#283 Andrei Danescu: How Dexory Uses AI & Robotics for Warehouse Management

What if your warehouse could see everything, all the time? In this episode of Eye on AI, we sit down with Andrei Danescu, CEO & Co-Founder of Dexory, to explore how AI and robotics are transforming logistics. Andrei shares his journey from Formula 1 engineering to building one of the fastest-growing robotics companies in Europe. He explains how Dexory's autonomous robots and real-time digital twins are giving warehouses unprecedented visibility, cutting errors, boosting efficiency, and even turning storage into a profit driver. We cover: Why warehouses struggle with visibility and how AI solves it The role of autonomous robots in scanning 10,000+ pallet locations per hour How digital twins unlock optimization in space, flow, and production Dexory's vision for the future of supply chains across Europe and the US The balance between human workers and automation in logistics If you want to understand the future of logistics, warehouse technology, and supply chain visibility, this conversation will give you a front-row seat. Subscribe for more deep dives on AI, robotics, and the future of work. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI (00:00) Intro (02:08) The Birth of Dexory and Its Mission (04:23) Building Autonomous Robots and Digital Twins (08:52) Optimizing Space, Storage, and Traffic Flow (12:15) DexoryView: The Platform Behind the Robots (16:03) Humans, Robots, and the Future of Warehouses (18:54) AI, SLAM, and the Tech Driving Dexory (23:17) Real-Time Visibility and Error Elimination (27:31) Multi-Site Insights and Supply Chain Potential (32:47) Scaling Dexory: Markets, Adoption, and Growth (37:01) Scanning at Scale: 10,000+ Pallets Per Hour (41:11) Roadmap: AI Agents, Simulations, and Next Steps (45:13) A Vision for Global Logistics Networks

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  If you think of a big space where you have a large number of pallets, because we can do the digital twin side of it, which is a 3D reconstruction of the space, we have a lot of volumetric information, we have a lot of visual information. So if you're a large warehouse that does, for example, e-commerce and you store the same goods in 10 locations, we can measure the quantity, the space occupied and say, okay, you're actually utilizing 20, 30% of the volume, the cube allocated for this pallet. So if you optimize the way you store them and optimize the position in space and the position in the warehouse, you can actually aggregate these 10 locations in three. We are focused exclusively on the intelligence layer in the warehouse space.

• 0:45Speaker 00

  So our robots go around the facility. They collect vast amounts of information and data. They literally create a 3D digital replica of that environment. And we use this information to drive optimization, to provide end-to-end visibility, to remove operational errors. So I'm Andre, I'm one of the co-founders of TechSery. I have a technical background. My background is in electronics, in robotic systems. So very, very passionate about product, very passionate about problem solving. And I have a special passion for the kind of input we're bringing to the logistics industry. And I'll tell you a little bit about why that is. So at the start of my career, I started working in motorsport.

• 1:31Speaker 00

  I started working in Formula One.

• 1:33Craig S. Smith?

  Wow.

• 1:34Speaker 00

  And yeah, it's been really, really good fun. So I was traveling around with the team. And my job was literally looking at sensor data and being able to make split-second decisions and relate that to the team and help us become faster, become stronger and obviously win the races.

• 1:53Speaker 00

  Now, I don't know if you know much about F1, but you know, the world of F1 is very, very fast. There's many races that happen around the world. So there's a lot of logistics involved in this. So basically, we would travel from one country to another and there were always a couple of glitches, a couple of hiccups. And I thought, you know, We have this incredible technology, incredible, super fast cars, very, very detailed information from the data. How can it be that logistics doesn't know exactly where everything is at every single point in time? So that kind of stuck with me for, for a while. And, and after I left F1, I, I decided that I want to build a company that uses autonomous robots.

• 2:35Speaker 00

  Robots have always been one of my, my big passions and one of my, my deep loves for technology. And I wanted to use autonomous robots to, to digitize offline spaces. And a big application for logistics is literally to provide this level of visibility and to eliminate any kind of operational errors from warehouses, from the wider supply chain. So that's the combination of, of some of my background and how we got to, to the technology that we have today at Dexory, which is solving exactly those problems, eliminating any visibility gaps and eliminating any operational errors for logistics.

• 3:12Craig S. Smith?

  Yeah, a couple of questions. Which team were you working on or did you work for several?

• 3:17Speaker 00

  And so I used to work for a team called Force India, which is now Aston Martin.

• 3:23Craig S. Smith?

  Yeah, yeah, you know, I, along with a lot of people, didn't know anything about Formula One until the Netflix documentary or series came out.

