Rail in the Time of AI

In this episode of “Don’t Dwell On It”, Harris + Sydney sit down with Amaro Luna, Chief Product Officer + co-founder at Telegraph, to explore why better data is truly the backbone of the modern rail ecosystem. Drawing on over a decade working in the transportation industry, Amaro explains how small information failures can create massive operational waste, + why shippers should care deeply about data quality as AI becomes more central to decision-making.

These three dive into the myths around rail data standards, the realities of EDI, + the cultural barriers that keep legacy industries from adopting new technology. Amaro + Sydney reflect on why better data isn’t just about efficiency, it’s about unlocking a new way of operating. For Amaro, the future of rail belongs to those who build the systems that make moving freight simpler, smarter, + more predictable.

About Telegraph

Telegraph is a leader in delivering digital solutions to railroads, shippers, logistics service providers, terminals, + railcar leasing companies. With an integrated platform that provides price transparency, shipment visibility, + proactive business intelligence, Telegraph empowers customers, and makes shipping by rail easier + more effective. For more details, visit www.telegraph.io and follow us on LinkedIn.

Transcript

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I am Sydnee Schreiber. And I'm Harris s. Ligon. You're listening to, don't Dwell on it, the podcast where we pause just long enough to figure out what's really moving Freight Rail forward. Let's get into it. Alright, well I'm really excited today. We've got a special guest up with us, Amara Luna, who is our Chief Product Officer and co-founder here at Telegraph. I've had the honor and pleasure of working with Amara. Way back in our Uber freight days, but he's come with a wide freight experience, I think over 10 years experience, but started off working with customers on the operation side, customer service, solving those day-to-day problems and flipping the switch to more of a product role, building products for customers and what they need. So I'm really excited to, to dive deep into our conversation today. But really, I think the most interesting thing about Amara. Is the fact that he's lived in, what is it, five cities now? You've really crossed all the United States tomorrow. Yeah, that's, it's a good point. I've, I've had my way at every major rail station in the us, you know, uh, I grew up in Texas. I was born and raised in El Paso was born in on, on the border. And half my family on, on one side of the border, half my family on the other side of the border moved around the state. Quite a bit and then landed in Chicago where I went to college. Spent some time in Chicago, then moved out to New York City, spent some time in New York City and then now, uh, find myself in the Bay Area. Definitely find myself coming to Chicago pretty often and will always consider it a second home. But it's been such a cool experience to just see the entire country get exposed to people all over, all over the country. For me, just have community in all these different places. It's something that's really special. Well, is Chicago your favorite city? Oh, I mean, I'm, I'm in Chicago. I can't, I can't say it's not So that, that was the right answer. Yeah. Yeah. So Amma, one of the things that, if I remember correctly, and this was many years ago now. Mm-hmm. That got you excited both about technology and transportation, was the element that there was a lack of data. But in that ecosystem, data is actually really critical to creating efficiency. Tell me a little bit more about why data and technology, like and in transportation really matters to you and how that got you excited about telegraph. Yeah, so when I think about. Some of the really cool things about telegraph, what I try to tell people all the time is it's very unique and very special to be able to build technology for the physical world. Mm-hmm. And a lot of technical people, engineers, data scientists, people in technology in general, um, they're dealing with data on a day-to-day basis. Big data and digital transformation, are these kind of keyword buzz phrases that consulting firms and Fortune 500 companies have had to, uh, leverage in their transformation as they try to get more insight about their consumers as they try to get more insight about their products and how to deliver their products more efficiently. And a lot of times that data is in a silo. Like you're, you're reading things about how certain, things are perceived in the media or how certain products are being consumed, but it's not really tangible or you're, you're building something for developers or you're kind of leveraging technology in a way that's like tech for the tech ecosystem. Mm-hmm. And so when we think about, data in the supply chain space. It is so tangible. It is so visceral. Like you could literally see, how, it affects the bottom line. And, uh, one anecdote that I like to point to where I got exposed to this for the very first time is, being in, like a financial role at Procter and Gamble and trying to understand, the actual production of chemicals that would ultimately be the precursor to a mouthwash. And, one of the. Elements of waste that was just fascinating to hear about is you have the distribution of these plants all over the US that are producing the chemical and you have all of the components that it takes to actually create a finished product. And so when you think about something as simple as a bo uh, as a bottle of mouthwash, it is essentially three or four components. You have the chemicals, so