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[on-hold music] Hey, everyone. Thanks so much for joining me on the Slice of Construction podcast. I'm your host, Jared Taylor.
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Joining me today is the CEO and co-founder of Look, Dominik Maier. How's it going? Real pleasure meeting you, Jared. Thanks so much for having me today. I'm excited to have you here. Let's dive right in.
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Tell the audience a little bit a-about yourself. Yeah. So it all started a few years back. I came out of UC San Diego, got my PhD in 3D computer vision.
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Um, we were building camera systems and AI for self-driving vehicles. Um, and that's where we figured out that, you know, all this camera technology could allow us to create 3D models,
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um, at a scalability point that previously wasn't doable, right? And that's exactly where Look came to be.
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Uh, so two, two and a bit few years ago, uh, we founded Look in my garage, you know, looking at how do we create technology to digitize our critical human infrastructure and to be able to improve how engineering workflows in the day-to-day field operations happen, and that's exactly what we built.
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So your, your background obviously very unique and m- aligns really well with what you decided to build here with Look. I, I guess maybe did you, did you always know you wanted to start a company?
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Did you know that, um... Was this always a space of, of interest? Or, I, I guess tell our audience w- when was that kind of light bulb moment to decide, "Okay, I wanna do this"?
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[lip smack] So it was a gradual journey, right? I've always loved building things. I always loved bringing technology to people and, uh, changing they work- the work they do, right?
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And that's really for, for the impact, uh, on society, on the impact on their lives.
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And as we started building this tech, right, we, we were like, "Wow, this really can have a massive impact onto these engineering workflows." Um, so we started testing it out, right?
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And we called up some general contractors saying, "Hey, can we take it to your site to try it out?" Um, and all of a sudden they were like, their eyes lit up.
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They were like, "Wow, now we can, in 3D, see everything that is being done on a daily basis," right? That's just a concept that, that is new.
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Um, and with that buzz we were like, "Okay, well, let's incorporate the company. Let's, let's get going. Let's build out the tech. Let's, let's hire some people. Let's go raise some money."
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So it was a really exciting journey that all kind of came together, uh, as we saw what the impact could be of this technology.
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Let's, let's give-- I, I know you gave us a little bit of an, an intro to Look, but give us the you run into someone on the street, they say, "Hey, what do you do?" What do you tell them? Yeah.
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So the two-second elevator pitch, right?
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We build 3D cameras and AI to digitize our critical human infrastructure, helping engineering companies, utilities identify mistakes and issues and faults early, and using these models to be able to optimize their engineering processes, help them fix things, help them improve, uh, what their current assets are.
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And then being able to put a glimpse into the future, right? Predictive maintenance. How do we now operate these assets?
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Um, and that's really where we're excited about Look is, is changing how we do day-to-day construction and engineering.
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One, one of the things I wanted to ask you is in a building basically 3D mapping technologies is not easy, right? Um,
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what were some of the challenges of transitioning to these technologies from, I guess, your, your early proof of concept to, um, to continue to be, like, one of the market leaders?
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So one of the ways we differentiate is, is we use cameras only and no lasers whatsoever, so no LiDARs, uh, some, some people may be fa-familiar with. Um, and using cameras itself is a challenging, uh, thing. Why?
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Because it's very difficult to have reliable 3D reconstructions at a quality level that is needed, uh, for this kind of engineering work.
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So my co-founder, Shreyas, um, and I, you know, spent a long time, you know, figuring out how do we get our system, our algorithms to a point of reliability that can scale, right?
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And that's really where, where the crux is. So he has developed, you know, the, the core AI algorithm that gets us to that stability point, that quality point that the industry is just blown away by, right?
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Like most surveyors, most engineers look at our data and are like, "Wow, you can just do this from cameras?" And the answer is yes.
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Purely from cameras we're able to reconstruct density of 3D points, measurements with accuracies, you know, sub-centimeter, uh, global positioning, reconstructing wires and power poles, all those features that have inherently been just very difficult to reconstruct from any kind of image-based, uh, methods.
