6 Innovative Fashion Tech Startups | PI Apparel

So welcome to the PI Apparel Startup Track. And my name is Julia Kranz, and I'm excited to guide you through this session where we'll be hearing from a number of exciting startups. And they all work with different things, but they share one goal, and that's to change the fashion industry using technology and make it better. Something short about me. I worked in fashion technology for the last 10 years. In 2014, I started the website Magic Fabric. And why I did that was because I was looking at how the game industry was working with cloth simulation and 3D software in character design and saw great potential for the fashion industry. And today, 10 years later, everything looks a little different. So it's exciting to see that a lot has happened in sort of this interface between XR, AI, and 3D. And I think Magic Fabric has grown into a bit of a think tank for this. And I've been particularly interested in how creativity and how creatives in the way that they're working with open experimentation and prototyping is driving how we use Technology and Society. But so with no further ado, I would want to invite our first speaker on stage, which is going to be Marta. Marta is the CEO of A. Valle Smart Hubs, and she works to completely change the way that designers, brands, ateliers, and manufacturers collaborate. Welcome on stage.

Thank you and good morning everyone. First of all, I would like to thank Joshua. He made my job much, much easier because many things that I bring here with App Valley, he already explained where the problems are and which problems we are trying to solve. So we are based in Portugal. I come from a family of textile a company, a factory actually, in the north of Portugal. I understand that many of you that work in the textile industry probably know this area for manufacturing high-quality clothing. So I would like to start with an applause to this fashion icon that passed away on the 1st of March, my birthday. That's why I bring her with me. And she is actually a big fashion icon. And for me, she— this date marks, I want it to be for me, the beginning of what I call the Greenessence era. This is a term that comes more or less in the opposition of the Renaissance. And we are in this beautiful room that resembles the Renaissance era with all its too much decoration, although beautiful. So this is what we have to go to, to the Green-naissance era, where we are more worried into collaboration for a more sustainable world. So these numbers, if you have all seen the McKinsey survey, you know what I'm talking about. So this is the, the, the, the estimations for production into in 2023, the amount of waste generated by this production, and the footprint of our industry, that it's around 3 or 80—

8%.

So these numbers come to us and say that we should do something, and all the companies, all the brands are now coming late to do something. So we need to speed up and do the best we can so we can also comply with the EU regulations that are coming into force. This is in the Europe, but I know for sure that this will go into other countries in the world. So we need to be aware of the waste regulation, we need to be aware of digital passport, and we need to be aware of the extended responsibility on everything we produce. So the shifting of the essential value for brands, for customers, is now how fast manufacturers can deliver. Then comes the price, compliance, and then at the last position, quality. So what can we do about this? Right now, fashion has to deal with these four areas when thinking about a collection. We have the price, we have quality and compliance, sustainability, and the time to market. This is our paradigm. And when the quantities come there, and Joshua mentioned that, the MOQs, most of the time are not compatible with what the brands really need to buy and sell. We need to get to technology so quantities become a no problem. And with technology, we get efficiency, we get fast time to market, We get precise quantities and waste prevention, not waste reduction. We want to prevent the waste, and there are ways to do it. And as Joshua also mentioned about the ideas of being sustainable and the organic cotton and all these buzzwords about being sustainable, in our view, sustainability doesn't come from materials. It comes from the the process we use and the quantity we use. So we have reached what I call a gestalt moment where everything is gathered together to make this happen and for our valley smart hub to be a value— valuable option for us to work with you. So we are already into what the environment needs from us social, and the narratives are pointing out for consuming less. Governance and politics are already aware what we need to do when imposing some regulations. We have technology and we are in the digitalization area. Everything that can be digitalized will be digitalized. So we have this gestalt moment and the ESG requirements that most of you are probably aware of and how important this is for all your companies. So what we do in our platform. So the step one is to— and I'm there. Step one is to connect designers with the development departments inside the supply chain using eco-design suggestions involving all the supply chain for to make an impact assessment. Then the step 2 is to mapping the supply chain net and the materials for traceability and transparency. Then we get to conscious production. So it's step 3 or phase 2 that we call precision production. Here we involve all these big data that Joshua mentioned. So So we can have market trends analytics, production schemes on demand, made to order, drop by drop, so the quantities that you really need integrated with the e-commerce stores, for instance, helping the factories to implement these production schemes on the smart hubs so we can have the small batches, customization, high quality standards, and speed and efficiency. So our solution benefits: we have an holistic approach, we want to establish and we are establishing smart hubs in Europe to achieve precision fashion. So agile go-to-market, the design and development process, sourcing and production impactment assessments, made-to-order production schemes, eco-modulation reports, and so on and so on, based on efficiency. So this is our beta version that we are already using with onboarding brands, onboarding manufacturers, agents, and the supply chain. We have our first implementation of the Smart Hub at Hap Valley in Portugal. That's why we are called Hap Valley, because that's exactly where we are, and we want to honor our origins. and we are into making a protocol with Fashion Techs that involves all these European countries and their design schools so we can start mapping in these countries the smart hubs we want the brands to work with for the nearshoring production and to have the genuinely made in. So thank you very much, and if you want to discuss more about these way to achieve precision fashion, the made-to-order. I will be hosting a roundtable in this same room tomorrow at half past— oh, 11:15, sorry. So thank you very much for your attention.