like the actual items that are. The liquid that's inside of the bottle. Mm-hmm. You have the bottle, you have the label on the bottle, and then you have the bottle cap. And so what's really fascinating is all of those things are segmented into different production lines for you know, division of labor and, and, and speed of, of production. But what was fascinating is. Understanding and seeing how the supply chain affected the actual financial outcome. Where if for whatever reason you didn't have enough bottle caps to cap the product, you would have a lot of waste, in terms of like the actual mouthwash that's produced because you can't leave it exposed to the elements. And so that is one example. And one way where for each commodity, there's data, there's information that informs how to. Actually produce something optimally. And at the end of the day, you want everything to be one-to-one, but waste exists in the system. And so when you look at other modalities and under other industries, uh, it's just pretty cool to see a, how we have information on all of this stuff. So we know the elevation that planes are flying, we know the speed that they're going at. We see the trains, we see the trucks, we see where they're at. Um, but there's so much optimization to be done and there's so much, room for technology to enable and empower these systems because you go on an airplane and you see the software that they're using to play the messages, and you're like, wow, that, that looks pretty old. Like, how, why does my like Amazon checkout screen look better than that? Or you, you go to the railroad and you see these different silos of where data is living. And it's not unified and, and made cohesive and actionable. So a lot of really cool things to, to play with, uh, in the data space for the physical world. So am Mario, you've spent your entire career in freight looking at the data. Yeah. Understanding the data, slicing it up. What in your opinion, makes rail good or high quality when it comes to data? Yeah, so. I think it doesn't apply to just rail. Mm-hmm. Um, data in and of itself, when, when, when you're handing like a project to a data analyst, for example, there is an entire phase of that project called like exploratory data analysis where the number one thing that you want to do as an analyst, the number one thing that you want do is really understand and contextualize. What is the shape of the data? What are the primary keys? Mm-hmm. So what are the things that exist in this data set that also exist in other data sets so that you can expand associate, combine. so what makes real data good or what makes it what will kind of uplevel it is making it easy to associate, making it easy to attribute, because there's a lot of elements that you're tracking in real time that you want to be able to associate to a shipment. And so the. Issue or one of the fundamental things that we see within the freight rail ecosystem is that there are many different sources for different elements of the data. Mm-hmm. And the data model that you want to get to eventually is kind of like, I guess like an example that I would use is think about your health data, for example. There are so many biomarkers within your body and there's so many things with respect to like your pulse or your sleep. Or your like, heart rate variability. And these are all like things that you can attribute and associate to your body and like that is one unit. Um, what is your favorite biomarker that you, what is my favorite biomarker? I think my favorite biomarker is VO two max. So like the maximum capacity of oxygen that your, that your lungs can hold. But anyways, coming back to like rail and, and, and the like the train space. Yeah. If you think about the unit, typically the unit that a shipper is gonna care about is, is the car. So at the car level, you have the actual container, you have the position of the container, you have the conditions of the container, you have what the container is holding. You have exogenous factors such as weather or light, or these other things that could affect the actual quality and conditions of the product. And so what will make rail data good and what is what I would consider. Good dataset is a data model that ultimately can attribute associate all these things together, and those are some of the fundamental problems that we're working on solving here. Yeah. So when, when you think about the data that may or may not exist, do you think that intermodal shipments require different, different data sets or that rail cars would, would benefit from, from additional data coming from, from the perspective of a shipper or maybe a beneficial cargo owner? A hundred percent. I think when you, when you put something like, uh, intermodal. Uh, side by side by with a rail car, they're competing in completely different markets. And so there are certain commodities that, um, by law you can only ship via a rail car, for example. fluid dynamics in general. Mm-hmm. In a truck. Like you can't just fill a truck with a bunch of water and then expect the brakes on that truck to have the same response if the fluids are like swooshing around and like moving. at the same time, what that liquid is also has a fundamental like effect, uh, from a regulatory standpoint as to like, what can you actually move? You can't be moving missiles on the like North American highway system, and so rail is one of the safest ways to move these. Chemicals that are necessary for like modern life. Mm-hmm. But they're also very dangerous from like a, just being hazardous or flammable or, or high risk. Mm-hmm. High risk, high value. Like that, that's what these commodities