1 00:00:07,760 --> 00:00:10,799 Speaker 1: Hi everyone, this is Lee Clascow and we're Talking Transports. 2 00:00:10,840 --> 00:00:14,760 Speaker 1: Welcome to the Bloomberg Intelligence Talking Transports podcast. I'm your host, 3 00:00:14,840 --> 00:00:19,280 Speaker 1: Lee Klaskows, Senior freight transportation logistics Analysts at Bloomberg Intelligence, 4 00:00:19,520 --> 00:00:22,400 Speaker 1: Bloomberg's in house research arm of almost five hundred analysts 5 00:00:22,400 --> 00:00:26,400 Speaker 1: and strategists around the globe. Before diving in a little 6 00:00:26,400 --> 00:00:30,200 Speaker 1: public service announcement, your support is instrumental to keep bringing 7 00:00:30,240 --> 00:00:33,800 Speaker 1: great guests and conversations to you, our listeners, and we 8 00:00:33,880 --> 00:00:36,840 Speaker 1: need your support. So please, if you enjoy this podcast, 9 00:00:37,000 --> 00:00:40,080 Speaker 1: share it, like it, and leave a comment. Also, if 10 00:00:40,080 --> 00:00:42,319 Speaker 1: you have any ideas for future episodes or just want 11 00:00:42,360 --> 00:00:45,240 Speaker 1: to talk transports, please hit me up on the Bloomberg terminal, 12 00:00:45,479 --> 00:00:49,680 Speaker 1: on LinkedIn or on Twitter at Logistics. Lee. Now on 13 00:00:49,760 --> 00:00:52,839 Speaker 1: to the episode. We're delighted to have Ryan Joyce with 14 00:00:52,920 --> 00:00:56,520 Speaker 1: us today. He's the CEO of Genlogs. Ryan previously spent 15 00:00:56,600 --> 00:01:00,400 Speaker 1: over fifteen years in the US intelligence community conducting counter 16 00:01:00,520 --> 00:01:05,199 Speaker 1: terrorism operations throughout the Middle East. Ryan holds a BA 17 00:01:05,280 --> 00:01:09,280 Speaker 1: from James Madison University, So go dukes. Ryan. Thanks so 18 00:01:09,360 --> 00:01:11,080 Speaker 1: much for being on the podcast with us today. 19 00:01:11,400 --> 00:01:13,319 Speaker 2: Lee, It's great to join. Thanks so much for having me. 20 00:01:14,000 --> 00:01:18,560 Speaker 1: All right, So you were in the intelligence industry and 21 00:01:18,640 --> 00:01:22,320 Speaker 1: now you're tracking trucks. So for those that don't know, 22 00:01:22,440 --> 00:01:24,480 Speaker 1: can you just give us a little background about what 23 00:01:24,840 --> 00:01:25,959 Speaker 1: gen logs is all about. 24 00:01:26,400 --> 00:01:30,520 Speaker 2: Absolutely, we are a truck intelligence platform, which really means 25 00:01:30,560 --> 00:01:34,040 Speaker 2: that we track all the patterns and locations of trucks 26 00:01:34,080 --> 00:01:36,679 Speaker 2: as they move around North America. Now there's a number 27 00:01:36,680 --> 00:01:39,800 Speaker 2: of reasons why we would do that. First and foremost, 28 00:01:39,840 --> 00:01:43,000 Speaker 2: there's one hundred and sixty billion dollar freight brokerage industry 29 00:01:43,040 --> 00:01:46,880 Speaker 2: that's that intermediary between shippers and the truck fleets or 30 00:01:46,880 --> 00:01:49,640 Speaker 2: carriers around the country, trying to make sure that they 31 00:01:49,640 --> 00:01:52,440 Speaker 2: can match with each other more efficiently. But there's just 32 00:01:52,560 --> 00:01:54,920 Speaker 2: never been one place that you can go to figure 33 00:01:54,960 --> 00:01:58,960 Speaker 2: out which truck operates in certain regions with the right equipment. 34 00:01:59,120 --> 00:02:01,760 Speaker 2: And that's where gen l comes online. We take what 35 00:02:01,960 --> 00:02:05,040 Speaker 2: historically used to have to be done through numerous phone 36 00:02:05,080 --> 00:02:07,720 Speaker 2: calls and a lot of manpower. We now automate that 37 00:02:07,880 --> 00:02:10,119 Speaker 2: so it's all in one place, right. 38 00:02:10,160 --> 00:02:13,720 Speaker 1: And I got to meet you guys at an event. 39 00:02:14,240 --> 00:02:18,320 Speaker 1: I'm trying to think it it was a manifest conference, Yeah, 40 00:02:18,360 --> 00:02:20,440 Speaker 1: and it was I found out to be very intriguing 41 00:02:21,360 --> 00:02:24,120 Speaker 1: just your your overall business. You are a new company, 42 00:02:24,120 --> 00:02:27,880 Speaker 1: an emerging company, and I think the technologies was pretty interesting. 43 00:02:28,520 --> 00:02:32,400 Speaker 1: So you know, just before we go into the technology 44 00:02:32,680 --> 00:02:35,560 Speaker 1: and a little more detail, can can you talk about 45 00:02:35,560 --> 00:02:40,160 Speaker 1: the ownership like and how you got on board on 46 00:02:40,240 --> 00:02:40,840 Speaker 1: gen Logs. 47 00:02:41,480 --> 00:02:44,280 Speaker 2: Sure, we founded the company back in April of twenty 48 00:02:44,280 --> 00:02:46,040 Speaker 2: twenty three, so we're coming up on our two year 49 00:02:46,080 --> 00:02:49,679 Speaker 2: anniversary next month with myself and two co founders, Blake 50 00:02:49,720 --> 00:02:52,640 Speaker 2: Balch and Joe Sherman. We all had worked together at 51 00:02:52,639 --> 00:02:56,160 Speaker 2: a previous company when we had the idea for gen Logs, 52 00:02:56,720 --> 00:02:59,760 Speaker 2: and we have raised venture capital through a few successive 53 00:02:59,800 --> 00:03:04,600 Speaker 2: rands about twenty one million dollars total the Genlogs. The 54 00:03:04,840 --> 00:03:08,560 Speaker 2: we're still controlling of the company and the board, although 55 00:03:08,600 --> 00:03:11,200 Speaker 2: we have fantastic partners when it comes to some of 56 00:03:11,240 --> 00:03:14,160 Speaker 2: our investors on our cap table. Two of our board 57 00:03:14,160 --> 00:03:17,680 Speaker 2: members from Venrock and Steel Atlas have been wonderful as well. 58 00:03:18,600 --> 00:03:22,480 Speaker 1: Okay, great, and I guess you're in the building mode, 59 00:03:22,680 --> 00:03:27,440 Speaker 1: so I guess the goal is to just continue to 60 00:03:28,320 --> 00:03:31,840 Speaker 1: build out. Are you guys in the market for investors 61 00:03:31,880 --> 00:03:34,480 Speaker 1: now or you're kind of happy where you are right now? 62 00:03:34,480 --> 00:03:36,280 Speaker 1: From a financial standpoint. 63 00:03:36,200 --> 00:03:38,920 Speaker 2: We're pretty happy where we are. We have some capital 64 00:03:38,960 --> 00:03:41,640 Speaker 2: to deploy, and we do have somewhat of a capital 65 00:03:41,680 --> 00:03:44,960 Speaker 2: intensive business in the sense of the way we track 66 00:03:45,080 --> 00:03:49,960 Speaker 2: trucks as we deploy sensors all over North America along 67 00:03:50,000 --> 00:03:53,440 Speaker 2: major interstates, freight lanes, highways. Each one of these have 68 00:03:53,600 --> 00:03:56,440 Speaker 2: cameras that collect on the front side and rear of 69 00:03:56,520 --> 00:04:00,640 Speaker 2: trucks as they pass by. So that takes some capital 70 00:04:00,640 --> 00:04:05,320 Speaker 2: expenditure just to get this network built. But given we 71 00:04:05,520 --> 00:04:07,800 Speaker 2: just closed our last round of funding in December of 72 00:04:07,840 --> 00:04:09,880 Speaker 2: last year, we're still in good shape for a while, 73 00:04:10,240 --> 00:04:13,320 Speaker 2: and we'll start having investor conversations again around Q three 74 00:04:13,360 --> 00:04:13,839 Speaker 2: of this year. 75 00:04:14,360 --> 00:04:17,360 Speaker 1: All right, let's talk about the network. So are you 76 00:04:17,839 --> 00:04:20,280 Speaker 1: So I'm assuming you're not all over the country yet, 77 00:04:20,320 --> 00:04:24,560 Speaker 1: because you're still building it out roughly, Where are you 78 00:04:24,600 --> 00:04:26,920 Speaker 1: in the buildout in the US, because I know you 79 00:04:27,400 --> 00:04:31,040 Speaker 1: were also eyeing other places in North America. 80 00:04:31,440 --> 00:04:34,280 Speaker 2: Absolutely, I would say right now we're about seventy percent 81 00:04:34,320 --> 00:04:37,239 Speaker 2: of the targeted build out, and by that our full 82 00:04:37,279 --> 00:04:41,080 Speaker 2: buildout in the United States, the lower forty eight is 83 00:04:41,120 --> 00:04:43,599 Speaker 2: really to get to the point that we are collecting 84 00:04:43,680 --> 00:04:45,839 Speaker 2: on more than ninety percent of trucks that are on 85 00:04:45,880 --> 00:04:49,160 Speaker 2: the roads each day, we're seeing them two or more times. 86 00:04:49,600 --> 00:04:52,520 Speaker 2: And the way we kind of targeted trying to redeploy 87 00:04:52,600 --> 00:04:55,640 Speaker 2: this network is we took essentially a map of how 88 00:04:55,760 --> 00:04:58,840 Speaker 2: trucks move around the United States and volumes on different roads. 