Thank you so much, Marta. Each startup has 8 minutes. I think you ended with laser precision, very impressive. It's very hard to do a pitch in just 8 minutes. So our next speaker is Heidi van Dyck. She's a CEO of Athena Studio, passionate about changing fashion design and making it more data-driven and sustainable using AI. Welcome on stage.

So about a month ago, When asked if design had a role, if AI had a role in design, Wes Gordon from Carolina Herrera answered, "Well, if there's one thing that history has taught us, it's to not to be afraid of newness." Now, I think our fashion industry has been a little bit afraid historically since we've been a bit slower than other industries in adopting technology and technological advancements. But I think if you look into this room today, I think in recent years we've started to catch up. Brands are adopting digital product creation, AI, stores in the metaverse, etc. So that's quite exciting. Now, however, beyond the excitement of technological acceleration, brands still face quite a few challenges. So for instance, with economic volatility and inflation, there's margin pressure. And as mentioned in previous talks, the growing demand for sustainability makes that it's very slowly but surely regulations are coming into the designer studio. Now, my name is Heidi, and I'm the founder and CEO of Athena Studio, a fashion tech company where design meets data. And in the next couple of minutes, I want to talk about a new and overlooked aspect of AI that we believe can be a real game changer in industry. But let's start with the basics. So I'm the third person to refer McKinsey, but McKinsey forecasted that over the next 3 to 5 years, AI would have the potential of $275 billion contribution to the fashion industry with applications in supply chain logistics, retail, marketing, and design. Now, if we double-click on design, when we say that AI in design, we often think about image generation solutions like Midjourney that have massively accelerated the design process for some brands. Now, our previous, uh, talker also mentioned Purple AI. I'll also highlight Raspberry AI, which is another specialized solution that essentially does the same thing as Midjourney but trains these algorithms on your own brand's our DNA and that bridges it with sales and trend data. So currently in the space of design, we have AI that helps us with creating better products faster. But if we think back about the two challenges of our industry—margin pressure, the need to reduce environmental footprints—they are currently not solved with AI. And if we double-click on product development, It's actually very interesting because over 80% of a garment's financial and environmental impact comes from decisions made during this process: the material choice, the trim selection, the supplier you work with. And yet this impact is not measured nor optimized for at this early stage. So I see that many people that I speak to confirm that, and the question is why? And I think we all know it's because the data is often not available to calculate these dimensions. And we also know that product development is a high-paced process, and nobody has extra time to go collect data or to run additional analysis. So the result of that is that brands are often developing collections without a view on how their styles perform versus their company targets. And I've seen in numerous cases that brands after collection sign-off have to discard styles because they're in the end not financially feasible. And I've also seen that there's always missed opportunities for improved gross margins or reduced product footprints. Now, this is an area that we believe that AI and especially machine learning could really make a difference. Because imagine a fashion industry where your product's margin and footprints are not just afterthoughts, but are optimized for from inception. Now, machine learning has the capacity to fill the typical data gaps, navigate its way through, and allow for a more informed and efficient product— product development process. Machine learning models can also, if they're well trained, automatically forecast the impact both financially and environmentally, thereby letting product developers immediately see the impact of the choices they make. But let's take it a bit further because that doesn't get us excited yet. So I think what is really cool about and really powerful about Midjourney is that based on one input, the platform can generate design after design, thereby speeding up, you know, the brainstorming and the integration phase. So what if we married that integrate— that generative capacity from Midjourney with those smart machine learning models? That help us with early impact estimates. And that is exactly what we sought out to do with Athena Studio. So we develop software solutions targeted at product developers that enable them to develop collections that meet their financial and sustainability targets. Now, how do we do that? Our platform analyzes each sample created and generates instant recommendations on smarter material and supplier choices that will bring you closer to your goals. Now, this helps product developers with adding more samples to consider, iterating over them, and going back to the supplier to have a discussion on this. Now, we know, of course, that— we mentioned it already— product developers don't have any time to waste. So that's why we use machine learning to make this entire journey from sketch to sample fully automated. And we smartly address any data gaps in the process. So overall, it's really our mission to make data-driven early-stage product optimization the new norm, and we thereby help brands to improve their product gross margins without sacrificing their brand DNA and their sustainability efforts. Now, I'll be around today and tomorrow, so if anybody wants to talk further about, you know, product development, the future of it, and the role of AI, Come find me over a coffee or a drink tonight at the reception. Thank you.

