1 00:00:07,440 --> 00:00:10,160 Speaker 1: Hi everyone, this is Lee Clascow when We're Talking Transports. 2 00:00:10,200 --> 00:00:13,520 Speaker 1: Welcome to Bloomberg Intelligence Talking Transports podcast. I'm your host, 3 00:00:13,640 --> 00:00:18,200 Speaker 1: Lee Clasgow, Senior Freight, transportation and logistics analysts at Bloomberg Intelligence, 4 00:00:18,520 --> 00:00:21,239 Speaker 1: Bloomberg's in house research arm of almost five hundred analysts 5 00:00:21,280 --> 00:00:24,280 Speaker 1: and strategists around the globe. A quick public service announcement 6 00:00:24,320 --> 00:00:27,280 Speaker 1: before we dive in. Your support is instrumental to keep 7 00:00:27,280 --> 00:00:30,440 Speaker 1: bringing great guests and conversations to you, our listeners, and 8 00:00:30,480 --> 00:00:32,919 Speaker 1: we need your support. So please, if you enjoy this podcast, 9 00:00:33,000 --> 00:00:35,440 Speaker 1: share it, like it, leave a comment. Also, if you've 10 00:00:35,440 --> 00:00:38,720 Speaker 1: got ideas, feedback, or just want to talk transports, I'm 11 00:00:38,760 --> 00:00:40,440 Speaker 1: always happy to connect. You can find me on the 12 00:00:40,440 --> 00:00:43,680 Speaker 1: Blueberg terminal, on LinkedIn, or on Twitter at logistics Late. 13 00:00:44,120 --> 00:00:46,720 Speaker 1: I'm very excited to have with us today. Harish Abbott, 14 00:00:46,720 --> 00:00:49,960 Speaker 1: the co founder and CEO of Augment and AI productivity 15 00:00:49,960 --> 00:00:53,960 Speaker 1: platform for logistics. Augment has raised about one hundred and 16 00:00:53,960 --> 00:00:58,000 Speaker 1: ten million from Red Point Ventures, eight VC and leading 17 00:00:58,040 --> 00:01:02,360 Speaker 1: logistics funds. Prior to launching Augment, Harish co founded Deliver, 18 00:01:02,640 --> 00:01:06,440 Speaker 1: an e commerce fulfillment platform acquired by Shopify for two 19 00:01:06,520 --> 00:01:10,160 Speaker 1: point one billion dollars in twenty twenty two. His career 20 00:01:10,160 --> 00:01:13,360 Speaker 1: includes pivot roles at Amazon, where he contributed to building 21 00:01:13,400 --> 00:01:21,080 Speaker 1: global fulfillment infrastructure. He holds degrees from Indian Institute of Technology, Rookie, 22 00:01:21,440 --> 00:01:25,720 Speaker 1: the University of Illinois, and an MBA from Stanford. Welcome 23 00:01:25,760 --> 00:01:27,800 Speaker 1: to talking Transports, Harish. 24 00:01:27,760 --> 00:01:30,319 Speaker 2: Thank you for having me. I'm excited to speak with 25 00:01:30,360 --> 00:01:30,679 Speaker 2: you here. 26 00:01:31,160 --> 00:01:33,800 Speaker 1: Yeah, it's great to have you. Can you talk a 27 00:01:33,840 --> 00:01:35,280 Speaker 1: little bit about what Augment does. 28 00:01:35,720 --> 00:01:41,240 Speaker 2: Sure, So, Augment is, you know, an AI productivity platform 29 00:01:41,280 --> 00:01:45,759 Speaker 2: for the logistics businesses. Our first product is Augie Augie. 30 00:01:45,880 --> 00:01:50,840 Speaker 2: Think of Augie as a teammate that can work twenty 31 00:01:50,880 --> 00:01:56,480 Speaker 2: four to seven across all modes in a business emails 32 00:01:56,520 --> 00:01:59,880 Speaker 2: and phone calls and text and messaging and your systems 33 00:02:00,120 --> 00:02:04,080 Speaker 2: record like TMS or w m S and it goes 34 00:02:04,120 --> 00:02:07,200 Speaker 2: and does work that is given to Augie as s 35 00:02:07,240 --> 00:02:12,960 Speaker 2: OPS or standard operating procedures is getting deployed to lots 36 00:02:12,960 --> 00:02:19,480 Speaker 2: of freight businesses today, mostly large freight brokers, UH freight 37 00:02:19,600 --> 00:02:25,440 Speaker 2: fleets and now shippers. Yeah, that's what that's what we 38 00:02:25,520 --> 00:02:27,480 Speaker 2: do and and reach. 39 00:02:27,880 --> 00:02:30,799 Speaker 1: Can you talk about how you know a broker might 40 00:02:30,919 --> 00:02:33,360 Speaker 1: use versus versus a shipper? 41 00:02:34,440 --> 00:02:38,200 Speaker 2: Sure so well, Like if you think about what does 42 00:02:38,240 --> 00:02:41,320 Speaker 2: a broker and do is you know their their job 43 00:02:41,400 --> 00:02:44,760 Speaker 2: is to match demand to supply and then execute against it. 44 00:02:44,800 --> 00:02:47,720 Speaker 2: They get demands from shippers of the world and then 45 00:02:47,760 --> 00:02:53,440 Speaker 2: the supplies are from several fleets, large fleets and small fleets. 46 00:02:53,840 --> 00:02:57,560 Speaker 2: They try to match them and then execute a shipment 47 00:02:57,680 --> 00:02:59,920 Speaker 2: end to end. Now, in the execution of the shipment, 48 00:03:00,080 --> 00:03:05,160 Speaker 2: there are multiple multiple steps today extremely manual. Right, there's 49 00:03:05,200 --> 00:03:07,800 Speaker 2: steps like, hey, I get a shipment in an email, 50 00:03:07,919 --> 00:03:10,760 Speaker 2: I need to now put that load up in a 51 00:03:10,800 --> 00:03:15,240 Speaker 2: TMS manually maybe or correct some feels from a load 52 00:03:15,280 --> 00:03:21,280 Speaker 2: that came through an EDI to Now I've got a shipment, 53 00:03:21,440 --> 00:03:24,440 Speaker 2: I need to now go source capacity for it. I've 54 00:03:24,440 --> 00:03:28,760 Speaker 2: got preferred carriers, I've got data on who's run these 55 00:03:28,840 --> 00:03:32,840 Speaker 2: lanes before. Or sometimes I get a load where I'd 56 00:03:33,040 --> 00:03:35,200 Speaker 2: never run this lane before, so I need to post 57 00:03:35,280 --> 00:03:40,640 Speaker 2: it on a public loadboard like DAT or truck stop. 58 00:03:42,240 --> 00:03:44,520 Speaker 2: Then there is this work of like hey, I've posted 59 00:03:44,560 --> 00:03:47,920 Speaker 2: the load, I need to now negotiate the load, go 60 00:03:47,960 --> 00:03:49,680 Speaker 2: back and forth to figure out how am I going 61 00:03:49,760 --> 00:03:52,640 Speaker 2: to make my margin on it. Once you figured out 62 00:03:52,680 --> 00:03:55,040 Speaker 2: like hey, this person is going to do this load 63 00:03:55,040 --> 00:03:58,040 Speaker 2: to this company. Then there's a whole execution step off. 64 00:03:58,600 --> 00:04:00,640 Speaker 2: I'm going to dispatch the load. I want to make 65 00:04:00,680 --> 00:04:03,920 Speaker 2: sure the driver understands how to get in. I have 66 00:04:04,040 --> 00:04:06,960 Speaker 2: to set up appointments for a load right on the 67 00:04:07,040 --> 00:04:10,120 Speaker 2: pickup side, on the drop off side. Then I have 68 00:04:10,200 --> 00:04:12,920 Speaker 2: to track the load. I have to keep updating the 69 00:04:12,920 --> 00:04:16,120 Speaker 2: customer on where the load is at, if there's delays, 70 00:04:16,720 --> 00:04:19,240 Speaker 2: what to do in terms of those delays. And then 71 00:04:19,279 --> 00:04:22,080 Speaker 2: when the load gets delivered, then there is the whole 72 00:04:22,080 --> 00:04:26,479 Speaker 2: paperwork cycle begins of collecting the delivery documents, collecting the 73 00:04:26,520 --> 00:04:32,440 Speaker 2: invoicing documents, and making sure if there's excessorials, detention lumpers, 74 00:04:33,400 --> 00:04:36,520 Speaker 2: those are collected so that the billing can happen fast. 75 00:04:37,000 --> 00:04:41,000 Speaker 2: So if you look at this life cycle for a broker, 76 00:04:42,040 --> 00:04:45,000 Speaker 2: you can now deploy Augie in any or all of 77 00:04:45,040 --> 00:04:51,440 Speaker 2: these steps, almost like a worker and saying, hey, Augie, 78 00:04:51,440 --> 00:04:54,640 Speaker 2: now you can be looking for all my emails and 79 00:04:54,760 --> 00:04:58,280 Speaker 2: look for tenders, and if you get a tender, maybe 80 00:04:58,360 --> 00:05:00,640 Speaker 2: start building the load in the TM And if you're 81 00:05:00,680 --> 00:05:03,600 Speaker 2: missing information, maybe you can come in and ask somebody 82 00:05:04,080 --> 00:05:07,320 Speaker 2: and saying, hey, I'm missing this information. What does this 83 00:05:07,720 --> 00:05:10,159 Speaker 2: shipper really mean, or go back and ask the shipper 84 00:05:10,600 --> 00:05:13,599 Speaker 2: so you can completely build the load. Or once the 85 00:05:13,600 --> 00:05:16,520 Speaker 2: load is built, like hey, just start working on capacity sourcing. 86 00:05:16,640 --> 00:05:22,039 Speaker 2: So collaboratively, work with the rep, understand their preferred carriers, 87 00:05:22,080 --> 00:05:24,360 Speaker 2: reach out to them, maybe post a load on a 88 00:05:24,480 --> 00:05:28,200 Speaker 2: dat get the incoming calls, take those calls, negotiate the load. 89 00:05:28,920 --> 00:05:32,599 Speaker 2: Some businesses say like pass the final negotiation to the rep. 90 00:05:32,680 --> 00:05:35,320 Speaker 2: Some people say, like, you can book the load, and 91 00:05:35,360 --> 00:05:37,760 Speaker 2: then like, let's start working on tracking the load or 92 00:05:37,800 --> 00:05:40,760 Speaker 2: tracing the load end to end, so making the dispatch calls, 93 00:05:41,600 --> 00:05:46,119 Speaker 2: understanding you know, the eled events, making the scheduling appointments, 94 00:05:47,120 --> 00:05:49,000 Speaker 2: and then all the way towards the end is like, hey, 95 00:05:49,000 --> 00:05:50,880 Speaker 2: the load's got delivered. Now we need to go and 96 00:05:50,960 --> 00:05:54,800 Speaker 2: chase documents. And you and I know nobody likes chasing documents. 