89 00:04:59,160 --> 00:05:02,760 Speaker 2: We targeted the most are the highest traffic volume first 90 00:05:03,160 --> 00:05:05,200 Speaker 2: and then are moving out to the edges. And so 91 00:05:05,200 --> 00:05:08,159 Speaker 2: we're seventy percent of trucks on the road each day 92 00:05:08,279 --> 00:05:10,679 Speaker 2: we do see in our data, and we're now pushing 93 00:05:10,720 --> 00:05:14,039 Speaker 2: that closer to that ninety to hopefully, you know, eventually 94 00:05:14,040 --> 00:05:17,680 Speaker 2: one hundred percent level, while also eyeing expansion down into 95 00:05:17,760 --> 00:05:19,560 Speaker 2: Mexico and up into Canada soon. 96 00:05:20,000 --> 00:05:22,599 Speaker 1: So currently you're not in Mexico or Canada that the 97 00:05:22,640 --> 00:05:24,560 Speaker 1: plan is to get to get there. 98 00:05:24,960 --> 00:05:28,320 Speaker 2: We have locations that we've secured to get our sensors 99 00:05:28,320 --> 00:05:31,120 Speaker 2: out there. We're working a little bit. There's it's quite 100 00:05:31,120 --> 00:05:33,400 Speaker 2: a lot that goes into making sure that the data 101 00:05:33,600 --> 00:05:38,640 Speaker 2: of which we're collecting terabytes today flowing through these cameras 102 00:05:39,080 --> 00:05:41,880 Speaker 2: can be processed at the edge and the data brought back, 103 00:05:41,880 --> 00:05:45,039 Speaker 2: and it's that data back hall that we're just looking 104 00:05:45,080 --> 00:05:47,919 Speaker 2: to solve. We've solved that throughout the United States and 105 00:05:47,960 --> 00:05:50,039 Speaker 2: looking to solve that in Canada and Mexico as well, 106 00:05:50,080 --> 00:05:52,320 Speaker 2: so that we can actually make sure that once we 107 00:05:52,440 --> 00:05:54,640 Speaker 2: deploy these sensors out there, we can get the data 108 00:05:54,640 --> 00:05:56,320 Speaker 2: back reliably and in real time. 109 00:05:57,160 --> 00:06:00,120 Speaker 1: So before we talk about the software, I'm just so 110 00:06:00,200 --> 00:06:04,320 Speaker 1: intrigued about the hardware. So are these like video cameras? 111 00:06:04,360 --> 00:06:06,599 Speaker 1: Are they like just taking Is there a sensor that 112 00:06:06,800 --> 00:06:09,760 Speaker 1: just takes a picture when something moves by? How does 113 00:06:09,920 --> 00:06:13,800 Speaker 1: the hardware work, Who maintains the hardware? How do you 114 00:06:13,839 --> 00:06:15,599 Speaker 1: figure out where to go? And kind of what I 115 00:06:15,600 --> 00:06:18,960 Speaker 1: guess what are those leasing arrangements like? And are you 116 00:06:19,040 --> 00:06:21,360 Speaker 1: on like billboards? Are you where are you? 117 00:06:22,160 --> 00:06:24,599 Speaker 2: Yeah? We started out with a theory that if we 118 00:06:24,640 --> 00:06:27,960 Speaker 2: could get enough density of sensors throughout the United States 119 00:06:28,000 --> 00:06:32,200 Speaker 2: on private property, then public entities that want access to 120 00:06:32,240 --> 00:06:36,000 Speaker 2: the same data to do counter human trafficking, counter narcotics 121 00:06:36,080 --> 00:06:38,440 Speaker 2: or fentanyl work would also then work with us to 122 00:06:38,520 --> 00:06:41,719 Speaker 2: deploy in additional areas. And that theory has played out 123 00:06:41,800 --> 00:06:45,479 Speaker 2: in the sense that we did start exclusively on private property, 124 00:06:45,520 --> 00:06:48,200 Speaker 2: so we are on the sides of warehouses or barns, 125 00:06:48,320 --> 00:06:52,359 Speaker 2: or on telecommunications structure wherever there's a structure within a 126 00:06:52,400 --> 00:06:56,760 Speaker 2: certain proximity to a highly truck trafficked lane, that's where 127 00:06:56,800 --> 00:06:59,640 Speaker 2: we look to deploy. Now, we have high requirements with 128 00:06:59,680 --> 00:07:03,400 Speaker 2: regards to the clear lines of sight to trucks as 129 00:07:03,440 --> 00:07:06,640 Speaker 2: they approach the side and then depart the area. We 130 00:07:06,680 --> 00:07:09,800 Speaker 2: need certain power requirements, a certain amount of data back 131 00:07:09,840 --> 00:07:13,000 Speaker 2: hall capabilities. So it takes the forty six thousand miles 132 00:07:13,000 --> 00:07:14,560 Speaker 2: of U in a center state and actually makes it 133 00:07:14,600 --> 00:07:17,400 Speaker 2: a very small subset of that that would work for 134 00:07:17,440 --> 00:07:20,600 Speaker 2: our purposes. And when it does work for our purposes, 135 00:07:20,640 --> 00:07:22,880 Speaker 2: we will go out and approach private property owners to 136 00:07:22,880 --> 00:07:26,040 Speaker 2: pay a monthly lease to host our sensors. These things 137 00:07:26,080 --> 00:07:29,480 Speaker 2: are nothing more than a giant ring cam, essentially, in 138 00:07:29,480 --> 00:07:31,560 Speaker 2: the way that a ring camera might sit at the 139 00:07:31,560 --> 00:07:33,960 Speaker 2: front of your house, overlooking the road in front of 140 00:07:34,000 --> 00:07:38,120 Speaker 2: your house, we're sitting on property overlooking public roadways and 141 00:07:38,160 --> 00:07:42,200 Speaker 2: collecting the data there. It's not motion sensing in the 142 00:07:42,280 --> 00:07:46,040 Speaker 2: sense that there are a classic emotion sensor that stands 143 00:07:46,040 --> 00:07:50,360 Speaker 2: apart to trigger the image collection we record twenty four 144 00:07:50,360 --> 00:07:54,760 Speaker 2: to seven, But we deploy truck detecting models or AI 145 00:07:55,000 --> 00:07:58,480 Speaker 2: computer vision models at the edge that work hand in 146 00:07:58,520 --> 00:08:02,400 Speaker 2: hand with our cameras to filter out all private vehicles 147 00:08:02,440 --> 00:08:05,960 Speaker 2: and only store images where trucks are seen present in 148 00:08:06,000 --> 00:08:09,480 Speaker 2: the image, and then pull that data back for additional processing. 149 00:08:10,600 --> 00:08:13,200 Speaker 1: And what's the average length of these leases that you 150 00:08:13,280 --> 00:08:15,400 Speaker 1: have with these private property owners. 151 00:08:15,680 --> 00:08:17,600 Speaker 2: We'd love to do them as long as possible and 152 00:08:17,640 --> 00:08:20,640 Speaker 2: as exclusive as possible. So we're on decades long leases 153 00:08:20,680 --> 00:08:22,760 Speaker 2: often with many of these property owners. 154 00:08:23,360 --> 00:08:27,080 Speaker 1: And you know, obviously with anything that's technology, something could 155 00:08:27,080 --> 00:08:32,480 Speaker 1: go wrong, whether you know, I guess a bug gets 156 00:08:32,480 --> 00:08:36,560 Speaker 1: squished on the lens, or weather or whatnot. How are 157 00:08:36,559 --> 00:08:38,760 Speaker 1: you maintaining these sensors? 158 00:08:39,280 --> 00:08:41,440 Speaker 2: As they say hardware is hard, and we would have 159 00:08:41,480 --> 00:08:44,959 Speaker 2: to agree with them. We've had, you know, different issues 160 00:08:45,000 --> 00:08:48,880 Speaker 2: with whether it's tornadoes coming through certain areas that bring 161 00:08:48,920 --> 00:08:51,560 Speaker 2: them down, We've had power outages in the areas, and 162 00:08:51,600 --> 00:08:55,600 Speaker 2: you're right, we have had certain issues with bird scat 163 00:08:55,720 --> 00:09:00,240 Speaker 2: on some of the lenses too. We have a multiple 164 00:09:00,280 --> 00:09:03,600 Speaker 2: field technician teams that are crisscrossing the country each and 165 00:09:03,640 --> 00:09:08,560 Speaker 2: every day with bucket trucks that go and both install 166 00:09:08,920 --> 00:09:12,280 Speaker 2: these sensors as we deploy them, but then also maintain 167 00:09:12,360 --> 00:09:15,560 Speaker 2: them as well. We have pretty rigorous standards for our 168 00:09:15,679 --> 00:09:18,400 Speaker 2: uptime both internally as a company and what we want 169 00:09:18,440 --> 00:09:21,720 Speaker 2: to deliver for our customers, and so we are often 170 00:09:21,760 --> 00:09:25,480 Speaker 2: making tweaks to some of the mechanics within these sensors 171 00:09:25,480 --> 00:09:27,600 Speaker 2: to ensure that that uptime can be maintained. 172 00:09:28,480 --> 00:09:30,440 Speaker 1: And you said it's kind of like a ring camera. 