are. Um, and within the intermodal space, I think for data to be contextualized, it needs to be correlated directly to its counterpart. In, in the trucking ecosystem. Mm-hmm. And so, ultimately what you want to provide a shipper insights into is you want to give them all of the information so that they can make the trade-offs financially, uh, from a time standpoint. Uh. To ultimately move their supply chain. And like we mentioned before, and, and are aware of not every commodity has the same kind of criticality. And so food, uh, that's perishable. Like you need to move that within a certain window, but rocks that you're getting from a quarry, um, oftentimes you see those on like the side of the highway and they're just like stacked in like little pyramid. Mm-hmm. Um, so yeah, it, it varies by commodity, it varies by shipment and ultimately it's all about providing the data to the shipper and giving them those insights. Definitely. What would you say are some common misconceptions when it comes to the data that powers the rail space, both intermodal and rail I mean, I think a, a common misconception would be that the standard is followed. And so one thing that I would say about that is, we live in a world where. We are still operating a lot of things off of like EDI messages and the EDI language. And so EDI stands for electronic data interchange. It's just a data format that essentially is line separation of values and it's positional. So like in this position for this segment, I will know this attribute. Those attributes can be things like the origin city, the commodity. The shipper, the person that's paying for the freight and EDI as itself is an encoded language. And so, when you think about the, the, the misconception that I would point at is like, oh, we are using a 3 22 message, or we are sending a 4 0 4 message. Mm-hmm. And your assumption is that across the entire. Real industry, so especially the largest players, that when they say they're using that standard, that it is uniform. Um, but within those standards, you have versions. And so one interesting nugget is for a lot of the way billing that happens today, we're still leveraging the 1997 standard. And, and that one hasn't been. Uh, replaced, uh, or for, uh, some other, elements. You're upgrading the standard every year, but the railroads are only adopting it every other year. And so those kind of key insights as you get deep into the data and you understand what is actually happening, uh, when is this being used? This version just got upgraded. Do I need to upgrade? Do I not need to upgrade? Mm-hmm. Those are secondary questions that you need to ask. To provide a good experience with the data that one would assume is standardized. Mm-hmm. And so, and do, what is it, do you, do you believe that in your work with a lot of shippers and three pls that they, when they're, when they've got a, you know, a large monolith ERP or they're running a really best in class TMS, that they are appropriately updated or made aware that these changes need to be made? And if so. Is this normally an area of focus where they want to spend a lot of engineering and technology resources getting on the correct standard to make sure that the, that the data flows correctly? Yeah, I mean, I think, I think there, it's a, it's a mix of reactions. It's a mix of different things succeeding and failing, uh, in the past. And so one of the primary reasons why you don't want to change the standard for sending an order to the railroad. is because that would be incredibly disruptive. Mm-hmm. and uh, within the rail ecosystem, it's an evolving mass of players, right? So you have, uh, what used to be thousands of railroads now, uh, consolidated down to a little bit over 600. Mm-hmm. And as a result of that, underlying data has changed, pretty, pretty significantly. And so it's interesting to see those changes and just. Try to be prepared and anticipate, but I don't think that most shippers and three pls want to be thinking about, do I need to update this? Do I need to upgrade that? I think they just want to focus on their supply chains. They want to focus on getting their products to their shippers, these data problems should be an afterthought. And so. What is really important is to provide that best in class experience and make it parallel to the experiences that you have in the rest of your life. Mm-hmm. why should the targeted ads that you get on social media be the best quality data and not the things that you're getting shipped to you on a day-to-day basis? What would you think? Targeted ads are high quality. I think that the mechanisms and the technology behind, everything that's gone into making a targeted ad convert is, is high quality. Like I think to a certain degree. It's pretty interesting. You go over to somebody's house, you're on their wifi, you look something up, and then they get an ad for the thing that you looked. That was in conversation and it's kinda like these ghost ads and like, I think we've all been freaked out at some point of like, how did you know that I was thinking about going Hawaii? Are you inside my brain right now? Now? Yeah, exactly. Right. And so those little things of like, who came up with that? Mm-hmm. Like who came up with, Hey, Sydnee comes over to hang out at my house, we're talking about this. She Googled something that I brought up in conversation that she had no idea. And then. And then now I'm getting a targeted ad to, to buy it. Like, it's pretty cool. I think one of, one of the interesting questions that, that, that I have, you know, either thinking about those ads or thinking about the, the high quality data and, you