97 00:05:55,279 --> 00:05:57,360 Speaker 2: Like we put Augie to work and it can chase 98 00:05:57,400 --> 00:06:01,440 Speaker 2: documents or our ds source or days sales outstanding can shrink, 99 00:06:01,480 --> 00:06:03,960 Speaker 2: and we can start money can start moving in this 100 00:06:04,080 --> 00:06:07,360 Speaker 2: business again and faster and quicker. So those are like 101 00:06:07,400 --> 00:06:10,560 Speaker 2: some of the use cases that people are using Augie 102 00:06:10,600 --> 00:06:14,080 Speaker 2: for today. As a broker. People are also using Augie 103 00:06:14,080 --> 00:06:18,279 Speaker 2: for like coding, you know, like they get lots of 104 00:06:18,839 --> 00:06:21,640 Speaker 2: requests for course, like hey, could you respond to my code? 105 00:06:22,040 --> 00:06:25,119 Speaker 1: Okay, so just a quick commercial. You know you mentioned 106 00:06:25,200 --> 00:06:27,640 Speaker 1: DAT and truck stop on the Bloomberg terminal. O. The 107 00:06:27,800 --> 00:06:30,400 Speaker 1: data is available and it can be found at BI 108 00:06:30,560 --> 00:06:33,320 Speaker 1: spaceed trc k Go on the Bloomberg terminal. So if 109 00:06:33,360 --> 00:06:36,080 Speaker 1: you're interested in that data, we have a spot contractual 110 00:06:36,120 --> 00:06:40,360 Speaker 1: struggle with market data from those two data providers you. 111 00:06:40,320 --> 00:06:41,000 Speaker 2: Know you mentioned. 112 00:06:41,040 --> 00:06:43,120 Speaker 1: So there's like three main customers that you guys are 113 00:06:43,120 --> 00:06:46,719 Speaker 1: going after, the shipper, the carrier and the broker. Right now, 114 00:06:46,800 --> 00:06:49,240 Speaker 1: kind of what is the mix and kind of because 115 00:06:49,279 --> 00:06:51,720 Speaker 1: I know you're in growth mode, what do you think 116 00:06:51,720 --> 00:06:54,880 Speaker 1: the optimal mix would be three years from now? 117 00:06:55,839 --> 00:06:59,600 Speaker 2: Yeah, I mean today, I think I would say sixty 118 00:06:59,640 --> 00:07:03,560 Speaker 2: percent of our businesses are brokers, right and they tend 119 00:07:03,640 --> 00:07:11,280 Speaker 2: to be the largest brokers about thirty five percent our fleets. 120 00:07:12,640 --> 00:07:15,080 Speaker 2: And then we're just starting to work with shippers, so 121 00:07:15,080 --> 00:07:19,360 Speaker 2: we're not as deep with shippers yet. I think as 122 00:07:19,400 --> 00:07:24,640 Speaker 2: we go forward, you know, our vision is that it 123 00:07:24,760 --> 00:07:28,440 Speaker 2: is not about I think step one Lee is about 124 00:07:28,480 --> 00:07:32,440 Speaker 2: taking this tedious, repetitive work off humans plates so they 125 00:07:32,440 --> 00:07:35,800 Speaker 2: can focus on more important things, right, that's what we're doing. 126 00:07:36,360 --> 00:07:41,680 Speaker 2: But if you imagine, you know, Augie's on a broker 127 00:07:41,760 --> 00:07:44,800 Speaker 2: side and a fleet side, and the two Augies can 128 00:07:44,840 --> 00:07:49,080 Speaker 2: now synchronously collaborate, then we can eliminate the need for 129 00:07:49,200 --> 00:07:51,960 Speaker 2: the phone calls and the text and the emails. Right. 130 00:07:52,480 --> 00:07:56,760 Speaker 2: So our mission is to make logistics better, and we 131 00:07:56,800 --> 00:08:00,520 Speaker 2: think the way to make that better is to get 132 00:08:00,560 --> 00:08:07,160 Speaker 2: to more real time synchronous collaboration between shippers and brokers 133 00:08:07,240 --> 00:08:12,320 Speaker 2: and fleets so that people can act faster. Right. So, 134 00:08:12,480 --> 00:08:14,720 Speaker 2: if you know your question was like three years from now, 135 00:08:14,800 --> 00:08:16,960 Speaker 2: I would sing three years from now, most of our 136 00:08:17,000 --> 00:08:20,880 Speaker 2: businesses like were equal mix of you know, shippers and 137 00:08:21,040 --> 00:08:25,920 Speaker 2: brokers and fleets, and we are really moving towards getting 138 00:08:25,960 --> 00:08:28,840 Speaker 2: all of them the signals they need so they can 139 00:08:28,880 --> 00:08:31,960 Speaker 2: act in their business faster. Right. Like as example would be, 140 00:08:33,320 --> 00:08:38,280 Speaker 2: well today, if a truck's running late, what happens is 141 00:08:39,720 --> 00:08:44,600 Speaker 2: and it's a broker load, the maybe the offshore team 142 00:08:44,840 --> 00:08:47,360 Speaker 2: or the overnight team at the broker has to like 143 00:08:47,559 --> 00:08:49,760 Speaker 2: keep an eye on it. If they get an alert 144 00:08:49,760 --> 00:08:52,760 Speaker 2: the truck's running late, then they might decide, oh, I 145 00:08:52,800 --> 00:08:56,320 Speaker 2: need to reset the appointment. The resetting of the appointment 146 00:08:56,440 --> 00:08:58,920 Speaker 2: means sometimes you can log into a portal. Sometimes it 147 00:08:58,960 --> 00:09:03,240 Speaker 2: means you need to email. And let's say, if it's 148 00:09:03,280 --> 00:09:06,640 Speaker 2: an email scenario, you email somebody, and if the receiving 149 00:09:06,679 --> 00:09:11,400 Speaker 2: warehouse is not working at that time, well till nine 150 00:09:11,440 --> 00:09:14,280 Speaker 2: am next morning, you're not going to get a new appointment. 151 00:09:14,880 --> 00:09:18,960 Speaker 2: So now the labor that the warehouse had planned is 152 00:09:19,000 --> 00:09:21,320 Speaker 2: going to get wasted because the truck's not showing up 153 00:09:21,320 --> 00:09:25,400 Speaker 2: on time. The truck when it shows up late, doesn't 154 00:09:25,440 --> 00:09:28,640 Speaker 2: have an appointment, so it might need to wait for hours, 155 00:09:28,679 --> 00:09:33,040 Speaker 2: sometimes days to be unloaded. And in the meantime, the 156 00:09:33,080 --> 00:09:40,200 Speaker 2: shipper's inventory is unsellable. Right, So everybody's hurting. The shippers hurting, 157 00:09:40,760 --> 00:09:43,640 Speaker 2: the warehouse is hurting, the truck guy is hurting, and 158 00:09:43,679 --> 00:09:46,720 Speaker 2: the broker's performance is hurting too because the shipper holds 159 00:09:46,760 --> 00:09:51,440 Speaker 2: them accountable. But now, if you imagine a scenario where 160 00:09:51,640 --> 00:09:55,920 Speaker 2: an augie on the broker side detects that truck's running late, 161 00:09:57,160 --> 00:10:01,240 Speaker 2: calls or synchronously communicates with an all on the warehouse, 162 00:10:01,280 --> 00:10:03,440 Speaker 2: I said, let's do find me a new appointment. That 163 00:10:03,520 --> 00:10:07,000 Speaker 2: appointment is now booked. The new the labor that was 164 00:10:07,040 --> 00:10:10,959 Speaker 2: allocated for the first appointment is reallocated to somebthing else 165 00:10:11,080 --> 00:10:13,880 Speaker 2: more productive, and now when the truck shows up, it 166 00:10:13,920 --> 00:10:17,520 Speaker 2: gets unloaded and the inventory gets available. Right, So that's 167 00:10:17,559 --> 00:10:20,240 Speaker 2: sort of the future. We think like AI has the 168 00:10:20,280 --> 00:10:23,760 Speaker 2: potential to enable, and we are starting to build for that, 169 00:10:24,120 --> 00:10:26,240 Speaker 2: and and that's a bad Like that's a better future 170 00:10:26,280 --> 00:10:31,280 Speaker 2: because now you're you're making supply chains more efficient, that 171 00:10:31,480 --> 00:10:34,120 Speaker 2: you're you're taking waste out of supply chains, right. 