173 00:09:30,600 --> 00:09:32,520 Speaker 1: Is that actually the size of it? Is it like 174 00:09:32,600 --> 00:09:33,720 Speaker 1: smaller than a shoe box? 175 00:09:34,120 --> 00:09:36,640 Speaker 2: No, it's actually quite a bit bigger. And each one 176 00:09:36,640 --> 00:09:40,960 Speaker 2: of these cameras are HD cameras that collect ten frames 177 00:09:41,000 --> 00:09:45,600 Speaker 2: per second. I mean, they're your maybe security camera looking 178 00:09:45,720 --> 00:09:51,880 Speaker 2: type type sensors that we have now they do as 179 00:09:51,880 --> 00:09:53,920 Speaker 2: a result. You know, they could be on the side 180 00:09:53,920 --> 00:09:56,720 Speaker 2: of a warehouse and you would never know that the 181 00:09:56,720 --> 00:09:58,840 Speaker 2: purpose of them is actually to be looking at some 182 00:09:58,960 --> 00:10:02,480 Speaker 2: of the truck patterns nearby. And actually, our partners are 183 00:10:02,520 --> 00:10:06,560 Speaker 2: real estate partners, enjoy and prefer those cameras because it 184 00:10:06,600 --> 00:10:10,120 Speaker 2: does do the double duty of deterring theft in the area. 185 00:10:10,559 --> 00:10:13,520 Speaker 2: When it does look like security cameras attached, We kind 186 00:10:13,520 --> 00:10:16,680 Speaker 2: of tuck away all of the parts into a smaller 187 00:10:16,720 --> 00:10:18,800 Speaker 2: box off to the side that does a lot of 188 00:10:18,800 --> 00:10:22,720 Speaker 2: the processing and the data xfilm gotcha. 189 00:10:22,360 --> 00:10:25,720 Speaker 1: And roughly how many employees do you have at gen Lux. 190 00:10:26,960 --> 00:10:29,840 Speaker 2: We have about fifty employees now when you count some 191 00:10:29,880 --> 00:10:32,839 Speaker 2: of our part time employees too that work with us, 192 00:10:33,160 --> 00:10:34,880 Speaker 2: but on the whole in terms of both W two 193 00:10:34,920 --> 00:10:36,920 Speaker 2: and ten ninety nine, we're about fifty now. 194 00:10:37,400 --> 00:10:40,000 Speaker 1: Okay, And is it like spill like half people in 195 00:10:40,040 --> 00:10:43,800 Speaker 1: the field and half programmers and management. 196 00:10:43,960 --> 00:10:46,439 Speaker 2: It makes me breaks down at about thirds. A third 197 00:10:46,559 --> 00:10:50,600 Speaker 2: is our sensor deployment team, and that includes both those 198 00:10:50,600 --> 00:10:52,880 Speaker 2: that are sourcing the leases for us to get our 199 00:10:52,920 --> 00:10:56,480 Speaker 2: sensors out, planning for the sensor deployments, and then the 200 00:10:56,480 --> 00:11:00,000 Speaker 2: field technician crews that are going and actually installing and maintains. 201 00:11:00,760 --> 00:11:04,280 Speaker 2: Another third of the team are engineers and data scientists 202 00:11:04,320 --> 00:11:06,920 Speaker 2: taking all of that data that's coming off of those sensors, 203 00:11:07,400 --> 00:11:09,720 Speaker 2: fusing it with other third party data sets that we 204 00:11:09,800 --> 00:11:12,439 Speaker 2: go out and collect, and then building the actual product 205 00:11:12,440 --> 00:11:14,439 Speaker 2: that we put in our customers hands. And then the 206 00:11:14,520 --> 00:11:16,480 Speaker 2: last third is a mixture of our sales and our 207 00:11:16,480 --> 00:11:21,280 Speaker 2: customer success folks, ensuring that the product hits the market 208 00:11:21,559 --> 00:11:25,120 Speaker 2: or exceeds in all cases what customers are expecting to get. 209 00:11:26,280 --> 00:11:30,360 Speaker 1: Gotcha, And I'm assuming that most of the equipment you 210 00:11:30,440 --> 00:11:32,920 Speaker 1: guys are buying it from third parties and putting it 211 00:11:32,960 --> 00:11:34,640 Speaker 1: together per your own specs. 212 00:11:35,040 --> 00:11:38,240 Speaker 2: This is all US manufacturers. We have multiple manufacturers that 213 00:11:38,280 --> 00:11:40,720 Speaker 2: we work with to take the concept of what we 214 00:11:40,800 --> 00:11:44,880 Speaker 2: needed to accurately collect on trucks as they fly by 215 00:11:45,000 --> 00:11:47,480 Speaker 2: seventy miles per hour in a road, especially when some 216 00:11:47,520 --> 00:11:49,320 Speaker 2: of the data we collect off them only need to 217 00:11:49,360 --> 00:11:51,959 Speaker 2: be about by law about one inch high some of 218 00:11:52,000 --> 00:11:54,520 Speaker 2: the US Department of Transportation numbers on the sides and 219 00:11:54,600 --> 00:11:57,520 Speaker 2: other marketings. So in order to get that, we had 220 00:11:57,520 --> 00:11:59,959 Speaker 2: to really ensure that we were working hand in hand 221 00:12:00,040 --> 00:12:04,000 Speaker 2: and with manufacturing partners to ensure that the entire process 222 00:12:04,040 --> 00:12:07,400 Speaker 2: could actually collect the data at that high speeds. And 223 00:12:07,440 --> 00:12:11,199 Speaker 2: so we do work with with US manufacturers to get 224 00:12:11,200 --> 00:12:12,520 Speaker 2: these built and then deployed. 225 00:12:13,520 --> 00:12:16,080 Speaker 1: All right, So I guess that's a good transition. Let's 226 00:12:16,080 --> 00:12:18,439 Speaker 1: talk about the technology. You know, you did mention a 227 00:12:18,520 --> 00:12:22,160 Speaker 1: nice buzzword AI. You know you put that in your 228 00:12:22,200 --> 00:12:24,840 Speaker 1: emission statement. You probably get a couple turns on your 229 00:12:24,920 --> 00:12:29,800 Speaker 1: valuation in today's market, but it truly is machine learning 230 00:12:29,880 --> 00:12:34,960 Speaker 1: AI enabled. You can you talk about, you know, what 231 00:12:35,000 --> 00:12:40,600 Speaker 1: you're looking for and the kind of use cases for genalogs. 232 00:12:41,880 --> 00:12:43,960 Speaker 2: Yeah, I would say we're starting to service a number 233 00:12:44,000 --> 00:12:47,000 Speaker 2: of different vertocols. We started with just focused on that 234 00:12:47,120 --> 00:12:50,560 Speaker 2: freight brokerage industry in the United States. Very quickly we 235 00:12:50,640 --> 00:12:54,120 Speaker 2: got connected with some very large shippers throughout the United 236 00:12:54,120 --> 00:12:57,640 Speaker 2: States that have trailers or different other assets themselves that 237 00:12:57,679 --> 00:13:00,760 Speaker 2: were missing, and we demonstrated early on on our ability 238 00:13:00,800 --> 00:13:04,640 Speaker 2: to find those in the wild where maybe a trailer 239 00:13:04,720 --> 00:13:08,880 Speaker 2: full of toys or electronics had been stolen and these 240 00:13:08,880 --> 00:13:11,439 Speaker 2: shippers were trying to track down who the thieves were. 241 00:13:11,960 --> 00:13:14,240 Speaker 2: There was no recourse for that back in the day. 242 00:13:14,320 --> 00:13:17,200 Speaker 2: Now Genlogs has the ability to set an alert on 243 00:13:17,240 --> 00:13:20,360 Speaker 2: a specific trailer that you're looking for by number, by logo, 244 00:13:20,920 --> 00:13:22,760 Speaker 2: and when that is seen anywhere in the United States, 245 00:13:22,760 --> 00:13:25,440 Speaker 2: you'll get an instant alert that also tells you what 246 00:13:25,600 --> 00:13:27,880 Speaker 2: the truck that is carrying that right now, where it is, 247 00:13:27,960 --> 00:13:31,040 Speaker 2: the direction. So that was another use case that has 248 00:13:31,080 --> 00:13:35,200 Speaker 2: expanded in terms of this asset location and recovery. Beyond that, 249 00:13:35,480 --> 00:13:38,440 Speaker 2: we are now dealing with the commercial auto insurance industry 250 00:13:38,440 --> 00:13:42,360 Speaker 2: helping them with better underwriting, continuous monitoring, as well as 251 00:13:42,360 --> 00:13:46,800 Speaker 2: claims investigations, and we are looking for a further expansion 252 00:13:46,920 --> 00:13:50,800 Speaker 2: into the federal and state government space as well as 253 00:13:50,840 --> 00:13:54,360 Speaker 2: financial institutions. So there's a lot of different use cases 254 00:13:54,400 --> 00:13:57,520 Speaker 2: for understanding the truck activity in the United States. If 255 00:13:57,559 --> 00:14:00,400 Speaker 2: you think about how goods flow to and from the 256 00:14:00,520 --> 00:14:03,600 Speaker 2: United States, they might be flown, they might be on ships, 257 00:14:03,640 --> 00:14:06,240 Speaker 2: but at some point they touch a truck. And that's 258 00:14:06,280 --> 00:14:09,360 Speaker 2: really why we've doubled down on the truck intelligence aspects 259 00:14:09,360 --> 00:14:12,559 Speaker 2: of what we're doing. Fourteen trillion dollars of goods filters 260 00:14:12,600 --> 00:14:16,080 Speaker 2: through the US economy each year with goods on trucks, 261 00:14:16,640 --> 00:14:19,240 Speaker 2: and that's why we focus there specifically. 262 00:14:20,280 --> 00:14:22,800 Speaker 1: So if I'm a freight broker like a sh Robinson 263 00:14:23,360 --> 00:14:27,120 Speaker 1: or an RXO, what is the use case? So, am 264 00:14:27,160 --> 00:14:28,520 Speaker 1: I is it just for tracking? 