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And, and which stakeholders would you say, uh, in this space care about this the most, um, that get-- uh, I'm assuming the ones that get the most excited when you, when you tell them. Um, but, but w-who, who are those?
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Like, what's that target audience look like, I guess? [lip smack] Yeah. So we really serve a few, uh, segments within the engineering sector. First of all, our surveyors, right?
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They're the ones actually, uh, on side of the road in areas with total stations and actually mapping out those environments to a really high degree of accuracy.
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[lip smack] And, you know, we, we help them save time, operational costs, capital costs on, on their workflows. The second tier of, uh, customers, um, are engineering companies, construction companies.
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So basically those that run big projects nationwide, statewide, right?
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Um, they, you know, upgrade our infrastructure, they fix our infrastructure, um, and they really use this technology as a way to get very fast feedback from the field.
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Because inherently, right, if you don't know what's happening outside, you can't design for it, you can't accommodate for it, you can't identify issues early, and that's really where Look comes in.
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And the final, you know, category of customers are really in the utility space, right? And those are the big utilities that are operating, uh, you know, large electrical communication assets.
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Um, and they're interested in looking at what is the state of health of these assets, right? Being able to get a glimpse on, you know, where are things going to fail? Where could they fail?
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What would it take to fix those areas? Um, so we really do span any area within infrastructure that interfaces with the built world.
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And, you know, if we look at our society and environment, everywhere you look, there is infrastructure we rely on. Is it roads? Is it power lines that gives electricity? Is it water pipes that get us water, right?
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That's-- these are all critical human infrastructure assets. And, and Dominik, starting a company is not easy. Building a company is, uh, even harder, right?
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What were some of the early challenges that you faced that you don't mind sharing, that you were able to get past and, um, you know, kind of how did, how did you end up doing that? Yeah.
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So, you know, a funny story is that we actually started with a robotic system.
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Um, so before we went to a handheld device, which is, which is our current product, um, we started with the assumption that, hey, wouldn't it be nice is if that every night on a construction site, a robot crawled around and captured all the data and brought it back to, to the office, and the next morning the superintendent sees all the mistakes, right?
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That would be an ideal world. Well, that was a massive failure, right? Like, we figured out that robots do not belong on a construction site. They don't climb stairs well. They don't deal with the mess that people leave.
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They don't deal with ditches, right? They don't deal with hazards. They fall off the side of, of concrete pours, right? Like, all these things are like, well, humans are really great. They know how to get around things.
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They know what to capture. And that was, I think, one of the first big like aha moments. You know, let's, let's build something to help humans rather than to necessarily replace them.
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I'm picturing like a Roomba falling off a building or fall like it-- or going in a ditch, right? Like that poor, that poor robot. Um, wow. Uh, so that's actually...
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that's super interesting 'cause in, in other spaces, right, like, so when you're talking about like the, the three-D mapping of a home, that made sense, right? Because what hazards could it run into?
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But when you really look at the construction space, there's just plentiful opportunities, whether, like you said, it's a ditch or falling off a concrete pour. Um, that is so interesting. Um, so how long did that--
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If you don't wanna share, that's fine. How long did it take to say, "Hmm, that's probably..."
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This is kinda like the actually the Amazon, uh, I don't know if you ever heard the Amazon knee pad story, uh, where they were pack- packing up boxes and, and, and Bezos said, "We need knee pads."
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And someone goes, "Maybe packing tables instead." Right? It's kinda like that moment. Um, how long did it take to say, "Okay,
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w-we need to create, uh, technology that people can use, not necessarily using a, a robot to be able to get that information"?
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So we maybe spent a month and a half building the robots, and then spent about a week tossing it in the garbage, starting on our new system.
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So, you know, in startup world, it's, it's a very much like you learn, you fail quickly, you build a, a new version.
