Ep 6. AI for Zero Waste Fabric, Sustainability and Traceability in Textile Factories with Gilberto Loureiro

[MUSIC PLAYING] Welcome back to another episode of the No Ordinary Cloud Podcast, where we bring you insights and experience from the leaders in the textile and fashion landscape who are transforming the very way we think about, make, and use fabrics. Hi, my name is Milita Rican, and I'm thrilled to bring you Gilberto Lureiro on the show today. Gilberto is the co-founder and CEO of SmartX, a very young and upcoming startup that is using AI and machine learning to minimize waste and boost sustainability in the textile factories. When we think about fashion, we have such a clamorous picture in our mind of runways and designers and trendsetters. But we rarely get a glimpse at the other end of the spectrum, the factory floors where these fabrics are first made. And Gilberto really opens up that world for us. And today, he shows us how many of these factories are in so many ways stuck in the past and hasn't modernized as many of the other industries have. He discusses the three pain points he has identified in the factories, especially the circular knitting factories, and three painkillers, as he calls them, that he's developed to solve these problems. He's also an incredibly inspiring startup founder who has grown his company quickly. And in just under five years, really built out a team of 150 and is growing fast. He has a lot of valuable insights for anyone who has a startup, whether it's in textiles or not. Gilberto has raised $40 million in investment, which is extremely impressive because I know how difficult it is to get investors in the fashion and textile sector. He talks about the importance of learning from others and leaning into mentors, particularly he discusses his investor, Tony Fidel, who developed the iPod and iPhone. You might know him as the father of the iPod, if you're old enough to remember the iPod. And he has many others who guide him in this journey. Gilberto is passionate and he's on a mission to transform the textile factories and he's unstoppable. So let's dive in and learn more. Hello, hello, and welcome Gilberto. It is absolutely wonderful to have you on the No Ordinary Plot podcast. I am looking forward to learning so much from you today, and particularly excited about hearing about smart X and how you're using AI and machine learning in very practical ways. And the fact is if you say AI to anyone today, they think chat GBT. But you're using AI in textile factories to reduce waste and increase transparency. And the impact you claim to have is quite incredible. I mean, in the short time that you've been running as a business, you say that your technology has saved over 961,000 kilos of fabric. That is mind blowing. That is fascinating. So I can't wait to dive in and find out more about your journey to creating solutions for the textile factories of the future. Are we ready, Gilberto? Thank you for the invitation. And we need more content like this, because the textile industry is probably the largest industry in the world that still isn't touched by internet in many ways, especially in textile factories. So let's dive in. Let's go. Yeah, great. So before we hear all about your work, I would love to just get to know you a bit, Gilberto. I know our listeners want to hear where you're from and where you grew up as a young boy. Tell us. Sure. So I grew up in the north of Portugal. In a textile hub, it is actually the second largest textile hub in Europe after North Turkey. Because of IndyTags, in Ouzada, Massimo, due to all of these brands in north of Spain, they still have lots of factories in the north of Portugal. So I was born and raised in a very, very small village in the middle of nowhere. And all my family works in textiles, since then. They are humble people, textile workers from textile factories. And then my parents, my cousins, my brother, like everybody works in textiles today. And that's part of the story, how I stumbled in many of these textile problems in factories. Because I actually used to spend a lot of my weekends and summers working in textile factories to get extra cash in school. So that was the moment that I was like, oh, wow. This is really manual, right? Like all the processes are manual. You have pen and paper everywhere next to the machines. You have people inspecting fabrics day and night, like human inspection for quality control. You have tons of waste. So that was my moment of like, hmm, this is a lot of room for improvement here. And because I was eventually a textile inspection worker, I decided to run away. And I promised to never get back to textile factories. Because it's really a hard work job. So that was motivation to leave to college. Right. OK. And what did you want to go and study in college? So I was very good at math and physics. I was a bit undecisive. But someone taught me my first rule of life, which is always make the choice that will open you more choices later. It was a finance mentor I had. He was teaching me about real options. So he was like, always choose the door that opens more doors. And physics is this course that was like, hmm, if you want to go to engineering later, you can or to mathematicians or to finance. So physics was like this mega course that you can choose more or less what you want to do later. So that was my choice. Brilliant. Going back to your family, I understand you have a brother. Yeah. Yeah. Yeah. And how did you guys spend your summers? So we used to work a lot again. OK. Because he was also working in textile factories. So very humble people, humble beginnings. We used to be very close and living in a very small house in the village. And in the village, everybody knows everybody. Like this very little village with a little church in the middle. So that has been our lifestyle. Although when I went to college, I was the first person in our family to go to college. I went to the city, which is more self of Portugal. And then eventually, life started going on. I moved to China. I moved to the US. I moved to Turkey. So I lived in many countries. And now, once in a while, I still go to the village to visit them. But I have been living in many other places now. Incredible. And your family still has the textile business? Yes. Yes. They still have a small textile warehouse. So you sort of rebelled against all of that, went away. And then sort of almost landed back in the same place. I'm sure your dad's very proud of you. Yes. I promised to never get back to textile factories. I was really annoyed by the whole thing, you know? Like lots of family businesses, lots of discussions. After the fashion world, you have like 10 different stages of the textile industry. You have like the garment, the dyeing, the finishing printing, embroidery. Then you have weaving, knitting, right? It will deeper to spinning yarn. And then you have fibers. All of these guys, they live in a different galaxy than the fashion brands. Like these worlds, they are disconnected. Yeah. And this world here, like these textile factories world, I was really shocked with the processes, the inefficiencies, the people, like nobody used like smartphones. Like nobody used, like there was pen and paper everywhere. So it's funny because now I see lots of companies, especially other startups talking about these AI, AI, AI, AI, which is amazing and blockchain and traceability. But then you go to textile factory and everything is in pen and paper. So good luck with the blockchain. That's not a problem here. The problem is we don't even have data from factories. That's the two galaxies that I have been describing. I saw that in the first person. So I wanted to run away. But then, yes, in college, when I was studying physics and then later on finance, all the things came back. And I was like, well, this machine learning thing is an inflection point in humanity. This was 2016, 2017. The first machine learning models were public. Like Google and Facebook, PyTorch, these type of models. And that was the trigger that we were like, maybe we can do something here to help the textile factories. So that was our first spark. Yeah. Yeah. Actually, I was reading through your modern textile factory report and we'll get more into that further into the discussion. But it was such an eye-opener to see how the textile factories are stuck in another age altogether. And so I'm really hoping we can shed some light onto that today. Yeah, definitely. We have a lot to talk on that front. I am extremely passionate about textiles. I'm a textile maker. And I also believe in the connections that we have with textiles as a material, whether it's a garment or a piece of cloth. So I'd like to get a little personal now, Gilberto. Is there any fabric or textiles that you hold some very personal connections with some intimate memories or stories that you have? I have a lot of memories in textiles, but more related with the machines and the processes. Interesting. Yeah. More than