265 00:14:29,360 --> 00:14:32,600 Speaker 2: It's actually tracking might come later right now. When we 266 00:14:32,640 --> 00:14:35,640 Speaker 2: work with freight brokerages, there's kind of three use cases 267 00:14:35,640 --> 00:14:39,280 Speaker 2: we focus on. First is we focus on these carrier 268 00:14:39,400 --> 00:14:43,920 Speaker 2: sales process. So traditionally, these freight brokerages exist because they 269 00:14:43,960 --> 00:14:46,800 Speaker 2: can see the long tail of the market that involves 270 00:14:46,800 --> 00:14:50,600 Speaker 2: them employees armies of young professionals to pick up the 271 00:14:50,600 --> 00:14:53,640 Speaker 2: phone every day call lists of truck carriers and ask 272 00:14:53,680 --> 00:14:56,400 Speaker 2: them a few basic questions like how many trucks do 273 00:14:56,440 --> 00:14:58,120 Speaker 2: you have, where do you like to operate? Where are 274 00:14:58,160 --> 00:15:00,640 Speaker 2: your trucks? Now, what type of a equipment do you 275 00:15:00,720 --> 00:15:04,280 Speaker 2: have with those trucks? We, by just nature of seeing 276 00:15:04,320 --> 00:15:06,600 Speaker 2: everything that's happening on the roads, know the answers to 277 00:15:06,640 --> 00:15:09,560 Speaker 2: all those questions, so we can absolutely cut down on 278 00:15:09,640 --> 00:15:11,600 Speaker 2: the number of people that need to be making those 279 00:15:11,640 --> 00:15:13,120 Speaker 2: calls or in the number of calls that have to 280 00:15:13,120 --> 00:15:15,840 Speaker 2: be made each day. So essentially going from a very 281 00:15:16,560 --> 00:15:19,280 Speaker 2: opaque process of understanding where trucks are to now a 282 00:15:19,320 --> 00:15:22,800 Speaker 2: transparent process for a H. Robinson for example, to fined. 283 00:15:22,800 --> 00:15:26,240 Speaker 2: So that's that carrier sales side. We then fuse the 284 00:15:26,320 --> 00:15:28,400 Speaker 2: data we collect on trucks out there in the roads 285 00:15:28,440 --> 00:15:31,240 Speaker 2: with other additional data sets in order to map back 286 00:15:31,320 --> 00:15:35,840 Speaker 2: to where those trucks came from their origins to their destinations. Now, 287 00:15:35,920 --> 00:15:38,040 Speaker 2: when you do that at scale, you get to understand 288 00:15:38,520 --> 00:15:41,680 Speaker 2: the shipper patterns and networks in the United States. So 289 00:15:41,720 --> 00:15:45,240 Speaker 2: we can take a Coca Cola, for example, and understand 290 00:15:45,400 --> 00:15:48,240 Speaker 2: where all of their suppliers are and where the trucks 291 00:15:48,280 --> 00:15:51,360 Speaker 2: come from from their suppliers to a Cocoa Cola bottling 292 00:15:51,440 --> 00:15:54,080 Speaker 2: plant for example, and then where the trucks go from 293 00:15:54,120 --> 00:15:56,920 Speaker 2: that Coco Cola bottling plant to then deliver to the 294 00:15:57,680 --> 00:16:01,520 Speaker 2: end locations. Grocery stores can be stores, So that's data 295 00:16:01,520 --> 00:16:04,680 Speaker 2: that we also have aggregated together with our AI. So 296 00:16:04,720 --> 00:16:08,040 Speaker 2: we then give h Robinson or other types of third 297 00:16:08,040 --> 00:16:12,600 Speaker 2: party logistics or freight brokerages insights into the shipper networks 298 00:16:12,640 --> 00:16:14,840 Speaker 2: and therefore those are the sh those are the customers. 299 00:16:15,200 --> 00:16:17,960 Speaker 2: And then lastly there's a compliance piece to this all 300 00:16:18,440 --> 00:16:21,600 Speaker 2: that we can validate that a truck carrier that says 301 00:16:21,600 --> 00:16:24,280 Speaker 2: that it operates on the roads actually does have the 302 00:16:24,320 --> 00:16:27,640 Speaker 2: equipments that they claim, almost making sure that the digital 303 00:16:27,680 --> 00:16:31,240 Speaker 2: footprint or what is claimed matches the physical footprint of 304 00:16:31,280 --> 00:16:34,200 Speaker 2: the actual ground truth. And so that's the compliance we 305 00:16:34,240 --> 00:16:37,000 Speaker 2: look when we deal with freight brokerages, we look at 306 00:16:37,280 --> 00:16:41,360 Speaker 2: carrier customers in compliance and we have a holistic solution 307 00:16:41,480 --> 00:16:41,840 Speaker 2: for them. 308 00:16:42,160 --> 00:16:45,440 Speaker 1: And so tracking might be down the road. Can you 309 00:16:45,520 --> 00:16:46,920 Speaker 1: just expand on what you meant by that. 310 00:16:47,360 --> 00:16:49,080 Speaker 2: I think there's a little bit has to do with 311 00:16:49,200 --> 00:16:52,880 Speaker 2: censored density. We're not at the point yet where, like 312 00:16:52,920 --> 00:16:54,840 Speaker 2: I said, our goal is to get to the point 313 00:16:54,880 --> 00:16:57,160 Speaker 2: that over ninety percent of the trucks on the roads 314 00:16:57,200 --> 00:16:59,960 Speaker 2: we're seeing two or more times, three, four, five times. 315 00:17:00,560 --> 00:17:02,640 Speaker 2: There's just going to be some additional sensors we need 316 00:17:02,680 --> 00:17:05,320 Speaker 2: to deploy. There's going to be additional work we need 317 00:17:05,359 --> 00:17:07,480 Speaker 2: to do to make sure that we can take the 318 00:17:07,600 --> 00:17:09,840 Speaker 2: different types of data points that we collect on a 319 00:17:09,880 --> 00:17:12,479 Speaker 2: truck and tie that down to the vin level so 320 00:17:12,480 --> 00:17:15,440 Speaker 2: that we know that it's not just acme trucking writ 321 00:17:15,560 --> 00:17:17,480 Speaker 2: large that we saw at a certain place, but it's 322 00:17:17,480 --> 00:17:22,199 Speaker 2: this specific truck or specific trailer that is underload that 323 00:17:22,440 --> 00:17:25,359 Speaker 2: is traveling from Dallas to Chicago that we see it 324 00:17:25,680 --> 00:17:30,000 Speaker 2: outside the confines of Dallas, midway in Missouri or Illinois 325 00:17:30,040 --> 00:17:33,200 Speaker 2: heading into Chicago. I think once we get to that point, 326 00:17:33,240 --> 00:17:35,840 Speaker 2: we will be in a position to automate a lot 327 00:17:35,840 --> 00:17:40,680 Speaker 2: of the tracking that occurs that currently necessitates providing the 328 00:17:40,720 --> 00:17:44,880 Speaker 2: actual permission of the electronic logging device or some other 329 00:17:44,960 --> 00:17:48,600 Speaker 2: type of cell phone tracking that has to be manually 330 00:17:48,680 --> 00:17:51,880 Speaker 2: enabled by a driver or fleet. We will be able 331 00:17:51,920 --> 00:17:57,360 Speaker 2: to automate that across all roads without needing that manual enabling. 332 00:17:58,400 --> 00:18:01,800 Speaker 2: And really, what we've found is that people don't really 333 00:18:01,840 --> 00:18:05,159 Speaker 2: want breadcrumbs. It's nice to have when you see something 334 00:18:05,200 --> 00:18:07,880 Speaker 2: every five minutes pinging on a road, but people want 335 00:18:07,920 --> 00:18:10,240 Speaker 2: to know two things are there. Is there shipment going 336 00:18:10,280 --> 00:18:12,400 Speaker 2: to be late or is it going in the wrong 337 00:18:12,440 --> 00:18:15,199 Speaker 2: direction potentially stolen. And if we can get to the 338 00:18:15,240 --> 00:18:18,040 Speaker 2: point that we can actually say, hey, your shipment is 339 00:18:18,119 --> 00:18:21,040 Speaker 2: on time, it's en route uh, and then say that 340 00:18:21,119 --> 00:18:23,399 Speaker 2: again a few hours down the road, and again and 341 00:18:23,440 --> 00:18:25,679 Speaker 2: we can do that all without needing any type of 342 00:18:27,240 --> 00:18:29,360 Speaker 2: enabling or the manual sharing, then I think that's where 343 00:18:29,359 --> 00:18:30,600 Speaker 2: we want to be. It's just going to take a 344 00:18:30,640 --> 00:18:33,040 Speaker 2: little bit more time for us, right. 345 00:18:33,400 --> 00:18:38,400 Speaker 1: And does the just the essence of what you guys do, 346 00:18:38,400 --> 00:18:41,359 Speaker 1: does it bring up any privacy concerns? You know, because 347 00:18:41,640 --> 00:18:46,119 Speaker 1: I guess you are filming the highways and you know, 348 00:18:46,359 --> 00:18:48,720 Speaker 1: some people may may or may not like that. 349 00:18:50,040 --> 00:18:52,760 Speaker 2: Great question. It's one we consider deeply before we started 350 00:18:52,760 --> 00:18:54,919 Speaker 2: gen logs, and we always wanted to make sure that 351 00:18:55,040 --> 00:18:58,280 Speaker 2: anything we did was taking privacy kind of first and 352 00:18:58,280 --> 00:19:00,679 Speaker 2: foremost in mind. I think it's important to keep in 353 00:19:00,720 --> 00:19:04,600 Speaker 2: mind that there are certainly cameras all over the roads already. 