• 3:34Speaker 00

  To be honest, it was before the change in, in, in management and before Netflix actually was a dying sport, believe it or not.

• 3:41Craig S. Smith?

  Oh, is that right?

• 3:42Speaker 00

  Yes. So the audience was declining because there wasn't that level of engagement with the fans and everything. And now it's massively spun back out and it's, it's huge everywhere.

• 3:53Craig S. Smith?

  Yeah, yeah, it really is remarkable. And, and your, your educational background was in engineering or in computer science or, or what?

• 4:04Speaker 00

  It was actually in robotics and basically electronics and control systems.

• 4:09Craig S. Smith?

  Yeah, you know, in Endeavour's technology, you're not, are you building robots? Or are you building sensor systems to track robots?

• 4:23Speaker 00

  So basically at Dexory, we're a full stack company. So we build autonomous robots. We're very proud to say that we have the world's tallest autonomous robots. But most importantly, part of being full stack, we build every part of the technology from the robots, which are used as data capturing, through to the digital twin platform, which is a software platform, the product that our customers actually interact with and spend their time getting value from.

• 4:51Craig S. Smith?

  Yeah, I've had Peter Chen on the podcast. He has a company with Peter Abiel out of Berkeley.

• 5:05Craig S. Smith?

  Called, they changed the name of it. It's, anyway, it used to be called Embodied Intelligence. It'll come to me, but, and they were focused on picking robots and the visual, computer vision.

• 5:30Craig S. Smith?

  Behind picking robots is, is that the same as you guys? Or are you more focused on, you know, retrieving bins in a warehouse and?

• 5:44Speaker 00

  No, that's very, very different. We are focused exclusively on the intelligence layer in the warehouse space. So our robots go around the facility. They collect vast amounts of information and data. They literally created a 3D digital replica of that environment. And we use this information to drive optimization, to provide end-to-end visibility, to remove operational errors and to ensure that things like picking robots, things like the warehouse operator, for operated forklifts, things like the.

• 6:19Speaker 00

  People that work in warehouses and actually do the physical action of picking and retrieving retrieving items can operate in the most efficient way. So the way we look at our technologies is a supercharger for the existing workforce. Because warehouses are difficult environments to operate in, so we strive to make them better. We strive to make people's jobs easier and provide this level of efficiency and make sure that we eliminate errors. So very much focused on the optimization data intelligence side rather than picking and physical actions or interacting with the space in the warehouse.

• 6:56Craig S. Smith?

  Right, but you do produce robots. So what are those robots doing?

• 7:02Speaker 00

  So we produce robots. Like I said, we have the world's tallest autonomous robots. They are going around the space, going around the warehouse and they collect data. They collect, they take pictures of pallets, they collect 3D scans of the warehouse. They transform or transpose the warehouse from the physical space into a 3D digital replica, enabling this level of optimization and this level of data processing on the physical space.

• 7:30Craig S. Smith?

  I see, but you're not building robots to move physical objects in the warehouse?

• 7:39Speaker 00

  No, no. So it's all about collecting data and gathering information.

• 7:43Craig S. Smith?

  Right. And is that for optimizing the human workers or is it also for optimizing robotic workers in the warehouse?

• 7:55Speaker 00

  So you can apply the technology to both. But I'm a big believer into solving the customer's problem in today's world. So rather than building a technology and then saying, okay, you know, in five years, 10 years' time, when we're going to have a lot more automation in the workforce, this is going to become even more relevant. We want to focus on helping our customers and solving their problems, their pressing problems today. And today, the vast majority of what you find in warehouses are people moving things around and doing the hard work, doing the hard jobs. So it's very much making their lives easier and making their jobs easier and their environment better.

• 8:36Craig S. Smith?

  Yeah, that's interesting. At the, so you create this digital twin of the warehouse. And can you give me an optimization example that you would use that digital twin to solve?

• 8:52Speaker 00

  Yeah, so if you think of a big space where you have a large number of pallets, because we, we can do the, the, the digital twin side of it, which is a 3D reconstruction of the space, we have a lot of volumetric information. We have a lot of visual information. So if you're a large warehouse that does, for example, e-commerce, and you store the same goods in, in 10 locations, we can measure the quantity, the space occupied and say, okay, you're actually utilizing 20, 30% of the volume, the cube allocated for this pallet. So if you optimize the way you store them and optimize the position in space and the position in the warehouse, you can actually aggregate these 10 locations in three.