know, advertising is somewhat fungible and maybe you, you convert or you don't convert. What, what do you think about, why should shippers care about great data? Like what, what, why does it matter? I mean, the industry's been around since 18 hundreds. The, the data standards that have existed. EDI has been in full effect since the eighties. Really came into be in the 1990s. Why in 2025 should participants in the rail ecosystem really care other than they're getting ads for it? No, I'm kidding. Yeah, so I think the reasons you should really care about the quality of the data. Is because it sets a precedent for what's to come, and it really kind of sets the standard of the evolution that we're inevitably going to. And so artificial intelligence is a really good example of the amalgamation of a lot of data being trained, uh, being leveraged in a specific way. And, what you've seen probably when you're trying to either build a mini model or you're building your own. like training data sources, the quality of the data strongly affects the output. Mm-hmm. And if that's the direction that we're going, like if that's the inevitable place that we're going, where again, at the end of the day, the thing that you want to do with technology is you want to think less, have better outcomes, have higher quality outcomes, and, and ultimately get to the thing better. And so, supply chain and transportation. Should in theory, be as cost efficient to increase the production, increase consumption, decrease waste. Mm-hmm. And so if we can get to a place where by having higher quality data and having more insights. Uh, in, in the ecosystem, you can, you can reduce that waste altogether. And so the fact that there's a lot of empty cars, for example, sometimes being used as like just storage for, for commodities at times. Like just kind of moving those commodities around when you're trading commodities at a global scale. and kind of like those insights as to like, where can things be and, and what's the most efficient way to position things. You want that to be done in an automated fashion. You want those decisions and those recommendations to be made for you so that you can do more of that. Mm-hmm. Instead of having to be very pointed and have like one very sharp person that's like arbitraging the entire system. Yeah. I mean, I, I think Amara what you really, what you really pointed out there, and I think you created a really interesting. Understanding as to why folks should care about data within the broader rail ecosystem, because it, it effectively powers a lot of the decisions that, that they may make or potentially not make, right? Mm-hmm. And so they may decide that based on the information that's coming to them, they can better plan the allocation of resources in a specific facility, or they may schedule their drayage pickups. In a, in a different way so that they can handle kind of the inbound or outbound flow of, of any of their like processing facilities. Right? I mean, is is that kind of what, where you're thinking a hundred percent? Yeah. Those are all very practical applications of like just seeing the thing. one of the things that I tell the team, and one of the things that, that we've talked about a lot is, you know, telegraph can sometimes be perceived as, oh, we, we really focused in on like. How do you see the shipment? Mm-hmm. And people always want to do more than that. And what I tell people is, you can't optimize something that you can't see. And so if you don't have the information mm-hmm. You're not gonna be able to do anything valuable without, with it. So, Sydney, I, one of the things that I, I kind of think back to your experience in trucking. Mm-hmm. And then right now more broadly in rail, why do you think some of these legacy industries. Struggle to really adopt new technology or see the need to actually increase the usefulness of the data. I think it's very much the problem of, well, we've always done it this way. Mm. You know, it's that classic. Why change, why shift? It's good enough. We've always done it this way. And kind of keeping, marching that same tune I think with where, you know, technology is in your real life tangibly, it's so much more advanced that we're starting to see that push of people wanting more, from their. Their workflow life from their, you know, matching their actual personal life mm-hmm. To their work life. Mm-hmm. Mm-hmm. And so I think that we definitely suffer from a good enough problem mm-hmm. In the industry. But Amaro, I'm curious as to why do you feel like from the data perspective, what we have, whether it's from EDI. It's good enough. How come there hasn't been a push until now? Why do you feel like the industry suffers from a, a good enough data problem of this is the way it's always been, been done? Yeah, look, I think at the end of the day. it goes back to if it's not broken, don't fix it. Mm-hmm. And a lot of people don't see the breakage. A lot of people don't see the fact that they're, that things can be done in a different way. Yep. And so, you know, our job is not to tell people this is how to do things. Our job is to show people this is how things can be done. And, you either opt into that or you don't. You either opt into doing things a different way. Or you don't. And at the end of the day, our job as a technology company is to predict where's the world going to be in 5, 10, 20 years from now and prepare for that eventual world. Mm-hmm. The reality of the supply chain and the logistics industry is oftentimes. You're thinking about what's in the top of my inbox today? Mm-hmm. How do I solve this critical problem? How do I move this ship from the Suez