354 00:19:04,600 --> 00:19:06,880 Speaker 2: And if you can go to any Department of Transportation 355 00:19:07,400 --> 00:19:09,520 Speaker 2: website right now, the five to one to one dot 356 00:19:09,640 --> 00:19:13,280 Speaker 2: orgs of Virginia or Texas or Florida, you can see 357 00:19:13,440 --> 00:19:16,200 Speaker 2: cars on the roads and you don't know who's actually 358 00:19:16,280 --> 00:19:18,800 Speaker 2: driving those cars. You just know that a car is there, 359 00:19:18,840 --> 00:19:21,439 Speaker 2: and we kind of took that mentality, I think, thinking 360 00:19:21,480 --> 00:19:24,240 Speaker 2: it further. You know, we talked about the ring cam example, 361 00:19:24,359 --> 00:19:27,600 Speaker 2: like all these ring cams are overlooking the roads as well. 362 00:19:27,760 --> 00:19:29,480 Speaker 2: But if you think about every Tesla on the road 363 00:19:29,520 --> 00:19:32,359 Speaker 2: right now having like eight to ten cameras on board 364 00:19:32,400 --> 00:19:35,200 Speaker 2: recording twenty four to seven, almost every truck on the 365 00:19:35,240 --> 00:19:38,000 Speaker 2: road now has a ford facing dash cam. And if 366 00:19:38,000 --> 00:19:41,879 Speaker 2: you actually look at Google Earth and specifically looking at 367 00:19:42,000 --> 00:19:45,119 Speaker 2: the Google street View, you can zoom in on trucks 368 00:19:45,119 --> 00:19:47,880 Speaker 2: and actually see the same data points that we pull 369 00:19:48,000 --> 00:19:50,439 Speaker 2: that Google is now putting that out there on the 370 00:19:50,560 --> 00:19:53,000 Speaker 2: road and the time that it was collected that image. 371 00:19:53,000 --> 00:19:57,440 Speaker 2: So we treat every truck as if it's autonomous, meaning 372 00:19:57,480 --> 00:19:59,080 Speaker 2: we don't know if there's a driver, and we do 373 00:19:59,160 --> 00:20:01,480 Speaker 2: not care if it's a driver, because we're not collecting 374 00:20:01,520 --> 00:20:05,520 Speaker 2: on the person. We're collecting on the inanimate commercial object 375 00:20:05,600 --> 00:20:07,399 Speaker 2: known as this truck that just happened to be at 376 00:20:07,400 --> 00:20:10,040 Speaker 2: a time and place. And when we collect that data 377 00:20:10,080 --> 00:20:12,919 Speaker 2: over time, you get a really rich understanding of how 378 00:20:13,000 --> 00:20:17,440 Speaker 2: let's say Walmart's fleet moves around or Pepsi's Newburn fleet 379 00:20:17,520 --> 00:20:20,639 Speaker 2: moves around the roads without knowing the actual people that 380 00:20:20,680 --> 00:20:21,879 Speaker 2: we're driving at that time. 381 00:20:23,480 --> 00:20:25,840 Speaker 1: He kind of mentioned that there's cameras everywhere, and there's 382 00:20:25,840 --> 00:20:28,080 Speaker 1: even cameras on the road right now. Your network is 383 00:20:28,240 --> 00:20:32,679 Speaker 1: you know, stationary. Is it possible for you guys to 384 00:20:32,720 --> 00:20:35,239 Speaker 1: tap into those networks, whether it's like, you know, you 385 00:20:35,280 --> 00:20:38,600 Speaker 1: want to partner with a large trucking company that has 386 00:20:38,640 --> 00:20:41,480 Speaker 1: cameras in their cabs, either you know, forward facing and 387 00:20:42,359 --> 00:20:46,200 Speaker 1: road facing cameras. Are are you guys able to tap 388 00:20:46,240 --> 00:20:46,600 Speaker 1: into that? 389 00:20:47,200 --> 00:20:49,280 Speaker 2: We are in some discussions to do that. I think 390 00:20:49,359 --> 00:20:52,600 Speaker 2: long term we want gen logs to be transformed into 391 00:20:52,680 --> 00:20:56,480 Speaker 2: kind of the the all things sensors of trucks on 392 00:20:56,520 --> 00:20:58,200 Speaker 2: the roads and however that might be. And I think 393 00:20:58,200 --> 00:21:00,679 Speaker 2: we're also looking at in the future, how do we 394 00:21:00,760 --> 00:21:04,919 Speaker 2: factor satellites into collecting on trucks that we confuse with 395 00:21:05,000 --> 00:21:07,679 Speaker 2: the data that we're collecting at the ground level, so 396 00:21:07,680 --> 00:21:10,280 Speaker 2: that we know what is seen by overhead matches, what 397 00:21:10,400 --> 00:21:14,000 Speaker 2: we're collecting with higher fidelity from the ground, and then 398 00:21:14,000 --> 00:21:16,359 Speaker 2: also looking at some of those existing networks like the 399 00:21:16,359 --> 00:21:19,919 Speaker 2: traffic camera networks, which we did evaluate early on in 400 00:21:19,960 --> 00:21:23,320 Speaker 2: our journey. The resolution just wasn't good enough to collect 401 00:21:23,359 --> 00:21:25,520 Speaker 2: the types of data points we need off the trucks. 402 00:21:26,000 --> 00:21:28,560 Speaker 2: But there is potentially, with more R and D, the 403 00:21:28,600 --> 00:21:32,360 Speaker 2: ability for us to uniquely fingerprint a truck on one 404 00:21:32,400 --> 00:21:35,480 Speaker 2: of our own deployed sensors and then see that same 405 00:21:35,520 --> 00:21:38,360 Speaker 2: truck two miles down the road on an existing Department 406 00:21:38,400 --> 00:21:41,720 Speaker 2: Transportation network sensor and know that those two are the 407 00:21:41,760 --> 00:21:44,120 Speaker 2: same truck, so that we can get closer to that 408 00:21:44,560 --> 00:21:49,360 Speaker 2: breadcrumb's type look of truck patterns without needing to deploy 409 00:21:49,480 --> 00:21:52,280 Speaker 2: sensors ourselves in all of these different areas. 410 00:21:53,119 --> 00:21:56,480 Speaker 1: Right, Okay, and you know you mentioned there's a use 411 00:21:56,560 --> 00:22:00,920 Speaker 1: case for law enforcement, and I'm assuming when someone uses 412 00:22:00,960 --> 00:22:03,679 Speaker 1: you it's an enterprise subscription. It's not just like a 413 00:22:03,720 --> 00:22:05,720 Speaker 1: one off find my trailer. 414 00:22:05,800 --> 00:22:09,720 Speaker 2: Right, That's correct. Although for anyone in the industry and 415 00:22:09,720 --> 00:22:12,040 Speaker 2: anyone listening to this, if you do have a trailer missing, 416 00:22:12,440 --> 00:22:15,760 Speaker 2: please do you can go to our website genlogs dot io. 417 00:22:15,880 --> 00:22:18,160 Speaker 2: On the upper right. We have a find my Lost 418 00:22:18,200 --> 00:22:21,359 Speaker 2: asset button. You can click that submit your details. We 419 00:22:21,359 --> 00:22:23,840 Speaker 2: will investigate it for you, and if we find it 420 00:22:23,880 --> 00:22:25,480 Speaker 2: we will send it over to you where that was 421 00:22:25,600 --> 00:22:27,840 Speaker 2: last scene so you could take action, and we will 422 00:22:27,840 --> 00:22:30,080 Speaker 2: do that for anyone in the industry for free for 423 00:22:30,119 --> 00:22:33,520 Speaker 2: a number of times, just to essentially give back and 424 00:22:33,560 --> 00:22:36,320 Speaker 2: demonstrate the value. I think long term, we hope that 425 00:22:36,359 --> 00:22:38,159 Speaker 2: people see that and say, well, I want to make 426 00:22:38,160 --> 00:22:41,040 Speaker 2: sure I get ahead of that theft. Let me make 427 00:22:41,040 --> 00:22:44,199 Speaker 2: sure I'm dealing with the real trusted carriers that are 428 00:22:44,200 --> 00:22:46,720 Speaker 2: out there in the roads. That's what Genlogs can bring 429 00:22:46,760 --> 00:22:49,120 Speaker 2: to the table. And if anything ever does go wrong, 430 00:22:49,160 --> 00:22:52,040 Speaker 2: we can help you recover it. And but we do 431 00:22:52,119 --> 00:22:55,760 Speaker 2: package all of that up on our enterprise subscription model 432 00:22:56,200 --> 00:22:59,960 Speaker 2: so that anyone in an organization you mentioned H Robinson earlier, 433 00:23:00,320 --> 00:23:04,280 Speaker 2: for example, like AH Robinson, their carrier folks could be 434 00:23:04,359 --> 00:23:07,199 Speaker 2: using the platform, their customer facing folks, as well as 435 00:23:07,240 --> 00:23:10,480 Speaker 2: their compliance and recovery folks, and all of that could 436 00:23:10,520 --> 00:23:15,600 Speaker 2: be done there together. You mentioned law enforcement, it's an 437 00:23:15,640 --> 00:23:18,560 Speaker 2: area we're exploring there. We've had law enforcement interest comes 438 00:23:18,560 --> 00:23:22,360 Speaker 2: in when there's truck related crimes that they just simply 439 00:23:22,440 --> 00:23:26,480 Speaker 2: don't have great data to figure out, especially because trucks 440 00:23:26,480 --> 00:23:30,760 Speaker 2: by nature often interstate and might be in Texas one day, 441 00:23:30,800 --> 00:23:33,520 Speaker 2: but then not to be seen again for a while, 442 00:23:33,560 --> 00:23:36,560 Speaker 2: and trying to understand where that truck came from and 443 00:23:36,600 --> 00:23:39,440 Speaker 2: where it's going, especially if you have limited data on 444 00:23:39,480 --> 00:23:42,280 Speaker 2: the markings on that truck and are trying to figure out, Hey, 445 00:23:42,320 --> 00:23:45,919 Speaker 2: who was the actual registered carrier that was involved in 446 00:23:45,960 --> 00:23:48,199 Speaker 2: either this hit and run or this theft or this 447 00:23:48,720 --> 00:23:53,800 Speaker 2: fentanyl related type of situation. We can now get involved 448 00:23:53,800 --> 00:23:56,439 Speaker 2: in a help law enforcement track down specific trucks. 