• 9:35Speaker 00

  And you make, or four, and you make space for another five, six pallets, which means you can put a lot more product through that environment, which means you, you make this transition from warehouses being a cost center into warehouses being a profit driver. So you can actually do a lot more with the existing space just by optimizing the way you run the operation.

• 9:55Craig S. Smith?

  I see, yeah. And so sort of storage, optimizing storage is one of the main things that you do. What about traffic patterns and things like that in, in the warehouse? Do you, do you analyze that as well?

• 10:20Speaker 00

  So we do, and this is where it gets really interesting, because we can have a, we can have a transition, we can have parallel to using AI and machine learning technology for this. So again, going back to the way we acquire the data, we have a digital twin, we have an understanding of how goods are moving around the warehouse. So the rate of depletion, the speed of picking, the way items are actually being collected and. They move through the space. And from there, we can infer, okay, this is um this is an aisle with a lot of fast-moving goods. So there's going to be a lot of people, there's going to be a lot of forklifts and machinery in the space. So therefore, there's always a a high-traffic and high-congestion area. So we can re-optimize the way the warehouse is being organized

• 11:06Speaker 00

  or the way the goods are being stored in order to distribute this high um highly populated or highly high-traffic area across two or three or four different aisles, massively alleviating the the way people are moving around this machinery and of course, enhancing a faster picking and better productivity for for everyone operating in that space.

• 11:27Craig S. Smith?

  Yeah, uh and this is uh the core product is is Dexory View, is that right? That is that the tall robot with all the sensors on it that glides around uh mapping the space?

• 11:44Speaker 00

  Um, sorry, can you repeat the first part of the question? I think we had a small break there.

• 11:48Craig S. Smith?

  Is the core prod- product called Dexory View?

• 11:54Speaker 00

  Yeah, so that's the that's the core product and that's um the offer, the product offering consists of the robots that acquire the information. And Dexory View is obviously the software platform where the digital twins are uh continuously updated and where customers can actually log in and interact with uh with the digital version of their warehouse.

• 12:15Craig S. Smith?

  Yeah, and then does uh does uh warehouse manager experiment with the digital twin? Or is there an optimization algorithm that that immediately uh gives you two or three configurations uh and the manager can decide among them?

• 12:37Speaker 00

  So at the moment, we have algorithms that offer various types of optimization. Um so the the volumetric, the the space utilization is an example and there's a number of of other options that we offer. And what we're building and what we have on the roadmap is e- exactly what you uh what you hinted to is the ability to run simulations, the ability to analyze different scenarios of the warehouse. And this is the kind of technology that uh we see a lot of customers being very excited about and what uh what we're looking to build and release over the next quarters into into the product as well.

• 13:09Craig S. Smith?

  Yeah, you said at the beginning that you want a product for today's use case, but warehouse robots are that spaces moving fast. Are there, are you integrated with any of the

• 13:30Craig S. Smith?

  robot companies that are putting robots into, I mean, ABB I think is one and and you know, there are a lot of them. I mean, Amazon has it bought that that I can't remember the name right now, but that robot company that it's renamed that has they were flat robots that you stack pallets on and then they move them around.

• 13:58Speaker 00

  Yeah, so those are those are different, different types of good movement solutions. So for example, you can have a goods to person system. You can have incredible companies like Locust that have picking robots where they rely on the robot going in the proximity of a person. And they put various products into the into the robot and then the robot carries on to another location. Or picking arms, like you mentioned from ABB. At the moment, we don't tend to integrate with the systems because they're relatively new into the space. But we have built the right level of APIs and we've built a system in a way

• 14:39Speaker 00

  that works with the latest and most up to date standards like VDA 5050 and so on. In order to make sure that in the future, as the systems become more and more present in the market, we're well positioned to be able to have this most pain free integration with such such additional technologies.

• 14:59Craig S. Smith?

  Yeah.

• 15:01Craig S. Smith?

  And and how long do you think before there's a completely automated warehouse? I mean, one of the things it's covariant was Peter Chen and Peter Beal's company. And they're working with ABB or were at least. One of the things that fascinated me is, one, you can operate a warehouse 24/7. And two, you could operate it in the dark because the the sensors don't necessarily need need.

• 15:36Craig S. Smith?

  Light to operate is how long do you think before? That is a reality where you know Dexterie goes in and does the digital twin and then you get that information to a robot warehouse company and and it uses that information to guide its robots.