the materials itself. I have lots of memories of the first knitting needles that I've seen and touched. So a knitting machine has thousands of needles. And those needles, they have a little latch, a little tongue, that opens and closes thousands of times per second or something like that to catch the yarn and neatly yarn together with the neighbor yarn to build the knitting structure. Those memories I still have from teenage wool, and I travel there a few times even today of the first time I've seen the engineering behind the knitting process and the weaving process. It's really fascinating to see all of those yarns and needles and air jets and things happening. So in terms of mechanical engineering, actually the industry is very evolved. And there are amazing things. machinery builders, especially in Germany, Belgium, in Italy, also in India and Japan, they are amazing engineering companies for textile building. When I talk about these two galaxies and the industry is not being connected, it doesn't have to do with the mechanical engineering of it, it has to do with the software part of it. I used to tell my investors that this will be the last industry to be fully automated compared with all the others. Why? Because it's so difficult to have robots or machines manipulating textiles, right? It's such a complex product. It's flexible, it's breathable, it's subjective. A defect for you might not be a defect for me, right? What you find beautiful, I might find not so beautiful. So there are so many variables to make a garment that is so difficult to automate it, especially to compete with the prices because garments manage to be cheap. Like since the 90s, they keep more or less in the same levels and all the other industries, they somehow manage to increase their prices. So all of these makes textile super challenge to be fully automated. So that's also why we came up with some initiatives there. I've never heard anyone speak so passionately and beautifully about the engineering behind textiles. And it is so true, we forget how complex the processes are for making a piece of fabric, you know, just to shirt or we just think, oh, it's a piece of cloth, but the amount of advancement in engineering is mind blowing. Thank you for just bringing that to our attention. You mentioned a mental earlier. Is there someone that you could share with us who's been a role model for you a mental that has guided your path? Sure. I try to be like absorbing knowledge from everyone. I try to find coaches everywhere I go. I have lots of mentors. I can point out one of my investors, Mr. Tony Fadel, the inventor of the iPod and iPhone and Google Nest. He has been someone really important for me in managing the business and helping me managing and growing the business. But I find mentors everywhere. I have textile mentors. I have business mentors. Even like spiritual meditation type of things. I try to find and drink knowledge from everyone around me. I have strong opinions about people that are coachable, like the absorbed knowledge from anyone and humble, right? And humble to be asking questions and not play as the smartest guy in the room and work hard, work an extra mile, like those guys fast answering emails, work an extra mile on the weekends and nights. If you have these three things, I really believe that you can do in three months what a normal person doesn't want to hear. When you are 30 years old, you can have as much experience as a 50-year-old average person. I really believe that. That's something that I try to put in people around me as well. How do you gather all these mentors? Because I can see that's a really important part of who you are and what you built. It is. Especially being a first time founder, right? I have never run a business of 150 people. I'm running now. It's better for me to be surrounded by people that have been doing this. Thankfully, we have a huge ESG impact, a huge sustainability impact. We can go there in a minute and also a social impact as well in the industry. We are improving a lot of lives on the contrary of some people say, "Oh, you guys are replacing jobs." No, no, no, no, that's not exactly like that. So, this type of mission, which is such an inspiring mission, many times attracts these type of investors and mentors and coaches. Sometimes it's not even the monetary part or the stock of the company. Of course, we compensate some of them. We give them the stock of the company. We try to bring them closer to our company for them to help us. We are very generous with stock in general. I think it is a mix of all of these things plus having fun with them that we can attract these type of guys. And I'm always asking for interest. So, I don't ping people randomly on LinkedIn. Sometimes I do, but usually is asking for interest to others. That's a great tip. Thank you. Okay. Now we're going to jump in and talk about your company or your baby, I would say, "Smartex." You are the founder and CEO of this incredible company. So, before we jump into what Smartex does, paint us a picture of what the textile industry is like. And what was the problem that you saw and felt like this has to be solved right now? Yeah. So, we identified a first pain that was very specific pain for a very specific client, which is the knitting and weaving factories. They are transforming yarns into fabrics. And they are producing some wastes in the process, some defects, some quality control issues, some folds. And this wastes, most of the times, goes to the supply chain until the garment stage or close to the garment stage and it becomes expensive wastes later on in the chain. That was the first initial pain that we found. And that's how we started with a camera system for quality control inspection and so on. But then we have been stumbling in larger and larger problems since then. For example, we started installing these cameras inside machines a few years ago. I remembered in Italy, in a big factory in Italy. And I asked the Italian owner, "Hey, can you give me the password of the internet so I connect my cameras so then I can see it's remote and so on?" And the Italian guy, he was like, "What do you mean by internet in a factory?" I don't need that. My workers will be on Facebook the whole day. I don't need that. So that was the moment when I was like, holy fuck, like this is much bigger than just textile inspection. So we started also deploying in the factories, routers, access points, cables. We started putting little screens in the machines so we can track workers, production orders, planning. So we started putting a bunch of other building blocks to create this smart factory ecosystem. And later on, now we are selling a solution. We can go that in the minutes about fact, core, loop, like we are selling a set of products to make the factory of the future, which we call the modern textile factory. Like that produces no waste, that is connected, and so on and so forth. And more recently, we are stumbling now in the traceability issue, which is another big thing and legislation is pushing for that. So since we are the only company that has hardware connected in the factory and we have the routers and access points and the connectivity, we are in a privileged position to get that precious data and transport it for traceability reasons and for legislation, compliance and all that stuff. So that was the pain that we started and the pain that we stumbled on. And so you created this whole ecosystem, basically, that's that you needed to create it because your solution alone wasn't going to work in a factory. Exactly. Imagine Uber, we thought the iPhone, right? Uber needs the smart phones to exist, right? But we need to build the phone, the app, the internet network, we need to build all the build lots. So the smart tech system, you mentioned the core, fact and loop. Could you explain each of these and what they do? Sure. So core was the initial products that I mentioned. So with cameras that we installed inside any machine, we are focusing a lot in circle and meeting machines that are weaving and knitting. We are focusing and knitting to start. And these cameras, they inspect in real time the fabrics, they stop the machine if necessary. So we can avoid waste at the very beginning of the process. Because if we don't stop that waste there, it will propagate until the end. So that's pain number one, defects and waste, pain killer number one, cameras and stop the machine. So we avoid that. Second one is fact, which is the platform, online platform, that I can see all the machines in real time. I can see the pictures, the role maps, I can see performance of machines and workers. It's like a digital factory dashboard. And so the production manager can access from home and see the machines in real time, see the workers and have planning and management and all that stuff. They are actually, he uses this platform to find problems before anyone else. It's like, hmm, machine number two is producing more defects than machine