449 00:23:56,920 --> 00:23:59,239 Speaker 1: And I'm just curious this might be too, just like 450 00:23:59,280 --> 00:24:01,760 Speaker 1: a little bunch in the weeds. But like so, if 451 00:24:01,760 --> 00:24:06,000 Speaker 1: a shipperd does use you guys to find a lost trailer, 452 00:24:06,400 --> 00:24:10,280 Speaker 1: and it's a customer and you find it, it's the 453 00:24:10,320 --> 00:24:13,240 Speaker 1: first call to the shipper, it's a law enforcement how 454 00:24:13,240 --> 00:24:14,440 Speaker 1: do you know? 455 00:24:14,520 --> 00:24:17,120 Speaker 2: We try to do it simultaneous. We had an incident 456 00:24:17,160 --> 00:24:19,560 Speaker 2: of this happened the other day in California where there 457 00:24:19,600 --> 00:24:23,480 Speaker 2: was a pet food distribution center. We had an unfortunate 458 00:24:23,520 --> 00:24:26,960 Speaker 2: case where a freight broker was involved and was not 459 00:24:27,119 --> 00:24:30,240 Speaker 2: using gen logs at the time and therefore tendered the 460 00:24:30,320 --> 00:24:34,320 Speaker 2: load to a fictitious carrier that came with an unmarked 461 00:24:34,359 --> 00:24:38,280 Speaker 2: truck to go pick up the act. The full truckload 462 00:24:38,320 --> 00:24:41,560 Speaker 2: of pet food and stole it. And essentially it turns 463 00:24:41,560 --> 00:24:44,240 Speaker 2: out that the trailer that they used was also a 464 00:24:44,280 --> 00:24:48,000 Speaker 2: stolen trailer from one of our other customers. So our 465 00:24:48,040 --> 00:24:51,439 Speaker 2: investigations team was quickly able to put tunes together. We 466 00:24:51,440 --> 00:24:54,760 Speaker 2: were able to find that truck on the roads, find 467 00:24:54,800 --> 00:24:59,040 Speaker 2: the trailer that was associated with it, called the actual 468 00:24:59,080 --> 00:25:01,240 Speaker 2: trailer owner and say, hey, can you tell us where 469 00:25:01,240 --> 00:25:03,840 Speaker 2: this trailer is right now? Because they had tracking on 470 00:25:03,880 --> 00:25:06,200 Speaker 2: that trailer, and then we were able to direct law 471 00:25:06,280 --> 00:25:09,560 Speaker 2: enforcement to go find where that trailer was and they 472 00:25:09,600 --> 00:25:12,120 Speaker 2: got to it too, fine the trailer at that time, 473 00:25:12,200 --> 00:25:15,680 Speaker 2: So that'd be an example where multiple parties were involved. 474 00:25:16,000 --> 00:25:19,440 Speaker 2: Gen Logs did the quarterbacking of it to help someone 475 00:25:19,480 --> 00:25:22,160 Speaker 2: that wasn't even our customer at the time, and since 476 00:25:22,200 --> 00:25:24,720 Speaker 2: then they've now signed up to VA gen Laws customer 477 00:25:24,720 --> 00:25:27,280 Speaker 2: that broker because they see, hey, we could have helped 478 00:25:27,280 --> 00:25:29,840 Speaker 2: them avoid that, but if something does go wrong then 479 00:25:29,840 --> 00:25:31,479 Speaker 2: we can help them get to the bottom of it 480 00:25:31,600 --> 00:25:32,240 Speaker 2: rather quickly. 481 00:25:33,640 --> 00:25:38,480 Speaker 1: Got ya, And I guess you know from a from 482 00:25:38,520 --> 00:25:45,199 Speaker 1: a carrier standpoint, they're using it primarily similar to the 483 00:25:45,240 --> 00:25:51,440 Speaker 1: brokerage use case, where you're kind of tracking and looking 484 00:25:51,480 --> 00:25:52,480 Speaker 1: at hippers as well. 485 00:25:53,600 --> 00:25:56,760 Speaker 2: The carrier use cases currently are for much larger carriers 486 00:25:56,760 --> 00:25:59,920 Speaker 2: that have kind of a larger trailer to truck race, 487 00:26:00,320 --> 00:26:03,399 Speaker 2: so they have unsecured trailers in certain areas that people 488 00:26:03,440 --> 00:26:06,640 Speaker 2: are stealing or misusing all the time. We're helping them 489 00:26:06,720 --> 00:26:09,600 Speaker 2: find get to the bottom of those misuses, although we 490 00:26:10,040 --> 00:26:13,040 Speaker 2: long term we do want to help carriers and kind 491 00:26:13,040 --> 00:26:15,400 Speaker 2: of go down market to help the smaller carriers. As 492 00:26:15,440 --> 00:26:18,840 Speaker 2: you know, ninety seven percent of the truck market is 493 00:26:18,880 --> 00:26:22,480 Speaker 2: made up of companies with ten or fewer trucks. And 494 00:26:22,520 --> 00:26:27,000 Speaker 2: what we've noticed is an unfortunate uptick in cases in 495 00:26:27,040 --> 00:26:32,960 Speaker 2: which bad actors, fraudsters, or thieves are posing as legitimate 496 00:26:33,080 --> 00:26:35,760 Speaker 2: truck carriers. So they will pull up to a distribution 497 00:26:35,920 --> 00:26:40,159 Speaker 2: center posing as ACME Trucking with a US Department of 498 00:26:40,200 --> 00:26:43,480 Speaker 2: Transportation number correctly put on the outside of their truck 499 00:26:43,840 --> 00:26:46,880 Speaker 2: that corresponds to ACME Trucking. It just so happens that 500 00:26:47,160 --> 00:26:51,000 Speaker 2: they're fraudster and they actually aren't hauling for ACME Trucking, 501 00:26:51,600 --> 00:26:54,639 Speaker 2: and a theft will occur, and either the shipp or 502 00:26:54,640 --> 00:26:57,679 Speaker 2: the freight broker will put in a negative report on 503 00:26:57,880 --> 00:27:01,320 Speaker 2: that carrier when it wasn't them involved. And so there's 504 00:27:01,359 --> 00:27:05,160 Speaker 2: a future state in which Genlogs is kind of doing 505 00:27:05,280 --> 00:27:09,080 Speaker 2: overwatch for smaller carriers to alert them in real time 506 00:27:09,560 --> 00:27:13,240 Speaker 2: whenever someone is posing as them and not actually part 507 00:27:13,280 --> 00:27:16,440 Speaker 2: of their fleet and giving them a heads up that, hey, 508 00:27:16,600 --> 00:27:18,800 Speaker 2: all of your trucks are on the East coast right now, 509 00:27:19,200 --> 00:27:21,119 Speaker 2: but we just saw someone on the West coast that 510 00:27:21,240 --> 00:27:23,800 Speaker 2: has your Department of Transportation number on the outside of 511 00:27:23,840 --> 00:27:26,440 Speaker 2: their truck that doesn't seem to be associated, and let's 512 00:27:26,440 --> 00:27:28,919 Speaker 2: get ahead of that before they do damage to your brand. 513 00:27:29,560 --> 00:27:33,840 Speaker 1: And I guess is the biggest competitor or risk to 514 00:27:33,920 --> 00:27:36,440 Speaker 1: the model is that there's a sensor in every truck 515 00:27:36,440 --> 00:27:37,240 Speaker 1: at every trailer. 516 00:27:37,520 --> 00:27:40,200 Speaker 2: Is that well, I would say that that already exists. 517 00:27:40,440 --> 00:27:43,879 Speaker 2: And back in twenty twelve, Congress mandated that all trucks 518 00:27:43,920 --> 00:27:47,919 Speaker 2: in America have this electronic logging device in their trucks 519 00:27:47,960 --> 00:27:51,200 Speaker 2: that are collecting their location, speed and all of that 520 00:27:51,200 --> 00:27:54,080 Speaker 2: all the time. Now, all of that data is stovepiped 521 00:27:54,480 --> 00:27:58,000 Speaker 2: within those trucking companies. It's not mandated to be shared publicly. 522 00:27:58,880 --> 00:28:00,480 Speaker 2: It just has to be stored and be able to 523 00:28:00,560 --> 00:28:04,960 Speaker 2: records turned over to authorities upon request. So I would 524 00:28:05,000 --> 00:28:07,840 Speaker 2: almost say that almost every large fleet and even small 525 00:28:07,880 --> 00:28:11,600 Speaker 2: fleet has trackers on their trucks, so the data is 526 00:28:11,640 --> 00:28:13,960 Speaker 2: out there, it's just not required to be in one place. 