• 16:03Speaker 00

  So that's that's definitely definitely an interesting question because I think that the biggest challenge or one of the biggest challenges we see in the logistics industry is that there's an ever increasing labor gap. So the labor shortage is real. So whilst the opportunities there, the industry is growing. Obviously, we're doing more and more as as humans as a species, we're doing more and more that relies on or heavily relies on logistics. We need to make sure that we first and foremost can thrive and create an efficient supply chain. So that's one of the that's one of the big drivers for us is how do we supercharge the existing workforce? To make sure that you can have the maximum impact with

• 16:47Speaker 00

  the relatively reduced number of people, the reduced workforce that you have now. So I think whilst dark warehouses are a reality now, it really depends on the type of operation that you're looking to run. So even in in highly automated environments, what we find is there are tasks that are just not lending themselves to automation. So it's a lot more complicated or it requires a level of technological sophistication that just doesn't have a direct application to make it commercially viable. And what we're also finding is that customers become more and more demanding. So they want to have a very versatile logistics partner. So there's there's a very

• 17:33Speaker 00

  good space and very good place for enhancing the existing workforce. Like I said, This enhancement comes in many ways. So if you have a team of ten, twenty, fifty people, if you're sending them around the warehouse trying to find the gaps that you have to store more goods, it's not maybe necessarily the best use of their time. If you can give them a completely optimized way of running their day-to-day operations, you can achieve a level of efficiency that has not been seen before. So I think the combination of a dark warehouse that can run twenty-four seven can can provide a level of optimization and efficiency in conjunction with a very powerful workforce is is what we're what we're looking at creating for the future

• 18:17Speaker 00

  and is definitely going to give us that robustness and flexibility to to have a supply chain that is as independent to external factors as we can make it.

• 18:29Craig S. Smith?

  Yeah, and the

• 18:34Craig S. Smith?

  the Dextrie it it maps with with the the Dex review or with the with the tall robot maps the space. What are the algorithms then used to for optimization?

• 18:52Speaker 00

  So when you look at when you look at breaking the technology down into some of the the component components or the subsystems. So we use the autonomous robots. They are fully autonomous, by which I mean they use SLAM to be able to map the environment, to be able to navigate. We use a combination of lidar and camera technology to be able to understand and perceive the environment in which they operate. And they then have the ability to do a 3D reconstruction of the space to be able to take pictures of pallet locations. So the human operator can then go into the digital twin and understand, okay, this is how my goods are actually looking in the warehouse. This is the environment in which I'm storing them.

• 19:33Speaker 00

  So for example, in a in a in an FMCG warehouse where you have perishable goods, you don't want to be storing them in inappropriate conditions, too hot or too humid and so on. So this is this is some of the the core technology that enables us to map the spaces, to be able to collect the sensor data. And then index or review as you go into the digital twin. We have a fairly large number of technologies that drive this optimization. On one hand, we have different computer vision algorithms that enable us to extract a lot of information from images. On the other hand, we have machine learning systems that can identify, that can extract and segment various images, various sort of like areas of the warehouse.

• 20:17Speaker 00

  And then when you put these data sets together, we have a layer of AI, a layer of

• 20:25Speaker 00

  different types of AI technology from small language models into agentic AI and so on, that can drive optimization, so it can tell you how to best organize certain elements, certain parts of the warehouse, can drive process optimization, can tell you how to best organize process in the warehouse. And then obviously, the agentic AI layer will be able to run this continuous simulation, will be able to run a lot of this what-if scenarios and simulation scenarios and analysis in order to tell you what the outcome would be of this of these actions or the proposed actions that the system could take.

• 21:02Craig S. Smith?

  Yeah, and do you have metrics on what kind of

• 21:09Craig S. Smith?

  what kind of improvements you can reach with this kind of optimization?

• 21:15Speaker 00

  Yeah, I mean, a lot of it revolves around error elimination. So being able, if you can run your operation with no errors or no eliminate all the inefficiencies, then you've pretty much achieved the holy grail. We have quite a few papers around the ROI and the speed to value that we drive for our customers. I think it's quite difficult to get into one unified metric because this technology is versatile and applies to a wide range of logistics operations. And various types of warehouses will tend to have various ways of running. You could have a high velocity operation where you have a lot of goods moving in and out.

• 21:58Speaker 00

  You could have a high volume where you have hundreds of thousands of pallets or a high value operation where it's very, very important to know at every single point in time what's going on because your storing items are incredibly valuable. Think of it as, I don't know, a pallet of iPhones is still one cubic meter, but it's completely different in terms of value to the same pallet of flour or rice or whatever else, sort of like goods we might have there. So so different types of warehouses have different ways of extracting value. But like I said, it revolves around eliminating errors. It revolves around this orchestration of the operations in the most efficient way possible.

