maybe someone I think will have a really interesting 11 00:00:40,280 --> 00:00:44,640 Speaker 1: take on a tech related issue. This week's question goes 12 00:00:44,680 --> 00:00:48,839 Speaker 1: to Saab Johal, the former Massive University Associate professor in 13 00:00:48,960 --> 00:00:54,240 Speaker 1: mental health, author, tech commentator, and YouTuber. I had Sab 14 00:00:54,400 --> 00:00:57,080 Speaker 1: on the show over a year ago to talk about 15 00:00:57,120 --> 00:01:02,200 Speaker 1: the debut of Apple's Vision pro augmented reality head hit Cyber' 16 00:01:02,200 --> 00:01:07,200 Speaker 1: is a massive Apple watcher. Cyb really understands human psychology 17 00:01:07,280 --> 00:01:10,600 Speaker 1: and he's a real tech enthusiast, which led me to 18 00:01:10,680 --> 00:01:15,280 Speaker 1: ask him this question. When I've been increasingly pondering as 19 00:01:16,000 --> 00:01:21,560 Speaker 1: artificial intelligence proliferates, what part of your life should always 20 00:01:21,560 --> 00:01:26,640 Speaker 1: stay analog no matter how smart tech gets. Here's what 21 00:01:26,800 --> 00:01:27,720 Speaker 1: CYB had to say. 22 00:01:28,280 --> 00:01:31,600 Speaker 2: For me, it's the slow, analogue moments that make space 23 00:01:31,800 --> 00:01:35,240 Speaker 2: for thought, like scribbling in a notebook or reading an 24 00:01:35,240 --> 00:01:38,800 Speaker 2: old paper encyclopedia with my kids and pausing to talk 25 00:01:38,840 --> 00:01:41,640 Speaker 2: about what we've just read. It might not be the 26 00:01:41,680 --> 00:01:44,240 Speaker 2: most up to day information, but it's good enough to 27 00:01:44,280 --> 00:01:47,520 Speaker 2: start a conversation, and that's the bit I care about, 28 00:01:47,760 --> 00:01:53,160 Speaker 2: the space to pause, reflect, and just muddle through together. 29 00:01:54,400 --> 00:01:56,840 Speaker 2: I live in a world full of tech. I film 30 00:01:56,880 --> 00:01:59,360 Speaker 2: with it, edit with it, and even use AI to 31 00:01:59,400 --> 00:02:03,200 Speaker 2: help me write sometimes, but not everything needs to be optimized. 32 00:02:03,280 --> 00:02:07,760 Speaker 2: In fact, some things shouldn't be. When I'm rising my hand, 33 00:02:07,760 --> 00:02:11,400 Speaker 2: for example, I'm not trying to be efficient. I'm trying 34 00:02:11,440 --> 00:02:15,680 Speaker 2: to feel what I think. That tiny bit of friction. 35 00:02:16,280 --> 00:02:20,400 Speaker 2: The slower pace makes it easier to notice what actually matters. 36 00:02:21,240 --> 00:02:23,839 Speaker 2: Because more and more research is telling us that it's 37 00:02:23,919 --> 00:02:28,080 Speaker 2: the dialogue between our left and right brains that's important. 38 00:02:28,800 --> 00:02:31,880 Speaker 2: The idea of having knowledge but not quite having the 39 00:02:32,040 --> 00:02:35,600 Speaker 2: language for it until we make space to allow the 40 00:02:35,680 --> 00:02:39,800 Speaker 2: relationship of language and deep knowledge held in the right 41 00:02:39,840 --> 00:02:45,280 Speaker 2: brain can eventually be verbalized by the left brain. It's 42 00:02:45,320 --> 00:02:49,160 Speaker 2: a slow groping in the dark gradual process, and at 43 00:02:49,200 --> 00:02:52,440 Speaker 2: the moment, all I see is AI models trying to 44 00:02:52,520 --> 00:02:57,600 Speaker 2: replicate left brain efficiency rather than a dialogue with our 45 00:02:57,760 --> 00:03:03,840 Speaker 2: more parallel processing summary of all things right brain. And 46 00:03:03,919 --> 00:03:07,440 Speaker 2: even outside of writing, sometimes I'd rather not have tech 47 00:03:07,639 --> 00:03:10,200 Speaker 2: jump in with the answer before I've even figured out 48 00:03:10,240 --> 00:03:14,919 Speaker 2: the question. There's value in the awkward pause, in not knowing, 49 00:03:15,880 --> 00:03:19,560 Speaker 2: in getting a little bit lost. So while I love 50 00:03:19,720 --> 00:03:22,880 Speaker 2: tech that makes life easier, I think there's a danger 51 00:03:22,880 --> 00:03:25,760 Speaker 2: when it makes things too easy, when we lose the 52 00:03:25,800 --> 00:03:30,480 Speaker 2: opportunity to engage, to struggle just enough to grow, to 53 00:03:30,639 --> 00:03:33,919 Speaker 2: connect in a more human way, not just with each other, 54 00:03:34,040 --> 00:03:38,840 Speaker 2: but with our own selves. So that's why I protect 55 00:03:38,920 --> 00:03:42,360 Speaker 2: the analog bits of my life, not because I'm nostalgic, 56 00:03:42,760 --> 00:03:45,840 Speaker 2: but because they keep me thinking, and they keep me present, 57 00:03:46,440 --> 00:03:49,120 Speaker 2: and they keep me connected with the richness of the 58 00:03:49,120 --> 00:03:53,720 Speaker 2: totality of me, and that to me is worth more 59 00:03:54,040 --> 00:03:55,640 Speaker 2: than perfect convenience. 60 00:03:56,360 --> 00:03:59,800 Speaker 1: I really love that protect the analogue parts of your 61 00:03:59,840 --> 00:04:03,480 Speaker 1: life to keep you thinking and to keep you present. 62 00:04:03,640 --> 00:04:08,000 Speaker 1: Great advice from Cyberjohal. I'll put links to Cyber's substack 63 00:04:08,360 --> 00:04:12,520 Speaker 1: and his YouTube channel in the show notes. My guests 64 00:04:12,520 --> 00:04:15,920 Speaker 1: this week are Lucy Pink and Hannah Hardy Jones, the 65 00:04:16,040 --> 00:04:21,039 Speaker 1: dynamic duo behind Contented AI, a christ Church startup that's 66 00:04:21,080 --> 00:04:25,600 Speaker 1: reimagining how we capture and extract value from our conversations. 67 00:04:26,440 --> 00:04:29,039 Speaker 1: If you've ever left a meeting or an interview thinking 68 00:04:29,080 --> 00:04:32,160 Speaker 1: there's gold in there, but how do I actually use it? 69 00:04:32,279 --> 00:04:36,280 Speaker 1: This episode is for you. Lucy and Hannah's journey is 70 00:04:36,320 --> 00:04:40,000 Speaker 1: a pretty fascinating one. Neither started as a technical founder. 71 00:04:40,160 --> 00:04:44,240 Speaker 1: Hannah comes from an HR and mental health background, having 72 00:04:44,279 --> 00:04:48,080 Speaker 1: built the well being app Kite after her own personal journey, 73 00:04:48,600 --> 00:04:52,320 Speaker 1: while Lucy's path took her from ethical marketplaces to teaching 74 00:04:52,360 --> 00:04:57,440 Speaker 1: herself to code during lockdown. Together they've built Contented AI, 75 00:04:57,800 --> 00:05:02,040 Speaker 1: a platform that transforms everything from council meetings to client 76 00:05:02,080 --> 00:05:07,119 Speaker 1: calls into actionable insights, tailored outputs, and even content ready 77 00:05:07,120 --> 00:05:09,440 Speaker 1: for LinkedIn. On the face of it, this sounds a 78 00:05:09,440 --> 00:05:13,600 Speaker 1: little bit like you services like Otter, even the transcriptions 79 00:05:13,600 --> 00:05:18,279 Speaker 1: available in Zoom and teams services like Fireflies as well, 80 00:05:18,320 --> 00:05:22,160 Speaker 1: But Contented has gone much much deeper here to deliver 81 00:05:22,400 --> 00:05:27,720 Speaker 1: real insights from interview transcripts or recordings of meetings. We'll 82 00:05:27,720 --> 00:05:31,240 Speaker 1: talk about how Lucy and Hannah met at the Ministry 83 00:05:31,240 --> 00:05:35,440 Speaker 1: of Awesome's Coffee and Jam event what it's like bootstrapping 84 00:05:35,440 --> 00:05:38,640 Speaker 1: an AI company and ar Tairoa, and why they believe 85 00:05:38,680 --> 00:05:43,080 Speaker 1: the future of work is all about making every conversation count. 86 00:05:43,520 --> 00:05:47,240 Speaker 1: Plus we'll dig into how Contented AI is already helping 87 00:05:47,400 --> 00:05:51,719 Speaker 1: legal firms, journalists, and even EWI organizations get more from 88 00:05:51,720 --> 00:05:56,840 Speaker 1: their words securely, accurately and with a uniquely Keiwi approach. 