172 00:10:34,440 --> 00:10:37,680 Speaker 1: Sure, And so you know, AI obviously it's a very 173 00:10:37,880 --> 00:10:42,800 Speaker 1: sexy industry, especially if you're doing it for transportation. It's 174 00:10:42,880 --> 00:10:45,480 Speaker 1: becoming a credit space because there's a lot of competitors 175 00:10:45,520 --> 00:10:49,600 Speaker 1: out there. Are you differentiate what what augment is doing 176 00:10:49,679 --> 00:10:52,040 Speaker 1: versus maybe some of the other competitors out. 177 00:10:51,920 --> 00:10:56,080 Speaker 2: There, Yeah, it is. It is a crowded space, partly 178 00:10:56,120 --> 00:10:58,760 Speaker 2: because it's so easy to build a prototype. You know, 179 00:10:58,800 --> 00:11:01,120 Speaker 2: you can vibe code of protein type, and if the 180 00:11:01,200 --> 00:11:04,520 Speaker 2: buyers are uninformed for them, it's very hard to tell 181 00:11:04,520 --> 00:11:07,240 Speaker 2: the difference. You know, the difference really comes in when 182 00:11:07,280 --> 00:11:10,480 Speaker 2: you actually take a product product to production at scale 183 00:11:10,520 --> 00:11:14,079 Speaker 2: and test to handle all these extenuating circumstances. And then 184 00:11:14,120 --> 00:11:18,479 Speaker 2: I think prototype starts to break the way we differentiate 185 00:11:18,559 --> 00:11:23,320 Speaker 2: is we have few core concepts or principles. The first 186 00:11:23,400 --> 00:11:27,400 Speaker 2: one is that you know you can really do mean 187 00:11:27,640 --> 00:11:30,160 Speaker 2: You can only do meaningful work when you have end 188 00:11:30,200 --> 00:11:34,480 Speaker 2: to end context. So point solutions of voice bots or 189 00:11:34,559 --> 00:11:38,240 Speaker 2: email or like a code solution, if they don't have 190 00:11:38,320 --> 00:11:41,160 Speaker 2: a full context of like here's my customer, this was 191 00:11:41,200 --> 00:11:45,440 Speaker 2: the customer's sop. This customer is an enterprise customer. This 192 00:11:45,520 --> 00:11:48,240 Speaker 2: has been my performance so far. If you don't have 193 00:11:48,320 --> 00:11:51,000 Speaker 2: that context when you're building the load, tracking a load, 194 00:11:51,040 --> 00:11:52,880 Speaker 2: when you have to make a decision if the truck's 195 00:11:52,920 --> 00:11:55,160 Speaker 2: running late, or you have to make a decision to 196 00:11:55,160 --> 00:11:57,600 Speaker 2: book a load at a negative mark, you can't do that. 197 00:11:58,360 --> 00:12:01,080 Speaker 2: And to bring true product activity you need to have 198 00:12:01,240 --> 00:12:04,800 Speaker 2: end to end context. So augment is building. I would 199 00:12:04,840 --> 00:12:08,720 Speaker 2: say an order to cash set of use cases for 200 00:12:08,880 --> 00:12:12,440 Speaker 2: all of our businesses, whether it's brokers or shippers or fleets, 201 00:12:13,000 --> 00:12:16,360 Speaker 2: and through those order to cash use cases, we can 202 00:12:16,360 --> 00:12:20,200 Speaker 2: now carry context and do a better job at each 203 00:12:20,280 --> 00:12:23,760 Speaker 2: step of the way. I think two is we believe 204 00:12:23,800 --> 00:12:28,040 Speaker 2: that you know, work is inherently multi modal, right, so 205 00:12:29,160 --> 00:12:32,000 Speaker 2: it's not just about calls or emails or tech, it 206 00:12:32,080 --> 00:12:34,960 Speaker 2: is about all of that like in some businesses, a 207 00:12:34,960 --> 00:12:38,800 Speaker 2: lot of work gets done on teams or Slack, and 208 00:12:39,120 --> 00:12:42,240 Speaker 2: there's a lot of discussion that is happening between people 209 00:12:42,280 --> 00:12:45,720 Speaker 2: on Slack, and it's extremely important for Augie to be 210 00:12:45,800 --> 00:12:48,360 Speaker 2: part of that conversations because there's a lot of context 211 00:12:48,360 --> 00:12:51,360 Speaker 2: in that. Right, somebody might say, hey, I just got 212 00:12:51,400 --> 00:12:54,480 Speaker 2: a call. This shipment has become an urgent shipment. We 213 00:12:54,600 --> 00:12:57,600 Speaker 2: need to now start tracking at fifteen minutes. Okay. If 214 00:12:57,640 --> 00:13:01,240 Speaker 2: Augie is part of that conversation on Slack, it now 215 00:13:01,320 --> 00:13:04,320 Speaker 2: knows that it can start adjusting its work. So the 216 00:13:04,360 --> 00:13:07,280 Speaker 2: shipment updates need to be sent every fifteen minutes, you know. 217 00:13:07,840 --> 00:13:12,600 Speaker 2: And so we believe that you have to meet where 218 00:13:12,640 --> 00:13:15,720 Speaker 2: people are and the work is not just any mode, 219 00:13:15,760 --> 00:13:18,760 Speaker 2: it's all modes all the time. And so we had 220 00:13:18,800 --> 00:13:21,880 Speaker 2: to build a teammate that almost looks and feels no 221 00:13:21,960 --> 00:13:25,040 Speaker 2: different than a remote employee. But it can be added 222 00:13:25,040 --> 00:13:28,960 Speaker 2: to any of these channels, you know. Third is we 223 00:13:29,040 --> 00:13:33,280 Speaker 2: are unlike many horizontal players like who are saying hey, 224 00:13:33,520 --> 00:13:37,079 Speaker 2: we have a AI platform, generic platform and you can 225 00:13:37,080 --> 00:13:42,160 Speaker 2: now implement it. We are purpose built for the logistics space. Right, 226 00:13:42,280 --> 00:13:47,120 Speaker 2: so we're saying we understand the ontology of freight and logistics. 227 00:13:47,440 --> 00:13:51,520 Speaker 2: We understand what a multi stop shipment is. We understand 228 00:13:51,559 --> 00:13:54,960 Speaker 2: what a flatbed truck is. We understand what does appointment 229 00:13:55,000 --> 00:13:58,000 Speaker 2: times mean. We understand sort of all the key players 230 00:13:58,040 --> 00:14:01,679 Speaker 2: that a highway fraud score or a you know, a 231 00:14:01,720 --> 00:14:04,320 Speaker 2: marker on dat or truck stop. What does that mean. 232 00:14:04,720 --> 00:14:08,520 Speaker 2: You don't need to teach Augie that it comes learned 233 00:14:08,600 --> 00:14:11,640 Speaker 2: about this space. So those are the three differences. One 234 00:14:11,800 --> 00:14:16,040 Speaker 2: is very holistic order to catch set of workflows. People 235 00:14:16,080 --> 00:14:18,120 Speaker 2: can pick and choose, of course, but our hope is 236 00:14:18,120 --> 00:14:22,760 Speaker 2: that they end up deploying Augie across a vast majority 237 00:14:22,760 --> 00:14:24,800 Speaker 2: of order to catch workflows and you get to see 238 00:14:24,800 --> 00:14:30,480 Speaker 2: more benefit. Very multimodal and you know, very purpose built 239 00:14:30,480 --> 00:14:31,520 Speaker 2: for logistics. 240 00:14:31,720 --> 00:14:35,600 Speaker 1: So when you say multimodal when you talk about carriers, 241 00:14:35,600 --> 00:14:40,120 Speaker 1: so you will do not only chuckload but lesser chuckload 242 00:14:40,160 --> 00:14:45,280 Speaker 1: and intermodal and other things that brokers are you know broking. 243 00:14:46,000 --> 00:14:51,400 Speaker 2: Yeah, I mean we today do all modes, right, all 244 00:14:51,440 --> 00:14:55,800 Speaker 2: modes across all channels of communication. Like that's what I 245 00:14:55,880 --> 00:15:01,080 Speaker 2: mean by multimodel. So LTL, FDL, flatbread auto, you know, 246 00:15:01,160 --> 00:15:03,400 Speaker 2: drage loads. So we do that and we build the 247 00:15:03,440 --> 00:15:06,600 Speaker 2: ontologies for all of those, But then the channels of 248 00:15:06,640 --> 00:15:11,240 Speaker 2: communication are also quite quite varied, right, Like, if you're 249 00:15:11,280 --> 00:15:15,240 Speaker 2: looking at LTL word one, in the LTL world, there's 250 00:15:15,280 --> 00:15:19,680 Speaker 2: really only twenty five thirty big LTL carriers in this space, 251 00:15:20,240 --> 00:15:22,320 Speaker 2: and the way you communicate to them is a lot 252 00:15:22,360 --> 00:15:26,360 Speaker 2: through their website actually, right, But in the FTL space, 253 00:15:27,000 --> 00:15:31,680 Speaker 2: it's a really fragmented industry, and the way you communicate 254 00:15:31,760 --> 00:15:34,200 Speaker 2: with them is maybe through emails and texts to the 255 00:15:34,280 --> 00:15:38,200 Speaker 2: dispatchers and calls sometimes to the drivers. And so allgie 256 00:15:38,200 --> 00:15:42,480 Speaker 2: needs to have sort of all of those channels of communication, 257 00:15:42,520 --> 00:15:44,800 Speaker 2: but needs to also understand the ontology of all of 258 00:15:44,840 --> 00:15:45,840 Speaker 2: these different modes. 259 00:15:46,680 --> 00:15:49,280 Speaker 1: And you mentioned the truckle and market being very fragmented 260 00:15:49,320 --> 00:15:52,640 Speaker 1: and very large. What does success mean for you guys 261 00:15:52,680 --> 00:15:54,600 Speaker 1: in terms of market share? Like how much of the 262 00:15:54,640 --> 00:15:58,080 Speaker 1: market do you think you guys could actually control of. 263 00:15:58,880 --> 00:16:00,680 Speaker 1: I don't know if you would measure by the AI 264 00:16:00,800 --> 00:16:04,920 Speaker 1: spend or just how many the percentage of the industry 265 00:16:04,920 --> 00:16:06,400 Speaker 1: that you're working with. 266 00:16:07,280 --> 00:16:09,520 Speaker 2: Yeah, great question. So I think, like, first, let me 267 00:16:10,160 --> 00:16:15,080 Speaker 2: the way we measure success is delivering ROI to our customers, right, Like, Hey, 268 00:16:15,920 --> 00:16:19,480 Speaker 2: by deploying Augie, what is the returnal investment? What is 269 00:16:19,520 --> 00:16:22,760 Speaker 2: the meaningful financial gain that you're able to get? Right, 270 00:16:22,840 --> 00:16:25,320 Speaker 2: And we actually measure that with every business we get into, 271 00:16:27,120 --> 00:16:29,560 Speaker 2: and I think if we do that, we hopefully acquire 272 00:16:29,600 --> 00:16:33,120 Speaker 2: more market But the way we see market share is 273 00:16:33,480 --> 00:16:36,440 Speaker 2: freight under management, Like how much freight inder management is 274 00:16:36,680 --> 00:16:42,160 Speaker 2: Augie assisted or Augie powered? You know, in this last year, 275 00:16:42,200 --> 00:16:45,120 Speaker 2: we've gone from almost nothing to now about forty billion 276 00:16:45,120 --> 00:16:47,720 Speaker 2: of freight under management that AUGI is getting deployed at. 277 00:16:48,280 --> 00:16:51,960 Speaker 2: Of course, you know, this market is close to you know, 278 00:16:52,520 --> 00:16:56,560 Speaker 2: eight hundred to nine hundred billion, and then if you 279 00:16:56,600 --> 00:16:59,280 Speaker 2: count the LTL market, you're getting closer to a trillion. 