• 22:42Craig S. Smith?

  Yeah, can you give an example of of and and optimizing, you you you talked about you know, increasing, you know, consolidating storage in a warehouse to create more space, but are there some other examples you can give of optimizations

• 23:08Craig S. Smith?

  that maybe allow people to do more or fewer people to do more than they would otherwise?

• 23:18Speaker 00

  Yeah, so I mean, we can look at, I think we touched quite a bit on three P L or logistics style warehouses, but we could look at warehouses that, for example, are serving production lines. So we work with a number of customers within automotive, within various types of OEMs. And for for them, for for this type of customers, the operation needs to continuously flow. So if you if you if you imagine a large automotive OEM that would stop a production line, that would cost millions of dollars in a very, very short amount of time. And those losses would start sort of accumulating over over days and so on. So being able to

• 24:04Speaker 00

  ensure that the production line never stops, that you never run out of materials, it's it's a big, it's a very, very big cost, cost benefit. And this is an example to automotive, but for example, we we have customers that produce electric motors. We have customers that produce furniture and so on. For them, equally important, they cannot stop the production line. They would start incurring a huge amount, huge bills of of losses. So we would be able to find any product anywhere in the warehouse. So if before you would spend hours trying to locate these goods in the warehouse, because you still need to be able to to produce the items and then obviously serve customers. With ExoReview, you have complete visibility over every single location you have in that facility.

• 24:50Speaker 00

  And so it means that you can actually go into the ExoReview, you can go into the platform. Look for whatever goods you are trying to find and immediately find where they are. See a picture, see when they was last scanned, basically be able to retrieve them ahead of any blockages or any kind of bottlenecks even happening.

• 25:09Craig S. Smith?

  Yeah, so so does DEXREVIEW also track inventory of in real time? So you always know your inventory levels for every SKU?

• 25:23Speaker 00

  Yeah, so it it tracks, it tracks the quantity. It's something that we call pick availability or pick face counting. So we track the amount of product you have available for picking, available for for taking to customers, whether that customer is, you know, the end customer, if it's a B to C, or whether that customer is a subassembly or subcomponent manufacturing line within the the larger plant.

• 25:49Craig S. Smith?

  Yeah, actually, that, you know, I'm I'm not the most organized person in the world. I'm always losing stuff in the house. And I've often thought it would be wonderful if you had a system that would track every item every time it was moved. So, you know, you could look into a digital twin of your house and do a search and it would tell you right where the thing is. Are there other applications for this beyond warehouses?

• 26:20Speaker 00

  Yeah, just just before I go there, for example, imagine the house would be a million square feet and you have a hundred thousand locations where you could be losing things. It's very easy to see how this can how this can snowball into something really, really inefficient very quickly. And any, for example, if we were to touch on on the latest tariffs and the the international trade, if before you could say, okay, my pallet is worth $10,000 and and I lost it, fine, I'll write off $10,000. Now with tariffs, that pallet could be 20, $30,000. Suddenly, the value has gone up a lot, which means there's a much, there's a much tighter focus on logistics excellence and operational excellence.

• 27:02Speaker 00

  And and pretty much all our customers are seeing the the ROI of our technology and our our product massively increased over probably the last two quarters because suddenly they started to have a very tight focus on how and and and where can they extract every bit of optimization from their operation? So if we were to look, for example, outside of warehousing, this has a very good effect at a wider industry level. So if you think that you're a logistics operator, you have multiple facilities and you have DEXO Review in all of them. The same would apply. You can literally log into one platform and you can see across all your location how they're progressing and how they're performing.

• 27:47Speaker 00

  You could have an operation that runs at ninety percent efficiency and then you could have two other operations that run at eighty, eighty-five percent efficiency. What DEXO Review enables you to do is virtually copy and paste intelligence. You can look at why is this operation running at ninety, ninety-five percent efficiency? What is the general setup and how does this compare in a unified platform against all my other facilities? And that's incredibly powerful because it's not just about being able to collect the data, but it's about being able to present this data in a unified way that enables you to compare and contrast across any facility you have pretty much anywhere in the world.

• 28:29Craig S. Smith?

  Yeah, that's fascinating.

• 28:32Craig S. Smith?

  And the

• 28:38Craig S. Smith?

  I'm sorry, I'll cut this out, but there was a question I was gonna ask.

• 28:49Craig S. Smith?

  Yeah, I was gonna ask about the speed that this operates. I mean, is, well, let me, yeah, something else. The optimization algorithms, are those proprietary? Are you using standard models from the research community?

