1 00:00:00,200 --> 00:00:04,400 Speaker 1: List assist from startup that's making it big in the States, 2 00:00:04,480 --> 00:00:07,520 Speaker 1: users AI to allow natural language queries for real estate listings. 3 00:00:07,520 --> 00:00:10,000 Speaker 1: The founder, Chris McGoldrick, is well, the it's Chris morning 4 00:00:10,000 --> 00:00:10,319 Speaker 1: to you. 5 00:00:11,000 --> 00:00:11,880 Speaker 2: Good morning. How are you. 6 00:00:12,000 --> 00:00:15,240 Speaker 1: I'm very well? Indeed, Now I featured you on the program, 7 00:00:15,320 --> 00:00:17,120 Speaker 1: and I think you probably heard it some way, somehow, 8 00:00:17,239 --> 00:00:20,280 Speaker 1: And my suggestion was, were you personally offended by what 9 00:00:20,320 --> 00:00:23,640 Speaker 1: I said? Were you deeply upset Chris by my analysis 10 00:00:23,640 --> 00:00:25,360 Speaker 1: of your company or what happened there. 11 00:00:25,920 --> 00:00:28,000 Speaker 2: Look, I was very oppressive of it. I had a 12 00:00:28,040 --> 00:00:30,000 Speaker 2: couple of friends and family reach out and say that 13 00:00:30,040 --> 00:00:33,880 Speaker 2: you mentioned a list of system and a few details around. 14 00:00:34,400 --> 00:00:36,360 Speaker 2: You know, you mentioned what would do that perhaps weren't 15 00:00:36,400 --> 00:00:38,600 Speaker 2: so accurate. I thought it was great. I think we 16 00:00:38,640 --> 00:00:41,080 Speaker 2: could do some great in commercials together. You starting with 17 00:00:41,280 --> 00:00:43,200 Speaker 2: the germ and gloom of why it could never work, 18 00:00:43,320 --> 00:00:45,320 Speaker 2: then me coming in and talking about all the things 19 00:00:45,320 --> 00:00:47,400 Speaker 2: we've done to bring it to life. So I'm all 20 00:00:47,520 --> 00:00:49,760 Speaker 2: for it. So was it? First? 21 00:00:49,840 --> 00:00:52,440 Speaker 1: We'll let me ask you some some startup questions. Was 22 00:00:52,560 --> 00:00:54,080 Speaker 1: raising money hard or not? 23 00:00:55,360 --> 00:00:58,120 Speaker 2: I think it's always really hard, to be honest, And 24 00:00:58,240 --> 00:01:00,680 Speaker 2: you know, we launched as kind of late twenty twenty two, 25 00:01:00,840 --> 00:01:04,039 Speaker 2: so it was sort of after I SATs the boom 26 00:01:04,080 --> 00:01:06,800 Speaker 2: period of late twenty twenty early twenty twenty one. So 27 00:01:07,720 --> 00:01:09,720 Speaker 2: it has been hard throughout the process, but we were 28 00:01:09,760 --> 00:01:12,399 Speaker 2: fortunate the first trip that I went to the US, 29 00:01:12,440 --> 00:01:15,360 Speaker 2: we got investment from the top Remax owner in the world, 30 00:01:15,800 --> 00:01:18,200 Speaker 2: and that gave us some really good initial credibility over there, 31 00:01:18,240 --> 00:01:20,600 Speaker 2: which has made that process a little bit easier over time. 32 00:01:20,800 --> 00:01:23,120 Speaker 1: And what do you see your growth pass as? Is 33 00:01:23,160 --> 00:01:25,160 Speaker 1: it exponential or you got a plan or what? 34 00:01:26,160 --> 00:01:28,640 Speaker 2: Yeah? Absolutely, I mean we're still really focused on just 35 00:01:28,640 --> 00:01:30,800 Speaker 2: putting one foot in front of the other and kind 36 00:01:30,840 --> 00:01:32,880 Speaker 2: of executing a lot of these recent deals that we've 37 00:01:32,920 --> 00:01:35,880 Speaker 2: boughked to life. So