280 00:17:01,240 --> 00:17:03,600 Speaker 2: So we're still very early, right like we're maybe like 281 00:17:03,640 --> 00:17:06,639 Speaker 2: barely four percent of the market. There's so much to 282 00:17:06,680 --> 00:17:09,720 Speaker 2: go and build, Like, I don't know what the upper 283 00:17:09,720 --> 00:17:11,960 Speaker 2: limit is. You know, there are some players who are 284 00:17:12,000 --> 00:17:14,359 Speaker 2: going to try to build this in house. There's some 285 00:17:14,400 --> 00:17:17,560 Speaker 2: players who are going to try to buy it. You know, 286 00:17:17,880 --> 00:17:22,000 Speaker 2: we're just really obsessively focused on saying, hey, if you 287 00:17:22,080 --> 00:17:24,760 Speaker 2: deploy Augie, can we get you seven to ten x ROI? 288 00:17:24,960 --> 00:17:27,320 Speaker 2: And if he can, we think more people will use us, 289 00:17:27,359 --> 00:17:28,960 Speaker 2: you know, So that's. 290 00:17:28,840 --> 00:17:31,680 Speaker 1: That's the goal of seven to ten ROI. 291 00:17:31,280 --> 00:17:34,600 Speaker 2: For our businesses. That's right. Yeah, can you talk. 292 00:17:34,480 --> 00:17:36,800 Speaker 1: About like some of the productivity gains that you have 293 00:17:37,000 --> 00:17:41,400 Speaker 1: generated for a broker, for a carrier, like any anecdotal 294 00:17:41,480 --> 00:17:43,760 Speaker 1: stories that you might have, or or if you have 295 00:17:43,840 --> 00:17:46,720 Speaker 1: some consolidated stats that'd be great too. 296 00:17:47,520 --> 00:17:50,199 Speaker 2: Yeah, let's let's do that. So, I mean I like 297 00:17:50,240 --> 00:17:52,560 Speaker 2: to go specifics, I think because that's where the like 298 00:17:53,200 --> 00:17:56,400 Speaker 2: people get start to hopefully learn from the color hair. Right. So, 299 00:17:57,080 --> 00:18:02,639 Speaker 2: one of our customers is fairly large HELTL focused, you 300 00:18:02,680 --> 00:18:05,359 Speaker 2: know three pl They have about a billion dollar of 301 00:18:05,920 --> 00:18:10,560 Speaker 2: freight ender management. And their first sort of issue that 302 00:18:10,600 --> 00:18:15,320 Speaker 2: they came to us was that that they had a 303 00:18:15,400 --> 00:18:21,440 Speaker 2: forty percent team, mostly offshore, that was reliant on spreadsheets 304 00:18:21,520 --> 00:18:27,440 Speaker 2: and fragmented homegrown systems to do tracking for urgent shipments 305 00:18:27,480 --> 00:18:30,160 Speaker 2: HEALTL shipments. So what they were really doing was they 306 00:18:30,160 --> 00:18:35,080 Speaker 2: were logging into all these different LTL carriers websites maybe 307 00:18:35,160 --> 00:18:39,560 Speaker 2: calling them to get a pro number. And they had 308 00:18:39,560 --> 00:18:42,199 Speaker 2: done a fair bit of tech investments lee before, but 309 00:18:42,600 --> 00:18:46,720 Speaker 2: thirty percent of their shipment sort of still lacked pro numbers. 310 00:18:47,280 --> 00:18:49,520 Speaker 2: And then after if you don't have the pro number, 311 00:18:49,600 --> 00:18:53,160 Speaker 2: you can get the real tracking. And so they deployed 312 00:18:53,200 --> 00:18:56,200 Speaker 2: Augie and said like, hey we want Augie do now 313 00:18:56,720 --> 00:18:59,199 Speaker 2: go get the pro number, Go get the latest update, 314 00:19:00,480 --> 00:19:05,840 Speaker 2: posting those updates into our system of record, the TMS. 315 00:19:06,560 --> 00:19:10,880 Speaker 2: And and now they have sort of freed up twelve 316 00:19:11,080 --> 00:19:15,200 Speaker 2: out of the forty people and repurpose them for more 317 00:19:15,359 --> 00:19:19,720 Speaker 2: growth related activity. Right, So I mean, what is that 318 00:19:19,880 --> 00:19:25,600 Speaker 2: about twenty thirty percent you know, productivity improvement on a 319 00:19:25,680 --> 00:19:31,800 Speaker 2: forty percent team. And and now but that's now. Now 320 00:19:31,920 --> 00:19:34,960 Speaker 2: the team hasn't has gotten confidence that oh I can 321 00:19:35,040 --> 00:19:39,520 Speaker 2: hand over all these urgent shipment tracking to Augie. Now 322 00:19:39,520 --> 00:19:43,240 Speaker 2: they're like, okay, what is the next thing that keeps 323 00:19:43,280 --> 00:19:46,439 Speaker 2: my team busy? And how can I help them, you know, 324 00:19:46,480 --> 00:19:49,320 Speaker 2: focus on things that I really want them to relationships 325 00:19:49,320 --> 00:19:52,320 Speaker 2: and and margins. So that's like an example of that. 326 00:19:53,680 --> 00:19:57,760 Speaker 2: There's another example of you know, like a very large 327 00:19:59,080 --> 00:20:04,920 Speaker 2: customers of ours do about four billion in revenues. They 328 00:20:04,920 --> 00:20:10,040 Speaker 2: have a network of close to eighty thousand carriers, and 329 00:20:13,040 --> 00:20:16,400 Speaker 2: you know, they had spent a lot of time and 330 00:20:16,440 --> 00:20:23,200 Speaker 2: built fairly extensive automation. You know, like their their automation 331 00:20:23,320 --> 00:20:25,919 Speaker 2: budget is in tens of millions a year, right's almost 332 00:20:25,920 --> 00:20:32,040 Speaker 2: sixteen million, and they were you know, for a small 333 00:20:32,119 --> 00:20:34,600 Speaker 2: percentage of their lows, call it fifteen to twenty percent 334 00:20:36,000 --> 00:20:38,920 Speaker 2: that was being covered through the public loadboards like the 335 00:20:39,000 --> 00:20:41,840 Speaker 2: Ato truck stop. You know, they were getting about thirty 336 00:20:42,040 --> 00:20:47,040 Speaker 2: five hundred calls and five thousand emails on a daily basis, 337 00:20:47,760 --> 00:20:51,840 Speaker 2: and a vast majority of them were getting unanswered, right 338 00:20:52,080 --> 00:20:55,480 Speaker 2: because like just to imagine, like the workforce, you need 339 00:20:55,520 --> 00:20:59,320 Speaker 2: to answer five thousand emails or thirty five hundred calls. 340 00:20:59,640 --> 00:21:01,399 Speaker 2: So what they do They put Augie on it and 341 00:21:01,400 --> 00:21:03,600 Speaker 2: they say, hey, Agie, I want you to be the 342 00:21:03,640 --> 00:21:08,080 Speaker 2: first line of defense, you know, answer these calls and 343 00:21:08,200 --> 00:21:11,119 Speaker 2: then you know, follow up on emails as you need to. 344 00:21:12,440 --> 00:21:15,359 Speaker 2: And so now you know, sticking all of the inlong 345 00:21:15,480 --> 00:21:21,399 Speaker 2: calls today, right every single day, and negotiating and booking loads, 346 00:21:21,400 --> 00:21:27,560 Speaker 2: and that's freeing up people to do I think more 347 00:21:27,640 --> 00:21:32,320 Speaker 2: higher judgment things. Now. If everything in a call is passed, 348 00:21:32,359 --> 00:21:35,960 Speaker 2: like their fraud scores is cleared up, they're not on 349 00:21:36,000 --> 00:21:38,680 Speaker 2: the do not use list, they meet the criterias of 350 00:21:38,840 --> 00:21:42,840 Speaker 2: the business. They have a good price, you know, the 351 00:21:42,880 --> 00:21:46,480 Speaker 2: freight fleet company is a good price. Then Augie is 352 00:21:46,520 --> 00:21:49,040 Speaker 2: passing that. But now it has done all the heavy 353 00:21:49,080 --> 00:21:53,160 Speaker 2: lifting of qualifying, getting a good bid, and then passing 354 00:21:53,200 --> 00:21:55,480 Speaker 2: that bid and transferring their call to a rep. So 355 00:21:55,520 --> 00:22:00,480 Speaker 2: when the rep takes the call, they're now really making 356 00:22:00,560 --> 00:22:03,439 Speaker 2: use of their time well right, and the same thing 357 00:22:03,480 --> 00:22:07,760 Speaker 2: with email. So through this plus about eight or nine 358 00:22:07,800 --> 00:22:11,320 Speaker 2: other use cases like this business you know expects somewhere 359 00:22:11,320 --> 00:22:18,000 Speaker 2: between six to twelve million improvement in IBITA over twelve months, 360 00:22:18,040 --> 00:22:19,840 Speaker 2: twelve to eighteen months, you know. So we were working 361 00:22:19,880 --> 00:22:25,760 Speaker 2: with them on that. You know, one of our customers 362 00:22:27,320 --> 00:22:30,600 Speaker 2: is this one we ever think published case study with them. 363 00:22:30,640 --> 00:22:37,560 Speaker 2: So it's called Armstrong Transport Group. They you know, they 364 00:22:37,600 --> 00:22:40,760 Speaker 2: are a large threpl it's sort of mixed. They have 365 00:22:42,359 --> 00:22:46,200 Speaker 2: agents and then they have W two brokers, so they 366 00:22:46,200 --> 00:22:52,800 Speaker 2: have a bit of both. And they had also a 367 00:22:52,800 --> 00:22:59,160 Speaker 2: fairly fairly large team that was doing day to day 368 00:22:59,200 --> 00:23:02,879 Speaker 2: tracking and tracing of loads. And you know, they are 369 00:23:02,920 --> 00:23:08,399 Speaker 2: now putting Augie as the first sign of defense to 370 00:23:08,480 --> 00:23:13,720 Speaker 2: you know, track these loads and raise escalations and whatnot, 371 00:23:14,600 --> 00:23:17,920 Speaker 2: and and it's made a remarkable difference. Like I think 372 00:23:18,080 --> 00:23:23,280 Speaker 2: the last we touched now forty of all TMS updates 373 00:23:23,640 --> 00:23:26,680 Speaker 2: on track and Trace are now made by Augie from zero, 374 00:23:26,800 --> 00:23:30,600 Speaker 2: you know, and roughly it's in's like thousands of updates 375 00:23:30,640 --> 00:23:33,879 Speaker 2: that needs to happen on a daily weekly basis on 376 00:23:33,920 --> 00:23:38,560 Speaker 2: these so it's many meaningful numbers. The other thing the 377 00:23:38,760 --> 00:23:42,720 Speaker 2: you know atg or Armstrong did with us was put 378 00:23:42,760 --> 00:23:46,119 Speaker 2: Augie to work on document collection, right like that was 379 00:23:46,160 --> 00:23:49,119 Speaker 2: actually the first use case. They say, like, hey, truly 380 00:23:49,160 --> 00:23:51,639 Speaker 2: no one likes document collections, so I always want Augie 381 00:23:52,040 --> 00:23:55,240 Speaker 2: and they're they're keyb was like, hey, when how many? 382 00:23:55,440 --> 00:23:59,720 Speaker 2: Like what can I do to reduce my dso hours? 383 00:23:59,760 --> 00:24:04,600 Speaker 2: Because so every hour, as you know, like brokers can 384 00:24:04,640 --> 00:24:08,119 Speaker 2: only get paid after the invoice, they have to pay 385 00:24:08,600 --> 00:24:11,320 Speaker 2: in some ways to the fleets before the shippers pay 386 00:24:11,359 --> 00:24:13,800 Speaker 2: them because of the term. So almost every day count 387 00:24:13,840 --> 00:24:17,119 Speaker 2: because they are otherwise running on you know, a credit 388 00:24:17,160 --> 00:24:21,439 Speaker 2: facility with the bank to cover this gap. So we 389 00:24:21,520 --> 00:24:24,960 Speaker 2: actually brought down Augie starts to chase these these documents 390 00:24:24,960 --> 00:24:29,480 Speaker 2: through emails and phone calls and texts and was able 391 00:24:29,520 --> 00:24:32,680 Speaker 2: to and then verify these documents whether they're signed not signed, 392 00:24:32,720 --> 00:24:34,760 Speaker 2: that they're matching the load number so that the billing 393 00:24:34,800 --> 00:24:37,919 Speaker 2: can happen in a really good way. And they were 394 00:24:37,960 --> 00:24:40,800 Speaker 2: able to bring down you know, the dso from I 395 00:24:40,880 --> 00:24:43,840 Speaker 2: think fifty eight hours before Augie do now like less 396 00:24:43,840 --> 00:24:46,920 Speaker 2: than thirty six hours and it's still coming down there, 397 00:24:46,920 --> 00:24:50,439 Speaker 2: optimizing it so across the board, like we're starting to 398 00:24:50,440 --> 00:24:55,440 Speaker 2: see you know, meaningful impact in like true financial metrics 399 00:24:55,480 --> 00:24:56,439 Speaker 2: for these businesses. 400 00:24:57,240 --> 00:25:00,159 Speaker 1: So given the focus in the trucking industry recently on 401 00:25:00,200 --> 00:25:05,080 Speaker 1: a non domicel CDL holders and English language proficiency, uh, 402 00:25:05,440 --> 00:25:09,639 Speaker 1: is Augie able to help brokers kind of like exclude 403 00:25:11,119 --> 00:25:16,119 Speaker 1: those that that I guess population of of the of 404 00:25:16,160 --> 00:25:17,040 Speaker 1: the driver market. 405 00:25:17,840 --> 00:25:21,040 Speaker 2: Yeah, so you know, we are a law compliant now 406 00:25:21,119 --> 00:25:23,880 Speaker 2: and so if that's the law, then we followed the law. 407 00:25:23,960 --> 00:25:28,240 Speaker 2: And there's a couple of cases there. One is it 408 00:25:28,400 --> 00:25:33,280 Speaker 2: Auto Today actually works with the Highway real time so 409 00:25:33,680 --> 00:25:35,880 Speaker 2: as soon as we get an email or or call 410 00:25:36,800 --> 00:25:39,400 Speaker 2: AGI before we even take or pick up the call. 411 00:25:40,240 --> 00:25:42,479 Speaker 2: We work with Highwave if you know who they are, 412 00:25:42,520 --> 00:25:45,520 Speaker 2: to get a really great company do fraud detection. But 413 00:25:45,560 --> 00:25:52,120 Speaker 2: they've also added cd L verification for the carriers themselves, 414 00:25:52,160 --> 00:25:53,720 Speaker 2: so you can get that signal from them. 415 00:25:53,960 --> 00:25:57,719 Speaker 1: We had one of their executives on the podcast earlier 416 00:25:57,760 --> 00:25:59,359 Speaker 1: this year. So if you're interested to hear more about 417 00:25:59,440 --> 00:26:03,960 Speaker 1: highway and D or preventing for success listeners to go 418 00:26:04,680 --> 00:26:07,239 Speaker 1: back and uh, take a take a listen to it. 419 00:26:07,320 --> 00:26:09,520 Speaker 2: So it's a big company. We enjoy working with them, 420 00:26:09,560 --> 00:26:13,280 Speaker 2: great partner to us. But yeah, we we basically you know, 421 00:26:13,359 --> 00:26:16,400 Speaker 2: Auguie will like Augie in a highway will like ping 422 00:26:16,440 --> 00:26:21,119 Speaker 2: each other. Highest sense. Highway senses the information both on 423 00:26:21,280 --> 00:26:23,879 Speaker 2: fraud but also on c d L, and then if 424 00:26:23,920 --> 00:26:28,200 Speaker 2: it's failing the CDL test, you know, we will obviously 425 00:26:29,000 --> 00:26:31,440 Speaker 2: not take the call and politely declient saying so I 426 00:26:31,480 --> 00:26:34,120 Speaker 2: don't think we can work with you, right now? What's that? 427 00:26:34,119 --> 00:26:34,600 Speaker 1: That's nice? 428 00:26:34,600 --> 00:26:38,240 Speaker 2: That is polite? I like that. Yeah, you know, like, yeah, 429 00:26:38,280 --> 00:26:41,080 Speaker 2: I think AI has like yeah, it is more patient 430 00:26:41,119 --> 00:26:47,040 Speaker 2: than humans, you know, and so and and and then 431 00:26:47,200 --> 00:26:49,960 Speaker 2: and then on top of that, like we we have 432 00:26:50,080 --> 00:26:53,560 Speaker 2: conditions where let's say we are making a call to 433 00:26:53,640 --> 00:26:56,000 Speaker 2: a you know, a driver and and and we are 434 00:26:56,040 --> 00:26:59,040 Speaker 2: sensing that they're not not able to pick up the 435 00:26:59,119 --> 00:27:03,560 Speaker 2: language English on that very well, right, we can we 436 00:27:03,600 --> 00:27:08,320 Speaker 2: can analyze the call, and we can we can rate 437 00:27:08,359 --> 00:27:12,080 Speaker 2: the call for proficiency in English, and that's a pretty 438 00:27:12,119 --> 00:27:14,359 Speaker 2: good signal. We can at least mark that to the 439 00:27:14,400 --> 00:27:17,320 Speaker 2: business like and saying hey, this is this is the 440 00:27:17,359 --> 00:27:20,679 Speaker 2: proficiency of English according to us in this call, do 441 00:27:20,760 --> 00:27:22,960 Speaker 2: you want to do business with this company or not? 442 00:27:23,119 --> 00:27:26,440 Speaker 2: Ultimately it's you know, the broker or the fleets or 443 00:27:26,840 --> 00:27:29,040 Speaker 2: the shipper's decision, not art. But we can give them 444 00:27:29,080 --> 00:27:32,200 Speaker 2: the data much more real time then it would be 445 00:27:32,240 --> 00:27:33,359 Speaker 2: available anywhere else. 446 00:27:33,800 --> 00:27:37,320 Speaker 1: Interesting stuff. And then so in addition to this, the 447 00:27:37,359 --> 00:27:43,800 Speaker 1: CDL and the English proficiency, So, as you mentioned, you're 448 00:27:43,840 --> 00:27:47,720 Speaker 1: in constant contact with Highway about fraud. Are there any 449 00:27:47,800 --> 00:27:51,520 Speaker 1: things that does by itself to detect fraud and make 450 00:27:51,560 --> 00:27:53,200 Speaker 1: sure that fraud is not happening? 