Thank you so much, Heidi. Our next person is Sergej Zlatik, CTO of Tronok. From Ljubljana, Slovenia, and Tronok is optimizing your design process using 3D visualization. And one of the things you're doing, for example, is building 3D configurators, which I personally think is super exciting and such a sleek and cool customer engagement tool, but also enabling companies to work with customization online. Welcome.

Hello everyone, my name is Sergey and I'm CTO of Trunok. And what we're trying to create are images for different companies. And here are some examples what we are doing. So we're working for automotive industry and we are also working for fashion industry. So our job is to create everything digitally, and we're of course also using AI inside our company. And here are already some examples how we're using AI to prepare, for instance, for the projects that we can create discovery phases. And out of this we can create, for instance, animations like that. Of course, those are not really created by AI. We are still doing it manually, but AI is helping us to create better images or better preparation of storyboards. And here are just some of our details, like our different examples of 3D. And here is video, one presentation of one of our projects for one of the customers where we prepared configurator. So we are going from 3D design to creation of the logic and later connectivity directly with the factory. And actually what I would really like to talk today about is how we're preparing image, images with help of AI, because actually there's quite a lot of problems with AI if we talk about like really creation of the image. And on this one we're showing, for instance, if you have a 3D software, how you can use your shape, for instance, 3D shape of a human, and then we can just generate whatever we need on this specific pose. So it means like different human, and of course, we can put different 3D clothes on it. And here's another example of a configurator, how we can use AI human and just put it like inside and use it more like— that would look much nicer for human, not just cloth, but actually also with human. Here's also another example, for instance, for Mix and Match, where we would be able to just create different, different style poses. What is good about AI is that we can actually, for instance, now you can see there's always the same pose, but actually we have different ethnic groups of people, and For instance, that kind of images we would be able to make for, for instance, for the same brand, but let's say that on different sides of the world, like Australia or Europe or India or wherever, you would be able to just recreate the image with different people. And here's another example. And what is also good about this is like we can also create different sizes. So on the left side, you can see the thin person, and on the right side, a bit stronger person. So we can create that kind of things, and it doesn't really take much time. And this is another example how we're using AI. So for instance, in automotive industry, sometimes you're not selling just the car as a product, but you're selling happiness, China's and that kind of things, and we are using AI also in that kind of examples. There are also other things you can do with AI. For instance, we can do old people, and of course we can do also child. So there's not like— of course there, there's a lot of limitations, but if you would need specific help, we can definitely help you with solutions, and we can custom develop it for you. So if you have questions, you can definitely contact us, and, uh, we will answer your email. Thank you.