number five or as oily or things like that. That's fact. And because fact exists, we have their precious information. We have role number, machine number, production order, worker, yarn composition. We have lots of precious information there. And that's why we created loop is a set of QR code systems that are impossible to remove from the fabrics. It's like a heat press QR code. And this QR code transports all of this information through the subsequent stages. The industry is very fragmental. There are like 10 different factories to process a textile roll, like the meeting, the wind, the embroidery, printing, dyeing, finishing. There are lots of processes there. So if we have QR codes tagged to the roll, this QR code will speak for the roll. By the way, fun fact. Today they use paper, pen and paper, sometimes attached to the roll. Super easy to cheat the system, super easy to swap rolls. This roll comes from China, this roll comes from India. You never know where it was made. All of the industry now works with pen and paper and human audits and human certifications. We can go that as well if you want, but it's a billion dollar industry just in human audits and it's also super easy to cheat the system. So when you talk about defects, what is a defect at a production stage? We can go philosophical here, because the defect sometimes for Mushi is not a defect for Zara. And sometimes they are producing in the same machine. That's why I believe machine learning is so powerful, because it can really understand like a human nuance and subjectivity of fabrics. So our systems, they look at the piece of fabrics and they look at the last few meters of production, they look at a billion of image database and understands this little line here is a yarnknot or is a contamination or is like part of the pattern because the pattern is like kind of crazy and it attributes a score to that particular fabrics, like from 0 to 10, you know, and then we teach our clients to see, like 10 is a defect for sure. Five is like you can accept it or not, maybe Gucci accepts, Zara doesn't accept, like a zero is like perfect clean fabrics. So we have this system of subjectivity, with machine learning that is really powerful. But to be honest, most of the times, like 90% we are talking about defects that are really like broken needles and create little holes on the fabrics or contaminations in the yarn that creates little white dashes in the fabrics. And these are usually defects that if nobody stops, they will continue for 100 meters, 500 meters, one kilometer, and all of that fabrics goes to the garbage or somehow it's destroyed somehow. These are the types of defects we are talking about. There are many, many types, hundreds of types. So how are you training the system to identify defects? You mentioned it briefly, but it'll be nice to just go into going to any machine learning company depends on high quality data to label things. So if I'm building a self-driving car, I need a bunch of pictures of cars, bicycles, sidewalks, and I need to have these pictures very well labeled. That's why they put people working for them. This is a bicycle, this is a sidewalk. Here we have also billions of images of textiles. In multiple wavelengths of lights, in multiple light conditions, we have these conditions of light. And then we have particular defects that we train. We don't trust anyone else to train. Actually, inside this company, we only have three people that we trust to label images. Because labeling textiles images is more a philosophical discussion than yes or no. We're not like bicycle or no bicycle. I don't know. We're like, this might be a bicycle. What do you think? Maybe Gucci would like this to be a bicycle. Maybe Zara wouldn't like this to be a bicycle. So we need to start giving weights and is a really very complex problem to solve. We also try to outsource labeling like many machine learning companies do. Never went good. We also try synthetic data. Many machine learning companies try it and it works for their applications. It didn't work for textiles. Textiles are really complex monsters. This industry deserves a special treatment. Even for software architecture, we do it very specifically for textiles. Because of all of these things that we mentioned, the subjectivity, the flexibility, the transparency, all of these variables. Infinite variables. So I'm guessing this database is constantly being updated. And it's being refined over time, the more information you collect as well. And we have played it to our clients as well. Because the clients that bought our system two years ago, they get new models every two weeks or every month. So they are feeling the benefit of also sharing some of the data with us. Of course, now we are becoming more and more careful with these data things, especially in Europe. So now we have a set of clients that we use to train models. And most of our clients, we don't touch their data. Is there any reason why you started with circular knitting machines? Is there a lot of waste in that specific industry or it's just where you started? Was more because of the family background. Usually knitting and weaving, they don't mix up a lot. Usually you have factories for knitting and factories for weaving. So for the audience, knitting is more sportswear, t-shirts, socks. And weaving is more like suits, shirts, home textiles. And usually the world splits into 50-50. 50% of the fabric is knitted and more or less. So we started knitting because of that background. But also we got the opportunity many times to go to weaving. We did some pylons in weaving, factories and so on. But strategically, also being a startup, we decided to kill it here, be like the best one in this segment. And then once we are inevitable here, we go for other segments. I have seen too many startups dying from trying to do everything at the same time and not focusing. So let's focus in one thing is very challenging, especially in this industry that everybody is asking you for features. Right. It's tempting, isn't it, to run in 10 different directions. We're tempting. There is no other technology. So they are like, hey, dude, can you bring me like a planning system, like a tablet screen, a television to the factory? So everybody wants new tech. But we need to focus and really be good in our segments. And then maybe later on, we can think about all the possibilities. This system can clearly be expanded to other textile processes such as weaving. But then you'll have to train the models again with woven fabrics then. Exactly. Exactly. That'll be a whole different system at that point. Are you allowed to share with us about some companies or countries where you have deployed? Yes. Martek successfully. Yes. So we started in Portugal is our sandbox. Also, the Portuguese industry is very resilient. And they kind of survive to the Asian hubs because they are closer to Europe. So we're usually faster on the deployments. And also they specialize the bit more in high-end fabrics. But still we find the whole supply chain here. So Portugal is our sandbox. What is a small market? Our largest market now is Turkey. It's where we have most of our deployments. But we also have a few clients in the Uzbekistan. Lots of clients now starting in Bangladesh, like at least 10 deployments already. Thailand, Indonesia, a few clients. And we are starting very strongly in India. And we are opening our first clients now and we have a big trade show now in March in Kui Mataar. Also, Egypt, a few deployments there, one deployment in Brazil. So we are going and chasing the early adopters. The factories that are the best factories of their hubs, the ones that are willing to try new things, the ones that are willing to change their processes. For example, the industry always has production, inspection, production, inspection. And by the way, sometimes the inspection happens two times for the same fabrics. Because you have exportation, exportation inspection. So before you ship, you need to inspect everything. And then the other guy on the other side, he really inspects again the same fabrics to receive it, double check if you are not sending. And sometimes they don't agree. Sometimes one sees more defects than the others. Like is an interesting discussion there. Sometimes there is a third party inspection, by the way. The referee. So we want to work with companies that are willing to change this data score, willing to change and challenge this really thing. Like, do I need to inspect this twice? Can I use a machine learning system to track this? Even if it is expensive and can cost millions of dollars to implement a full factory, hundreds of machines. It is expensive. But these factories, they are willing to change the start to school and really challenge the waste, the things, the quality control things, the human inspection things. And the ones that are willing to do that, they find a good ROI. Sometimes like in less than two years, they have the systems paid and