451 00:27:53,600 --> 00:27:57,440 Speaker 2: So a few things. So one is that it supplements 452 00:27:57,480 --> 00:28:00,199 Speaker 2: what we get from Highway and almost all all of 453 00:28:00,200 --> 00:28:03,040 Speaker 2: our businesses have their own do not use list for 454 00:28:03,080 --> 00:28:05,439 Speaker 2: a variety of different reasons. They could be fraud, but 455 00:28:05,480 --> 00:28:08,840 Speaker 2: they could also be for performance related reasons or pay 456 00:28:09,040 --> 00:28:13,600 Speaker 2: related reasons, you know. So it supplements the signals it 457 00:28:13,640 --> 00:28:19,879 Speaker 2: gets from Highway with you know, the businesses data and 458 00:28:20,000 --> 00:28:23,600 Speaker 2: does that. That's I think one thing. I think the 459 00:28:23,680 --> 00:28:26,240 Speaker 2: only other piece where we can help or help is 460 00:28:26,400 --> 00:28:31,439 Speaker 2: in track and trace. One of the frauds in this 461 00:28:31,560 --> 00:28:36,160 Speaker 2: business is that the truck starts to go off route, 462 00:28:36,720 --> 00:28:39,880 Speaker 2: and it's let's say it supposed to go from Atlanta 463 00:28:39,920 --> 00:28:42,880 Speaker 2: to Chicago, and there is a route for it, and 464 00:28:43,760 --> 00:28:46,600 Speaker 2: you know it starts there, but then it starts to 465 00:28:46,600 --> 00:28:50,680 Speaker 2: go off route. Now, typically you can try to set like, 466 00:28:52,400 --> 00:28:55,520 Speaker 2: you know, a geofense and saying hey, if it's within 467 00:28:55,560 --> 00:28:58,080 Speaker 2: this geofense, I'm good. But if it crosses the geofense, 468 00:28:58,160 --> 00:29:00,440 Speaker 2: I need to know. This is a fraud. Somebody is 469 00:29:00,440 --> 00:29:03,440 Speaker 2: literally stealing, right. It's like it's the worst kind of product. 470 00:29:03,480 --> 00:29:07,640 Speaker 2: They're just off. They know there's a high like high 471 00:29:07,720 --> 00:29:10,120 Speaker 2: valued goods in the container and they pick it up 472 00:29:10,160 --> 00:29:12,840 Speaker 2: and then they want to just steal or go to 473 00:29:12,920 --> 00:29:18,560 Speaker 2: a warehouse and replace the the trailer and then still 474 00:29:18,600 --> 00:29:21,480 Speaker 2: do a delivery, but but with like very different goods 475 00:29:21,560 --> 00:29:24,720 Speaker 2: or whatnot. You know. So in that case, you can 476 00:29:24,720 --> 00:29:26,840 Speaker 2: put ugue to say like, hey, I want you to 477 00:29:26,880 --> 00:29:30,520 Speaker 2: be like a watchman, right and say like, hey, watch 478 00:29:30,600 --> 00:29:33,120 Speaker 2: these signals and if it's on its way, we're good. 479 00:29:33,160 --> 00:29:36,280 Speaker 2: But if it's not, priests start to alert internally, whether 480 00:29:36,320 --> 00:29:40,920 Speaker 2: it's through calls or you know, teams or slack messages 481 00:29:40,960 --> 00:29:43,360 Speaker 2: to people and saying hey, I think there's a potential. 482 00:29:44,040 --> 00:29:46,239 Speaker 2: We you want me to call the driver, You call 483 00:29:46,320 --> 00:29:48,560 Speaker 2: the driver, or maybe you want to step in and saying, hey, 484 00:29:48,560 --> 00:29:51,640 Speaker 2: why are you going this route? I don't know we 485 00:29:51,680 --> 00:29:55,560 Speaker 2: can prevent the fraud, but we can like maybe by 486 00:29:55,600 --> 00:29:58,080 Speaker 2: this way we can at least alert the authorities faster. 487 00:30:00,080 --> 00:30:02,200 Speaker 2: You know, there's a little bit more like knowing that 488 00:30:02,280 --> 00:30:04,640 Speaker 2: this can happen also can prevent the fraud. 489 00:30:05,360 --> 00:30:07,240 Speaker 1: So just changing gears a little bit. So like the 490 00:30:07,280 --> 00:30:10,600 Speaker 1: brokers industry is pretty fragmented. Obviously there's there's a bunch 491 00:30:10,640 --> 00:30:13,120 Speaker 1: of huge players, but there's also a lot of little players. 492 00:30:13,120 --> 00:30:16,640 Speaker 1: And you know, the broker industries was kind of an 493 00:30:16,640 --> 00:30:19,720 Speaker 1: industry where all you needed was a Rolodex and a 494 00:30:19,760 --> 00:30:22,640 Speaker 1: phone and you can be find success. You know, fast 495 00:30:22,680 --> 00:30:27,240 Speaker 1: forward today obviously do need technology and technology is becoming 496 00:30:27,280 --> 00:30:30,240 Speaker 1: more and more important. If I'm like a small broker 497 00:30:31,200 --> 00:30:34,760 Speaker 1: and I you know, understand that I start really need 498 00:30:34,840 --> 00:30:39,680 Speaker 1: to start using tools like augmented and algy Where are 499 00:30:39,760 --> 00:30:43,440 Speaker 1: like the first places that you suggest a broker to 500 00:30:43,480 --> 00:30:47,360 Speaker 1: start using AI and their workflow, you know, as they're 501 00:30:47,400 --> 00:30:49,760 Speaker 1: kind of dipping their toe in the water because they 502 00:30:49,840 --> 00:30:52,520 Speaker 1: but they might not just have the funds to go all. 503 00:30:52,400 --> 00:31:00,120 Speaker 2: In, Yeah, it's a a good question. So I think 504 00:31:01,080 --> 00:31:03,200 Speaker 2: there's sort of two lenses to look at it, right, 505 00:31:03,320 --> 00:31:08,360 Speaker 2: especially for small brokers, delivering an incredible service level to 506 00:31:08,480 --> 00:31:11,440 Speaker 2: customers is how they grow their business. Right, So if 507 00:31:11,480 --> 00:31:14,160 Speaker 2: you signed up for you know, if you have some 508 00:31:14,280 --> 00:31:16,440 Speaker 2: business from a Target or a Walmart or you know, 509 00:31:16,520 --> 00:31:18,680 Speaker 2: you line like, Okay, how did you do on my 510 00:31:18,760 --> 00:31:21,800 Speaker 2: fifty loads? Really matters because that's how you earn your 511 00:31:21,800 --> 00:31:25,680 Speaker 2: next fifty and your next hundred, And a big part 512 00:31:25,720 --> 00:31:28,480 Speaker 2: of the service is that, hey, was there on time, pickup, 513 00:31:28,600 --> 00:31:32,040 Speaker 2: on time delivery and load detention, like okay, can you 514 00:31:32,080 --> 00:31:35,280 Speaker 2: always make sure those happens? So the brokers obsess about 515 00:31:35,280 --> 00:31:39,000 Speaker 2: that and they care about like delivering those services. So 516 00:31:39,040 --> 00:31:41,320 Speaker 2: I think one of the easiest and early places to 517 00:31:41,400 --> 00:31:45,640 Speaker 2: put AI on is just that it's like, hey, can 518 00:31:45,760 --> 00:31:48,720 Speaker 2: you like take over the whole track and trace type 519 00:31:48,760 --> 00:31:53,840 Speaker 2: of initiative for me and put me in in loop 520 00:31:53,960 --> 00:31:57,120 Speaker 2: or get me in and saying like if there is 521 00:31:57,400 --> 00:32:02,040 Speaker 2: an extenuating circumstances is an issue, me step in. But 522 00:32:02,040 --> 00:32:04,480 Speaker 2: but like having a pair of eyes that is like 523 00:32:04,960 --> 00:32:09,600 Speaker 2: twenty four to seven on it all the time is 524 00:32:11,600 --> 00:32:14,400 Speaker 2: because like most small brokers also don't have night shifts, 525 00:32:14,400 --> 00:32:19,520 Speaker 2: they don't have sometimes overseas resources and so and trucks 526 00:32:19,520 --> 00:32:22,160 Speaker 2: around twenty four seven. So having an augie as like 527 00:32:22,200 --> 00:32:25,160 Speaker 2: your night shift person at a minimum, or an AI 528 00:32:25,320 --> 00:32:27,600 Speaker 2: that can just monitor things all the time and alert 529 00:32:27,720 --> 00:32:29,719 Speaker 2: you when you need it to, I think it can 530 00:32:29,760 --> 00:32:31,960 Speaker 2: go a long way in earning trust of the shippers. 531 00:32:32,280 --> 00:32:35,320 Speaker 2: So's I would say, it's almost like less about costs 532 00:32:35,400 --> 00:32:38,560 Speaker 2: to serve for small shippers, but it's much more about 533 00:32:38,960 --> 00:32:44,160 Speaker 2: delivering an exceptional service level to the sorry for small brokers, 534 00:32:44,200 --> 00:32:46,959 Speaker 2: but delivering an exceptional service levels to their shipping customers. 535 00:32:47,000 --> 00:32:47,280 Speaker 2: You know. 536 00:32:48,320 --> 00:32:52,280 Speaker 1: So has the current freight recession hindered growth for you 537 00:32:52,320 --> 00:32:55,360 Speaker 1: guys or is it kind of help growth? As I 538 00:32:55,360 --> 00:32:58,560 Speaker 1: guess people are trying to get more productive with the 539 00:32:59,480 --> 00:33:02,200 Speaker 1: you know, the low revenues that are coming in. Another 540 00:33:02,240 --> 00:33:03,600 Speaker 1: broker and carrier said. 541 00:33:04,000 --> 00:33:06,920 Speaker 2: I think net net it has helped initiatives and companies 542 00:33:06,960 --> 00:33:09,360 Speaker 2: like ours and AI in general because there's just a 543 00:33:09,360 --> 00:33:13,120 Speaker 2: tremendous amount of cost pressure, right Like I think we 544 00:33:13,200 --> 00:33:15,880 Speaker 2: have what Lee, I think three and a half years 545 00:33:15,880 --> 00:33:19,160 Speaker 2: into now for a kind of a price recession. Definitely 546 00:33:19,160 --> 00:33:23,040 Speaker 2: in this industry. And so margins are raised thin across 547 00:33:23,040 --> 00:33:25,600 Speaker 2: the board, right, like whether you're a broker or you're 548 00:33:25,640 --> 00:33:27,760 Speaker 2: a fleet I think these are some of the lowest 549 00:33:27,760 --> 00:33:33,480 Speaker 2: margins maybe ever, certainly in the last five years that 550 00:33:33,520 --> 00:33:37,920 Speaker 2: you have seen. And so having AI ttarting to take 551 00:33:38,000 --> 00:33:44,480 Speaker 2: some cost out, you know, almost becomes impertative and a 552 00:33:44,600 --> 00:33:47,920 Speaker 2: nice hope in this in this environment that hey. And 553 00:33:47,960 --> 00:33:52,760 Speaker 2: then two, as it turns around, which it will, you know, 554 00:33:53,200 --> 00:33:58,040 Speaker 2: as a market, now now you have like you can 555 00:33:58,080 --> 00:34:02,080 Speaker 2: you can handle the sicklycality better, right right, because like 556 00:34:02,120 --> 00:34:05,880 Speaker 2: before this business was like it's so cyclical that as 557 00:34:06,200 --> 00:34:08,080 Speaker 2: market gets hard, you just need to hire so many 558 00:34:08,120 --> 00:34:11,200 Speaker 2: people to cover freight. Well, now you have AI all 559 00:34:11,280 --> 00:34:14,279 Speaker 2: theis of the world that can help you not go 560 00:34:14,600 --> 00:34:18,000 Speaker 2: so high and so low. It can be like this bridge. 