Thank you so much. This is very inspiring. Also probably one of the most visual presentations that we're going to see today, so that was fun. Next up, we have Thomas Casquiola, who's head of sales at Nice Bespoke Technologies from Spain, and he'll be talking about how they're solving fit with the power of data. And we all know the size of that problem in the industry right now. So super excited to hear your pitch. Welcome.

Okay, does it work? Yeah. How you doing? So, well, here, I'm here today to talk about, like, how we try to solve size and fit issues, okay, based on the power of data. Because actually, I would like to start with a number that I got really impressed with, which is that we are now 8 billion people on this planet. So it's quite a huge number. Isn't it? And so it's true also that we are like different body types, different people, different attitudes, behaviors. But the reality is that we are like 8 billion, and we must fit all of us within limited size range. And this kind of size ranges might vary across country, across region, or within collections. Okay? But that's nothing new because Actually, it's everything that has been done so far because as consumers, we know that sizing might be inconsistent among brands, okay? Because that actually has its own logic because different fashion brands, they try to reach different target consumers. Therefore, they create different fitting models and they name them with different sizing, okay? However, the problem can also happen within the same brand. And that's what we found out with some partners, that also the reality is sad but true reality, because you might have also that within the same collection and the same product category, you might have different sizes, okay? As you can see in this example with Denim, with a Denim brand. So that's the great question. So when, what's my size? When you when you need to buy or need to go shopping, normally you struggle with understanding what's the perfect size for you. And here we come to the online challenge where the tangible part of the fashion industry is not playing a crucial game as it plays in the retail, right? Because you cannot try the item, you cannot touch it, you cannot see how it fits on you. And that's the solution that many e-commerces are providing. So, okay, we have the size guide, take your own measurements, but actually nobody probably has a measuring tape. And also it's like quite hard to measure yourself, you know, especially for the chest or for the leg length. So it's not as easy. And if you want, I have plenty of them, so for you. And so that's the reality, but consumers are facing that they need to measure themselves. And it's not easy because first, they don't have a tape. You will have afterwards, but they don't have a tape. And second, they don't know how to take the measurements, especially like chest, bust, or leg length, as I told you before. So we're facing now that the consumers have like some frictions, okay, to complete the purchase. And that's how we help them, okay, with our Size and Fit Advisor, okay, where we help e-commerce have like a virtual fitting room or call to action in product page that help the customers fill in some basic information, okay, like gender, age, height, weight, and also like body shape, or to upload the photos in some cases, okay, if the brand is willing to interact more with the users and gather more data. And our algorithm, okay, with machine learning, they calculate up to 21 different measurements of the human body. Okay, according to the gender type, according to the age, the weight distribution might be different, and so they will get a personalized size recommendation for them. Okay, so now we understand that, okay, this is the first part of the equation. So how to know what's Thomas's size, so my size, on a specific item? I must know two things. First, the human body, and we got it. Okay? Second part is to know the specific SKU, which is the tricky part. Because— okay, that's another slide that you can see the journey, okay, that the user does. So they put their basic input, they upload a photo, or they can ask and fulfill like morphology questions such as waist, such as hip, belly. And they will get a personalized size recommendation on that specific item, as I was saying. So the next part is understanding that fashion is way more than a bunch of measurements or size chart. And what we do ask for brands to understand how the collections are is first, as an easy option to plug and play, let's say, we ask for their size charts or garment measurements or product specs. We have an in-house pattern-making team that analyze and collaborate with brands to understand the collections and their measurements requirements and so on. But the reality is that we noticed that the measurements are not enough to understand how a textile product really fit on you. So that's why we offer additionally two options. One is having fitting try-ons on focus group and our lab in La Coruña. And the other one is doing these fitting try-ons at the brand's place, either the warehouse or the stores, or the headquarters. And so that's how our algorithm and technology can match the size recommendation and enhance the maximum accuracy by basing on the real fit of the final product and not just only measurements. So these are some examples of impact of e-commerce. Especially you can see that the return is quite a big challenge, and they were able to reduce the return rate but also to increase the confidence such as in Moschino and in luxury, and also like increased conversions. And I needed to put this slide because actually sizing at ecommerce level is just the, the peak of the iceberg. And I'm not the first one probably to put an iceberg slide in a presentation, but I thought that it was worth it. And we understood that we must go more in depth, okay? Because also product is involved in making this challenge solved. And so how can product team also benefit from this and from fitting try-ons to understand in depth how the collection fit on their customers? And I brought 3 challenges, okay, 3 different scenarios that we have with our partners. The first is that the challenge was to know if consumers have the same size in the entire collection. So we did, as you can see here on the side, like data from people participating in the fitting room try-on in the focus group. And the findings were that they had up to 4 different sizing in trousers and jeans. So the actions taken by the brand, thanks to this information, was to revise, revise pattern and correct them. Another one was, okay, are we offering the right size range to our target customers? And we noticed, as you can see here, that 25% was NA, not available. So which, which means that they needed to offer larger sizes, okay, to adapt to real consumer demand based on data coming from e-commerce, okay, and from the fitting try-ons. And finally, also, if our consumers are morphologically similar across different markets, and of course different markets might have different different, uh, tastes and fit requirements. Okay, so we can provide BMI data and also minimum and height data. So that's just to end the presentation because I'm seeing I'm sticking to the time. And that's just to say that of course these are some of our numbers, okay, of testers we've been collaborating with and fitting trials realized. And this is the smart catalog where you can now have access to this data. And that's more or less everything. Thank you for your attention. Thank you.