527 00:28:14,000 --> 00:28:16,879 Speaker 2: And that's been why genlogs emerged in the scene and 528 00:28:16,880 --> 00:28:20,480 Speaker 2: has had the growth we've seen, is because because that 529 00:28:20,560 --> 00:28:23,320 Speaker 2: data is all stovepipe, our ability to collect it on 530 00:28:23,440 --> 00:28:25,960 Speaker 2: all trucks breaks down those stovepipes and we get full 531 00:28:26,040 --> 00:28:29,800 Speaker 2: visibility and transparency into the market without needing anyone to 532 00:28:29,800 --> 00:28:30,880 Speaker 2: share data with us. 533 00:28:32,560 --> 00:28:35,440 Speaker 1: And could you just talk about the genesis of genlogs 534 00:28:35,560 --> 00:28:38,920 Speaker 1: between you and your co founders, like, you know, you 535 00:28:38,960 --> 00:28:41,320 Speaker 1: guys playing poker and someone said, hey, you know what 536 00:28:41,320 --> 00:28:43,720 Speaker 1: we should do? Like how did the idea come about? 537 00:28:43,880 --> 00:28:46,520 Speaker 2: Well, we talked about my background the US intelligence community, 538 00:28:46,520 --> 00:28:49,040 Speaker 2: and the other two co founders have similar backgrounds where 539 00:28:49,040 --> 00:28:52,400 Speaker 2: they worked at times with the intelligence community. I myself 540 00:28:52,440 --> 00:28:57,000 Speaker 2: spent a spell of my career at CIA conducting counter 541 00:28:57,080 --> 00:29:00,800 Speaker 2: terarism operations, and my job was to fruit sources in 542 00:29:00,880 --> 00:29:04,040 Speaker 2: these foreign terists networks to give us information. Now before 543 00:29:04,040 --> 00:29:06,600 Speaker 2: we could validate or vet that information or act on it, 544 00:29:07,000 --> 00:29:09,360 Speaker 2: we would see what they told us and how that 545 00:29:09,440 --> 00:29:12,400 Speaker 2: looked about what we were seeing from sensors, satellites, or 546 00:29:12,400 --> 00:29:14,760 Speaker 2: even hearing from other sources. So we called this an 547 00:29:15,160 --> 00:29:19,320 Speaker 2: all source intelligence approach to vetting the new information that 548 00:29:19,360 --> 00:29:21,680 Speaker 2: we received and then when we could actually validate that, 549 00:29:21,720 --> 00:29:24,400 Speaker 2: then we could act on it. And so it was 550 00:29:24,440 --> 00:29:27,120 Speaker 2: a process by which we took data from disparate sources 551 00:29:27,520 --> 00:29:29,680 Speaker 2: stitched it all together. In fact, we were doing what 552 00:29:30,000 --> 00:29:32,320 Speaker 2: pollunteer does now for the US government. We had kind 553 00:29:32,360 --> 00:29:35,560 Speaker 2: of pioneered that before Pollunteer came onto the scene, and 554 00:29:35,640 --> 00:29:37,760 Speaker 2: as we were starting to think about, hey, where is 555 00:29:37,800 --> 00:29:39,760 Speaker 2: there an application for what we were doing in the 556 00:29:39,800 --> 00:29:43,720 Speaker 2: counter terrorism field to potentially in private industry. It was 557 00:29:43,760 --> 00:29:45,640 Speaker 2: at a time where I opened up I think it 558 00:29:45,680 --> 00:29:48,520 Speaker 2: was the Economist or potentially in Bloomberg and saw something 559 00:29:48,560 --> 00:29:52,960 Speaker 2: about the increase in truck fraud and theft, and there's 560 00:29:53,120 --> 00:29:56,360 Speaker 2: thirty billion dollars of cargo theft going on in the world, 561 00:29:56,360 --> 00:30:00,360 Speaker 2: but specifically to trucking, there was this upswing of that 562 00:30:00,440 --> 00:30:03,320 Speaker 2: was leading the theft known as double brokering, and I 563 00:30:03,440 --> 00:30:05,280 Speaker 2: just went down a rabbit hole there and it was 564 00:30:05,320 --> 00:30:07,400 Speaker 2: just so crystal clear to me that the methods that 565 00:30:07,440 --> 00:30:10,800 Speaker 2: we had used in the counter terrorism space could now 566 00:30:10,840 --> 00:30:14,400 Speaker 2: be applied and kind of this all source intelligence gathering 567 00:30:14,440 --> 00:30:18,000 Speaker 2: and stitching to ensure that what people were telling us 568 00:30:18,040 --> 00:30:20,920 Speaker 2: could actually be validated, and that led us down this road, 569 00:30:20,960 --> 00:30:22,560 Speaker 2: and we kind of liked to joke at Genlogs we 570 00:30:22,640 --> 00:30:25,120 Speaker 2: used to track terrorists and now we track trucks. And 571 00:30:25,160 --> 00:30:27,640 Speaker 2: it's essentially just taken the methodology that we did for 572 00:30:27,680 --> 00:30:30,200 Speaker 2: two decades at the Global War and tear and applying 573 00:30:30,200 --> 00:30:31,360 Speaker 2: it domestically on trucking. 574 00:30:32,040 --> 00:30:34,960 Speaker 1: Got youa So you've worked both in the private and 575 00:30:35,000 --> 00:30:39,200 Speaker 1: public sector during your career, obviously you know very different 576 00:30:39,280 --> 00:30:42,360 Speaker 1: kinds of organizations. Can you talk a little bit about 577 00:30:42,400 --> 00:30:45,720 Speaker 1: what you enjoy most about your current role at Genlogs. 578 00:30:46,360 --> 00:30:48,920 Speaker 2: Yeah, I think there's a lot to love. First of all, 579 00:30:48,920 --> 00:30:51,400 Speaker 2: it's the people that have joined Genlogs from of the 580 00:30:51,440 --> 00:30:55,800 Speaker 2: most brilliant data scientists, engineers, operation folks that are working 581 00:30:55,840 --> 00:30:58,560 Speaker 2: on a frankly, a really tough problem. I think a 582 00:30:58,560 --> 00:31:00,680 Speaker 2: lot of investors thought we were kind of crazy. Early 583 00:31:00,720 --> 00:31:03,920 Speaker 2: on we talked about getting this nationwide network of hardware 584 00:31:03,960 --> 00:31:07,360 Speaker 2: deployed in order to collect on trucks and yet censored 585 00:31:07,360 --> 00:31:10,360 Speaker 2: by censor by sensor. We've deployed that, so one is 586 00:31:10,440 --> 00:31:12,560 Speaker 2: just a privilege to be able to work with incredibly 587 00:31:12,600 --> 00:31:15,240 Speaker 2: talented and bright people, but number two, to see that 588 00:31:15,400 --> 00:31:19,200 Speaker 2: data that we collect be transformed into insights that is 589 00:31:19,760 --> 00:31:23,520 Speaker 2: truly driving customers business forward. I think that is where 590 00:31:23,880 --> 00:31:25,920 Speaker 2: I just love that each and every day is when 591 00:31:25,920 --> 00:31:29,600 Speaker 2: we customers on their own accord write to us by 592 00:31:29,640 --> 00:31:31,360 Speaker 2: email say I just want to let you know I 593 00:31:31,480 --> 00:31:34,440 Speaker 2: love your platform, and it helped us do X, Y 594 00:31:34,600 --> 00:31:38,920 Speaker 2: or Z that resulted in this recovery of this stolen trailer, 595 00:31:39,320 --> 00:31:42,880 Speaker 2: this new customer opportunity that was a six figure opportunity 596 00:31:42,920 --> 00:31:45,800 Speaker 2: that was only sourced directly to you or hey. Our 597 00:31:45,840 --> 00:31:49,120 Speaker 2: ability to now see trucks on the roads with the 598 00:31:49,160 --> 00:31:52,560 Speaker 2: most niche equipment to carry heavy haul, or you know, 599 00:31:52,680 --> 00:31:55,800 Speaker 2: satellites for SpaceX or whatever it might be that they 600 00:31:55,840 --> 00:31:57,720 Speaker 2: need to get from point A to point B that 601 00:31:57,840 --> 00:32:00,360 Speaker 2: before they were kind of stuck, but now they have 602 00:32:00,840 --> 00:32:04,280 Speaker 2: actual data to make those decisions. Like I just love 603 00:32:04,960 --> 00:32:06,640 Speaker 2: hearing that from our customers. 604 00:32:06,840 --> 00:32:09,440 Speaker 1: O got ya, And I'm just going curious, you know, 605 00:32:09,440 --> 00:32:11,640 Speaker 1: because you know we were talking about the use cases, 606 00:32:11,840 --> 00:32:15,880 Speaker 1: So is the brokerage community currently your largest market. 607 00:32:16,480 --> 00:32:18,560 Speaker 2: That's been where we've focused and that's where we've seen 608 00:32:18,560 --> 00:32:22,880 Speaker 2: the most traction we've We've we just have ramped up 609 00:32:22,920 --> 00:32:26,040 Speaker 2: completely on the brokerage community. We now have more than 610 00:32:26,080 --> 00:32:29,440 Speaker 2: fifty percent of the top twenty brokerages are actively on 611 00:32:29,480 --> 00:32:32,480 Speaker 2: our platform right now. We continue to expand out through 612 00:32:32,480 --> 00:32:35,160 Speaker 2: the rest of the top one hundred and beyond. The 613 00:32:35,640 --> 00:32:39,440 Speaker 2: traction's been quick there. We've just started to look at 614 00:32:39,480 --> 00:32:43,240 Speaker 2: what happens beyond there and breaking into the commercial auto 615 00:32:43,320 --> 00:32:46,560 Speaker 2: insurance space. Now we have two of the larger commercial 616 00:32:46,600 --> 00:32:50,920 Speaker 2: auto insurance companies that are our customers as well, that 617 00:32:50,960 --> 00:32:53,120 Speaker 2: are using the data that we're collecting in the roads 618 00:32:53,640 --> 00:32:57,960 Speaker 2: via APIs to automate their underwriting process. Given that we 619 00:32:58,160 --> 00:33:00,800 Speaker 2: are able to deliver into their hands about one thousand 620 00:33:00,880 --> 00:33:04,440 Speaker 2: times more data points than they historically had on trucks 621 00:33:04,480 --> 00:33:08,840 Speaker 2: gathered through US Department of Transportation inspection data. So for them, 622 00:33:08,880 --> 00:33:12,640 Speaker 2: it's a completely huge shift in terms of orders of 623 00:33:12,720 --> 00:33:16,560 Speaker 2: magnitude of more data that they can make better underwriting decisions. 