561 00:34:18,040 --> 00:34:20,239 Speaker 2: You know that if the market gets hard, you can 562 00:34:20,320 --> 00:34:24,560 Speaker 2: just put AI to work to more use cases and 563 00:34:25,160 --> 00:34:27,799 Speaker 2: you don't need to hire that many people. So I 564 00:34:27,800 --> 00:34:30,120 Speaker 2: think there's like two benefits. But yeah, in that net, 565 00:34:30,160 --> 00:34:32,200 Speaker 2: if I were to take a guest, I think it's 566 00:34:32,520 --> 00:34:34,280 Speaker 2: it's it certainly helped. 567 00:34:34,440 --> 00:34:38,680 Speaker 1: Yeah, great, And then you know what has been the 568 00:34:39,719 --> 00:34:44,680 Speaker 1: biggest challenge you face since founding Augment. 569 00:34:44,560 --> 00:34:49,440 Speaker 2: The biggest challenge, Like I'll put it like in two terms. 570 00:34:49,440 --> 00:34:52,600 Speaker 2: One is for Augment, you know as building the company, 571 00:34:52,640 --> 00:34:55,319 Speaker 2: but then also working with our customers I think like 572 00:34:55,400 --> 00:35:00,880 Speaker 2: delivering value. I think working and building the company. It's talent, 573 00:35:01,880 --> 00:35:06,000 Speaker 2: you know, like very AI native talent is an extremely 574 00:35:06,080 --> 00:35:09,319 Speaker 2: high demand and there are companies in every layer, whether 575 00:35:09,360 --> 00:35:13,080 Speaker 2: it's the LM layers or the data center layers, and 576 00:35:13,080 --> 00:35:16,319 Speaker 2: we are in the application layer right every layer there 577 00:35:16,400 --> 00:35:18,640 Speaker 2: is just a tremendous demand of that talent. And it's 578 00:35:18,680 --> 00:35:21,799 Speaker 2: not a lot of talent, it's you know, that talent 579 00:35:21,920 --> 00:35:25,839 Speaker 2: is getting groomed, the AI native talent. So you've got 580 00:35:25,880 --> 00:35:29,080 Speaker 2: gone from I think like zero to like one hundred 581 00:35:29,120 --> 00:35:33,960 Speaker 2: engineers or so. But but you know, in short amount 582 00:35:33,960 --> 00:35:35,839 Speaker 2: of time I would say, like less than less than 583 00:35:35,840 --> 00:35:39,880 Speaker 2: a year's and but it wasn't easy, you know, to 584 00:35:39,920 --> 00:35:44,560 Speaker 2: get one hundred engineers. We probably looked at, you know, 585 00:35:44,600 --> 00:35:49,920 Speaker 2: close to five thousand engineers, you know, and then obviously 586 00:35:49,920 --> 00:35:54,319 Speaker 2: people trickle through the interview loop and then when you 587 00:35:54,400 --> 00:35:56,680 Speaker 2: make them an offer, a lot of people have several 588 00:35:56,680 --> 00:35:59,600 Speaker 2: offers and you have to compete on why they should 589 00:35:59,680 --> 00:36:02,439 Speaker 2: join Augment, you know, and not open AI. 590 00:36:03,600 --> 00:36:06,520 Speaker 1: But it's to work, it's to work with you, of course, 591 00:36:06,560 --> 00:36:07,759 Speaker 1: I mean that's the number one. 592 00:36:08,000 --> 00:36:09,920 Speaker 2: I hope that goes a little bit, but it is. 593 00:36:09,960 --> 00:36:12,280 Speaker 2: It is not easy, and we spend an enormate amount 594 00:36:12,280 --> 00:36:15,560 Speaker 2: of time to build a teams. So if we think 595 00:36:15,640 --> 00:36:18,839 Speaker 2: like talent comes first in this space, now working with 596 00:36:18,840 --> 00:36:23,200 Speaker 2: our customers, I think the biggest there are two challenges, 597 00:36:23,239 --> 00:36:27,600 Speaker 2: but the biggest challenges change management like AI. When AI 598 00:36:27,719 --> 00:36:32,319 Speaker 2: does something, somebody is not doing that thing, and AI 599 00:36:32,400 --> 00:36:34,440 Speaker 2: is taking on real work, like it is taking on 600 00:36:34,680 --> 00:36:38,440 Speaker 2: end to end work right, which means that the company 601 00:36:38,480 --> 00:36:43,680 Speaker 2: needs to go and repurpose and retrain employees that you 602 00:36:43,719 --> 00:36:45,279 Speaker 2: don't need to do this, but I need you to 603 00:36:45,400 --> 00:36:51,080 Speaker 2: retrain to do this right. And that requires a tremendous 604 00:36:51,120 --> 00:36:57,319 Speaker 2: amount of effort and fortitude and courage to do. And 605 00:36:57,360 --> 00:36:59,759 Speaker 2: there are leaders who are leaning in when they are 606 00:36:59,760 --> 00:37:03,960 Speaker 2: also leaders will like weight and see mode because it 607 00:37:04,080 --> 00:37:07,880 Speaker 2: is so much work and it is it's also courageous, 608 00:37:07,920 --> 00:37:11,400 Speaker 2: like oh, I'll have to go and take five hundred 609 00:37:11,480 --> 00:37:15,520 Speaker 2: people and retrain them on new set of skills, right, 610 00:37:15,600 --> 00:37:17,719 Speaker 2: And they were just so set of doing this one thing. 611 00:37:17,800 --> 00:37:20,000 Speaker 2: But now AI is doing that, and I don't need 612 00:37:20,000 --> 00:37:21,480 Speaker 2: them to do this thing, but I need them to 613 00:37:21,520 --> 00:37:28,040 Speaker 2: do this new thing, and so a natural instinct is 614 00:37:28,040 --> 00:37:31,000 Speaker 2: that maybe next quarter, maybe in two quarters, you know, 615 00:37:31,680 --> 00:37:35,360 Speaker 2: versus today. So what that does is for companies like ours, 616 00:37:35,680 --> 00:37:42,200 Speaker 2: is it pushes the ROI where you can show that 617 00:37:42,320 --> 00:37:45,640 Speaker 2: people need to do less. But those folks are not 618 00:37:45,719 --> 00:37:50,680 Speaker 2: repurposed and flowing through the books, it gets harder to show, 619 00:37:51,560 --> 00:37:54,239 Speaker 2: you know. And so I think there's just a lot 620 00:37:54,280 --> 00:37:56,239 Speaker 2: of work that needs to be done in this industry. 621 00:37:57,040 --> 00:37:59,680 Speaker 2: And maybe there are consulting firms and whatnot who can 622 00:37:59,719 --> 00:38:02,799 Speaker 2: help for folks who've done this, Like, how do you 623 00:38:02,880 --> 00:38:07,200 Speaker 2: take folks like we are technology providers and we have 624 00:38:07,360 --> 00:38:13,560 Speaker 2: some now some experience a bit now observing across several 625 00:38:13,560 --> 00:38:16,880 Speaker 2: of our customers, how what good looks like, whatnot. But 626 00:38:16,960 --> 00:38:19,319 Speaker 2: we're not the experts of change manager Like we need 627 00:38:20,120 --> 00:38:22,400 Speaker 2: folks who can take these very large three four to 628 00:38:22,440 --> 00:38:26,360 Speaker 2: five thousand people organizations and run them through change management. 629 00:38:26,680 --> 00:38:30,440 Speaker 2: But the reality is that every single organization in two 630 00:38:30,520 --> 00:38:32,799 Speaker 2: years time is going to look very different than what 631 00:38:32,840 --> 00:38:35,359 Speaker 2: it does look today. Right. 