not getting ahead of ourselves with that, 38 00:01:36,000 --> 00:01:38,720 Speaker 2: but yeah, certainly a planned for exponential growth over time. 39 00:01:38,959 --> 00:01:41,959 Speaker 1: Have you built the AI part of it that's unique 40 00:01:42,000 --> 00:01:43,920 Speaker 1: to you? Do you in other words, do you do 41 00:01:44,000 --> 00:01:45,760 Speaker 1: something no one else can do? Or is this just 42 00:01:45,760 --> 00:01:47,400 Speaker 1: a bit of AI that someone else will think of 43 00:01:47,440 --> 00:01:48,280 Speaker 1: eventually anyway? 44 00:01:49,200 --> 00:01:51,040 Speaker 2: No, so we've kind of built a lot of our 45 00:01:51,040 --> 00:01:53,640 Speaker 2: own technology. And one of the things that you reference 46 00:01:53,880 --> 00:01:55,880 Speaker 2: was these sort of things only being as good as 47 00:01:55,880 --> 00:01:58,400 Speaker 2: the information given from the agent, and that kind of 48 00:01:58,400 --> 00:02:01,600 Speaker 2: being the barrier is to buy this maybe wouldn't become 49 00:02:01,960 --> 00:02:04,560 Speaker 2: as effective as you might hope. What we do is 50 00:02:04,600 --> 00:02:07,360 Speaker 2: we enrich the data with things like our own computer 51 00:02:07,480 --> 00:02:10,000 Speaker 2: vision that we've built, which is our ability to understand 52 00:02:10,040 --> 00:02:13,679 Speaker 2: property images. We've built our own AI models too, which 53 00:02:13,800 --> 00:02:16,720 Speaker 2: enable us to create another layer of data. We also 54 00:02:16,760 --> 00:02:20,600 Speaker 2: incorporate things like locational data. I know you mentioned stuff 55 00:02:20,639 --> 00:02:22,440 Speaker 2: like you wouldn't be able to say close to this 56 00:02:22,480 --> 00:02:25,600 Speaker 2: school or close to this restaurant. All of that capability 57 00:02:25,680 --> 00:02:27,720 Speaker 2: we have at the moment, and our approach is a 58 00:02:27,760 --> 00:02:30,080 Speaker 2: little bit different. So we actually go to existing real 59 00:02:30,200 --> 00:02:34,040 Speaker 2: estate websites and essentially take over their search and enable 60 00:02:34,120 --> 00:02:37,440 Speaker 2: this AI search on their website. So it's slightly different 61 00:02:37,480 --> 00:02:39,480 Speaker 2: to some of the other type of approaches we've seen 62 00:02:39,520 --> 00:02:40,160 Speaker 2: around search. 63 00:02:40,680 --> 00:02:43,720 Speaker 1: Was I correct though, in saying that no matter where 64 00:02:43,800 --> 00:02:47,000 Speaker 1: you get your material from, it's only as rich as 65 00:02:47,040 --> 00:02:49,839 Speaker 1: that material pool of the pool doesn't have what I'm 66 00:02:49,880 --> 00:02:51,600 Speaker 1: looking for, it can't bring it to me. 67 00:02:52,639 --> 00:02:55,800 Speaker 2: No, because again, we enrich the data so that an 68 00:02:55,840 --> 00:02:58,760 Speaker 2: agent may overlook the fact that it's got a pool 69 00:02:59,560 --> 00:03:02,240 Speaker 2: or a certain type of backyard as where you ingest 70 00:03:02,280 --> 00:03:05,720 Speaker 2: the data and process things like the images. We add 71 00:03:05,720 --> 00:03:08,160 Speaker 2: all these different layers of data to it which enable 72 00:03:08,240 --> 00:03:10,160 Speaker 2: us to essentially match them to what a user is 73 00:03:10,200 --> 00:03:10,600 Speaker 2: asking for. 74 00:03:10,800 --> 00:03:13,639 Speaker 1: How much labors involved in that? In other words, you're 75 00:03:13,639 --> 00:03:16,760 Speaker 1: taking what I was trying to say was you're looking 76 00:03:16,800 --> 00:03:21,280 Speaker 1: potentially to include every single piece of detail so that 77 00:03:21,400 --> 00:03:23,640 Speaker 1: no matter what I ask, it'll go, I've got the 78 00:03:23,639 --> 00:03:26,079 Speaker 1: house right there for you. So how much input does 79 00:03:26,080 --> 00:03:26,680 Speaker 1: that require? 