• 29:11Speaker 00

  So we use a combination. Of course, the models per se are, there's a lot of open, kind of, there's a lot of open research work that you can leverage.

• 29:24Speaker 00

  But what is really important to make the best use of this technology within the vertical space that you're operating in, the data becomes the most important asset. And to be able to acquire this data, we have obviously built a technology that enables us to do so. But we have huge amounts of data, which in turn allows us not just to best to create these bespoke models and to create the bespoke algorithms that we use in our optimizations. But also it enables us to to train various models for these specific applications. Because then I was I was recently listening to a to an OpenAI discussion and they were saying, look, there's like

• 30:07Speaker 00

  ten fifteen percent of the world's information is is available on the web. But the vast majority of it sits behind, you know, closed doors and industrial environments in in the in the real world applications in the, let's say, classical industries. And that's where you can drive a huge amount of value. If you can access that data, if you can start harnessing the insights and the power of that information, you can really drive a huge impact for the industries and and have and bring real benefit to the way we're we're actually doing things.

• 30:38Craig S. Smith?

  Yeah, this optimization part, are there,

• 30:47Craig S. Smith?

  I mean, outside of warehouses, for example, could you apply it to? I mean, is it specific to to

• 30:57Craig S. Smith?

  internal configurations or or you you talk about the supply chain generally, maybe between sites or or globally and moving product around? Could it apply to a larger footprint like that? I mean, could you create a digital twin of of your entire?

• 31:23Craig S. Smith?

  Workflow from factory to customer and use the optimization algorithms on that.

• 31:31Speaker 00

  Um. So I would say, yes, it's not something that we're focusing on because we're very, very focused on the specific industry applications we have now. But as we evolve and expand the DEXREVIEW platform, um, the vision we have is to to build this into a warehouse operating system and then into a broader supply chain operating system. So you can have a good example to to what we were discussing. You could have um, DEXREVIEW present in a warehouse that serves a production line. You could have DEXREVIEW present in warehouses that serve third-party logistics, or they even serve the um, the retail stores and the retail centers. Being able to close the loop and being able to provide this level of of feasibility and optimization across the wider

• 32:16Speaker 00

  supply chain and logistics industry means that we have the um, we're best positioned, I would say, to be able to drive this um, this level of efficiency across the wider industry. And that that's very much where we're looking to take the technology is um, a lot broader and um, a lot wider than the beating heart of the logistics industry, which is the warehouse where we started.

• 32:41Craig S. Smith?

  Yeah, and you you formed the company in two thousand and fifteen, is that right?

• 32:47Speaker 00

  Yeah, so uh, me with the with my other co-founders, we started this business in two thousand and fifteen. Uh, we bootstrapped for a long period of time, uh, because as a full-stack business, there wasn't uh, let's say that the robotics wasn't as exciting back then as it is today as an investment thesis for a lot of people.

• 33:07Craig S. Smith?

  Yeah, and then uh, uh, how does somebody deploy this? Uh, the the the package includes the scanning robot uh, and the platform, I mean, uh,

• 33:22Craig S. Smith?

  How how long does it take for a company to get the system into their warehouse?

• 33:30Speaker 00

  So it's incredibly easy to get started and deploy the technology. So the it's we're operating based on a subscription style of agreement. And we've created the technology with the end customer in mind. So we spend a lot of time working with the industry and understanding how people see robots coming into warehouses. Because at the end of the day, robotic system is is a means to an end. It's the ability that it is the ability it gives you to collect the information, to collect the data. So we created a technology that requires no infrastructure changes. You literally don't have to make any adaptations or any changes to your environment. You don't have to put markers. You don't have to really do anything.

• 34:13Speaker 00

  We would come on site, we will install the robot. It takes three, four days to be able to install and map the entire facility to build the digital twin. And within less than five days, you can actually see value from the product. You can see value from the technology. Obviously, that includes a training element because digital twins and and the platform like we've created, it's not something that the industry has seen before. It's a very, very new product into the market. So the the training is a very important part of it to make sure that our customers have the best experience with the product. And also to make sure that we answer their pressing needs and and and we answer the the pressing problems.

• 34:53Craig S. Smith?

  Yeah, well, in when you began productizing this, did you work with any of the F1 teams?

• 35:01Speaker 00

  No, we didn't actually. They tend to have a very, very different style of operations, not necessarily something that would lend itself to this.

• 35:09Craig S. Smith?

  Right, right. And and you're located in London still?