624 00:33:17,160 --> 00:33:18,800 Speaker 1: Right and there are a lot of trucking companies like 625 00:33:18,840 --> 00:33:22,800 Speaker 1: you know, jbon Knight Warner that had their own brokerage units. 626 00:33:22,880 --> 00:33:27,160 Speaker 1: So if one of those large players are using the platform, 627 00:33:27,320 --> 00:33:29,960 Speaker 1: are they using across their businesses or is it kind 628 00:33:30,000 --> 00:33:32,600 Speaker 1: of just rank fenced around the brokerage business. 629 00:33:33,920 --> 00:33:36,800 Speaker 2: We often will go in start through the brokerage business, 630 00:33:36,840 --> 00:33:39,600 Speaker 2: and once the brokerage business gets to see what we're 631 00:33:39,600 --> 00:33:42,120 Speaker 2: able to do, then they'll bring in their their asset 632 00:33:42,160 --> 00:33:45,360 Speaker 2: tracking folks, they'll bring in their customer sales folks. And 633 00:33:45,600 --> 00:33:48,200 Speaker 2: within some of those organizations you mentioned, we are in 634 00:33:48,240 --> 00:33:50,720 Speaker 2: with them. Some of them have been acknowledged, like Werner 635 00:33:51,280 --> 00:33:54,400 Speaker 2: and others that are using us across the spectrum of 636 00:33:54,440 --> 00:33:56,840 Speaker 2: their operations now great, and this. 637 00:33:56,760 --> 00:33:58,760 Speaker 1: Is a question I always like to kind of end 638 00:33:58,800 --> 00:34:02,160 Speaker 1: the conversation with. I'm always interested to hear what people 639 00:34:02,240 --> 00:34:05,320 Speaker 1: are reading, and I know our listeners like to seek 640 00:34:05,360 --> 00:34:07,640 Speaker 1: out books that they might have not heard before. You 641 00:34:07,800 --> 00:34:11,680 Speaker 1: Do you have any favorite books on either transportation, business 642 00:34:11,800 --> 00:34:13,640 Speaker 1: or leadership that's kind of close to your heart? 643 00:34:14,600 --> 00:34:18,040 Speaker 2: You know one on transportation that I read recently that 644 00:34:18,120 --> 00:34:20,960 Speaker 2: was kind of maybe outside your normal transportation topics was 645 00:34:20,960 --> 00:34:24,440 Speaker 2: a book called Long Haul, written by an Associate Deputy 646 00:34:24,480 --> 00:34:28,359 Speaker 2: Director of the FBI, Frank Fabuzi I believe his name is, 647 00:34:28,760 --> 00:34:30,760 Speaker 2: and he talks about the fact that there are over 648 00:34:30,880 --> 00:34:35,560 Speaker 2: eight hundred serial murders that they believe are linked to 649 00:34:35,880 --> 00:34:38,040 Speaker 2: long haul trucking over the last two decades, and they 650 00:34:38,040 --> 00:34:42,880 Speaker 2: believe there are some active, unfortunately cases ongoing that the 651 00:34:43,000 --> 00:34:45,600 Speaker 2: FBI just never had a response. They formed a task 652 00:34:45,640 --> 00:34:47,320 Speaker 2: force to try to track it down, but they lacked 653 00:34:47,320 --> 00:34:50,640 Speaker 2: the data to actually act on this. And that's part 654 00:34:50,640 --> 00:34:53,040 Speaker 2: of what Genlog's mission is is to keep the bad 655 00:34:53,080 --> 00:34:55,880 Speaker 2: actors out so that the good actors can flourish, because 656 00:34:56,320 --> 00:34:58,800 Speaker 2: ninety nine percent of the truck drivers on the roads 657 00:34:59,160 --> 00:35:02,120 Speaker 2: and nine percent of brokerages out there are just trying 658 00:35:02,160 --> 00:35:04,400 Speaker 2: to do great work and a few bad actors are 659 00:35:04,440 --> 00:35:06,960 Speaker 2: spoiling it and making it more difficult and more costly 660 00:35:07,000 --> 00:35:09,319 Speaker 2: for them. And that was an eye opening book to 661 00:35:09,400 --> 00:35:12,200 Speaker 2: just read. Frankly, it brought us through what the lens 662 00:35:12,320 --> 00:35:14,600 Speaker 2: of someone that does long haul trucking is. So the 663 00:35:14,680 --> 00:35:17,760 Speaker 2: author hops in with a long haul trucker and follows 664 00:35:17,800 --> 00:35:20,920 Speaker 2: him around and then talks through the law enforcement eyes 665 00:35:20,960 --> 00:35:23,680 Speaker 2: of what they're working on as well. So that was 666 00:35:23,719 --> 00:35:26,359 Speaker 2: an interesting read and I give that out to all 667 00:35:26,480 --> 00:35:30,319 Speaker 2: new employees that join gen logs. Yeah, so I'll kind 668 00:35:30,320 --> 00:35:31,080 Speaker 2: of leave it there. 669 00:35:31,200 --> 00:35:33,239 Speaker 1: All right, that's great. I'ven't heard about that one, so 670 00:35:33,280 --> 00:35:35,759 Speaker 1: I'll definitely check that out and you know, I know 671 00:35:35,960 --> 00:35:39,120 Speaker 1: as an entrepreneur and you know, a co founder early 672 00:35:39,160 --> 00:35:42,520 Speaker 1: on in the company, the job is more or less 673 00:35:42,520 --> 00:35:45,520 Speaker 1: a twenty four hour, seven day a week kind of job. 674 00:35:45,560 --> 00:35:49,080 Speaker 1: But you know, we do have some some rye in time, 675 00:35:49,160 --> 00:35:50,920 Speaker 1: we'll call it. What do you like to do? 676 00:35:51,719 --> 00:35:53,960 Speaker 2: You know, I got a farm out in the Shenandoah 677 00:35:54,000 --> 00:35:56,400 Speaker 2: River in Virginia. That's my pride and joye. So I 678 00:35:56,560 --> 00:35:58,920 Speaker 2: like to just spend time out there away from a 679 00:35:58,960 --> 00:36:04,080 Speaker 2: computer chopping, would enjoy nature. And so it's very few 680 00:36:04,120 --> 00:36:06,920 Speaker 2: and far between that I do get that that these days, 681 00:36:07,239 --> 00:36:08,800 Speaker 2: but that's where I love to spend my time. 682 00:36:09,560 --> 00:36:11,399 Speaker 1: So you say a farm, is it a working farm? 683 00:36:11,640 --> 00:36:13,960 Speaker 2: It's not a working farm per se. It used to be. 684 00:36:14,520 --> 00:36:18,280 Speaker 2: Right now it's farm for hay. But it's absolutely peaceful 685 00:36:18,360 --> 00:36:20,040 Speaker 2: and we'll see once again if we could turn it 686 00:36:20,040 --> 00:36:21,560 Speaker 2: into a little bit more of a working farm in 687 00:36:21,560 --> 00:36:23,840 Speaker 2: the future. I like to like to say that beyond 688 00:36:23,880 --> 00:36:27,080 Speaker 2: genlogs and beyond doing counter terrorism, I would love to 689 00:36:27,120 --> 00:36:29,000 Speaker 2: just be a peaceful farmer someday. 690 00:36:29,280 --> 00:36:30,440 Speaker 1: Just like George Washington. 691 00:36:30,520 --> 00:36:33,080 Speaker 2: You got it all right, Well. 692 00:36:32,840 --> 00:36:36,000 Speaker 1: Thanks so much for your time. I thought the conversation 693 00:36:36,200 --> 00:36:38,520 Speaker 1: was great and it was really interesting to learn more 694 00:36:38,520 --> 00:36:40,160 Speaker 1: about genlog, So thanks for that. Ryan. 695 00:36:40,320 --> 00:36:42,399 Speaker 2: Yeah, thanks Lee, thanks for having me all. 696 00:36:42,360 --> 00:36:43,920 Speaker 1: Right, and I want to thank you for tuning in. 697 00:36:43,960 --> 00:36:46,680 Speaker 1: If you liked the episode, please subscribe and laver review. 698 00:36:47,080 --> 00:36:49,480 Speaker 1: We've lined up a number of great guests for the podcast, 699 00:36:49,480 --> 00:36:54,560 Speaker 1: so please check back to your conversations with C suite executives, shippers, regulators, 700 00:36:54,760 --> 00:36:58,000 Speaker 1: and decision makers within the freight markets. Also, if you 701 00:36:58,000 --> 00:37:00,359 Speaker 1: want to learn more about the freight transportation market, check 702 00:37:00,400 --> 00:37:03,560 Speaker 1: out our work on the Bloomberg Terminal at big or 703 00:37:03,680 --> 00:37:06,320 Speaker 1: on social media. Take care of let's keep those supply 704 00:37:06,400 --> 00:37:07,440 Speaker 1: chains moving. Thanks