profit. But they need to be willing to change. So you seem to go directly to the factories that have the machine set up and running already. Are you working closely with manufacturers of machines, itself, and to integrate it at that stage? Yes, yes, as well. The OEM business, as we call it, is very strategic and important for us. Although if we really want to make an impact in the world, while I still don't have many gray hairs, we need to go after the existing part of machines. Because these machines last forever. These machines have like 20 or 30 years old of lifetime. So we have like the new machines in the market represent like 1% per year or maybe not even that. So we are targeting both sides, but mostly the existing part. What are some of the challenges or resistance that you have faced when you try and approach factories with this technology? Many, okay. We are having tough skin here. And that is nothing easy on this business. Inside smart techs we have like four companies. We have a machine learning company, software. We have a hardware development company. We need to develop our own camera, sensors, lenses, LED, PCBs. We have a logistics company, shipping stuff, installing stuff. And then we have a software business like to create these platforms and so on. So software, hardware, logistics, and machine learning. These could be four different companies. So we have like 30 people flying around the world to install our system in existing machines in very tough places. We are not going, where are we going to Brazil? We are not going to Rio de Janeiro. We are going to the jungle, right to the middle of the world. Where we go to Bangladesh, we go to DACA, four hours driving from DACA in the middle of tough places. So I got lots of food poisoning and all the experiences of traveling in these places. But that's on the personal side, on the travel side. On the business side and tech, this industry because of what you mentioned, doesn't have much technology in. Typically we are talking about family owned businesses, people that are really skeptical about technology. And it's like businesses that come from the grandfather, the father, the son. Usually environments like this are not the most tech savvy environments. Super price sensitive. Sometimes we are negotiating these counts of 0.5%. Like a build breaker, you know. If I, you know, do me this count, I don't close the deal, you know. It's very price sensitive. So all of these ingredients combine plus the fact that we are talking about these countries very far away from each other with a lot of challenges, politically. You see turkish lead inflation crisis, you see Bangladesh crisis on the banking industry, they cannot take dollars outside. All of these systems, all of these complex variables combine and make us our life really challenging as you can imagine. And then tech wise, we need to be training and teaching people to use a very complex system that, we try to simplify it as much as possible, but we need some basic interactions. Sometimes our workers that cannot read illiterate people, right? So we are putting machine learning in state of the art tech in the hands of people that never have seen a computer in their lives. And they are interacting right there in the factory floor. And We need to train them and this connection, when it happens is magical. And it's something that really gives me a lot of energy is to see these skeptical people using it. And when they see the machine stopping with a defect, they are like, wow, did it just stop with a micro defect? Like, the defects are invisible. Everybody knows that the micro defect is invisible, but it's like, is there? And that's really magical. - Oh, that's amazing. How do the employees feel about this? Because again, as you mentioned earlier, when you hear AI, everyone's like, I'm gonna lose my job. - Yeah. - What has been the response to from the actual people on the floor of the factory, not the CEO, but the guys on the floor? Are they feeling threatened? - They are in the first moments, but we try to put those cars in the table right in the beginning, whether we are training them, it's like, hey guys, no one here is going to be fired, because this will make your work to be twice as more productive. We see that a lot in Europe and in Turkey, now even now in Bangladesh in these first deployments, we are seeing that in general, the world is having lack of people to work in textiles. Like, nobody wants to work in a textile factory, right? People usually want to go to other jobs as soon as they can. So the ones that stay, so that is that's pressure, that is lack of people to work in textiles. So we need to make the few people that are there, much more productive. So instead of being eight hours looking to an inspection machine, that same guy can inspect 10 times more roles, just with a computer next to him. And even sometimes he is going to inspect visually again, sometimes he finds in our platform, "Mmm, this role number three looks very weird, let me inspect that one." And he goes straight to role number three. So this guy, John, is not threatened, he's actually much more productive now. So we see many times our clients, we have now more than 100 clients, but the ones that are really deployed and really using the system in the full potential, they remove sometimes some people from the inspection machines, and they put them working in other parts, mostly in the machinery in the production. They can produce more with the same resources. And by the way, when we do our arrow-wide calculations, to convince them to pay a big amount of money, right? The main variable there is the savings due to raw material. Yarn or cotton that you are not going to waste, that's like the biggest saving. If you fire your inspection team, that is not even in the same order of magnitude. Wow, give it a go. You've managed to do like you said, it's four mini companies sitting under this umbrella almost of Swatix, isn't it? And you only started in 2018. Tell us a bit about that. How did that come about? What's the journey been for you personally to found a startup and then grow it so quickly to where it is today? Usually founders tend to sugarcoat at this stage, how awesome it has been. It has been awesome, it's really awesome. And I've been learning from the best of the world. But there is nothing easy here. It is tough. No vacations, no weekends, no Christmas. Like of course I try to manage the family and life and everything, but the sprints are real and the stress is real and having tough clients, sometimes insulting in your face, you know, things, it's really tough and chasing investors and all of these things. But somehow we found a cycle here that we can attract clients, attract investors and have a lot of fun in the middle. Because in the end of the day, life is about the people you work with and what you do, the products we build, right? And these two, we couldn't be more proud of the things we are building because they create lots of benefits to the world. And the team that we work in is like super fun to work, super smart people. We have 17 nationalities here in the office, Canadian Americans, Brazilians, Turkish. So we have, we found a way to have tons of fun so it doesn't feel like work. But it's really, really challenging. And we've got some luck strikes, some talented moves that we have done to be able to get here. Because if you think about it, there were no computer vision companies that were able to scale in the textile industry. You have a few players in the market, but usually they deployed some tens of devices in very specific situations. Nobody has ever sold thousands of devices in two years like we do. So wow, we believe we cracked here a pricing problem and we cracked the tech problem with machine learning. So that's really, really, we are really proud of that. And that makes us feel even more energized to the many challenges that are away. I used to say to my team, we will laugh that we start as a defective tech. I think it's much more than that. It's like the whole industry operative system is in our hands and we can do it if we want. - So the, I mean, the ripple effect of your technology is mind blowing. Could you quantify that a bit? How the impact that you've technology has had? - Yeah, so because we are building the plumbing system of the textile industry, right? We are building the pipelines for the data to flow, right? We are doing the dirty job. So then in the future software companies can run their systems on top of our infrastructure, right? We are kind of the Amazon logistics things behind things and then other companies can come on top of us and do traceability services and do stock management. Imagine in the future fashion brands will have as little as stock as possible, right? They will be selling on demand. You start seeing that trend with Cheyenne, with some other brands. They are selling on demand online or in shops and they are producing almost in the same time and the factories will be