• 35:15Speaker 00

  So we're based just we're based outside of London, kind of between London and Oxford, I would say. Probably about an hour's drive from from Heathrow Airport.

• 35:27Craig S. Smith?

  Yeah, and excuse me, where is the bulk of your market? I mean, it's obviously a global market and, as you said.

• 35:39Craig S. Smith?

  Fulfillment centers and logistics industry is exploding with the shift to online commerce and all of that. Where is your largest market?

• 35:54Speaker 00

  So the markets of focus for us at the moment are Europe and America.

• 36:02Speaker 00

  I would say the larger market is the American market and we're seeing very, very strong interest and we're seeing some incredible customers and incredible partners in the market that have really embraced the technology and have achieved incredible return on investment and incredible benefits from it. So if you were to ask me the question and I'm looking at the global ecosystem, I would say the American market is one of the, is basically the largest for us. But it's also one that's incredibly fast moving and fast growing and fast adopting of the technology.

• 36:39Craig S. Smith?

  Yeah, you said a couple of times that the scanning robot is the tallest robot, is that how you put it? How tall is it? And if it's, you know, warehouses can be pretty high inside, so how does it scan, you know, thirty, forty feet up?

• 37:04Speaker 00

  So the height of the product, the current, the current version we have in the market is forty six feet.

• 37:10Craig S. Smith?

  Oh, wow.

• 37:11Speaker 00

  It's a ground based robot, so it's got a telescopic tower on it and it scans. It scans in one swift pass, so that's, that's again, so like it goes back to what we were discussing is we've built this product by leveraging the market and having very deep conversation and a lot of product discovery with customers. It's a ground based robot, it enables it to operate under any conditions, day or night with people around it, it doesn't really matter. And it scans continuously, so it moves on the side of the rack. It moves on the side of the rack and collects the data, but it can also scan this information in. Block stack environments where you don't really have racking and it's exactly the same principle.

• 37:54Speaker 00

  Um. Being able to to leverage this, the fact that it's a ground based robot is very, very tall. We can scan well over ten thousand pallets an hour. So we've we've had location, we've had, we've had customers and locations where we scan twelve, thirteen thousand pallets an hour. So. A very important point for our customers is the speed of scanning, but more important than that. The speed of transforming that scan data, that raw data into insights for them. That's really what uh what drives uh operational efficiency and it's really what drives excellence. So we can scan the warehouse, but how fast can you actually tell me what I need to do? In order to drive uh improvements in my day to day operation.

• 38:35Craig S. Smith?

  Yeah, and I can see that after an initial scan, the creation of a digital twin. And the optimization, maybe you wanna. You know, move these pallets over here and. Re-up space there and put something, you know, that kind of configuration, but that happens. Um, you know, once every so often, it's not an ongoing. Uh, thing, but does the robot continue uh to scan on a real time basis to track inventory? Or alert? If something is being. Uh. Misplaced uh according to the optimization plan.

• 39:20Speaker 00

  Um. Yeah, absolutely. So the short answer is yes. Actually. It scans um at least once a day, sometimes multiple times a day, depending on the operation. Um. And the big thing about the optimization is that you drive this. You you basically run this continuously. Because uh today you're selling a certain type and amount of product and then as, for example, as the week progresses. Customers want something else that changes and evolves continuously. You don't drive changes in the warehouse, so you don't change, move the racks around and so on, but. You could continuously optimize the way you lay down the space and where you store the products to make sure that you. Have the best use of that facility.

• 40:03Speaker 00

  So the robots would scan continuously, eliminate any kind of inventory errors, track this real time availability of product for picking, for making sure that everything is best utilized, and provide this information in real time. So you see this information pretty much at every single point of time in the day or night, index or review. You don't have to wait to the end of the scan. You can see as the as the robot moves through the warehouse, you see this information being pushed in the digital twin.

• 40:33Craig S. Smith?

  Yeah. So where is this going?

• 40:38Craig S. Smith?

  You know, you talked about

• 40:41Craig S. Smith?

  how warehouse robots and dark warehouses and that sort of thing are are going to be common in the future. For Dexory,

• 40:54Craig S. Smith?

  where are you guys looking to go? Is it, are you just in in the phase now building market share with your product? Or are you doing research on new products

• 41:08Craig S. Smith?

  coming out?

• 41:10Speaker 00

  So we're, I would say we're continuously developing new functionality for our products. The way we look at products is is by combining Dex review, the digital twin, as well as the robotic systems and the autonomous robots used to to collect the the data and the information. I would say we have a very a very rich roadmap for where we are looking to take the technology. And we touched on a couple of aspects around AI. We touched on a couple of aspects around the agentic side of things. Because the way we see it is being able to to create an ecosystem where you can run any type of product, any type of goods, any type of of warehouse layout with any type of resources you have available.