80 00:03:27,639 --> 00:03:30,480 Speaker 2: Yeah, so in terms of a consumer asking or in 81 00:03:30,560 --> 00:03:30,840 Speaker 2: terms of. 82 00:03:30,960 --> 00:03:36,000 Speaker 1: No, in terms of you creating the information that's so 83 00:03:36,280 --> 00:03:39,200 Speaker 1: rich and so detailed that no matter what I ask, 84 00:03:39,280 --> 00:03:40,720 Speaker 1: it's got a good accurate answer. 85 00:03:41,600 --> 00:03:43,560 Speaker 2: Yeah. So obviously, as I mentioned, we've been going for 86 00:03:43,600 --> 00:03:46,440 Speaker 2: a couple of years and building throughout that time, so 87 00:03:46,720 --> 00:03:49,240 Speaker 2: it has taken a long time. It's one thing too 88 00:03:49,280 --> 00:03:51,680 Speaker 2: that it's never kind of finished, you know, So as 89 00:03:51,680 --> 00:03:54,520 Speaker 2: we've kind of I think where you might have seen 90 00:03:54,600 --> 00:03:56,560 Speaker 2: us as in that story that we've taken over search 91 00:03:56,960 --> 00:04:00,720 Speaker 2: for the largest independent brokerage in America, and so things 92 00:04:00,760 --> 00:04:03,760 Speaker 2: like understanding how their users are actually using it, the 93 00:04:03,800 --> 00:04:07,080 Speaker 2: types of searchers that enables us to continue to iterate 94 00:04:07,160 --> 00:04:10,160 Speaker 2: and get better and understand the areas that we need 95 00:04:10,200 --> 00:04:13,240 Speaker 2: to improve on and the evolution that we need to, 96 00:04:13,440 --> 00:04:15,000 Speaker 2: you know, kind of bring to our models. 97 00:04:15,520 --> 00:04:17,880 Speaker 1: Will you get to a point and I'm asking from 98 00:04:17,920 --> 00:04:20,159 Speaker 1: ignorance here, I don't know whether it's doable. I see 99 00:04:20,200 --> 00:04:22,760 Speaker 1: a house that I quite like. Will there come a 100 00:04:22,839 --> 00:04:25,760 Speaker 1: day when I can somehow show AI or your tool 101 00:04:26,080 --> 00:04:30,880 Speaker 1: this photo, see this house, AI show me seventeen virtually the. 102 00:04:30,839 --> 00:04:34,920 Speaker 2: Same, absolutely, and there's stuff like that kind of you know, 103 00:04:35,000 --> 00:04:37,960 Speaker 2: not too far away. One of the progressions that we're 104 00:04:38,000 --> 00:04:40,080 Speaker 2: working on is as you go to let's say one 105 00:04:40,120 --> 00:04:42,440 Speaker 2: of our American clients and you say, I want a 106 00:04:42,480 --> 00:04:44,680 Speaker 2: red house with a blue door and a big backyard 107 00:04:44,800 --> 00:04:48,000 Speaker 2: near that school. As we start showing you properties, you'll 108 00:04:48,040 --> 00:04:50,200 Speaker 2: soon be able to go, well, I don't like that kitchen, 109 00:04:50,600 --> 00:04:53,560 Speaker 2: I like that backyard. And what that does is continues 110 00:04:53,560 --> 00:04:56,720 Speaker 2: to build information on you at lead, which gets passed 111 00:04:56,720 --> 