89 00:05:57,600 --> 00:06:01,120 Speaker 1: Lucy and Hannah were both keynote speakers at Electrify our 90 00:06:01,200 --> 00:06:05,680 Speaker 1: tairo last week here in Wellington, a great conference centered 91 00:06:05,680 --> 00:06:08,080 Speaker 1: on women founders. I got along to a few sessions 92 00:06:08,120 --> 00:06:10,679 Speaker 1: and it really was a top notch event. I'd encourage 93 00:06:10,680 --> 00:06:13,919 Speaker 1: you to go next year. So let's get into it. 94 00:06:13,960 --> 00:06:16,960 Speaker 1: Here's the co founders behind up and coming AI startup 95 00:06:17,320 --> 00:06:36,160 Speaker 1: Contented AI. Hannah and Lucy. Welcome to the business of tech. 96 00:06:36,200 --> 00:06:37,839 Speaker 1: How are you both doing good? 97 00:06:38,160 --> 00:06:38,600 Speaker 3: Really well? 98 00:06:38,640 --> 00:06:43,560 Speaker 1: Thanks? So I've heard just recently about Contented AI, and 99 00:06:43,600 --> 00:06:46,480 Speaker 1: I'm on a mission to showcase some of the AI 100 00:06:46,600 --> 00:06:49,960 Speaker 1: startups that are coming out of New Zealand. I get 101 00:06:49,960 --> 00:06:52,200 Speaker 1: the feeling that we're a little bit slow, really too, 102 00:06:53,520 --> 00:06:56,040 Speaker 1: taking all of these great large language models and then 103 00:06:56,040 --> 00:06:58,839 Speaker 1: turning them into businesses, maybe slower than Australia, but this 104 00:06:58,960 --> 00:07:02,960 Speaker 1: year in particular, I'm seeing a lot of great companies 105 00:07:03,000 --> 00:07:05,679 Speaker 1: as a launch coming up this week for yet another 106 00:07:05,720 --> 00:07:09,159 Speaker 1: one in Wellington, So it's starting to happen. The pair 107 00:07:09,200 --> 00:07:11,320 Speaker 1: of you really got together. What was it in twenty 108 00:07:11,360 --> 00:07:15,040 Speaker 1: twenty three? The content had launched in April twenty twenty three, which, 109 00:07:15,080 --> 00:07:18,360 Speaker 1: when you think about it, wasn't that long after the 110 00:07:18,440 --> 00:07:21,280 Speaker 1: debut of Chat GPT, which changed the game when it 111 00:07:21,320 --> 00:07:26,160 Speaker 1: comes to generative AI. Take us back to the origin story, 112 00:07:26,480 --> 00:07:29,080 Speaker 1: after two of you meeting and having done quite a 113 00:07:29,080 --> 00:07:32,240 Speaker 1: bit in sort of business, but not necessarily as technical founders. 114 00:07:32,640 --> 00:07:35,280 Speaker 3: Yeah, so we met at the Ministry of Awesome. We 115 00:07:35,360 --> 00:07:39,840 Speaker 3: actually a few years prior to starting Contented. We were 116 00:07:39,840 --> 00:07:42,880 Speaker 3: brought together as we're both speaking at an event called 117 00:07:42,880 --> 00:07:46,200 Speaker 3: Coffee and Jam, which was just showcasing early startups and 118 00:07:46,280 --> 00:07:49,280 Speaker 3: their ideas. We were both starting in the sort of 119 00:07:49,360 --> 00:07:53,560 Speaker 3: Tiohaka incubator program and it was Yeah, it was just 120 00:07:53,600 --> 00:07:55,960 Speaker 3: amazing to meet each other here about what we were 121 00:07:56,000 --> 00:07:58,640 Speaker 3: doing with our startups, hear about the problems that we 122 00:07:58,640 --> 00:08:03,080 Speaker 3: were solving. We became really close over that time really 123 00:08:03,120 --> 00:08:05,880 Speaker 3: just supporting each other as solo founders, you know, like 124 00:08:05,960 --> 00:08:08,600 Speaker 3: just having that community and being able to bounce ideas 125 00:08:08,880 --> 00:08:12,600 Speaker 3: off each other. And we got to the point it 126 00:08:12,680 --> 00:08:16,120 Speaker 3: was really when chetchybt launched that we saw the power 127 00:08:16,200 --> 00:08:18,640 Speaker 3: of AI and how it could help our own startup. 128 00:08:18,760 --> 00:08:22,560 Speaker 3: So just being able to power up what we were doing, 129 00:08:22,720 --> 00:08:25,480 Speaker 3: you know, with this lack of time and resource and capital. 130 00:08:25,640 --> 00:08:29,160 Speaker 3: We had suddenly a tool that could really transform what 131 00:08:29,240 --> 00:08:33,120 Speaker 3: we were doing. And that's when we decided to come 132 00:08:33,160 --> 00:08:36,959 Speaker 3: together to create something to help you know, businesses and 133 00:08:38,000 --> 00:08:41,440 Speaker 3: people use this technology. So that was the start of 134 00:08:41,800 --> 00:08:42,400 Speaker 3: the journey. 135 00:08:42,480 --> 00:08:45,880 Speaker 4: Really we basically were Chatchybt consultants, you know for a 136 00:08:45,920 --> 00:08:48,240 Speaker 4: few months, you know, and we were because it was, 137 00:08:48,520 --> 00:08:53,280 Speaker 4: you know, yourself, as a startup founder, like can't mentioned 138 00:08:53,600 --> 00:08:56,360 Speaker 4: you're trying to make the most out of anything you get, 139 00:08:56,360 --> 00:08:59,560 Speaker 4: and AI was one of them. And so we became 140 00:08:59,760 --> 00:09:04,040 Speaker 4: in credibly proficient with chatjibt so quickly because finally we 141 00:09:04,080 --> 00:09:07,400 Speaker 4: could create content, we could do research, we could create 142 00:09:07,480 --> 00:09:11,840 Speaker 4: LinkedIn posts all based on kind of using a chatbot. 143 00:09:11,880 --> 00:09:15,400 Speaker 4: And so when we became really good at it, family friends, 144 00:09:16,200 --> 00:09:20,920 Speaker 4: legal firms, schools were asking for our help because we 145 00:09:21,200 --> 00:09:24,400 Speaker 4: just with somebody and people that just dived in. What 146 00:09:24,480 --> 00:09:27,680 Speaker 4: we found very quickly was a it's good if you're 147 00:09:27,920 --> 00:09:30,720 Speaker 4: sort of an individual person and you know what good 148 00:09:30,760 --> 00:09:33,480 Speaker 4: looks like, and you can use AI in a great way. 149 00:09:33,520 --> 00:09:36,200 Speaker 4: You know how to prompt, you know what is false 150 00:09:36,240 --> 00:09:38,640 Speaker 4: and what is true. But when you're trying to roll 151 00:09:38,679 --> 00:09:40,960 Speaker 4: that out across an entire legal firm and you're trying 152 00:09:40,960 --> 00:09:43,480 Speaker 4: to get people to use CHATCHBT and teach a whole 153 00:09:43,559 --> 00:09:47,240 Speaker 4: variety of people, that's when it gets very difficult. And 154 00:09:47,280 --> 00:09:50,040 Speaker 4: so we paed back really with chatchept, didn't we because 155 00:09:50,040 --> 00:09:52,760 Speaker 4: we were like, actually, we don't actually know if we 156 00:09:52,840 --> 00:09:54,840 Speaker 4: love the interface, We don't know if we love the 157 00:09:55,280 --> 00:09:58,640 Speaker 4: technology just yet. And we kind of went back to 158 00:09:58,640 --> 00:10:01,720 Speaker 4: the drawing board and started to talk to the businesses 159 00:10:01,760 --> 00:10:03,760 Speaker 4: we were working with to go, actually, what is it 160 00:10:04,120 --> 00:10:07,079 Speaker 4: with your pain points? What is it around your business 161 00:10:07,080 --> 00:10:09,200 Speaker 4: that you're wanting to use AI for? Is it actually 162 00:10:09,240 --> 00:10:13,199 Speaker 4: a problem? And so even though we were AI consultants, 163 00:10:13,240 --> 00:10:15,800 Speaker 4: we hardly ever spoke about AI. We're kind of just 164 00:10:15,880 --> 00:10:18,760 Speaker 4: talking about problems. And then that's kind of when Contented 165 00:10:18,880 --> 00:10:22,800 Speaker 4: started to become a product. But it really took about 166 00:10:22,800 --> 00:10:24,640 Speaker 4: a year and a half of R and D to 167 00:10:24,640 --> 00:10:27,760 Speaker 4: get to this point, which is where we're at. 168 00:10:28,120 --> 00:10:31,240 Speaker 1: Yeah, so we're going to get into exactly what content 169 00:10:31,320 --> 00:10:34,960 Speaker 1: it does, but really intrigue at this mix of skills 170 00:10:35,360 --> 00:10:39,720 Speaker 1: and backgrounds that you bring to Contented. You know, Hannah, 171 00:10:39,720 --> 00:10:41,920 Speaker 1: I think the Kite program I'd love to hear more 172 00:10:41,960 --> 00:10:44,760 Speaker 1: about that. You've got sort of quite a mental health 173 00:10:45,000 --> 00:10:48,360 Speaker 1: and well being sort of background, and then Lucy obviously 174 00:10:48,400 --> 00:10:51,520 Speaker 1: debt to Daddy. Interesting to hear about that. So quite 175 00:10:51,520 --> 00:10:54,760 Speaker 1: an eclectic mix of different types of businesses that you've 176 00:10:54,760 --> 00:10:57,320 Speaker 1: been involved and tell us the sort of the background 177 00:10:57,400 --> 00:11:01,079 Speaker 1: to your businesses you were in coming to Contented. 