Canal mm-hmm. So that it stops all of this rerouting. Yeah. Um, and those are critical, important problems that people do need to be focused on today. The thing is, how do you solve those problems more quickly so that you can get into that long-term planning and run a more efficient supply chain? Mm-hmm. And so. There is a lot of inefficiency. Uh, when you think about things like pricing, for example. when you run year long RFPs and you're asking transportation companies to essentially give you pricing for an entire year, they have no idea what's gonna happen. over the course of the year, you might have more oranges coming out of Florida, one year. Than the other. Or you might have a wildfire that disrupts like the transportation of certain goods from a certain region of the country. And that's going to create a very dynamic system that needs to be reactive to what's happening in the real world. And what that does is that gap between what was predicted and the actual starts to come out, as the supply chain operates through that course of the year. So that inefficiency exists. And, at the end of the day, our job is to predict it. Our job is to kind of see where the goalpost is and try to like get there, ahead of time and ultimately give people the tools that can accelerate that planning, uh, lifecycle and ultimately make it more efficient at their jobs. It's interesting when you said breakage, because to me that is so true as a former operator. You know, you're so much in the headspace of playing the game of whack-a-mole. Uh, what fire do I need to put out? Right? What thing do I need to focus on? Right? What has my urgent attention and let me prioritize that. You get so Hellen on maybe old processes or just getting, you know, your hands dirty and doing it manual. You can't even think about 10 steps ahead of you. What's in the future. Correct. How can I actually advance this with technology, with better data, with better processes? Yeah. I think it's an interesting, you know, point that we, we kind of face in our industry today, right? I, I think that the, the rail industry has benefited from economies of scale and efficiency. Yeah. And all, all of the tailwinds that make it such a good choice to move things from point A to point B. The challenge with that is oftentimes you can lose the thesis of. Sometimes investments that we all might, might make. I mean, the, like the core thesis around making an investment somewhere is that at some point you anticipate an expected payoff, right? Mm-hmm. At some point, that is why you would, you would exert the energy or set aside the, the capital or the resources to, to make that decision. One of the things that I think we oftentimes lose sight of, because we, I mean supply chain and transportation, part of the reason why it makes it so fun is you're oftentimes just reacting to a lot of things that are going on, totally reacting. And so it's hard to understand why data upstream flows into a better process later on. It matters later on. Yeah. And, and, uh, when we think about, you know. An entity that is a 140,000 miles of track and 600 different railroads all operating together, it's hard to, to even go back to the idea of a standard. Mm-hmm. Because that means all of the different bodies would have to invest in that same ideology in the same way and in a somewhat competitive environment. That kind of goes against the way that I think folks wanna operate. Yeah. A hundred percent. Alright, so we've been thinking about, and. Honestly, candidly, talking quite a bit about why data matters at a, at a very high level, and we, we've talked about some applications, but I mean, candidly, we, we started Telegraph to make a difference in the lives of other people in the rail industry. Mm-hmm. And so Amaro, I, I'm super interested. Before we kind of dive into like the industry, how do you feel like good data has affected you personally? And, and maybe give, give some examples of like, like why data matters in your own. Life and how it changes the way that, like between the time you get up and the time you go to bed, like how does it change what you do or decisions you make? Yeah. I think good data at the end of the day is something that you don't question. So it's about trusting. The information, it's pretty easy to know when something is messed up and you'll be skeptical about it. I've spent a lot of time in spreadsheets. I know kind of like what to look for and really, oh, yeah, you, you know this for a fact. Uh, kind of know what to look for and like when something looks fishy or funky, but. Good data is honestly, it shouldn't interfere. It should just be reality. It should be perfect information. And I think a couple of examples of people doing this in their day-to-day lives is it was not uncommon to go to multiple stores on any given weekend and you're looking to buy this one specific thing and you're, you're going to Best Buy and Circuit City. Mm. And like these different like places to. Figure out am I getting the best price? And today most consumers will go online and they will take a price at face value, right? Mm-hmm. Like you will not do like the price comparison, like you will. You want your thing to be shipped to you in two days. I'm gonna click buy now and I'm not questioning it because I assume that the pricing has been done for me and that I'm getting the fair deal, right? Like, yes, there's all these other things that could be causing a shortage in the specific thing, like we recently went. Through the the eggs pricing thing. And like you, most people don't order their eggs online, but for the most part, like you knew that there was a shortage. You knew that the