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And we have gotten to the point that because of how fast our engineering team is, we can get prototypes up and running in, in a matter of a week, right?
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So you just iterate, iterate, iterate, test it out, get it to the field, bring it back, listen to your customers, listen, listen, listen.
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And with that feedback loop, we were able to really narrow down to what is a product that people will absolutely love, right? It's compact, it's small. They can travel with it. Um, it looks good, right?
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And these are small things, but as you start scaling it, as people start seeing it in their environment, they care about that. And that's what we really care about at VP as well. What's, what's next for you?
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So I'm, I'm assuming outside of like the construction space too, there's, there's other potential applications for this technology. Do you have any plans to go, um...
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you know, we obviously focus on the, on the construction side of things on this show, but always curious to, do you have plans to expand into other industries as well? Yeah.
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So any industry that has physical assets, right, we really can target. Um, we are an AI-first company. And what we mean with that is that we are heavily focused on driving the intelligence piece of these assets, right?
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Um, so what we've built now is the technology to be able to capture them at a scale point that previously wasn't available.
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And what we're doing now is really focusing on what the intelligence piece comes into play, right? So let me use some examples here. [lip smack] Uh, let's look at some of the power line infrastructure, right?
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Uh, you know, today that is inspected on average every five to 10 years because utilities can't keep up with that inspection schedule. Our technology allows them to do that all faster.
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We're talking once every one to three years. And with that, all of a sudden we have the ability to go back and say, "Look, this is the list of, of issues or things that could be a-an issue in the future."
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So bringing that AI piece in there allows us to really future safe, uh, the technology for a market that inherently needs more intelligence around these, you know, ancient assets.
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Um, 70% of our critical human infrastructure assets are over 25 years old, right? Uh, and with that, they're falling apart. We, we need to be able to find better ways to, to, to fix them and, and improve them.
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Um, and really that's, that's where it comes in. In terms of other sectors, you know, is it oil and gas? Is it, um, more on Department of Transportation assets? Is it bridges? Is it dams? Right?
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All of these assets, they are old. They need maintenance. They need better technology. So our technology will directly impact all of those, uh, as, as we scale. If you don't mind, Dominik, walk me through, um,
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with, with one of the surveyors that you're working with today. You introduce your technology to them. They decide to use it. What does that kind of day in the life look like for them
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using the technology for the first time? Yeah. So maybe it's important for me to start on what a day of their life looks like before they use the technology and, and then after they, they use it.
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So, you know, they normally have a set of crews, survey crews, so let's say anywhere between two and five crews of two people, one truck.
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Um, they go out to a site, and then they set up control for a few hours, and then they put up their total stations and then take measurements, and then take those notes on what those measurements are, where they are, and that'll go, on average, I'm gonna say, uh, you know, for three to eight hours per day, right?
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So these can be long days, very tedious, very repetitive tasks. Uh, now with our technology, you know, they, they can maybe wake up an hour or two later, uh, so they don't have to get up at 3:00 in the morning anymore.
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Uh, because when they get to sites, all of a sudden all their work can be done in a matter of an hour, right? Um, so what that means is that, first of all, they need less people on their crews, right?
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So they can go to a single person crew, uh, for safety reasons, uh, because our technology does not mean they need to stand in the middle of the road for three hours.
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Um, they can, uh, start getting a lot more information for their clients, uh, a lot faster. They can get the feedback quickly on whether the data they, they need is, is, is good, right?
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And that's, that's a really important part. And what that means is now they start doing two to three jobs in the same day, right?
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So they just knock out two to three times the amount of work, and at the end of it they're like, "Well, this was super efficient."
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So all of a sudden we've seen that these survey crews and survey companies are excited about, "Hey, we're getting a lot more value out of our manpower in the field.
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We're getting a lot more value out, out of the time we're investing. We're improving safety conditions.
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And bottom line, well, we're saving money and making more money because this tool allows us to do so much more work for a lot less."