00:04:59,440 Speaker 2: on to agents. So by the time an agent gets 112 00:04:59,560 --> 00:05:02,440 Speaker 2: kind of your information instead of a traditional lead which 113 00:05:02,520 --> 00:05:05,400 Speaker 2: knows Mike's interested in five Smith Street or whatever it 114 00:05:05,440 --> 00:05:08,400 Speaker 2: may be, we now know Mike isn't like these bathrooms, 115 00:05:08,440 --> 00:05:10,640 Speaker 2: he likes kitchens that look like this. He wants to 116 00:05:10,680 --> 00:05:13,000 Speaker 2: be in this area. So it just builds so much 117 00:05:13,000 --> 00:05:14,760 Speaker 2: efficiency in that sales cycle. 118 00:05:15,200 --> 00:05:17,920 Speaker 1: Are you a AI freak or are you just an 119 00:05:18,080 --> 00:05:21,520 Speaker 1: entrepreneur who's who's seen an opportunity here just. 120 00:05:21,480 --> 00:05:23,719 Speaker 2: In the more entrepreneurial path I would I would think 121 00:05:23,800 --> 00:05:25,880 Speaker 2: trying to be an AI freak, but probably more in 122 00:05:25,920 --> 00:05:27,599 Speaker 2: the entrepreneurial side of that. 123 00:05:28,200 --> 00:05:30,400 Speaker 1: Right, So where are you at in the broad based 124 00:05:30,400 --> 00:05:33,040 Speaker 1: global discussion that AI is going to tip us upside 125 00:05:33,080 --> 00:05:35,159 Speaker 1: down and change the world in which we know versus 126 00:05:35,360 --> 00:05:37,760 Speaker 1: it'll probably do some cool stuff, but we've overreacted. 127 00:05:38,680 --> 00:05:40,640 Speaker 2: Yeah, Look, I'm probably more in that it'll probably do 128 00:05:40,680 --> 00:05:43,560 Speaker 2: some cool stuff and we've overreacted. It's been a really 129 00:05:43,560 --> 00:05:46,240 Speaker 2: interesting learning curve for us. I think there's been so 130 00:05:46,440 --> 00:05:48,479 Speaker 2: much hype around it and a lot of the discussions 131 00:05:48,520 --> 00:05:51,200 Speaker 2: that we've had with with different people in America, they've 132 00:05:51,200 --> 00:05:54,039 Speaker 2: been like, look, I just kept seeing solutions to problems. 133 00:05:54,080 --> 00:05:56,120 Speaker 2: I don't think I have. People are so quick to 134 00:05:56,120 --> 00:05:59,039 Speaker 2: bring these products to market without you know, real life 135 00:05:59,120 --> 00:06:01,800 Speaker 2: use cases. So you know, for us, we've been hyper 136 00:06:01,800 --> 00:06:04,560 Speaker 2: focused on real world proof points. You know. So we 137 00:06:04,680 --> 00:06:06,960 Speaker 2: got a couple of really good clients early on and 138 00:06:07,080 --> 00:06:11,000 Speaker 2: just nailing that execution and bringing value and generating real 139 00:06:11,240 --> 00:06:13,360 Speaker 2: rois kind of in what our focus has been on, 140 00:06:13,839 --> 00:06:15,880 Speaker 2: and that's proved pretty effective so far in. 141 00:06:15,880 --> 00:06:17,680 Speaker 1: Terms to go well with it. Might appreciate it very much. 142 00:06:17,720 --> 00:06:21,360 Speaker 1: Chris mcgoldwick, who is the founder of list assist out 143 00:06:21,360 --> 00:06:22,400 Speaker 1: of the States. 144 00:06:22,400 --> 00:06:25,320 Speaker 2: For more from the Mic Asking Breakfast listen live to 145 00:06:25,440 --> 00:06:26,000 Speaker 2: news talks. 146 00:06:26,000 --> 00:06:29,200 Speaker 1: It'd be from six am weekdays, or follow the podcast 147 00:06:29,240 --> 00:06:30,120 Speaker 1: on iHeartRadio.