178 00:11:01,559 --> 00:11:04,480 Speaker 3: Yeah, so I started the Kite program. I'd actually come 179 00:11:04,480 --> 00:11:08,520 Speaker 3: from an HR psychology background. I'd also really struggled from 180 00:11:08,520 --> 00:11:11,120 Speaker 3: a mental health perspective after having my first baby, So 181 00:11:11,160 --> 00:11:14,040 Speaker 3: it really changed kind of where I saw myself. And 182 00:11:14,080 --> 00:11:17,960 Speaker 3: so Kite was based on you know, tiny steps micro learning. 183 00:11:18,040 --> 00:11:21,160 Speaker 3: It was an app. It could be customized to different 184 00:11:21,400 --> 00:11:25,320 Speaker 3: customers and different you know eating disorders or you know, 185 00:11:25,400 --> 00:11:27,960 Speaker 3: workplace well being, and so that was my whole passion 186 00:11:28,000 --> 00:11:30,800 Speaker 3: and I built it like I can hand on heart 187 00:11:30,880 --> 00:11:33,119 Speaker 3: say now, like it was built on my own experience 188 00:11:33,160 --> 00:11:36,400 Speaker 3: and what I thought people needed, and I did not 189 00:11:36,640 --> 00:11:40,720 Speaker 3: enough user research, and I went barreling down this path 190 00:11:40,800 --> 00:11:42,559 Speaker 3: of I've got to build this app and it's going 191 00:11:42,600 --> 00:11:44,600 Speaker 3: to be a global sensation, you know. And I think 192 00:11:45,040 --> 00:11:48,800 Speaker 3: there's a lot of learnings from there. But going through 193 00:11:48,840 --> 00:11:52,880 Speaker 3: that process and kind of being I can sell really well, 194 00:11:52,960 --> 00:11:55,320 Speaker 3: but I don't think I had really spent the time 195 00:11:55,360 --> 00:11:58,840 Speaker 3: to understand the customer. And so Lucy will share more 196 00:11:58,840 --> 00:12:02,000 Speaker 3: about this, but you know, that's when our skills come 197 00:12:02,040 --> 00:12:05,880 Speaker 3: together really well, because everything that I don't have, Lucy 198 00:12:05,920 --> 00:12:09,160 Speaker 3: has and potentially there by way around. So that was 199 00:12:09,400 --> 00:12:11,040 Speaker 3: that was my journey with kit. 200 00:12:11,440 --> 00:12:14,840 Speaker 4: Yeah, and I think and Hannah's being low key about 201 00:12:14,840 --> 00:12:17,880 Speaker 4: it because you know, the selling and the passion also 202 00:12:17,960 --> 00:12:20,680 Speaker 4: helps with landing amazing customers like you know, the National 203 00:12:20,760 --> 00:12:24,000 Speaker 4: History Museum in London, you know, being able. I think 204 00:12:24,120 --> 00:12:27,800 Speaker 4: me and Hannah really collecked because we are such doers, 205 00:12:28,160 --> 00:12:30,240 Speaker 4: like once we've got it in our head that this 206 00:12:30,280 --> 00:12:32,439 Speaker 4: is where we want to be and we see the impact, 207 00:12:32,640 --> 00:12:35,480 Speaker 4: there's no stopping it, you know. And Hannah's ability to 208 00:12:35,520 --> 00:12:38,679 Speaker 4: stretch her impact all the way over to London US 209 00:12:38,800 --> 00:12:43,240 Speaker 4: and rally some amazing companies is testament to her. I'm 210 00:12:43,400 --> 00:12:46,880 Speaker 4: the opposite in the sense that I loved talking to 211 00:12:47,160 --> 00:12:50,720 Speaker 4: everyday people, understanding what it looked like. And as a consumer, 212 00:12:50,800 --> 00:12:55,960 Speaker 4: I built kind of an ethical marketplace and built and 213 00:12:56,000 --> 00:12:58,280 Speaker 4: started to learn to code based on my own problem, 214 00:12:58,280 --> 00:13:00,400 Speaker 4: which was I wanted to know more about where products 215 00:13:00,400 --> 00:13:03,640 Speaker 4: were from and if they were ethical, and I spent 216 00:13:03,920 --> 00:13:06,200 Speaker 4: a lot of time on Reddit. I'd be talking to 217 00:13:06,200 --> 00:13:09,280 Speaker 4: one hundred users around what is it around an ethical 218 00:13:09,280 --> 00:13:11,680 Speaker 4: white T shirt? You know, what is ethical? What does 219 00:13:11,720 --> 00:13:13,960 Speaker 4: that mean? How do you buy it? Where do you 220 00:13:14,000 --> 00:13:17,199 Speaker 4: want to buy it? So I did the opposite, and I, 221 00:13:17,280 --> 00:13:20,040 Speaker 4: as a comfort zone, went to building a product and 222 00:13:20,120 --> 00:13:22,280 Speaker 4: hardly selling. You know, I just wanted to build this 223 00:13:22,320 --> 00:13:25,360 Speaker 4: beautiful product and had the assumption that if you did that, 224 00:13:25,520 --> 00:13:28,400 Speaker 4: people would come to you, which is completely the wrong 225 00:13:28,440 --> 00:13:32,960 Speaker 4: approach because I think you're nothing without sales or revenue. 226 00:13:33,600 --> 00:13:36,600 Speaker 4: That combined we kind of came together. But going back 227 00:13:36,600 --> 00:13:39,400 Speaker 4: to Dada Daddy, it was an amazing way for me 228 00:13:39,520 --> 00:13:42,520 Speaker 4: to learn about startups and but building a business. My 229 00:13:42,600 --> 00:13:46,040 Speaker 4: boss met. He was incredible because he was so passionate 230 00:13:46,040 --> 00:13:49,600 Speaker 4: about you know, accounts receivable, maybe not the most glamorous 231 00:13:49,640 --> 00:13:54,360 Speaker 4: topic that you saw everyday guy wanting to build some 232 00:13:54,480 --> 00:13:57,240 Speaker 4: tech in software and a solution around it and build 233 00:13:57,240 --> 00:13:59,040 Speaker 4: an incredible business. So I kind of got a front 234 00:13:59,120 --> 00:14:02,720 Speaker 4: row seat that looks like. So that kind of propelled 235 00:14:02,720 --> 00:14:06,240 Speaker 4: me into startup world and having the kind of motivation 236 00:14:06,320 --> 00:14:07,240 Speaker 4: to do my own thing. 237 00:14:07,600 --> 00:14:09,760 Speaker 1: And I understand it was sort of in the depths 238 00:14:09,760 --> 00:14:11,960 Speaker 1: of COVID that you decided I'm going to sit down 239 00:14:12,000 --> 00:14:14,719 Speaker 1: and learn to code. What inspired that move and what 240 00:14:14,760 --> 00:14:17,400 Speaker 1: was it like sort of doing that at a time 241 00:14:17,559 --> 00:14:19,360 Speaker 1: when you might have been sort of a bit more 242 00:14:19,400 --> 00:14:22,160 Speaker 1: isolated from the sort of people who are going to 243 00:14:22,160 --> 00:14:23,200 Speaker 1: help you on that journey. 244 00:14:24,480 --> 00:14:28,760 Speaker 4: Yeah, exactly. I was during COVID, and it was the 245 00:14:28,800 --> 00:14:32,720 Speaker 4: first time I actually had the brain time, and it 246 00:14:32,840 --> 00:14:35,480 Speaker 4: was in a privileged position of, you know, living with 247 00:14:35,520 --> 00:14:38,840 Speaker 4: my parents who basically were making the amazing dinners, and 248 00:14:38,880 --> 00:14:41,640 Speaker 4: I got to spend my nights and days finally building 249 00:14:41,680 --> 00:14:44,080 Speaker 4: the software that I couldn't stop talking about. If you 250 00:14:44,120 --> 00:14:45,480 Speaker 4: talk to me, if you talk to the team at 251 00:14:45,480 --> 00:14:47,480 Speaker 4: deada Jelly, all I talked about was on you in 252 00:14:47,520 --> 00:14:51,520 Speaker 4: this marketplace. So it was this kind of perfect stars 253 00:14:51,520 --> 00:14:55,280 Speaker 4: alignment moment. What I had to do was I watched 254 00:14:55,640 --> 00:14:58,680 Speaker 4: countless YouTube videos. I started to build, I started to 255 00:14:58,760 --> 00:15:02,440 Speaker 4: learn to code. Was driven by a problem. So I 256 00:15:02,440 --> 00:15:04,720 Speaker 4: think if you if you asked me to do like 257 00:15:04,800 --> 00:15:07,920 Speaker 4: a software engineering pay for at UNI, I would have 258 00:15:07,920 --> 00:15:11,040 Speaker 4: hated it. But because I was driven by building this 259 00:15:11,120 --> 00:15:13,720 Speaker 4: thing that I've always dreamt off, I managed to do it. 260 00:15:13,760 --> 00:15:16,360 Speaker 4: And I loved engineering and I loved software and it 261 00:15:16,400 --> 00:15:18,760 Speaker 4: was always something that I loved to do and for 262 00:15:18,920 --> 00:15:22,400 Speaker 4: that school, and so yeah, I think what I did 263 00:15:22,440 --> 00:15:25,320 Speaker 4: find was I hated bugs, I hated checking code. I 264 00:15:25,360 --> 00:15:28,560 Speaker 4: had no patience for it. So again Star's alignment. AI 265 00:15:28,640 --> 00:15:33,240 Speaker 4: comes along, and I could build quickly fastly, I could 266 00:15:33,240 --> 00:15:36,760 Speaker 4: build well, and I could use AI to do all 267 00:15:36,800 --> 00:15:38,400 Speaker 4: the stuff I really didn't want to do. 268 00:15:38,600 --> 00:15:44,320 Speaker 1: So yeah, yeah, and you know, I've just built a 269 00:15:44,320 --> 00:15:48,840 Speaker 1: website with an AI agent called Replet. It took literally 270 00:15:49,200 --> 00:15:52,360 Speaker 1: a couple of minutes. There's a whole host of these 271 00:15:52,400 --> 00:15:55,200 Speaker 1: AI agents now, so it's absolutely changing the game in 272 00:15:55,280 --> 00:15:59,640 Speaker 1: terms of software development. But Hannah tell us about contented AI, 273 00:16:00,000 --> 00:16:02,040 Speaker 1: and you know, it's built around this idea that every 274 00:16:02,120 --> 00:16:05,720 Speaker 1: conversation can be turned into extraordinary outcomes. I think that's 275 00:16:05,720 --> 00:16:08,040 Speaker 1: how you describe it, and it very much comes down 276 00:16:08,120 --> 00:16:12,160 Speaker 1: to voice and conversations. And this is at the heart 277 00:16:12,160 --> 00:16:15,080 Speaker 1: of what I do talking to people, doing interviews and 278 00:16:15,120 --> 00:16:20,160 Speaker 1: then getting transcripts. I'm using tools like Otter and Fireflies 279 00:16:20,480 --> 00:16:23,640 Speaker 1: and even teams to make sense of all of these 280 00:16:23,680 --> 00:16:26,520 Speaker 1: conversations that I'm having and get contexts. So all of 281 00:16:26,520 --> 00:16:29,880 Speaker 1: these tools were sort of being infused with AI around 282 00:16:29,920 --> 00:16:34,240 Speaker 1: twenty twenty three. Tell us about this particular take you 283 00:16:34,280 --> 00:16:39,320 Speaker 1: wanted to have on conversations and extracting value from them. 284 00:16:39,560 --> 00:16:42,400 Speaker 3: Yeah, so when we first started, we were doing lots 285 00:16:42,440 --> 00:16:45,000 Speaker 3: of different sort of mini projects. Some of them were 286 00:16:45,280 --> 00:16:48,000 Speaker 3: voice inputs, some of them were imaged, some of them 287 00:16:48,000 --> 00:16:51,200 Speaker 3: were data, and we found time and time again that 288 00:16:51,280 --> 00:16:56,360 Speaker 3: the most valuable input into these tools is voice data. 289 00:16:56,440 --> 00:17:00,320 Speaker 3: You know, a transcription of incredible words is so much 290 00:17:00,320 --> 00:17:03,080 Speaker 3: more meaningful than just some data or a few prompts. 291 00:17:03,760 --> 00:17:08,399 Speaker 3: And so we realized that it was conversation, not just meeting. 292 00:17:08,480 --> 00:17:10,120 Speaker 3: So a lot of the tools that you'll see will 293 00:17:10,160 --> 00:17:12,879 Speaker 3: be note takers. They're very focused on these sort of 294 00:17:12,880 --> 00:17:16,720 Speaker 3: transactional turning a meeting into some summaries. We wanted to 295 00:17:16,800 --> 00:17:20,399 Speaker 3: take this value of conversation and just really push that, 296 00:17:20,840 --> 00:17:23,640 Speaker 3: so we talk about wasted words. You know, in a business, 297 00:17:23,720 --> 00:17:25,680 Speaker 3: the amount of words that we say in a day 298 00:17:26,160 --> 00:17:29,880 Speaker 3: that are valuable that could actually have an outcome to them, 299 00:17:30,359 --> 00:17:33,720 Speaker 3: whether it's turned into content or you know, a proposal 300 00:17:34,040 --> 00:17:36,960 Speaker 3: or something for the team to share. You know, this 301 00:17:37,160 --> 00:17:41,480 Speaker 3: huge value to that. So that's where where we come 302 00:17:41,520 --> 00:17:46,280 Speaker 3: in is it's not about just a traditional business meeting 303 00:17:46,800 --> 00:17:49,679 Speaker 3: that needs an output. In our businesses that we work with, 304 00:17:50,000 --> 00:17:55,880 Speaker 3: they're using conversation from musings through to team meetings through 305 00:17:55,920 --> 00:17:59,400 Speaker 3: to a conference speaking session. You know that there's so 306 00:17:59,520 --> 00:18:03,239 Speaker 3: much richness across everything, and we like to see it 307 00:18:03,320 --> 00:18:05,480 Speaker 3: being you know, we talk about it sometimes being like 308 00:18:05,520 --> 00:18:09,879 Speaker 3: canvas for conversations. It's one conversation asset can turn into 309 00:18:10,280 --> 00:18:13,320 Speaker 3: so many different things, and that's what really excites us. 310 00:18:13,400 --> 00:18:15,919 Speaker 3: But that's also what excites our customers so much, is 311 00:18:16,000 --> 00:18:19,720 Speaker 3: just this world of opportunity from this one conversation. 312 00:18:20,400 --> 00:18:24,240 Speaker 1: You're using some of the top large language models, you know, 313 00:18:24,359 --> 00:18:29,480 Speaker 1: presumably from open Ai and maybe Claude and others as well, 314 00:18:29,880 --> 00:18:33,080 Speaker 1: and they're really attuned to natural language processing and making 315 00:18:33,160 --> 00:18:37,600 Speaker 1: sense off conversations. But what process did you have to 316 00:18:37,640 --> 00:18:41,119 Speaker 1: go through actually adapting all of those to your specific needs. 317 00:18:42,640 --> 00:18:45,840 Speaker 4: We were really fortunate that a couple of years ago 318 00:18:46,960 --> 00:18:49,320 Speaker 4: a man called Pedal the Fort came on to our 319 00:18:49,400 --> 00:18:51,119 Speaker 4: kind of doorstep. It was his last day in New 320 00:18:51,200 --> 00:18:53,920 Speaker 4: Zealand on holiday and he saw Contented the sign and thought, 321 00:18:54,320 --> 00:18:56,600 Speaker 4: what is this AI company? And it was really early 322 00:18:56,800 --> 00:18:59,400 Speaker 4: in the sort of age of AI. So he had 323 00:18:59,400 --> 00:19:01,680 Speaker 4: a coffee with me and Hannah which ended up being 324 00:19:01,760 --> 00:19:05,160 Speaker 4: kind of a four hour conversation, and Peter had worked 325 00:19:05,200 --> 00:19:07,720 Speaker 4: at Microsoft for a really long time and he really 326 00:19:07,840 --> 00:19:11,040 Speaker 4: understood our vision which Hannah just mentioned before, which is 327 00:19:11,480 --> 00:19:16,359 Speaker 4: seeing conversations being an incredibly rich data source that previously 328 00:19:16,400 --> 00:19:19,639 Speaker 4: without AI, was untapped, you know, and people were having 329 00:19:19,720 --> 00:19:22,919 Speaker 4: meetings without any traction or outcomes coming from it. So 330 00:19:23,000 --> 00:19:25,840 Speaker 4: we contented we're finally seeing that. But to be able 331 00:19:25,880 --> 00:19:28,640 Speaker 4: to earn the right to kind of work with businesses, 332 00:19:28,640 --> 00:19:31,040 Speaker 4: to do that, you have to be accurate, You have 333 00:19:31,160 --> 00:19:34,040 Speaker 4: to have safeguards, you have to work with great models, 334 00:19:34,080 --> 00:19:37,800 Speaker 4: you have to have great kind of hallutionin hallucination prompting. 335 00:19:37,840 --> 00:19:39,360 Speaker 4: You have to be able to provide all of these 336 00:19:39,400 --> 00:19:43,520 Speaker 4: frameworks and the person or the industry to help us 337 00:19:43,520 --> 00:19:46,439 Speaker 4: build out. With journalists, we worked with journalists over in 338 00:19:46,480 --> 00:19:50,240 Speaker 4: the US to build the kind of first version of Contented, 339 00:19:50,680 --> 00:19:53,200 Speaker 4: which was to take kind of five hour council meetings 340 00:19:53,560 --> 00:19:56,320 Speaker 4: and turn them into news stories. Because in the US 341 00:19:56,359 --> 00:19:59,000 Speaker 4: at the moment, you're seeing a kind of decrease in 342 00:19:59,119 --> 00:20:03,239 Speaker 4: publications news agencies. So you've got incredible individuals who are 343 00:20:03,320 --> 00:20:06,440 Speaker 4: kind of taking that on. And so we have a 344 00:20:06,520 --> 00:20:09,760 Speaker 4: large customer base in the US that users contented to 345 00:20:09,880 --> 00:20:13,480 Speaker 4: turn council meetings into news to keep their communities informed. 346 00:20:14,119 --> 00:20:16,640 Speaker 4: But in order to do that, you have to be accurate. 