much more automated and shorter orders. This thing of producing 1,000 T-shirts, small, 1,000 M, 1,000 large, this won't be the future, right? Because you have that stocks and a bunch of waste. The industry is going into this trend and everybody wants to catch this wave, but first things first. Let's make sure the factories don't run in pen and paper first. Let's make sure we have cameras and sensors and things connected to the machines that can speak with the outside world. Because today we are light years away from that reality and that's what we are building. You did mention that textile and fashion industries extremely price sensitive. Everyone's trying to lower their prices, brands want production to be done at lower prices. How did you navigate this to reach a business model that makes sense? Yeah, super challenging, super super challenging and education of the clients as well. We believe that this company would only be possible now. If we were 10 years earlier or 10 years later, wouldn't be possible. And let me explain you why. 10 years ago, the hardware would be much more expensive. Cameras, sensors. Now, especially because of the smartphone industry and mobile phones, it's much more democratized. We can find electronics and cameras, high-resolution cameras with much more affordable prices. That's actually why we chose to have Tony Fadel in one of our main investors. Because of all his experience with hardware at Apple, at Google, he's helping us a lot in that front. Now, 10 years ago, it was not possible. Now, it's possible to have a full system with 10 cameras, high-resolution at a very low price. So then we can make an interesting profit in our sales. That's first. Second machine learning is now democratized. Everybody can do a machine learning model. Like three kids in college can launch machine learning models in an Amazon machine. So that's much more democratized now. Third, there are much more talent. There is much more talent in the world. I mean, 10, 20, 50 years ago would be much more difficult to find software engineer. Now, we have people from everywhere in the world, amazing talents, connect, we can work remote. So it's much easier to create a technology company and scale it than before. So because of all of these factors, this is a cocktail, an explosive cocktail that makes possible for us right now. And not just for us, for many companies. I really believe that who is not in tech in 20 years will suffer. I used to tell to my doctor friends, my accounting friends, my lawyer friends. Guys, find a way to go to tech. Even if you want to be a lawyer, find a way to be a lawyer in the tech world. Because this is a cocktail that is exploding now, right? What happened in the 90s was just the tip of the iceberg. Now is what we are seeing in the world connected. Plus that has never been so much money in the world invested in technology. And a guy like me based in Europe, I can attract funds from Silicon Valley and build a company of software company, a very cash capital intensive from Europe. That would not be possible 20 years ago. I needed to be in Silicon Valley to do this. And again, hardware, it's really, really difficult to get investors to invest in hardware startups. It's a big challenge. How did you crack that? Yes. And not to in Silicon Valley. Yeah, we were very sales oriented in the beginning because we got lots of clients to sign. That's like, I'm interested in this. I want to buy this to show that initial traction. Before we even had the product, that was a very, a very good move and investors love that move. But indeed, you are right. There are many investors that they don't really open the door to speak with me just because we have a strong component of hardware. We cracked that problem somehow because our business is first hardware. We live to sell and install hardware. But our main product and our main strategy is the software that runs on top of the existing hardware that we deploy. It's like Apple. Apple, employees, iPhones are on the world. But then they make their living from the services that they charge on top of the phones. And then we have a Mac, the iPod, the iPod, the iPhone. You are locked. You will never leave, right? If you lose your phone, you need to buy another iPhone because you have the whole ecosystem. You have photos, you know, your memories. So we are trying to become Apple of textiles in that sense that we deploy hardware. But then we create something bigger on top of the hardware. And that's usually something that is much more big picture and attractive to investors. Of course, it's also the way that we need to-- the path that we need to make to transform this industry is not just about the hardware. Although, I don't want to push water up the hill. If my clients want to buy hardware, and then we wrap up software services on it, let's do it. I don't want to be a fundamentalist size founder that just sells subscriptions. We need to wait. adapt to the world that we are. So that's why when investors talk with us and they are very obsessed about Sass, Sass, Sass, usually that's not the right investor for us. We prefer to have investors like Agenem is a good investor of ours. Tony Fadell, light speed ventures, bomb big scapital, also textile focus. You might find some good speakers for your podcasts there. - Brilliant, yeah. - Because these people are more focused in the big picture of the industry. How we are going to really transform it and impact the world. Then with Sass, software metrics. - You produced the modern textile factory report. And that was quite an undertaking where you had input from factories, policymakers, brands, companies. Companies were introducing innovative tech into textile factories. What was your motivation to compile this report? - Sure, I feel and we felt a lot, me and Max, Max was the author. We felt many times that fashion brands and the startup worlds, they were living in a different galaxy as the factories. And when you ask a fashion brand what they really want for the future, flexibility, sustainability, ESG, digital product passport, I want to know all the information about this garment, how much water did it consume? Where was it made? Is this China cotton or is this Turkish cotton? Where are the materials from? And which process has been true? All of these questions that everybody wants to have, the answer, they depend on the factories and the machines. And nobody knows, and sometimes even inside fashion brands, nobody knows exactly which machines are used, which processes, where are these factories? Again, not their fault, not their fault. The industry is very complex and fragmented. The fashion brands they buy garments from the tier one, but tier one has a thousand of possibilities to buy finished fabrics. And the tier two has thousands of possibilities to buy yarn and so on and so forth. So it's really complex. Nobody knows how to crack this problem. Who says they know? They don't know. And we have lots of brands that they say, I identify and I have everything mapped, but it's so easy to slip. So we felt the need of showing the world the importance of textile factories before we ask for the beautiful things that the fashion world wants, as we need to solve aid to then go to be. And that's exactly what it was. It was such an eye-opener for me to read through this report. And there was an interesting quote in there by Ken Pucker. And he said, if you were able to turn back the clock about half a century and visit a tannery or a cut and so factory, much would look the same apart from the address. And that really hit me, you know? Yeah, it is. The technology, the mechanical technology, the machines, they generally became faster. They are a produced faster. They are better. They are more electrical and so on. But the process itself, because of the complexity of materials, is really the same. And we believe that is a massive opportunity here. Trillions of dollars are being generated without access to the internet. And we can be the ones connecting the world on that sense. So only depends on us. That's what really excites us. And by the way, for other startups that might hear this podcast, we are constantly looking for bridges to build bridges with all the technologies around, like all the pressability hubs, pressability startups. I used to mock a bit on the blockchain platforms because it's like, no, why do I need blockchain? If I don't even have data to put the blockchain. But I'm more than happy to collaborate with these guys, because I really believe this won't be a one company solving the industry. We might solve part of the meeting, maybe weaving, maybe tier two. But we need to have more bridges to the other sides. I'm very excited about chemical traceability, for example, for the fiber or other types of traceability that we can use. As you said, the textile industry is so complex. And you need as many hands on the deck as possible to try and align the supply chain. Definitely. What was a couple of the most surprising insights that you learned from the report yourself? So because we had so many different people on that report, we had people from factories, but also from the brands, also from fibers, like renewacel type of-- when we put all of these people in some sessions, and we had a few sessions. We had like 10 sessions or five sessions. We had some sessions together during the year to discuss particular topics. It's interesting because I am obsessed with the factories and with the factory processes. But seeing other perspectives, like Christine, she's used to work with Pangaya and Gucci and Balenciaga as a sustainability director there, she was much more focused in other things that's usually we don't see lots of people caring about having factories. Like, oh, I want to know more about the salaries of the people. Like the wages and stuff. It's interesting to see that that trend is there, although my point still remains, which is, for example, if I want to know more about the salaries and the people, I need first to know at least where is the factory, the name of the factory, right? Is it Mr. Gilberto Factory in Portugal or Mr.