632 00:38:35,480 --> 00:38:38,720 Speaker 1: It's interesting, you know, you keep on mentioning retraining people 633 00:38:38,840 --> 00:38:43,680 Speaker 1: versus reducing head count, So you think most of the 634 00:38:44,520 --> 00:38:48,239 Speaker 1: brokers and carriers that implement this are not going down 635 00:38:48,239 --> 00:38:49,000 Speaker 1: that road. 636 00:38:49,320 --> 00:38:53,399 Speaker 2: So I think there's like there's offshore labor and near 637 00:38:53,480 --> 00:38:58,520 Speaker 2: shore we're seeing their reductions, right, They're not like there 638 00:38:58,560 --> 00:39:01,120 Speaker 2: we are seeing very clear like, hey, these teams I 639 00:39:01,160 --> 00:39:05,280 Speaker 2: need to reduce and they're usually not on the payroll 640 00:39:05,320 --> 00:39:08,560 Speaker 2: there contractors, right, But I think the folks on the 641 00:39:08,600 --> 00:39:14,080 Speaker 2: payroll most businesses are saying, like, can I the institutional 642 00:39:14,160 --> 00:39:18,160 Speaker 2: knowledge the culture, like can I actually see if I 643 00:39:18,200 --> 00:39:22,640 Speaker 2: can repurpose them for growth? Okay, Like they're they're they're 644 00:39:22,680 --> 00:39:24,960 Speaker 2: looking at it from that angle. They're saying, can I 645 00:39:25,040 --> 00:39:27,919 Speaker 2: deliver more superior? But they already know who I am, 646 00:39:27,960 --> 00:39:31,319 Speaker 2: they know how I work, you know, they know my systems, 647 00:39:31,480 --> 00:39:34,400 Speaker 2: they know my customers. Like that would be a waste 648 00:39:34,400 --> 00:39:36,600 Speaker 2: to say, like I can have this talent go out 649 00:39:36,640 --> 00:39:39,680 Speaker 2: of my door. Right, It's just the skills that they 650 00:39:39,680 --> 00:39:42,880 Speaker 2: were working on is now different and now so I 651 00:39:42,920 --> 00:39:45,279 Speaker 2: think most companies are thinking that way. I don't know 652 00:39:45,280 --> 00:39:49,960 Speaker 2: how much will end up on can sales or customer 653 00:39:49,960 --> 00:39:54,040 Speaker 2: relationships or new business expansion absorb all of that. I 654 00:39:54,040 --> 00:39:55,840 Speaker 2: think the jury is still out on it. 655 00:39:56,640 --> 00:40:00,920 Speaker 1: Right, And so just again changing gears a little bit. 656 00:40:00,960 --> 00:40:02,319 Speaker 1: Can you talk about a little bit how you got 657 00:40:02,360 --> 00:40:06,759 Speaker 1: into transportation, you know, what made you kind of as 658 00:40:06,760 --> 00:40:10,480 Speaker 1: a technologist kind of gravitate towards the freight world. 659 00:40:11,520 --> 00:40:14,440 Speaker 2: Yeah. So, like I started off my career at Amazon, 660 00:40:15,160 --> 00:40:17,640 Speaker 2: and I you know, wrote a lot of software which 661 00:40:17,680 --> 00:40:21,520 Speaker 2: now runs fulfillment by Amazon Service. There we were big 662 00:40:21,560 --> 00:40:25,200 Speaker 2: consumers of transportation, right, like trucks coming from hosts the 663 00:40:25,320 --> 00:40:30,359 Speaker 2: certainly are distributing all over the country. I mean now 664 00:40:30,400 --> 00:40:34,200 Speaker 2: Amazon maybe after Walmart or a couple other players, probably 665 00:40:34,239 --> 00:40:38,720 Speaker 2: one of the larger trucking consumers in in in in 666 00:40:38,719 --> 00:40:42,719 Speaker 2: in the US. And then several of my businesses, whether 667 00:40:42,760 --> 00:40:45,759 Speaker 2: it was delivered or even the one before, we were 668 00:40:46,360 --> 00:40:50,720 Speaker 2: either builders or consumers or both of the freight word. 669 00:40:52,520 --> 00:40:56,400 Speaker 2: And so you know, from that I understood both the 670 00:40:56,440 --> 00:41:00,759 Speaker 2: problems in this space, right, Hey, why does it you know, 671 00:41:02,000 --> 00:41:09,160 Speaker 2: why is it so tedious? Right? And like why there 672 00:41:09,160 --> 00:41:12,760 Speaker 2: are so many inefficiencies in this space. It's a large space. 673 00:41:12,800 --> 00:41:17,120 Speaker 2: It's so crucial and important too, you know for augmented 674 00:41:17,160 --> 00:41:19,040 Speaker 2: So that's how I got into it. But I think 675 00:41:19,200 --> 00:41:23,600 Speaker 2: the way become a sort of a computer science math 676 00:41:23,640 --> 00:41:25,719 Speaker 2: type of person, and I look at things as like 677 00:41:25,760 --> 00:41:30,520 Speaker 2: this node arc networks. You know, you have nodes and 678 00:41:30,560 --> 00:41:32,920 Speaker 2: you have arcs, and like arcs can be like ships 679 00:41:32,960 --> 00:41:36,799 Speaker 2: and trucks and planes, and and nodes are like distributor 680 00:41:36,800 --> 00:41:44,239 Speaker 2: warehouses or or you know, like small distribute like last 681 00:41:44,320 --> 00:41:49,439 Speaker 2: mile distribution warehouses or cross crosstocking depots or so there's 682 00:41:49,440 --> 00:41:52,080 Speaker 2: like different nodes, you know, and truck is a is 683 00:41:52,120 --> 00:41:54,839 Speaker 2: an ARC that connects almost all nodes in America, I say, 684 00:41:54,960 --> 00:41:57,719 Speaker 2: is the most common form of So like, okay, if 685 00:41:57,719 --> 00:42:00,200 Speaker 2: you want to go and change logistics in general or 686 00:42:00,280 --> 00:42:04,080 Speaker 2: supply chain broadly, let's start with the most important arc. 687 00:42:04,560 --> 00:42:08,600 Speaker 2: Through that we will learn the ecosystem, the warehousing, the 688 00:42:08,760 --> 00:42:13,560 Speaker 2: distribution ecosystem, and from that we can now branch out 689 00:42:13,760 --> 00:42:17,799 Speaker 2: to those verticals if you will. We were starting off 690 00:42:17,800 --> 00:42:20,760 Speaker 2: with trucking, but augments mission is broader, right. Our mission 691 00:42:20,840 --> 00:42:24,719 Speaker 2: is that can we make logistics better? And that requires 692 00:42:24,719 --> 00:42:27,120 Speaker 2: coordination with nodes and arcs or different types. 693 00:42:27,600 --> 00:42:30,200 Speaker 1: All right, well, well good luck with that. With that 694 00:42:30,239 --> 00:42:33,800 Speaker 1: goal in mind, you know, before we go, I always 695 00:42:33,800 --> 00:42:37,320 Speaker 1: like to ask my guests if they have a favorite 696 00:42:37,360 --> 00:42:40,759 Speaker 1: book about transportation leadership or in your case, technology that's 697 00:42:40,840 --> 00:42:42,960 Speaker 1: kind of close to your heart that you might want 698 00:42:42,960 --> 00:42:45,120 Speaker 1: to recommend our listeners to take a look at. 699 00:42:45,600 --> 00:42:48,279 Speaker 2: Okay, so I think I'll give you maybe two. I 700 00:42:48,280 --> 00:42:51,000 Speaker 2: think the first one is is a very technical book. 701 00:42:52,200 --> 00:42:55,920 Speaker 2: It's about how to optimize network is called network Flows. 702 00:42:57,360 --> 00:43:03,400 Speaker 2: The main author is Rabia Ahuja. It's like a seminal 703 00:43:03,400 --> 00:43:08,239 Speaker 2: book on how do you model physical networks into sort 704 00:43:08,280 --> 00:43:10,960 Speaker 2: of this computer science way of thinking graph theory, and 705 00:43:11,000 --> 00:43:13,560 Speaker 2: then now you can optimize. It's very dense, but it's 706 00:43:13,600 --> 00:43:16,880 Speaker 2: fascinating and you can start to see networks in everything, 707 00:43:17,040 --> 00:43:20,480 Speaker 2: like you know, like an airline is a network where 708 00:43:20,680 --> 00:43:23,120 Speaker 2: you know a airplane is going from one to the other. 709 00:43:23,840 --> 00:43:27,040 Speaker 2: Wheels or networks, ships are network and how do you 710 00:43:27,080 --> 00:43:29,279 Speaker 2: optimize it? And it's a you know, it's a it's 711 00:43:29,320 --> 00:43:32,000 Speaker 2: an interesting read. It might for you to sleep if 712 00:43:32,040 --> 00:43:36,239 Speaker 2: you if you read it on your bedstand. I think 713 00:43:36,280 --> 00:43:38,719 Speaker 2: in terms of leadership, I like this the hard things 714 00:43:38,760 --> 00:43:41,319 Speaker 2: about hard things, you know, it's a it's a great 715 00:43:41,360 --> 00:43:45,040 Speaker 2: book about grit that how leaders just need to lean 716 00:43:45,080 --> 00:43:49,919 Speaker 2: in on on the toughest the toughest calls, and and 717 00:43:50,360 --> 00:43:55,759 Speaker 2: the complexity of decisions that you need to make and 718 00:43:55,960 --> 00:44:00,279 Speaker 2: the discipline of execution to build anything of meaning. You know. 719 00:44:01,239 --> 00:44:04,399 Speaker 2: So I love that book. I re reiated every time 720 00:44:04,440 --> 00:44:08,040 Speaker 2: I have, you know, tough days building businesses, I go 721 00:44:08,120 --> 00:44:10,120 Speaker 2: back and read that book and say like, Okay, it's 722 00:44:10,200 --> 00:44:11,040 Speaker 2: not that bad. You know. 723 00:44:13,360 --> 00:44:15,200 Speaker 1: Well, I really want to thank you for your time, 724 00:44:15,239 --> 00:44:19,279 Speaker 1: Harish and your insights today. This is definitely an interesting conversation. 725 00:44:19,080 --> 00:44:20,960 Speaker 2: So thank you for having me. It was great chatting 726 00:44:21,000 --> 00:44:22,040 Speaker 2: with you, and I want to. 727 00:44:22,040 --> 00:44:23,960 Speaker 1: Thank you for tuning in. If you'd liked the episode, 728 00:44:24,000 --> 00:44:26,640 Speaker 1: please subscribe and leave a review. We've lined up a 729 00:44:26,680 --> 00:44:28,879 Speaker 1: number of great guests for the podcast, so please check 730 00:44:28,920 --> 00:44:33,120 Speaker 1: back to your conversations with C suite executives, shippers, regulators, 731 00:44:33,160 --> 00:44:36,120 Speaker 1: and decision makers within the freight markets. Also, if you 732 00:44:36,120 --> 00:44:38,719 Speaker 1: want to learn more about the freight transportation markets, check 733 00:44:38,760 --> 00:44:41,400 Speaker 1: out our work on the Bloomberg Terminal at Bigo and 734 00:44:41,520 --> 00:44:44,239 Speaker 1: on social media. This is Lee Klaskal signing off and 735 00:44:44,280 --> 00:44:52,840 Speaker 1: thanks for talking transports with me. Bye.