Our next speaker is Fanny Bungermann. She's the founder and CEO of Vestas Labs, and one of her goals is to use 3D to reduce the number of physical samples to make it easier to iterate and prototype designs. Welcome on stage.

Good morning, everyone, and thank you very much for the opportunity today to present Vestas Labs. I'm Fanny, and I'm the founder of Vestas Labs. So, I love fashion. It's a beautiful, creative, and dynamic industry. I've immersed myself in the fashion industry for more than 15 years. But after these long years, I've seen firsthand its inefficiencies, wastefulness, and also slow adaptation to digitalization. And I want to change that. So at Vestes Labs, we have a mission to make the fashion industry more efficient, transparent, collaborative, and also future-ready. So we have talked a lot about the challenges that we are facing this morning, and totally on point and totally agree with, um, all of the challenges that have been said. But I want to highlight also the challenges that propelled me in starting this venture. First is the slow digitalization. So, um, we live in a very digital world today. But the fashion industry is still lagging. Much of the industry is still slow in adapting new technologies and still rely heavily on outdated tools. There is scattered data information and inconsistent data management. Not only that, the product development process still involves excessive multiple physical samples being created prior to production. Also, the complexity in the supply chain make it challenging for fashion brands to be truly transparent, which is the basis for sustainable and circular business practices. And to wrap it up, currently there is still limited availabilities of digital solutions out there which is accessible and affordable, and this is slowing down digital adaptations, especially among the SMEs. Which makes more than 95% of the fashion industry in Europe. So we need a new way forward, and this is where Vestis Labs comes in. So we provide a digital solution which is simple, practical, and accessible. We help brands to work more efficiently, enhance transparency, and also facilitate circular business model. Our business is divided into two main parts. One is the digital product development service, and secondly is the integrated digital collection management with features such as digital library integrated on the platform as well as digital ID. So we have very exciting features on the pipelines as well, such as the automated certification management and sustainable and transparency tool to help brands prepare for the upcoming legislations. So our digital product development service is integrated onto the platform. We focus on creating digital assets which are fit for productions. That means we embed the garment construction skills onto the workflow, making sure of bridging the gap between the physical and digital. Also, by doing digital product creation from the design phase, you can reap the full benefits of digital product creation. On average, we are able to reduce the sample, physical sample, by up to 70% and sample cost by up to 50%. So our digital collection management platform is an integrated solutions built on modular system. To make it flexible and scalable. Brands are able to visualize and manage their designs in 2D and 3D, manage their materials, product development process. They have a centralized communication and collaboration tools to be able to communicate effectively with internal and external teams. The platform is user-friendly, doesn't require an IT team, and requires only minimum onboarding. Brands have full control of their data as well as level of access. So the digital library is also integrated onto the platform. Brands are able to manage their materials, digital assets, integrate all the suppliers' database, including their certifications and credentials, onto the workflow from the design and development stage. So beyond storage, the digital library also acts as an— digital hub connecting brand suppliers and different actors within the ecosystem, um, so that they can collaborate and, um, share resources, as well as turning this into a robust digital ecosystem. Now, our, uh, Digital ID features also directly integrated onto the platform, so onto the product level So this minimizes the need for data transfer from multiple sources and also minimizes the integration time and costs. Each product will have an automatically generated unique ID detailing all the entire product journey. But more than this, more than increasing transparency and traceability, this actually increases connection and engagement. Post-purchase, the end consumers are able to connect themselves with the garment, increasing their relationships with the garment, with the suppliers and brands, and also the makers. These connections increases the value of the items and also the utility of the items, unlocking new opportunities and possibilities, such as easy access for repair and second— end-of-life solutions, such as resale, rental, or recycling. On top of that, brands are able to collect and gather invaluable insights, pre-sales and post-sales, such as product performance, durability, consumers' behaviors, as well as consumers' preferences. So we have time, so I'm going to quickly show a video. If you can play the video there. So this is one of the projects that we did with, um, circular— a new circular brand. And, you know, you can start by scanning a digital data carrier. It can be QR code. You can start telling the story of the fabric, then connect yourself post-purchase and engage with the garment, and as well as you know, any other initiatives that the brands can have post-purchase.