stores were going to jack up the prices and that there was gonna be a limit to the number that you could buy. And you kind of accept that as a consumer, that like the price is what the price is. And so I think it needs to be non-disruptive and it needs to be something that you fundamentally trust. again, in, in the consumer perspective. when it comes to other things like your health, for example, there are people that are always seeking higher quality data. And so, anecdote that I would point to is, Harris, you've run a marathon, for example, and the most intense people that you see at the, at the marathon, they're wearing the, like, the chest, the heart, heart rate runners. Yes. And they're, they're trying to get a much more precisive. Precise reading of their heart rate so that they could be in like a specific heart rate zone. but for, I don't know, I would call us more like amateur runners. Like we would just, I wouldn't even call myself that. Well, you know, like you, you probably are happy with the output of your Apple Watch and Yeah. You, you know that it's not gonna be as precise, but, but you trust it. That's good enough. Exactly. And so that's the thing is like the ma, the amount that it. Matters to you is gonna make you seek out the, the higher quality, alternative. But there is such a thing as like the 80 20 principle where it's like, if something is like good enough, I'm gonna accept it. And that's the reality of, of how a lot of things are in, in the data ecosystem is like, you accept it, it's good enough for you, or you seek out that best thing because you have a specific goal that you're, that you're going for. And so I. It kind of sounds like what you're saying is that there is a, there is a difference between just accepting good enough versus actually striving for, and there actually is a measurable and remarkable difference between good enough and actually high quality. Mm-hmm. So much so that if you are planning a billion dollars in transportation, spend. High quality actually really matters to you a hundred percent. Do you think at times there have been Marvel movies? Good one. That's a good one. Thank you. Do you think at times there have been periods, especially in supply chain and transportation, where data has suddenly been made available or new data sets or out there, or you could potentially combine different data points? For sure, but people have not seen the value in that right away. And give me an example of that. Yeah, I think an example that I like to point to is the ELD mandate that came mm-hmm. Uh, into the trucking industry. going back to a very. Tangible point in time that we can look at where people have done things a certain way for a long period of time. And for the audience to just kind of contextualize what is an ELD, it's an electronic logging device. It's a device that was put into trucks to monitor driver, safety. So like attentiveness position, but also just like measure driver hours. Mm-hmm. And that's like the number one thing. So the Department of Transportation has regulations on the number of driver, the number of hours that a driver can drive consecutively. So no more than 10. And the number of hours that a driver can, drive on any given day. So out of the 24 hours of a day, they can drive 14 hours. That's, that's pretty crazy if you think about it. Some of these drivers are completely built different, and in terms of making the industry safer, like you want to measure that. And how did that used to be? done pre 2016? Pen and paper and paper. So literally you would write down, this is when I started driving, this is when I stopped driving. And as you can imagine. the incentives for a driver are the more I drive, the more money I can make. So there's an inherent incentive to, not be honest, not be truthful. I'm not accusing anybody of not being truthful, but the reality is that that's, that's something that would happen and, you would see that come into effect where there was like the DOT week in the trucking land mm-hmm. Where you're actually checking the logs, of the drivers to make sure that they're being compliant. and at that point in time you would always see a shortage of drivers. Mm-hmm. And obviously that tells you one thing. It means that there are bad actors in the space. And so when you look at the, the actual mandate of ELD devices, um, at first it was all predicated on this idea of safety. You know, it's a government regulation. Mm-hmm. Mm-hmm. The objective is safety and the data that people are looking for are. It is, are the roads going to be safer? Mm-hmm. Are we going to have less accidents? Are we gonna have more compliance with respect to like these, these hour, uh, regulations? What people did not see was the derivative effect of an entire market that that created. And so what you have now by virtue of measuring driver hours, and you also have essentially a GPS device in these trucks, is you now create an entire market for aggregating. Those ELD devices into one place and provide that visibility. And so fast forward to today, you don't have to do that call because you know the position of the driver. And so whose life got better? The driver's life got better because I'm not getting pestered by Sydney like asking me every 20 minutes. Like, you're, you're never, where am I? I promise and Sydney's happy'cause she doesn't have to deal with my frustration of her calling me as a driver. and getting those updates. And so it's a win-win, uh, situation. It's a paradigm shift. and then that leads to the moment you have that data, the moment that you have that information, it leads to so many more things that can be done. and it just takes