-- I need to know at least locations where the fabrics is made? And that's the first step that we want to solve with technology. Because then, once we have that building block, we can start collaborating with other startups for social and labor securities and the interest ability and ESG impact measurements. Then we can do a bunch of cool stuff on top of the infrastructure. Could you briefly explain to us about the five pillars of a modern textile factory that was mentioned in the report? I will share the report in the show notes if that's OK. Cool. I think it's absolutely fascinating for everyone to read it. But if you could just summarize the five pillars. Yeah, there are many angles there. I guess, we can summarize it in one big pillar to summarize. Which is the data. Having access to data, I guess, is-- and everybody that agreed multiple times is like, yes, you know, I've been working with two or three traceability platforms. Yes. And the factories, they need to pay for five different certifications. They need to pay to SGS certification guys to go to the factory once a year to write a beautiful report. Then they pay to another to God's certification. They pay to many other certifications. It's an investment that the factories need to make to have a few flags that they're-- and in the end, when we were looking to all of these challenges, everything goes around data, having reliable data from the factory. For example, the guys that come from SGS to do a beautiful report about compliance and stuff, they go through the invoices, they go through the machines, they see the fire extinguishers, they see, and they write a report about the general safety of the factory, the general production, and so on. All of this could be done with technology, without needing to fly a person from Zurich to Dhaka. All of this can happen if you have things monitored in the factory, if you know that that textile roll came from that factory and was dyed in the other and was gutted in the other. If you know the locations and you can prove it with QR codes, cameras, tech, you can pass a bunch of these manual processes in front. So data is, I would say, the main words of that report. And then it goes to all the other pillars, one of the pillars was the social thing, another pillar was the automation, data driven decisions, to take decisions to become more efficient, to avoid waste. All of these comes from the same source, which is having reliable data from what a hell is happening inside that factory, which many times, even the factory owners don't really know very well. So let's solve that problem first, and then all the other ramifications will come from there. When I look at your website, you quantify the impact that your technology has had. And as I mentioned in the beginning, so 961,000 KGs of textiles that were saved. How are you getting those numbers? And what are some of the other facts that you can share with us about the impact of your solution? Very important there, because we are stopping the machines in real time. So we are avoiding those defects to propagate to the front. So you can tell me, how do you learn to-- how can you say that you saved 1,000 kilos here? Because you stopped the machine in the first one. You don't know how big of a disaster it was going to happen after you stopped, right? And that's a very valid point that we have-- that explanation also in our website, how we calculate. We base on that same factory, that same machine, a few months before. We usually install our camera systems, and we let them be there for a few weeks or a few months. So we have a baseline to subtract. Of course, this is not exact science. You can argue with me, how can you prove that the factory was behaving in the same way before and after? I mean, we can go into the details, and we can go into green washing conversations, and really squeeze it. And it's like, oh, maybe they are exaggerating here. We can do that exercise. It's fine. But I'm not so much concerned in getting into the details of the numbers. I'm more concerned about, hey, look at this factory. 2% waste before 0.1 after. OK, it might not be 0.1. It's 0.2. It was not 2%. It was 1.5. I don't really care about those data. We made these difference here. Now, we know the average water per kilo, water usage, to die one kilo of fabrics. And you might say, but how do you know that? Because you need more water to die black fabrics or white fabrics. It's like, sorry, my friends, it's an average. I know the industry is not ready to have that traceability at that level. Actually, that's one of the problems we are trying to solve is having the exact traceability, right? For now, just with the defect, inspection, and detection, we can say machine by machine, how many kilos we save per month. Then we transform kilos to energy, kilos to water, kilos to chemicals based on the factory, consumptions, the country where they are, and things like that. And you might ask, how important is this? We don't obsess. these numbers like we need to get to 100 milliliters of water. We don't obsess with that. It's just for us to have a north star of like the north of magnitude of the impact we are creating here. That's also more and more important for for for everything to attract talent to attract investors to even for our own mission goals and to like measure our impact in this world. And sometimes we are doing so many efforts like I will become vegan. I will never drink in plastic straw. I will have like we start making so many efforts but when you look to the order of magnitudes of what you do like oh my god working in smart techs I can like I have so many I have so much much more impact working in smart techs my eight hours per day than being vegan the whole life and drive electric cars and things like that. It's just for us to have an order of magnitude about how much impact you are creating and is really insane especially like even that we started selling two years ago is really insane. Honestly, Gilberto it's really really mind blowing what you've managed to achieve and this is where I'd love to hear your sort of personal journey in sort of evolving with the company and the new hats that you've had to wear every you know so quickly as the company grew and expanded. Yeah. What's that been like for you personally? It's been a quite a journey and indeed you are right we need to evolve as CEOs as founders like I always have been the sales guy but now I'm evolving more into like more leadership type of profile and I have my sales team that helped me a lot in the field in trade shows and so on although I still go a lot to clients and travel a lot so I have been evolving with my co-founders as well because we are first-time founders and that's why we hang so much in these mentors and coaches because they have been there they've done that they have grew companies from zero to IPO so they have seen these these challenges before that's indeed a big challenge and I'm always thinking a bit of this how do you call it in English imposter syndrome type of thing like I'm not enough for this now I'm the first time founder I need to improve more so maybe that gives me extra motivation to you know always be working more always be like fast answering you know flying around the world working on weekends working on vacations you know I guess that's a good thing and bad thing in terms of in terms of stress but in general we try to convert it into positive energy and have lots of fun and we have lots of fun yeah because a lot of founders tend to burn out after point especially when their company starts to grow which is when they need to read their shoppers but they've already burned out at that point so you need to look after yourself as well definitely definitely and having fun and doing things with people you like is half the way through sometimes I'm so much impressed sometimes I