Thank you.

Important detail. Our next speaker is from Norway, Petter Helvik CEO of Scyzer, and they use feedback loops to enable you to get personalized fit recommendations online.

Thank you. Yeah, we do that among other things, and it feels wonderful, I have to tell you, to know that I stand between you and your break, your coffee. Hope there's not any smokers in here. But I have only 8 minutes, so this will be quick. And having only 8 minutes means I have to prioritize pretty hard on what topics to cover. There are so many things about Sizer that I would love to speak to, tell you about, but these slides had to be ready by Friday last week. And Friday happened to be— anyone recognize the date? It was International Women's Day. And for me, a guy living together with 3 women, That day does not go by unrecognized, I can tell you that. So that kind of set the tone for my theme on sizing today. And I almost launched an Instagram campaign that Friday that were to say, stop asking— and please do not feel offended— stop asking my daughters about their weight and body shape. Because my daughters, they are 16 and 19, they have grown up in a world and they have grown accustomed to a world where online shopping for clothing equals answering questions basically about what they do not like about themselves, about their complexes. And that's how a technical development guy like me kind of fell into the rabbit hole of sizing because I I thought there has to be a better way of doing this. And how is Sizer different? What have we done to solve this sizing? How can I give you the right size recommendation without asking you these questions? And if you look at it from a statistical point of view, an average point of view, because that's what you do. If you tell me your height and your weight, I can calculate your BMI. And if you also tell me your age, I can correct it further. Or if you send me some pictures, that's fine, maybe I can work on that. And as a tech father, I always tell my girls, do not upload photos of yourself without clothing to those services. Please don't do that. But from an average point of view, obviously some of the recommendations will be right. But the problem is that average is a statistical term, it's a mathematical term. Is used to describe data. It's not used to describe— it's not supposed to be used to describe people because none of you are average. People are not average. I'm not average. I'm shy of 180 high, so you can say kind of average, but I have negative ape index. I have very short arms. They're not like T-Rex arms, but they are shorter than they're supposed to be. Took me years to find this manufacturer of shirt that actually can— I can go off the rack and buy shirts from. So basically in the middle there, you can say that we have all these solutions that yes, eventually they will give you the right size recommendation, but most of the time they will not. And it doesn't help me that they are 70% accurate. They need to be above 90 at least for me to trust them. And so basically the size recommendation will be wrong. Because if you are a computer and you know my height and you know my weight, but I ask you as a computer for a personal size recommendation for a fit, let's say a formal shirt, you have a problem knowing that I have a short upper body. So the size recommendation will be wrong. And let's just pause there for a minute because what's actually a wrong and a right size recommendation? Here the other day I said to my 19-year-old daughter that I can mathematically prove that I can give you right-size recommendations for jeans. Jeans, yeah, right, how are you going to do that? You're thinking the same thing, right? Without asking you a lot of questions, how would I do that? And I said that's fairly easy from a mathematical point of view. If I give you a recommendation, say 26, 32, and you are able to pull them on, zip it, close the button, and they don't fall off, you have the right size recommendation. And she said, "Haha." And