347 00:20:16,760 --> 00:20:21,679 Speaker 4: So Contented today was built off the back of challenging 348 00:20:21,720 --> 00:20:25,280 Speaker 4: circumstances and the sense that I would spend a summer 349 00:20:26,160 --> 00:20:31,000 Speaker 4: building and creating incredible frameworks around these llms and choosing 350 00:20:31,000 --> 00:20:34,240 Speaker 4: the right llms, and at that stage, every week there 351 00:20:34,280 --> 00:20:37,280 Speaker 4: was a new model, and every model would let me 352 00:20:37,320 --> 00:20:39,720 Speaker 4: down technically, you know, and I'd still have to build 353 00:20:40,119 --> 00:20:43,320 Speaker 4: huge prompt frameworks around these models to make sure that 354 00:20:43,359 --> 00:20:45,760 Speaker 4: they were accurate. They were long, and they were good, 355 00:20:45,920 --> 00:20:49,879 Speaker 4: good writing. And one of our kind of publications was 356 00:20:49,960 --> 00:20:54,320 Speaker 4: the Palm Springs Post, and they had worked with some 357 00:20:54,480 --> 00:20:59,840 Speaker 4: incredible publications. They've come from amazing backgrounds, and so his 358 00:21:01,480 --> 00:21:04,600 Speaker 4: kind of his perception of writing was incredibly high, so 359 00:21:04,840 --> 00:21:07,800 Speaker 4: we had to make sure we were building some great outputs. 360 00:21:07,880 --> 00:21:10,479 Speaker 4: So yeah, so we've been challenged across all of our 361 00:21:10,520 --> 00:21:12,439 Speaker 4: building and I think that's why people love us is 362 00:21:12,440 --> 00:21:17,000 Speaker 4: because our level of accuracy and outputs is next level. 363 00:21:17,520 --> 00:21:21,240 Speaker 1: Yeah, that's you know, it just resonates so much with me. 364 00:21:21,320 --> 00:21:25,760 Speaker 1: Obviously in terms of democracy itself, you know, newsrooms are shrinking, 365 00:21:26,080 --> 00:21:29,119 Speaker 1: We don't have enough journalists. You know, Luckily there's some 366 00:21:29,160 --> 00:21:34,479 Speaker 1: funding through the Democracy Public Journalism funding from the government 367 00:21:34,520 --> 00:21:36,960 Speaker 1: that is continuing to allow people to go and cover 368 00:21:37,640 --> 00:21:41,400 Speaker 1: council and local court. But there's just too much information 369 00:21:41,440 --> 00:21:43,119 Speaker 1: and not enough people to sift through it. So if 370 00:21:43,160 --> 00:21:46,080 Speaker 1: you can record all of that and actually make sense 371 00:21:46,119 --> 00:21:48,719 Speaker 1: of it in a way that's useful for the needs 372 00:21:48,720 --> 00:21:52,960 Speaker 1: of democracy and journalists. So, Hannah, how does that sort 373 00:21:52,960 --> 00:21:57,080 Speaker 1: of concept apply to other types of businesses that you 374 00:21:57,160 --> 00:21:59,640 Speaker 1: work with? It seems like what mind mapping and business 375 00:21:59,680 --> 00:22:03,640 Speaker 1: plan So turning all of those conversations and transcripts actually 376 00:22:03,640 --> 00:22:07,639 Speaker 1: into output that's useful for a business trying to decide 377 00:22:07,640 --> 00:22:08,280 Speaker 1: where it's going. 378 00:22:08,560 --> 00:22:11,800 Speaker 3: Yeah, so we we're identified in the hub of contented 379 00:22:11,960 --> 00:22:14,480 Speaker 3: is sort of some standard outputs that everyone can create, 380 00:22:14,600 --> 00:22:18,960 Speaker 3: so a SWAT analysis, a summary, a risk matrix, human 381 00:22:19,040 --> 00:22:23,480 Speaker 3: centered OUTPUTSLACK, people's struggles and motivation. So those are across 382 00:22:23,560 --> 00:22:26,080 Speaker 3: all of the businesses that we work with. The benefit 383 00:22:26,160 --> 00:22:28,600 Speaker 3: is that each business also gets to have their own 384 00:22:28,960 --> 00:22:32,679 Speaker 3: custom outputs that we have developed alongside the industry. So 385 00:22:32,800 --> 00:22:36,439 Speaker 3: within finance, and within law and within health. So you know, 386 00:22:36,520 --> 00:22:39,480 Speaker 3: these buttons that people can create that are very specific 387 00:22:39,560 --> 00:22:42,359 Speaker 3: to the conversations they're having in that industry, and we 388 00:22:42,440 --> 00:22:46,879 Speaker 3: find that works incredibly well. It makes contented, relatable and 389 00:22:46,960 --> 00:22:50,320 Speaker 3: accessible to everyone, and then each kind of customer base 390 00:22:50,359 --> 00:22:53,040 Speaker 3: can also have best practice from their industry and kind 391 00:22:53,080 --> 00:22:56,240 Speaker 3: of you know, it's almost like having an incredible consultant 392 00:22:56,640 --> 00:22:59,679 Speaker 3: coming alongside your conversations and turning it into things. But 393 00:22:59,720 --> 00:23:03,119 Speaker 3: they get to click a button, and that's really important. 394 00:23:03,119 --> 00:23:05,320 Speaker 3: And I think from a you know, even working with 395 00:23:05,800 --> 00:23:07,600 Speaker 3: you know, if someone's trying to get an output from 396 00:23:07,640 --> 00:23:10,160 Speaker 3: say chat TOBT, the hard thing is is you're prompting 397 00:23:10,480 --> 00:23:12,200 Speaker 3: to the level of you know, you have to put 398 00:23:12,240 --> 00:23:14,639 Speaker 3: so much effort into that. And so we make this 399 00:23:14,760 --> 00:23:18,439 Speaker 3: accessible that anybody in any industry can literally click a 400 00:23:18,440 --> 00:23:22,359 Speaker 3: button and get this absolutely high level output every time, 401 00:23:22,520 --> 00:23:25,640 Speaker 3: and there's a lot of comfort in that for people too, 402 00:23:25,720 --> 00:23:27,920 Speaker 3: because they don't want to have to do all that 403 00:23:28,040 --> 00:23:29,920 Speaker 3: hard yards with a chatbot. 404 00:23:30,359 --> 00:23:33,440 Speaker 1: Yeah, and look, you said earlier that you don't really 405 00:23:33,760 --> 00:23:36,159 Speaker 1: talk about the AI aspect of it. You're trying to 406 00:23:36,200 --> 00:23:40,880 Speaker 1: make it really simple and seamless. So do you adapt 407 00:23:41,119 --> 00:23:44,080 Speaker 1: the app for different industries or does everyone get the 408 00:23:44,080 --> 00:23:46,920 Speaker 1: same dashboard to look at? We adapt? 409 00:23:47,040 --> 00:23:49,879 Speaker 4: So a good example might be if you're a mortgage 410 00:23:50,000 --> 00:23:53,640 Speaker 4: broker or an insurance firm. They're a really good example 411 00:23:53,680 --> 00:23:57,840 Speaker 4: because there's so many requirements and needs and outputs from 412 00:23:57,960 --> 00:24:01,320 Speaker 4: just one conversation. So let's say it's discovery conversation with 413 00:24:01,400 --> 00:24:03,879 Speaker 4: a couple who are looking to buy their first home. 414 00:24:04,359 --> 00:24:08,600 Speaker 4: You know they would create and contented their perfect proposal 415 00:24:08,640 --> 00:24:10,280 Speaker 4: back to them to say this is what I'm hearing. 416 00:24:10,640 --> 00:24:13,360 Speaker 4: Within the same vein, they've worked with us to create 417 00:24:13,760 --> 00:24:17,159 Speaker 4: the mortgage application form and the outputs needed for that. 418 00:24:17,400 --> 00:24:20,520 Speaker 4: So from just that twenty minute conversation, they're creating the 419 00:24:20,600 --> 00:24:22,800 Speaker 4: form that they will then send to the bank. They're 420 00:24:22,840 --> 00:24:25,480 Speaker 4: also creating a compliance checklist, so they need to be 421 00:24:25,480 --> 00:24:28,680 Speaker 4: able to show that they've provided the right advice. They've 422 00:24:28,680 --> 00:24:31,000 Speaker 4: made sure that they're not a vulnerable client, so they 423 00:24:31,040 --> 00:24:33,000 Speaker 4: need to show that they can tick that off. They've 424 00:24:33,000 --> 00:24:37,080 Speaker 4: asked the right questions, so they create that and then actually, 425 00:24:37,119 --> 00:24:40,280 Speaker 4: because they're asking the right thing and they're providing advice 426 00:24:40,440 --> 00:24:43,800 Speaker 4: and on that day they're finding great advice, they actually 427 00:24:43,840 --> 00:24:46,840 Speaker 4: then go into our marketing module and they create a 428 00:24:46,920 --> 00:24:49,879 Speaker 4: LinkedIn post or a LinkedIn blog to say, hey, if 429 00:24:49,920 --> 00:24:51,320 Speaker 4: you're a first home buyer and you're about to go 430 00:24:51,400 --> 00:24:53,200 Speaker 4: to auction, these are the things you need to prep for. 431 00:24:54,000 --> 00:24:57,000 Speaker 4: And then potentially if they're looking to get in a 432 00:24:57,080 --> 00:25:00,960 Speaker 4: new team member or they're wanting to train another advisor, 433 00:25:01,640 --> 00:25:04,080 Speaker 4: they could then go and create a cheat sheet to go, hey, 434 