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What's, what's next that you'd like our audience to know in terms of, uh, what's next for the company?
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So we're very excited about some of the AI releases that are, that are gonna be upcoming, um, in the different space across survey, across utilities. So stay tuned as we release those product features.
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Um, and we're excited to be continuing to work with a lot of the leaders in the industry, right from the largest engineering companies to the surveyors, right? Running, running their, running their companies.
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And, uh, you know, s- feel free to reach out for any questions about our technology, and we're happy to, to walk through customers. I always like to say, "Don't believe anything we say," right?
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Like, we care about trust, and that c- trust comes through accuracies. The proof is in the pudding, which is in the data. So, uh, come on out, see how it works, uh, believe in the data and take it from there.
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Why do you think now is the best time for this type of technology? So we're just at the cusp of really seeing a shift towards 3D capture, uh, from what is traditional methodologies, right?
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And, uh, as our society and as our industry swims in a sea of data, it's becoming es- increasingly important for us to extract value out of that data, right?
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I have way too many companies that come to me saying, "Hey, I have 100 terabytes of LiDAR data, you know, that we don't know what to do with. We can't even open them on our computer."
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And that's where like, okay, well let's look at the problem. Let's, let's figure out what is actually needed.
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Um, and with Lok, we've really focused on extracting the key parts of information, the key parts of value to help processes, uh, but to not overburden and not to overwhelm what this industry is.
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And I think now with the AI, um, appearance, right, we are, have been able to really value what comes out of these workflows rather than just overwhelm people with a ton of data.
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Uh, so we're excited about getting, getting that actionable value out to, to everyone.
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Well, Dominik, I'm really excited to continue to see how you grow the company, and we'll definitely have to have you come back on in the near future to give us an update on where things are at.
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But, uh, kudos to you and the team for everything that you've built thus far and for being an AI-first company. I would like to hear more people start saying that. Absolutely. Awesome. Thank you so much, Aaron.
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Would love to be on a future show as well. [upbeat music]
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[on-hold music] Hey, everyone. Thanks so much for joining me on the Slice of Construction podcast. I'm your host, Jared Taylor.
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Joining me today is the CEO and co-founder of Look, Dominik Maier. How's it going? Real pleasure meeting you, Jared. Thanks so much for having me today. I'm excited to have you here. Let's dive right in.
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Tell the audience a little bit a-about yourself. Yeah. So it all started a few years back. I came out of UC San Diego, got my PhD in 3D computer vision.
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Um, we were building camera systems and AI for self-driving vehicles. Um, and that's where we figured out that, you know, all this camera technology could allow us to create 3D models,
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um, at a scalability point that previously wasn't doable, right? And that's exactly where Look came to be.
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Uh, so two, two and a bit few years ago, uh, we founded Look in my garage, you know, looking at how do we create technology to digitize our critical human infrastructure and to be able to improve how engineering workflows in the day-to-day field operations happen, and that's exactly what we built.
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So your, your background obviously very unique and m- aligns really well with what you decided to build here with Look. I, I guess maybe did you, did you always know you wanted to start a company?
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Did you know that, um... Was this always a space of, of interest? Or, I, I guess tell our audience w- when was that kind of light bulb moment to decide, "Okay, I wanna do this"?
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[lip smack] So it was a gradual journey, right? I've always loved building things. I always loved bringing technology to people and, uh, changing they work- the work they do, right?
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And that's really for, for the impact, uh, on society, on the impact on their lives.
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And as we started building this tech, right, we, we were like, "Wow, this really can have a massive impact onto these engineering workflows." Um, so we started testing it out, right?
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And we called up some general contractors saying, "Hey, can we take it to your site to try it out?" Um, and all of a sudden they were like, their eyes lit up.
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They were like, "Wow, now we can, in 3D, see everything that is being done on a daily basis," right? That's just a concept that, that is new.