the paradigm shift to happen. And so I think we're seeing a lot of those paradigm shifts in rail, as well. And it's, it's very interesting to see how the industry's evolving. Yeah. Yeah. So I mean, it sounds like what you're saying is there was this completely unknown. Opportunity. Mm-hmm. That happened as a result of regulation and new technology being developed, and then o obviously applied. And I think that's, that's one of the really, really interesting things. I agree with you, that I think we are seeing in the rail space that we don't know what it's gonna look like in five years, but going back to your point tomorrow, I, I think you, you've said a lot that. Our job in the technology space is to actually plan for the future. Yeah. And so we have to lay a lot of that foundation today. And so Sydney, I mean, I mean, kind of thinking about that, are there some advances in technology that you're particularly excited about? I would say telematics has me, jazz. Okay. And I think interesting. Telematics in my mind is similar to the ELD mandate. You know, it ties back to not having to just assume or constantly check, is this car gonna be on time for delivery? Where is it at currently? Right, right. I know where it is. And that is something that I think is gonna catapult the industry forward, light years in the next few years as well. For sure. Amara, I'm curious as to what is a technological advancement? You are jazz about or you think is something we should be thinking about in the rail space? Yeah, I think you, you hit it right on the head and that that's like the most practical thing that we could point to and look at. Where, there is going to be an evolution of the data layer within the rail ecosystem. That's good point. And going back to where is the future going? the future is gonna go towards more connectivity. There is gonna be more data. So these data problems are gonna be exacerbated. Mm-hmm. But at the end of the day. It is so clear, and you can see it in our platform today, the tabulation between what is A-C-L-M-A car location message look like versus a telematics event. And the very clear shift between filling in these gaps where there are sometimes miles in between scanners that are scanning these cars and giving you a location update versus seeing that in real time and the additional data that you get on top of it. And so I think. As far as ex excitement, getting that data in is gonna be awesome for position. Right? Yeah. It's gonna be awesome for prediction. Yep. It's gonna be awesome for performance. Mm-hmm. Right. You're going to be able to also have derivative associations that come as a result of that. And so with telematics in the telematics industry, what I would say to the rail industry is. You actually don't need to equip every rail car. Now you just need to hit the critical mass because the moment you hit a mass of 10 to 20 to 25%, I don't know what the exact number is gonna be on any given mile long train. If you have that data point and you can attribute it to the rest of the train, correct. You now have a much higher fidelity view of, mm-hmm. What is happening with all these commodities? What is where, what is the performance? Where are there maybe delays or inefficiencies in the system? And again, some of these things are things that you don't control. And so being able to see that in real time, being able to predict that is going to ultimately catapult the industry to be, more predictive and less reactive. Well, I think it's interesting too because you're talking about the ripple effect of what is telematics and what are those ripple effects it's gonna have. And when you think about it with telematics, you have filling in data gaps that we might currently have in this space. Sure. Absolutely. With that, you can actually start to trust more route optimization. Yeah, true. And what is the best, most effective route, and what are those actual transit times? And that has a segue into pricing and how do you actually start to automate rates and pricing from that. So it starts to really, I think, catapult and Oh yeah. Spin off in a positive way. Right. Yeah. And I think at the end of the day, it's. Competition is good in the broader supply chain ecosystem. And uh, yes, we work within rail and we want rail to succeed, but at the end of the day, the decision is up to the shipper. Mm-hmm. It's up to the shipper to decide what experience do I want to have? And you have to make the trade-offs very clear to them in a very, transparent way. And so if you are purchasing. If you're procuring transportation and you understand the price of a trucking shipment mm-hmm. Uh, the price of a rail shipment, the time that it's going to take for that truck, the time that it's gonna take for that train and the carbon emissions associated with the truck. Bingo. Yeah. Versus the train, you're gonna make trade-offs within your own supply chain and, ultimately, pick what's best for you. Mm-hmm. And our job is to make it as easy yes as it is currently to procure a truck as it is to procure a train and help. People procure that rail car for the very first time. Mm-hmm. And help them see how easy it is to actually ship by rail. And that's the beauty of having really good, useful data is we can just make it easier. A hundred percent. Absolutely. So folks really, really appreciate you taking the time to listen in to another episode of Don't Dwell on It. Amma, thanks for making the trip out to Chicago. Great to see you this week Sydney. Thanks again for joining. and let's see where this journey takes us. Awesome. Thanks for having me, y'all. Appreciate it. 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