interview a lot of people like to like job interviews and it's so impressive how many the percentage of people in the world that doesn't like what they do or they don't like their boss or they don't like their team or they don't like their mission but they leave like that for years and it's really impressive because sometimes it's not about money it's not about the salary it's not about sometimes when you just squeeze it is is about those two things I was mentioning in the beginning is the people you work with and what you are building like the products you are building if you nail these two and you are good at these two the rest will kind of sort out around the money the growth the experiences if these two are there if you optimize for these two you will be unstoppable before you founded smart techs you were working as a researcher at the Center for Nanotechnology and Smart Materials sent to you right yeah where you were involved in developing smart tech stars that's totally my cup of tea would you tell us briefly about the work that you were doing there yeah so in physics where I was in college I always have been in this interception between technology but because of the family background I was always looking to textiles you know conductive yarns such such pads in fabrics magnetic coatings or anti antibacterial coatings and things like that and when I saw that research center which is also in north of Portugal they support these companies to become more sustainable with the chemicals are good chemicals and things like that when I saw that they were doing an internship position I was like oh wow this is like my my cup of tea right yeah it's the intersection between textiles and tech and was amazing years I actually worked there for quite a few years because I was an intern then I started being a full-time but I was amazing because there are so many things to explore other than factories right materials which I guess is you are most of what you are more interested in in many ways like the coatings the functional textiles the the functionalities you can put in governments to be more breathable to have sensors embedded on it is really a huge universe that I guess unfortunately didn't take off yet despite some projects like Google Jack hard for some other projects but I can't wait to for it to take off and maybe be kind of part of it you were part of the fashion for good accelerator program what were some of the big learnings and takeaways that you had from this experience yeah again asking for help to run the people that's where how we got into fashion for good I was living in China in that year was 2019 I was living in China in Chen Zhen I was prototyping hardware electronics soldering stuff and I remember I was asking all of my investors because my first investor he was American but he had an office in in Chen Zhen to help he start up doing robots so I was there I was constantly asking them for introductions to fashion brands and they were like dude you have nothing to sell to a fashion brand right now and I was like yeah you were right indeed I don't have anything to sell to a fashion brand not even to my clients I have but I want to know what they are what they're looking for so I can at least build my product try to go in that way because all my clients they always say the profit is fashion brands if you want to maximize traceability whatever tools it's with fashion brands so I was always with an eye on it and they connected me with fashion for good based in Amsterdam I was there in some events speaking with some fashion brands and we ended up being accepted in their acceleration program which is basically a program that we went to Amsterdam a few times during the year to have a few sessions with fashion brands and some pilots that they would overview and help us getting through it was more about the friends and mentoring and the people around we still go there quite often to other events and to help other startups because I really believe more things like these are needed podcasts like this are needed to have this intersection between innovation and textiles fashion for good as well they ended up being our investors as well fashion for good have a small investment fund they invested in our capital as well so all of these is needed and I would say much much more is to be done because there are very little startups of success and very little cases of success in the tech world with textile what would be some advice that you would give other people who might be starting off in the textile industry as a small startup it's a tough one to break into you know that but you sort of had some history and connections already with your family in the business but yes how would others go about it I see I see essentially three types of innovation going into textiles or at least we can divide it in three buckets one is like materials new materials alternative letters you know like recycled fibers that's a whole world there then I see like the middle being more or less where we are machinery textile fabrics being produced more efficient way softwares to the supply chain in general until the garments are made and then shipped dying with weather chemicals things like that and the third bucket would be retail like 3D type of fabrics 3D experiences retail returns which is also a big problem I would divide in these three buckets for simplification I hope nobody gets mad with me but for the bucket number two I would definitely advise to do exactly what we do is like signing a bunch of clients even before you have a product interviewing a bunch of clients even before you start laying the product and being obsessed with the clients for them to implement and to pay sometimes you forget that last part we just implement a pilot and a pilot never closes because actually what we do what we are doing there is useless and nobody cares let's make sure somebody is putting the the skin in the game and be obsessed with that in the early days and you don't need to be like a 20 people team to do that you can be like a three people team to do this stage and then raise your pre-see or raise your seed round and there are many many books with this playbook I would suggest the book "Build" by Tony Fadell I would suggest a lot the book "The Great CEO Within" by Matemov-Charrie I would suggest the classic the classic 0 to 1 like the classic startup books hard things about hard things from Ben Horowitz there are a lot of great books to go to this journey but I would suggest that on the other two buckets to be I don't want to be here telling telling them what to do because there are really different challenges there but this thing of being client obsessed and identifying a pain and a pain killer from the beginning and very specific and kill it there I think this applies for most cases of companies some exceptions but I would double down there is there something you would have done differently if you were to start your business today oh yeah many things oh yeah that is one of my favorite books is a book called "Thinking Fast and Slow" by Daniel Kahneman and he has an entire chapter about the fallacy of planning everybody fails planning by borders of magnitude this is insane and indeed the unknown unknowns of building a new product because you have technical risk in the new market because you have market risk in a new country a new geographies you have political risks the amount of unknown unknowns is insane for any startup but specifically in textiles. So of course I would do many things differently. For example one of the classic mistakes founders do at this stage is because we have cash constraints. We are always frugal. We tend to hire more junior people, which is great as long as they are machines, they are a players, they have the energy. I love to work with young guys, but we need to have the right mix with very senior people as well. We try a lot to bring external advisors, investors, very experienced people here. But maybe if I was now I would hire a bit more senior folks from the beginning and avoid some technical depth. Now it's easy to say because we raised a bunch of money. We raised 40 million dollars. But a few years ago when we were like sleeping in the same bed with my co-founders, it was a bit more difficult to say this. Your mission has been to empower factories to produce zero waste and have full traceability. How do you envision that in the next five to ten years for smart techs and for other companies? So the mega trends in textiles are about shorter orders, on demand, much more automation, less human dependency, less paper dependency. These are the trends like paperless factories, robots everywhere. We are going in these mega trends for the next ten years. So I see smart techs being a part of it in some different ways. One is with the computer vision stack, as far as we know we are the only ones with such a complete computer vision stack for role-to-role production and scaling that in thousands of units. That will be for sure