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Um, and with that buzz we were like, "Okay, well, let's incorporate the company. Let's, let's get going. Let's build out the tech. Let's, let's hire some people. Let's go raise some money."
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So it was a really exciting journey that all kind of came together, uh, as we saw what the impact could be of this technology.
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Let's, let's give-- I, I know you gave us a little bit of an, an intro to Look, but give us the you run into someone on the street, they say, "Hey, what do you do?" What do you tell them? Yeah.
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So the two-second elevator pitch, right?
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We build 3D cameras and AI to digitize our critical human infrastructure, helping engineering companies, utilities identify mistakes and issues and faults early, and using these models to be able to optimize their engineering processes, help them fix things, help them improve, uh, what their current assets are.
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And then being able to put a glimpse into the future, right? Predictive maintenance. How do we now operate these assets?
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Um, and that's really where we're excited about Look is, is changing how we do day-to-day construction and engineering.
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One, one of the things I wanted to ask you is in a building basically 3D mapping technologies is not easy, right? Um,
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what were some of the challenges of transitioning to these technologies from, I guess, your, your early proof of concept to, um, to continue to be, like, one of the market leaders?
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So one of the ways we differentiate is, is we use cameras only and no lasers whatsoever, so no LiDARs, uh, some, some people may be fa-familiar with. Um, and using cameras itself is a challenging, uh, thing. Why?
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Because it's very difficult to have reliable 3D reconstructions at a quality level that is needed, uh, for this kind of engineering work.
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So my co-founder, Shreyas, um, and I, you know, spent a long time, you know, figuring out how do we get our system, our algorithms to a point of reliability that can scale, right?
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And that's really where, where the crux is. So he has developed, you know, the, the core AI algorithm that gets us to that stability point, that quality point that the industry is just blown away by, right?
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Like most surveyors, most engineers look at our data and are like, "Wow, you can just do this from cameras?" And the answer is yes.
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Purely from cameras we're able to reconstruct density of 3D points, measurements with accuracies, you know, sub-centimeter, uh, global positioning, reconstructing wires and power poles, all those features that have inherently been just very difficult to reconstruct from any kind of image-based, uh, methods.
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And, and which stakeholders would you say, uh, in this space care about this the most, um, that get-- uh, I'm assuming the ones that get the most excited when you, when you tell them. Um, but, but w-who, who are those?
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Like, what's that target audience look like, I guess? [lip smack] Yeah. So we really serve a few, uh, segments within the engineering sector. First of all, our surveyors, right?
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They're the ones actually, uh, on side of the road in areas with total stations and actually mapping out those environments to a really high degree of accuracy.
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[lip smack] And, you know, we, we help them save time, operational costs, capital costs on, on their workflows. The second tier of, uh, customers, um, are engineering companies, construction companies.
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So basically those that run big projects nationwide, statewide, right?
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Um, they, you know, upgrade our infrastructure, they fix our infrastructure, um, and they really use this technology as a way to get very fast feedback from the field.
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Because inherently, right, if you don't know what's happening outside, you can't design for it, you can't accommodate for it, you can't identify issues early, and that's really where Look comes in.
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And the final, you know, category of customers are really in the utility space, right? And those are the big utilities that are operating, uh, you know, large electrical communication assets.
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Um, and they're interested in looking at what is the state of health of these assets, right? Being able to get a glimpse on, you know, where are things going to fail? Where could they fail?
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What would it take to fix those areas? Um, so we really do span any area within infrastructure that interfaces with the built world.
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And, you know, if we look at our society and environment, everywhere you look, there is infrastructure we rely on. Is it roads? Is it power lines that gives electricity? Is it water pipes that get us water, right?
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That's-- these are all critical human infrastructure assets. And, and Dominik, starting a company is not easy. Building a company is, uh, even harder, right?
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What were some of the early challenges that you faced that you don't mind sharing, that you were able to get past and, um, you know, kind of how did, how did you end up doing that? Yeah.
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So, you know, a funny story is that we actually started with a robotic system.