everyone knows that because it's all about how she feels. It's all about how that size makes her feel. And that might be a right size recommendation for her, but for her friend that looks quite similar, it might be the wrong size recommendation because she prefers a wider fit. So back to this, we can all agree that average is not the way to go ahead solving sizing. So that's why we have in our mission that respectful sizing for everyone, among other things. When we started working on SIZER several years ago, we asked hundreds of end users things about sizing. They literally said the same thing. Everyone basically this and everything from depending on the situation, I prefer different fittings. I can gain, I can lose weight. My body shape changes over time. I don't like intrusive personal questions about my weight, etc., etc. And even more important to us, I don't want to be put into gender brackets or I don't want to start over every time I visit an online store. So obviously we have a different approach, I might say a very different approach. So enter Sizer ID and solving sizing is just one part of the equation. The other part of the equation is, and I very much agree with the former speaker here, that we need to take that experience from the physical store and implement it into the online store. Because to me, fashion as an industry exists because it is supposed to make people feel good. That's why fashion is around and makes us able to express ourselves. So that means that when Kim, in this instance, comes into my online store, I would say, "Hi Kim, you would look great in a US 4," instead of saying, "Hi Kim, welcome to my store, how much do we weigh today?" Or, "Hi Kim, could you please send me some nude photos? I would love that." You would never do that in a physical store. And moving on, how do we know that Kim would love a size US 4? Without asking her all these questions? And how would we know that she prefers on her blazers a snug fit? How do we know all that? Or Siri, and this is my wife actually, I'm married to Siri, that's annoying when Apple called their personal assistant Siri as well, but leave that for later. How do we know that she is actually a size IT 36 in boots? And as an avid marathon runner, she prefers her running shoes in size 13. IT38. Because it varies, right? This is actually live. This is from a POC we're doing with NAP these days. These days we are tested in UK, US, Hong Kong, and Deutsch— and Deutschland, what's that in German? That's called, uh, really cool. So here it, what it look like. And what we do is we collect fit data. So we, when we integrate into the stores, all your online purchases are added to your personal profile. You can, if you like, go in and say, yeah, that fits perfectly, but— there's always a but, right? So you can tailor your personal fit preferences. Technical slide at the end. Yes, it works. It has been tested, has been proven. Not too much work for you guys. AI works for us, it works for you. So we have integrated some of the largest online stores in the world in less than 48 hours. Please get your mobile phones up, scan that QR code, because everyone knows how does it work. That takes you to my and my lovely colleague Ruben's calendars. Please book a meeting with us and we would love to chat with you about how SASE works. Thank you.

Thank you, Petar. Unfortunately, we were a little bit behind schedule, so we're going to have to move forward with the agenda, but I do want to thank all the speakers. You're truly awesome out there in the trenches building stuff, changing the industry. It's super hard running a startup, so you're troopers. Thank you so much, and thank you to everyone who listened in. Make sure to grab the opportunity to continue the conversation with these speakers. All startups depend on feedback to move fast, So they need your ideas and your heavy experience. Thank you so much. And you can now continue straight to the exhibition hall for the next session. Thank you so much.