00:25:04,280 --> 00:25:06,719 Speaker 4: these are the questions I asked, and actually these are 435 00:25:06,720 --> 00:25:09,600 Speaker 4: the questions I should have asked too, and create some 436 00:25:09,720 --> 00:25:13,480 Speaker 4: internal training so that kind of if you can visualize that, 437 00:25:13,720 --> 00:25:17,439 Speaker 4: it's one conversation, like Hannah mentioned before, sweated into so 438 00:25:17,600 --> 00:25:22,120 Speaker 4: many amazing assets, and I think for advisors they shouldn't 439 00:25:22,160 --> 00:25:24,600 Speaker 4: have to go into LinkedIn to go, shit, you know 440 00:25:24,600 --> 00:25:26,119 Speaker 4: what am I going to write about today? And what 441 00:25:26,160 --> 00:25:29,359 Speaker 4: does that look like? They're actually they've got the expertise 442 00:25:29,400 --> 00:25:32,080 Speaker 4: and advice there in these conversations, and that's what they 443 00:25:32,080 --> 00:25:32,800 Speaker 4: should tap into. 444 00:25:33,080 --> 00:25:35,639 Speaker 1: You talk about things like you're creating LinkedIn posts and 445 00:25:35,960 --> 00:25:39,080 Speaker 1: that sort of content. A lot of people will probably 446 00:25:39,119 --> 00:25:42,040 Speaker 1: be doing that in chat, GPT or maybe even Microsoft 447 00:25:42,040 --> 00:25:44,840 Speaker 1: Copilot if they've got a subscription. But we're seeing services 448 00:25:44,920 --> 00:25:48,680 Speaker 1: like Contented, which is doing all of that for you. 449 00:25:48,800 --> 00:25:52,520 Speaker 1: So how do you position yourselves in terms of saying 450 00:25:52,560 --> 00:25:54,919 Speaker 1: to customers you can actually do all of this. You 451 00:25:54,920 --> 00:25:58,159 Speaker 1: don't need this proliferation of subscriptions, you can do it 452 00:25:58,200 --> 00:26:03,000 Speaker 1: all through our PlantForm. And related to that, how do 453 00:26:03,040 --> 00:26:06,240 Speaker 1: you avoid just being seen as a feature rather than 454 00:26:06,440 --> 00:26:08,479 Speaker 1: a business a feature that potentially is going to get 455 00:26:08,520 --> 00:26:12,520 Speaker 1: built into one of these big platforms like Otter in future. 456 00:26:12,560 --> 00:26:15,760 Speaker 1: How do you position yourself so that it's worth having 457 00:26:15,760 --> 00:26:18,520 Speaker 1: a subscription to Contented instead of you know, some of 458 00:26:18,560 --> 00:26:20,640 Speaker 1: these other big sort of platforms that have emerged. 459 00:26:21,000 --> 00:26:22,919 Speaker 3: Yeah, I mean the big things for us is our 460 00:26:22,960 --> 00:26:25,439 Speaker 3: transcription is better than anything on the market. If we 461 00:26:25,480 --> 00:26:29,639 Speaker 3: can get a potential customer to experience Contented, that's it. 462 00:26:29,720 --> 00:26:33,760 Speaker 3: They don't need any other convincing because it's so clearly obvious, 463 00:26:33,880 --> 00:26:36,880 Speaker 3: you know, And it's all those things that in person meetings, 464 00:26:36,960 --> 00:26:38,760 Speaker 3: we thrive and you know, being able to just use 465 00:26:38,800 --> 00:26:42,400 Speaker 3: your phone and have that being able to analyze really 466 00:26:42,480 --> 00:26:47,440 Speaker 3: quickly with noise and accents today you know, multiple speakers, 467 00:26:47,440 --> 00:26:49,480 Speaker 3: So particularly in New Zealand, obviously we've got a lot 468 00:26:49,480 --> 00:26:53,399 Speaker 3: of EWE lead EWE owned organizations that love us because 469 00:26:53,400 --> 00:26:56,840 Speaker 3: of how we treat today a Maori and so there's 470 00:26:56,840 --> 00:26:58,919 Speaker 3: that side of it, and then there's obviously just the 471 00:26:59,320 --> 00:27:04,720 Speaker 3: unbelievable quality of output which is just incomparable, you know, 472 00:27:05,840 --> 00:27:08,200 Speaker 3: And that's a huge part of it. And I think 473 00:27:08,240 --> 00:27:11,440 Speaker 3: because you can have it rolled out across a whole organization, 474 00:27:11,520 --> 00:27:14,000 Speaker 3: and we're working with some really large New Zealand businesses 475 00:27:14,520 --> 00:27:19,320 Speaker 3: because it taps into everybody's roles, you know, and everybody's conversations. 476 00:27:19,359 --> 00:27:22,199 Speaker 3: It's so easy to roll that out and it be 477 00:27:22,840 --> 00:27:26,879 Speaker 3: solidified and everything, whereas most other tools are just pockets, 478 00:27:27,040 --> 00:27:29,480 Speaker 3: you know, and some people love them, some people don't. 479 00:27:29,560 --> 00:27:31,840 Speaker 3: So that's where we're really leaning into the fact that 480 00:27:31,880 --> 00:27:35,000 Speaker 3: it's just so easy to adopt it too because it 481 00:27:35,080 --> 00:27:35,640 Speaker 3: works so. 482 00:27:35,600 --> 00:27:43,040 Speaker 1: Well, right and Lessie, it's clearly very useful for strategic conversations, 483 00:27:43,560 --> 00:27:47,280 Speaker 1: risk management, that sort of thing needs are quite sensitive conversation, 484 00:27:47,520 --> 00:27:52,640 Speaker 1: So how do you deal with that putting your customers 485 00:27:52,680 --> 00:27:55,159 Speaker 1: sort of minds at ease that this stuff is going 486 00:27:55,240 --> 00:27:58,120 Speaker 1: to be private. It's going to be it's sensitive information 487 00:27:58,200 --> 00:27:59,840 Speaker 1: that it's you know, so how do you store it 488 00:28:00,160 --> 00:28:03,040 Speaker 1: and make sure that all of those conversations actually stay private? 489 00:28:03,119 --> 00:28:06,720 Speaker 1: But you get these great outputs from them, and I guess. 490 00:28:06,520 --> 00:28:09,320 Speaker 4: We work with the company. So for example, if you 491 00:28:09,560 --> 00:28:13,199 Speaker 4: are a high needs customer, you might want to do 492 00:28:13,280 --> 00:28:16,320 Speaker 4: things like PII reduction, which basically means you can redact 493 00:28:16,760 --> 00:28:19,959 Speaker 4: names before they even hop into an AI model. So 494 00:28:20,520 --> 00:28:24,480 Speaker 4: we offer a menu of ways to customize how you 495 00:28:24,520 --> 00:28:27,119 Speaker 4: know private, you want these conversations to be honest. We 496 00:28:27,280 --> 00:28:32,720 Speaker 4: take our role really really responsibly. So data, I guess 497 00:28:32,720 --> 00:28:35,280 Speaker 4: before the age of AI, you know, people kind of 498 00:28:35,359 --> 00:28:38,160 Speaker 4: knew what companies did with it, but you know, they 499 00:28:38,240 --> 00:28:40,840 Speaker 4: kind of just let it go and happen. But now 500 00:28:40,840 --> 00:28:44,320 Speaker 4: you've got these questions like is this company training our 501 00:28:44,440 --> 00:28:47,040 Speaker 4: data on AI? And tech firms are having to come 502 00:28:47,080 --> 00:28:50,360 Speaker 4: to the fore to kind of deal with this situation 503 00:28:50,440 --> 00:28:53,040 Speaker 4: where now a lot of users want to know what's 504 00:28:53,040 --> 00:28:56,480 Speaker 4: happening with their data. So as a new company, we 505 00:28:56,560 --> 00:29:00,200 Speaker 4: get to work in a world that accommodates this. We 506 00:29:00,240 --> 00:29:03,160 Speaker 4: know that the local pizzeria wants to know that their 507 00:29:03,240 --> 00:29:05,760 Speaker 4: data is going to be stored in New Zealand or 508 00:29:05,920 --> 00:29:09,440 Speaker 4: if closely you know, Australia. They want to be able 509 00:29:09,480 --> 00:29:12,000 Speaker 4: to have that, which is before, I guess before they 510 00:29:12,000 --> 00:29:14,400 Speaker 4: didn't even have that opportunity. So we offer that to 511 00:29:14,480 --> 00:29:17,160 Speaker 4: our customers in terms of where that data is stored 512 00:29:17,200 --> 00:29:19,920 Speaker 4: and what that looks like in terms of you know, 513 00:29:20,080 --> 00:29:25,400 Speaker 4: risk conversations or sensitive conversations. The outputs we create are 514 00:29:25,440 --> 00:29:27,800 Speaker 4: based with their customer. So for example, if it is 515 00:29:27,840 --> 00:29:30,120 Speaker 4: a risk matrix, we work with them to go what 516 00:29:30,240 --> 00:29:32,400 Speaker 4: actually is risk? How do you infer risk? 517 00:29:32,680 --> 00:29:33,480 Speaker 3: What is risky? 