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Um, so before we went to a handheld device, which is, which is our current product, um, we started with the assumption that, hey, wouldn't it be nice is if that every night on a construction site, a robot crawled around and captured all the data and brought it back to, to the office, and the next morning the superintendent sees all the mistakes, right?
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That would be an ideal world. Well, that was a massive failure, right? Like, we figured out that robots do not belong on a construction site. They don't climb stairs well. They don't deal with the mess that people leave.
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They don't deal with ditches, right? They don't deal with hazards. They fall off the side of, of concrete pours, right? Like, all these things are like, well, humans are really great. They know how to get around things.
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They know what to capture. And that was, I think, one of the first big like aha moments. You know, let's, let's build something to help humans rather than to necessarily replace them.
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I'm picturing like a Roomba falling off a building or fall like it-- or going in a ditch, right? Like that poor, that poor robot. Um, wow. Uh, so that's actually...
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that's super interesting 'cause in, in other spaces, right, like, so when you're talking about like the, the three-D mapping of a home, that made sense, right? Because what hazards could it run into?
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But when you really look at the construction space, there's just plentiful opportunities, whether, like you said, it's a ditch or falling off a concrete pour. Um, that is so interesting. Um, so how long did that--
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If you don't wanna share, that's fine. How long did it take to say, "Hmm, that's probably..."
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This is kinda like the actually the Amazon, uh, I don't know if you ever heard the Amazon knee pad story, uh, where they were pack- packing up boxes and, and, and Bezos said, "We need knee pads."
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And someone goes, "Maybe packing tables instead." Right? It's kinda like that moment. Um, how long did it take to say, "Okay,
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w-we need to create, uh, technology that people can use, not necessarily using a, a robot to be able to get that information"?
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So we maybe spent a month and a half building the robots, and then spent about a week tossing it in the garbage, starting on our new system.
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So, you know, in startup world, it's, it's a very much like you learn, you fail quickly, you build a, a new version.
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And we have gotten to the point that because of how fast our engineering team is, we can get prototypes up and running in, in a matter of a week, right?
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So you just iterate, iterate, iterate, test it out, get it to the field, bring it back, listen to your customers, listen, listen, listen.
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And with that feedback loop, we were able to really narrow down to what is a product that people will absolutely love, right? It's compact, it's small. They can travel with it. Um, it looks good, right?
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And these are small things, but as you start scaling it, as people start seeing it in their environment, they care about that. And that's what we really care about at VP as well. What's, what's next for you?
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So I'm, I'm assuming outside of like the construction space too, there's, there's other potential applications for this technology. Do you have any plans to go, um...
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you know, we obviously focus on the, on the construction side of things on this show, but always curious to, do you have plans to expand into other industries as well? Yeah.
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So any industry that has physical assets, right, we really can target. Um, we are an AI-first company. And what we mean with that is that we are heavily focused on driving the intelligence piece of these assets, right?
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Um, so what we've built now is the technology to be able to capture them at a scale point that previously wasn't available.
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And what we're doing now is really focusing on what the intelligence piece comes into play, right? So let me use some examples here. [lip smack] Uh, let's look at some of the power line infrastructure, right?
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Uh, you know, today that is inspected on average every five to 10 years because utilities can't keep up with that inspection schedule. Our technology allows them to do that all faster.
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We're talking once every one to three years. And with that, all of a sudden we have the ability to go back and say, "Look, this is the list of, of issues or things that could be a-an issue in the future."
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So bringing that AI piece in there allows us to really future safe, uh, the technology for a market that inherently needs more intelligence around these, you know, ancient assets.
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Um, 70% of our critical human infrastructure assets are over 25 years old, right? Uh, and with that, they're falling apart. We, we need to be able to find better ways to, to, to fix them and, and improve them.
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Um, and really that's, that's where it comes in. In terms of other sectors, you know, is it oil and gas? Is it, um, more on Department of Transportation assets? Is it bridges? Is it dams? Right?