part of this factory of the future because you will need to be looking at digital twins of textile rolls and to do all of that stuff. So cameras will be needed everywhere. Like if you go to a car factory or electronics factory, you already see that, right? It's not like this guy is a genius, he's like really, he's seeing what others don't see. No, it's like just look to all the other industries. He's just that this one is more complex to automate because of the complexity of the materials and the price point, right? So let's just look at the other industries and see what is working very well there. So is that automation, paperless cameras and computers and everything. So I believe smart techs can be really an intrinsic part of that future by having a bunch of cameras deployed as much as possible, a bunch of routers and access points and infrastructure things that will be part of the backbone of the industry. Like IBM used to be for banks, you know, like the systems, the interconnected systems. And eventually if we build that pipeline, that plumbing system of textile industry, we will be able to welcome other startups and other companies to build things, cool things on top of us. So that's my vision for it and that's what we have been working in the last few years, day and night to what shape. So we have the capital, we have the big market there waiting for us, client-spain. So if somebody fucks up, he's on us, he's on me. But do you feel there is a market pull for new technologies like this? Are you still trying to create awareness and convince companies of what these new technologies can do? Another mandatory book for any founder listening to this is a book called Crossing the Casm. Actually for B2B companies, once you close your first few deals, that book is mandatory because the world usually divides in like 20% of the people they are early adopters, they like to test the new iPhone, they like to be the ones driving the new Tesla in their city, they like to be the ones exposed to some risk in exchange to be the new guys having a new tech. 80% of the world hates that and it's like the majority of the world is risk-averse. They don't want to be the first one driving the first Tesla. They want to be the last one driving the first Tesla, right? They want to be when enough people are using Tesla's, they are like, okay, now my fear is not being too early, now my fear is being too late. So I want the Tesla as well. So most of people they operate like this. So we need to find the first 20% and those ones, definitely they are pulling. We feel that the calling us, they are insisting, they are paying for it and that's great. The second one and it's really a sharp division there. The book is called Crossing the Cousin because the Cousin is the space between these two groups. The second one is really really difficult to crack and that's actually where most startups die. So I believe that the textile Cousin is a huge challenge for us. We didn't pass it for sure. We still have many clients, some clients that are in the second group that they try at our system. They are not in love with it, okay, to be honest. They are not, they don't want more. The first group, the 20%, they love it. They want more. They can't wait for the new releases. They help us developing and stuff. I would divide my answer in these two groups. The first 20% are loving the second 20%, 80% we are not killing it yet. That's very honest. I love that. And now coming back to where we started, which is your family and the factories that your family has. Now the big question is, are they using your tech conditions? Let me tell you exactly what my parents do just to justify this. My parents used to work many years in textile factories, like textile workers, machinery operators. More recently when I was a child, like eight years old, my father decided to start his own business in textiles with a small warehouse, a small middleman trading, there are many of these around the world. He works with my mom and with my brother. The wife of my brother, so it's like a four people company type of family business, very small, but self-sustainable and so on. They don't use my system because they don't have machines or production. But it's really interesting because now we have these QR codes in our factories. They start seeing rules going by in the trucks and in the van and in the containers. They start seeing those QR codes and they're like, "Huh, what's this? A smart text. My father called me one of these days. Hey, I saw this QR code thing. I love it. Why do we need this?" And I was like, I was in the phone and I was thinking, damn, we didn't think about this. We didn't think about what the middleman will think about the QR code. We need to have some messaging. And I called my co-founders guys. Let's put some messaging in the fucking label. Let's put the duct tape saying, "Find the QR codes. Can it see it?" So people like my father, he can really have the benefit of it. And he starts calling the factory saying, "Hey, I want this QR code more often and then we can increase sales." So I still see my family like every week. I have been with them and we talk about these things. And my father, he tells me a lot of little cases of, you know, that guy is cheating the God system. That guy is cheating the recycled yarn thing because that yarn is not recycled, but he mixed up with the other yarn. So he knows all of the little nuances of the textile industry and all of these rabbit holes that usually brands don't go into. So we make our products to try to be bulletproof because we know that people will cheat the system when they can. He sounds like he's an encyclopedia on textile factories and what goes on in there. So definitely, we're picking his brains as a great place. Yeah, never. Well, you're worth a, what a fascinating conversation. Honestly, I love your passion, your energy, your commitment and what you've, and the fact that you've got a solution that you're able to take to market and solve a huge problem that the textile industry is facing. And the ripple effect of that, probably we don't get to see today, but we will in the next five to ten years. And I'm really looking forward to just seeing how you grow and keeping a very close eye on smart techs and all that you're going to evolve into as a company. Thank you so much, Milly. It was, was a pleasure. And please continue because we need more content like this. Thank you. My pleasure. Ciao. Ciao. How amazing and insightful was that conversation. I mean, I learned so much from Gilberto today and hope you did too. I think an observation from my side is how focused he is. He could be going in all kinds of directions and all kinds of people asking him for solutions to problems, maybe in the weaving sector or any other part in the supply chain, but he is laser focused on being the best in the area that he has carved out and started in, which is the circulating machine factories. And I think this is really important for startups. Stay laser focused on very specific pain points that you're solving for very specific target group. This right here is a secret to success. It's also great to meet founders who are open and share about their struggles as well in starting up their company. And it was clear from his journey that it wasn't an easy one, but he plowed through and worked hard to build a company to where it is right now in a very short time. As he said, you just don't know the unknown unknowns when you start a business. And when things don't go according to plan, it's your vision and the purpose that helps you stay on track. The modern textile factory report link is in the show notes. So please check that out. It is a really valuable resource. Challenges in the fashion and textile industries are very, very complex and cannot be solved by one startup. New technologies such as AI and machine learning are enabling solutions that are never possible before. And there are so many opportunities for innovation. And I hope this inspires you to find solutions to some of the pressing needs that we have in our industry. My hope is that this podcast is interesting to those in the textile industry as well as those in the tech industry because I want to encourage and promote cross disciplinary collaborations. We all hold different pieces of the puzzle and we need to come together to solve challenges and innovate urgently to transform the textile and fashion landscape. Please do follow and subscribe to this podcast and leave me a review. I love to hear from you and if you could spare just two minutes. I would love it if you could fill out a little survey. This would really, really help me get an insight into who's listening in and what kind of content you would love to hear more of. The link to the survey is in the show notes below and thanks again for tuning in guys. I really appreciate it. Join us again as we weave together a tapestry of textile innovations one episode at a time. Bye for now. This is your host, Militaricend.