518 00:29:34,080 --> 00:29:37,400 Speaker 4: And so actually every output is kind of pretty customized 519 00:29:37,480 --> 00:29:41,240 Speaker 4: to that customer because risk might look a lot different 520 00:29:41,480 --> 00:29:45,080 Speaker 4: to another business. And so I think that's where you know, 521 00:29:45,200 --> 00:29:48,000 Speaker 4: chatchept goes wrong because if you put in their conversations 522 00:29:48,000 --> 00:29:51,200 Speaker 4: and make me a risk matrix, you're you're asking chat 523 00:29:51,280 --> 00:29:54,320 Speaker 4: chept to quantify that risk and you don't know where 524 00:29:54,320 --> 00:29:57,360 Speaker 4: that's from. So for us, it's a lot around personalization 525 00:29:57,800 --> 00:29:59,200 Speaker 4: and using AI to help with that. 526 00:30:00,000 --> 00:30:02,120 Speaker 1: You've got I think over one hundred customers now and 527 00:30:02,160 --> 00:30:05,760 Speaker 1: a lot of international customers. How did you go international 528 00:30:05,840 --> 00:30:10,400 Speaker 1: as a small New Zealand AI startup based in christ Church. 529 00:30:10,440 --> 00:30:13,880 Speaker 1: What was your sort of key drivers of your international adoption. 530 00:30:13,960 --> 00:30:16,440 Speaker 1: Did you sort of what goes straight to newsrooms first 531 00:30:16,440 --> 00:30:19,360 Speaker 1: and target them or was there a particular segment that 532 00:30:19,400 --> 00:30:20,440 Speaker 1: you went after initially. 533 00:30:21,120 --> 00:30:24,840 Speaker 3: Our whole go to market currently, honestly is word of mouth. 534 00:30:25,680 --> 00:30:29,600 Speaker 3: You know. We got an amazing sports publishing company in 535 00:30:29,640 --> 00:30:32,800 Speaker 3: London as a customer and they had just seen someone 536 00:30:32,840 --> 00:30:37,280 Speaker 3: talking about it, you know, online on Twitter from America. 537 00:30:37,440 --> 00:30:39,200 Speaker 3: You know, like this is the level of you know, 538 00:30:39,400 --> 00:30:41,480 Speaker 3: we'll speak at a conference or we'll partner with a 539 00:30:41,520 --> 00:30:45,160 Speaker 3: conference to create like a version of contented for them. 540 00:30:45,400 --> 00:30:49,720 Speaker 3: It spreads so fast and so we haven't targeted necessarily 541 00:30:50,240 --> 00:30:54,880 Speaker 3: actual you know, geographical regions. It's just been this organic focus, 542 00:30:54,920 --> 00:30:57,080 Speaker 3: which is a really exciting part for us into this 543 00:30:57,160 --> 00:31:00,280 Speaker 3: next phase, which is when we can target We do 544 00:31:00,360 --> 00:31:03,520 Speaker 3: have the capital to be able to really launch you 545 00:31:03,560 --> 00:31:07,200 Speaker 3: know into a particular region or city, we can really 546 00:31:07,280 --> 00:31:10,160 Speaker 3: nail that. But yeah, it's been an organic process. 547 00:31:10,600 --> 00:31:13,680 Speaker 1: Wow, that's great, And where you at on your sort 548 00:31:13,680 --> 00:31:18,320 Speaker 1: of capital journey you're obviously you know, generating revenue now, 549 00:31:18,320 --> 00:31:21,640 Speaker 1: which is fantastic. Have you raised money or are you 550 00:31:21,720 --> 00:31:22,840 Speaker 1: looking to raise more money? 551 00:31:23,160 --> 00:31:23,680 Speaker 3: We're soon. 552 00:31:24,160 --> 00:31:27,880 Speaker 4: Yeah, we're bootstrapped and proud of it, and we're stoked 553 00:31:27,920 --> 00:31:30,920 Speaker 4: because it meant that we've built a business on our 554 00:31:30,960 --> 00:31:34,000 Speaker 4: own terms, you know, and we've felt what building a 555 00:31:34,040 --> 00:31:37,160 Speaker 4: business looks like, and we know that we can take 556 00:31:37,200 --> 00:31:39,160 Speaker 4: a risk and a risk can pay off because we've 557 00:31:39,160 --> 00:31:41,120 Speaker 4: got our own capital behind it and we've got our 558 00:31:41,160 --> 00:31:44,400 Speaker 4: own team. So we've been able to do this, you know, 559 00:31:44,480 --> 00:31:47,280 Speaker 4: for across the last couple of years, and what we're 560 00:31:47,280 --> 00:31:49,720 Speaker 4: really excited for, like I mentioned, is to be able 561 00:31:49,760 --> 00:31:51,560 Speaker 4: to actually put a bit of capital behind this. So 562 00:31:51,600 --> 00:31:55,480 Speaker 4: we will be looking to kind of start the conversations 563 00:31:55,520 --> 00:31:58,640 Speaker 4: around an investment across the next couple of months, just 564 00:31:58,680 --> 00:32:02,000 Speaker 4: to see if we can find some great investors who 565 00:32:02,080 --> 00:32:04,880 Speaker 4: kind of understand our vision, who want to jump on 566 00:32:04,960 --> 00:32:07,720 Speaker 4: board and to help us with this kind of next 567 00:32:07,760 --> 00:32:11,040 Speaker 4: stage of growth, which will be exciting. 568 00:32:11,560 --> 00:32:15,800 Speaker 1: Okay, fantastic. Well, hopefully someone's listening who really likes the 569 00:32:16,360 --> 00:32:18,720 Speaker 1: sound of this business and it has some money to invest. 570 00:32:18,800 --> 00:32:21,640 Speaker 1: But just finally, for both of you, where do you 571 00:32:21,680 --> 00:32:26,320 Speaker 1: see Contented going and in terms of this sort of 572 00:32:26,360 --> 00:32:31,040 Speaker 1: technology around voice and conversations. For instance, I see the 573 00:32:31,320 --> 00:32:33,440 Speaker 1: game changer that's coming in it's sort of possible now, 574 00:32:33,480 --> 00:32:36,560 Speaker 1: but it's a bit clunky having these new conversations but 575 00:32:36,640 --> 00:32:40,800 Speaker 1: then combining that with all of your institutional data and 576 00:32:40,960 --> 00:32:44,800 Speaker 1: past conversations to make sense of where thinking has gone 577 00:32:44,800 --> 00:32:47,240 Speaker 1: in an organization. So it's you're not just having the 578 00:32:47,280 --> 00:32:50,440 Speaker 1: same conversation again and again as new people come into 579 00:32:50,440 --> 00:32:54,880 Speaker 1: the organization. You're retaining that institutional knowledge and supplementing it 580 00:32:54,880 --> 00:32:57,480 Speaker 1: with new conversations. And I'd love to see more in 581 00:32:57,520 --> 00:33:00,760 Speaker 1: that space, but interest in your views on the features 582 00:33:00,760 --> 00:33:03,120 Speaker 1: that are coming or that you know you really would 583 00:33:03,120 --> 00:33:04,360 Speaker 1: love to see in Contented. 584 00:33:06,160 --> 00:33:09,080 Speaker 4: Yeah, and we're spearheading at the moment, so you can 585 00:33:09,120 --> 00:33:14,480 Speaker 4: imagine now, customers trust us with their sensitive conversations, and 586 00:33:14,600 --> 00:33:16,400 Speaker 4: trust is at the center of what we do. So 587 00:33:16,760 --> 00:33:21,280 Speaker 4: the vision of Contented will always be around trust and security. 588 00:33:21,520 --> 00:33:24,120 Speaker 4: And with that trust, it means that we've aren't the 589 00:33:24,200 --> 00:33:27,400 Speaker 4: right to be able to build incredible features like horizontal 590 00:33:27,400 --> 00:33:29,560 Speaker 4: insights like you just said. You know, you've got a 591 00:33:29,600 --> 00:33:33,400 Speaker 4: council that's put through all of their council conversations, that 592 00:33:33,480 --> 00:33:37,000 Speaker 4: council meetings imagine if you could go back and create 593 00:33:37,080 --> 00:33:39,720 Speaker 4: some insights around, Okay, what we're we talking about? You know, 594 00:33:39,760 --> 00:33:42,200 Speaker 4: what were the problems? Where do we keep on, you know, 595 00:33:42,240 --> 00:33:45,360 Speaker 4: talking about that same problem. How can we do a 596 00:33:45,440 --> 00:33:48,840 Speaker 4: council meeting better? Who is roadblocking? What does that look like? 597 00:33:49,680 --> 00:33:51,800 Speaker 4: We've spent the last couple of years, you know, creating 598 00:33:51,840 --> 00:33:55,320 Speaker 4: an LTP. Are we getting anyway close to this? You know, 599 00:33:55,360 --> 00:33:58,800 Speaker 4: this is the LTP. This is six months of council meetings? 