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All of these assets, they are old. They need maintenance. They need better technology. So our technology will directly impact all of those, uh, as, as we scale. If you don't mind, Dominik, walk me through, um,
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with, with one of the surveyors that you're working with today. You introduce your technology to them. They decide to use it. What does that kind of day in the life look like for them
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using the technology for the first time? Yeah. So maybe it's important for me to start on what a day of their life looks like before they use the technology and, and then after they, they use it.
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So, you know, they normally have a set of crews, survey crews, so let's say anywhere between two and five crews of two people, one truck.
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Um, they go out to a site, and then they set up control for a few hours, and then they put up their total stations and then take measurements, and then take those notes on what those measurements are, where they are, and that'll go, on average, I'm gonna say, uh, you know, for three to eight hours per day, right?
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So these can be long days, very tedious, very repetitive tasks. Uh, now with our technology, you know, they, they can maybe wake up an hour or two later, uh, so they don't have to get up at 3:00 in the morning anymore.
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Uh, because when they get to sites, all of a sudden all their work can be done in a matter of an hour, right? Um, so what that means is that, first of all, they need less people on their crews, right?
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So they can go to a single person crew, uh, for safety reasons, uh, because our technology does not mean they need to stand in the middle of the road for three hours.
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Um, they can, uh, start getting a lot more information for their clients, uh, a lot faster. They can get the feedback quickly on whether the data they, they need is, is, is good, right?
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And that's, that's a really important part. And what that means is now they start doing two to three jobs in the same day, right?
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So they just knock out two to three times the amount of work, and at the end of it they're like, "Well, this was super efficient."
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So all of a sudden we've seen that these survey crews and survey companies are excited about, "Hey, we're getting a lot more value out of our manpower in the field.
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We're getting a lot more value out, out of the time we're investing. We're improving safety conditions.
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And bottom line, well, we're saving money and making more money because this tool allows us to do so much more work for a lot less."
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What's, what's next that you'd like our audience to know in terms of, uh, what's next for the company?
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So we're very excited about some of the AI releases that are, that are gonna be upcoming, um, in the different space across survey, across utilities. So stay tuned as we release those product features.
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Um, and we're excited to be continuing to work with a lot of the leaders in the industry, right from the largest engineering companies to the surveyors, right? Running, running their, running their companies.
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And, uh, you know, s- feel free to reach out for any questions about our technology, and we're happy to, to walk through customers. I always like to say, "Don't believe anything we say," right?
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Like, we care about trust, and that c- trust comes through accuracies. The proof is in the pudding, which is in the data. So, uh, come on out, see how it works, uh, believe in the data and take it from there.
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Why do you think now is the best time for this type of technology? So we're just at the cusp of really seeing a shift towards 3D capture, uh, from what is traditional methodologies, right?
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And, uh, as our society and as our industry swims in a sea of data, it's becoming es- increasingly important for us to extract value out of that data, right?
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I have way too many companies that come to me saying, "Hey, I have 100 terabytes of LiDAR data, you know, that we don't know what to do with. We can't even open them on our computer."
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And that's where like, okay, well let's look at the problem. Let's, let's figure out what is actually needed.
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Um, and with Lok, we've really focused on extracting the key parts of information, the key parts of value to help processes, uh, but to not overburden and not to overwhelm what this industry is.
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And I think now with the AI, um, appearance, right, we are, have been able to really value what comes out of these workflows rather than just overwhelm people with a ton of data.
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Uh, so we're excited about getting, getting that actionable value out to, to everyone.
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Well, Dominik, I'm really excited to continue to see how you grow the company, and we'll definitely have to have you come back on in the near future to give us an update on where things are at.
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But, uh, kudos to you and the team for everything that you've built thus far and for being an AI-first company. I would like to hear more people start saying that. Absolutely. Awesome. Thank you so much, Aaron.
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Would love to be on a future show as well. [upbeat music]