• 41:54Speaker 00

  So regardless of the amount of space you have, or however way you want to equip that facility, being able to deploy Dex review means you can run, you can have the certainty that you run that operation with maximum efficiency. And I think that's one of the the big, big drivers that you we want to bring into the industry. Is this transformation that you don't have to. You don't have to know if you have an idea or if you want to run a type of product, you don't have to have past experience. You can leverage the fact that this technology is so widely adopted. And and then have that certainty that the the way you're running your operation is going to be maximum efficiency is going to bring you the maximum benefit for for that setup.

• 42:38Craig S. Smith?

  Yeah, you were talking about the data that you've collected or that you collect in these. Uh, scans, is there a central of. You know, down a lake where all that data goes that that continues to optimize. Uh, the algorithms uh or train the algorithms is is I I remember a number. Warehouse robotics companies I've spoken to, you know, like a picking robot, it's. It's learnings will be fed back to the central. AI system and then all of the.

• 43:18Craig S. Smith?

  Robotic arms uh attached to that system are updated with that learning is there is there that kind of uh.

• 43:29Craig S. Smith?

  You know, fleet learning going on.

• 43:32Speaker 00

  Yeah, absolutely. I mean, this this level of um of distributed intelligence is something that um we've is is one of the the core principles on which we build the system because. That benefits pretty much anyone that uses the the Dexter review platform and Dexter's technology. So it's learning about running more efficiently is learning about scanning faster, but also. The perception system being able to extract more information from the the the scans and the data we collect. Is then something that benefits everyone that um leverages this technology to be able to drive efficiency in their business. So it's a very, very important point and.

• 44:12Speaker 00

  Um, we're operating a lot of processing directly on the robots, directly on the device because we scan a huge amount of data, we scan hundreds of gigs an hour. There's no real infrastructure to be able to transmit all that information. So we process the data, we extract the insights, and then we keep only the valuable parts of that data set for for further learning and further improvement.

• 44:35Craig S. Smith?

  So, Andre, is there anything else that you want the audience to hear? I'm excited about the prospect of a fully automated global logistics system. How far away do you think that is? As I said, with minimal human workers in warehouses, Dexory, View, sort of providing the data, and then a fleet of robotic arms or or mobile robots, you know, moving things around in the warehouse. Is that five years away, 10 years away?

• 45:21Speaker 00

  That's a very, very good question. I think it's a lot closer than than we would envision. You look at some of the latest developments in the in the field of robotics, in the field of automation, but also in the field of intelligence and data analytics. And I think that that's shaping up to be a very exciting future. And as I was saying earlier, I think it's really about how do we create the perfect symbiosis between between humans and the machines that can actually enable us to do our jobs better, faster, easier. I think once we once we start making this a reality, we're going to have a very robust and resilient global supply network.

• 46:06Speaker 00

  I don't like to call it supply chain because I think it's it's about breaking the chains, about creating this interconnected networks. It's about driving to to get to this seamless visibility so you can optimize end to end. So we can be efficient and we can actually be a lot better and in the way we're doing things, in the way we're pretty much moving everything around the world.

• 46:29Craig S. Smith?

  Yeah. And finally, what what sort of how big is the market? It seems like it's massive and growing. And and how much of that market are you serving? Are you still at the very beginning?

• 46:49Craig S. Smith?

  You know, is there sort of endless growth ahead of you?

• 46:53Speaker 00

  I would, I would like to believe there is endless growth ahead of us, of course. I don't like to, to look at the world with a, with a limited or fixed mindset. I mean, if, even if you, if you were to limit this technology and limit the product and applicability of what we do to the warehousing space, you know, this is a massively growing industry and it's, it's accelerating and you look at some of the trends that we're seeing now with bringing manufacturing at the forefront of what we do and so on. You can't bring manufacturing and make, make a flagship industry out of it without having a very, very strong supply network around it.

• 47:34Speaker 00

  So when you look at the wider industry, there's probably two, 300,000 warehouses, as we know in the world right now, where we're definitely just getting started. But I think, you know, there's applicability for, for DEX to review as a technology to be a household name in pretty much every single one of these locations. This is what we're, we're looking to build. This is where we're going towards. Warehouse operating systems and a wider logistics operating system means the ecosystem will be what's going to be powering the industry for, for the foreseeable future.