600 00:33:58,920 --> 00:34:01,880 Speaker 4: What does this look like? So being able to have 601 00:34:01,960 --> 00:34:08,320 Speaker 4: horizontal insights across multiple conversations, even from a small company perspective, 602 00:34:08,480 --> 00:34:11,359 Speaker 4: is incredible. So well, you'll start to see that soon 603 00:34:11,600 --> 00:34:14,719 Speaker 4: was contented. So the bigger, I guess for us, the 604 00:34:14,719 --> 00:34:18,600 Speaker 4: big vision also was contented is the ability to use 605 00:34:18,640 --> 00:34:21,400 Speaker 4: it for more personal conversations, for home life, to be 606 00:34:21,440 --> 00:34:24,040 Speaker 4: able to be putting in your parent teacher conversations so 607 00:34:24,080 --> 00:34:26,160 Speaker 4: you can share it with you know, a loved one 608 00:34:26,160 --> 00:34:28,880 Speaker 4: who couldn't make it all the way to doctor conversations. 609 00:34:28,960 --> 00:34:32,480 Speaker 4: We can see contented kind of being home to the 610 00:34:32,480 --> 00:34:36,040 Speaker 4: world's most important conversations, whether that's at home or you know, 611 00:34:36,440 --> 00:34:39,960 Speaker 4: in big contacts like the un You know, that's the dream. 612 00:34:40,280 --> 00:34:42,040 Speaker 1: I'm sort of feeling it now where I sort of 613 00:34:42,080 --> 00:34:45,440 Speaker 1: want to record every conversation I have. You know, if 614 00:34:45,480 --> 00:34:49,239 Speaker 1: I'm at a public event, that's fine, but because I 615 00:34:49,280 --> 00:34:52,400 Speaker 1: want to get these this context, and I find that 616 00:34:53,239 --> 00:34:55,799 Speaker 1: when I'm having conversations, I'm just not able to listen 617 00:34:55,880 --> 00:34:59,799 Speaker 1: as and pick up the context as well as as 618 00:34:59,800 --> 00:35:02,520 Speaker 1: the something like arto which will remind me of things, well, 619 00:35:02,920 --> 00:35:05,040 Speaker 1: these points were made that you should maybe cover in 620 00:35:05,040 --> 00:35:08,759 Speaker 1: the story. So I guess culturally we're entering a place 621 00:35:08,800 --> 00:35:11,240 Speaker 1: where we're going to need to get more comfortable about 622 00:35:11,800 --> 00:35:13,280 Speaker 1: recording everything all the time. 623 00:35:14,360 --> 00:35:17,400 Speaker 3: Yeah, I think, and I think it's probably quite a 624 00:35:17,440 --> 00:35:19,759 Speaker 3: long way from that. You know, people we find we 625 00:35:19,880 --> 00:35:22,000 Speaker 3: have to you know, coach them a bit of how 626 00:35:22,040 --> 00:35:24,880 Speaker 3: to ask for recording consent and how to not feel 627 00:35:24,880 --> 00:35:27,320 Speaker 3: awkward about it. And often it's about the other person, 628 00:35:27,440 --> 00:35:29,400 Speaker 3: you know, I would like to actually be able to 629 00:35:29,440 --> 00:35:31,760 Speaker 3: engage in this conversation with you, and if I write notes, 630 00:35:31,760 --> 00:35:34,879 Speaker 3: I can't and making it framing it. But I think 631 00:35:34,880 --> 00:35:37,680 Speaker 3: that that's definitely the future that we see is that 632 00:35:37,960 --> 00:35:42,160 Speaker 3: you know, all conversations, you know, are recorded and can 633 00:35:42,200 --> 00:35:44,640 Speaker 3: be used in amazing ways. 634 00:35:44,680 --> 00:35:48,560 Speaker 4: So yeah, and that word consent is complicated, and for 635 00:35:48,680 --> 00:35:52,439 Speaker 4: us at the moment, our mobile app has a pop 636 00:35:52,520 --> 00:35:55,359 Speaker 4: up which, you know, while it is gimmicky, it's a 637 00:35:55,400 --> 00:35:58,359 Speaker 4: really great reminder to go, I have consent, click on it, 638 00:35:58,640 --> 00:36:00,600 Speaker 4: and it actually is just a nice one to ask 639 00:36:00,640 --> 00:36:04,880 Speaker 4: for permission. But the future of consent I think could 640 00:36:04,880 --> 00:36:08,320 Speaker 4: be more AI focused. It could be literally everyone saying 641 00:36:08,400 --> 00:36:11,280 Speaker 4: with their voice, I have consent before a meeting starts, 642 00:36:11,640 --> 00:36:15,600 Speaker 4: and that then you know, initiates recording or there's going 643 00:36:15,640 --> 00:36:18,600 Speaker 4: to be some really interesting ways of obtaining it. And 644 00:36:18,600 --> 00:36:20,839 Speaker 4: we're kind of researching this at the moment with some 645 00:36:20,960 --> 00:36:23,840 Speaker 4: great companies, but it's it is going to be a 646 00:36:23,880 --> 00:36:27,279 Speaker 4: different world, I think, especially for doctors. You know, we 647 00:36:27,360 --> 00:36:28,920 Speaker 4: often get a lot of g PEO ple in next 648 00:36:29,000 --> 00:36:31,880 Speaker 4: asking about I'm not sure about recording it, you know, 649 00:36:32,000 --> 00:36:35,360 Speaker 4: in recording our client conversations whatever. But actually on the 650 00:36:35,400 --> 00:36:38,200 Speaker 4: other side, we are hearing from the patients, you know, oh, 651 00:36:38,320 --> 00:36:40,799 Speaker 4: I've been recording our doctor conversations and then you asked 652 00:36:40,800 --> 00:36:42,319 Speaker 4: them not did you ask the for mession and they're 653 00:36:42,360 --> 00:36:46,040 Speaker 4: like no, you know, and so you've really got to 654 00:36:46,120 --> 00:36:48,399 Speaker 4: sort of yeah, it's going to be an interesting couple 655 00:36:48,440 --> 00:36:49,799 Speaker 4: of years around how we navigate this. 656 00:36:50,320 --> 00:36:54,480 Speaker 1: Well, I've been doing exactly that with my dad who's 657 00:36:54,480 --> 00:36:57,040 Speaker 1: had cancer, and just so he and I can make 658 00:36:57,080 --> 00:37:00,320 Speaker 1: sense of what the specialists are saying. I've been hoarding 659 00:37:00,360 --> 00:37:03,080 Speaker 1: it and it's really helped. So I think healthcare is 660 00:37:03,800 --> 00:37:07,480 Speaker 1: a huge area of opportunity. There are patient portals give 661 00:37:07,560 --> 00:37:10,680 Speaker 1: us all of this jargon and all of this impenetrable 662 00:37:10,719 --> 00:37:13,759 Speaker 1: information about our health. But to be able to sit 663 00:37:13,800 --> 00:37:16,319 Speaker 1: down with a GP or a specialist and get that 664 00:37:16,400 --> 00:37:19,279 Speaker 1: in plane language summary and be able to refer to 665 00:37:19,320 --> 00:37:22,200 Speaker 1: that as is huge and many other industries as well. 666 00:37:22,320 --> 00:37:26,640 Speaker 1: So congratulations on bootstrapping your way to a really successful 667 00:37:27,160 --> 00:37:30,359 Speaker 1: AI company and all the best for the future. Thanks 668 00:37:30,400 --> 00:37:31,960 Speaker 1: so much for coming on the Business of Tech. 669 00:37:32,440 --> 00:37:33,200 Speaker 3: Thanks so much. 670 00:37:38,200 --> 00:37:42,120 Speaker 1: That's it for this week's episode of the Business of Tech. 671 00:37:42,239 --> 00:37:46,000 Speaker 1: A huge thank you to Lucypink and Hannah Hardy Jones 672 00:37:46,040 --> 00:37:49,840 Speaker 1: for sharing their story from solo founders to building Contented 673 00:37:49,920 --> 00:37:54,320 Speaker 1: AI into a platform trusted by over one hundred businesses worldwide. 674 00:37:54,800 --> 00:37:56,560 Speaker 1: And what really stood out to me, I think is 675 00:37:56,560 --> 00:37:59,239 Speaker 1: they're not just building tech for tech's sake or jumping 676 00:37:59,320 --> 00:38:03,520 Speaker 1: on the AI. They're focused on real world problems and 677 00:38:03,560 --> 00:38:07,000 Speaker 1: doing it from New Zealand, making AI accessible and genuinely 678 00:38:07,200 --> 00:38:11,960 Speaker 1: useful for everyone from journalists to mortgage brokers, and as 679 00:38:11,960 --> 00:38:14,760 Speaker 1: we heard, they're doing it with privacy, security and trust 680 00:38:15,000 --> 00:38:17,760 Speaker 1: at the core. If you want to learn more about 681 00:38:18,000 --> 00:38:21,600 Speaker 1: contented AI, what you're curious about AI could help your 682 00:38:21,600 --> 00:38:25,239 Speaker 1: business turn conversations into outcomes, check them out. And if 683 00:38:25,280 --> 00:38:28,640 Speaker 1: you enjoyed this episode, don't forget to subscribe, share and 684 00:38:28,800 --> 00:38:30,960 Speaker 1: leave us a review. It really helps us bring more 685 00:38:31,000 --> 00:38:36,360 Speaker 1: great Kiwi tech stories to your ears. We're on iHeartRadio, Apple, Spotify, 686 00:38:36,560 --> 00:38:39,480 Speaker 1: wherever you get your podcasts. Show notes are in the 687 00:38:39,560 --> 00:38:44,200 Speaker 1: podcast section at Businessdesk dot co dot Nz. Thanks so 688 00:38:44,280 --> 00:38:46,319 Speaker 1: much for tuning in. I'm Peter Griffin and I'll catch 689 00:38:46,360 --> 00:38:48,600 Speaker 1: you next time on the Business of Tech.