FASHION MADE: A Better Fit with BodyBlock
In this episode of Fashion Made, host David interviews Greg Moyes, CEO of FIT3D and BodyBlock AI, about the company’s 3D body‑scanning ecosystem and how it is reshaping apparel fit. Moyes explains that FIT3D has deployed over 1,500 patented scanners in 47+ countries, generating a continuously growing dataset that is approaching millions of full‑body scans. The massive, anonymized body‑shape library is used to conduct sizing studies for the world’s three largest apparel brands, informing pattern‑block design, consumer‑persona shape spaces, and predictive fit algorithms. The conversation dives into the “Find My Fit” widget (exemplified by Rhone.com), where users input height, weight, age, and gender, then select representative body silhouettes; the system matches these inputs to the closest scan clusters, predicts precise measurements, and recommends the optimal garment size. Moyes highlights that the platform validates predictions against a live user base, achieving 2‑3× higher conversion than traditional size charts and up to a 7% reduction in returns. A major theme is the futility of standard sizing; Moyes argues that brands should move to a body‑first approach where size labels become irrelevant because the algorithm maps each consumer to a specific body model. He also discusses data‑quality pipelines—automated AI checks that flag scans with clothing, accessories, or posture issues—to keep the dataset “crystal clean.” The interview touches on challenges such as handling extreme body types (e.g., very tall or very heavy individuals) and ensuring inclusivity. Sustainability is framed as a downstream benefit: better fit reduces waste from returns, packaging, and logistics, and a more precise shape‑first design could enable on‑demand, additive manufacturing, cutting fabric waste. Finally, Moyes shares a blue‑sky vision of an industry where fit data powers personalized, on‑demand apparel production, positioning FIT3D as a foundational layer for that future.
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Hey, welcome to another episode of Fashion Mades. Today I've got the pleasure to be joined by Greg Moyes, the CEO of FIT3D and Bodyblock AI. Greg, hello. Hello, David.
How are you?
I'm really well, thank you. You as well?
Good, good. Yes, I'm doing great. And thanks for having me.
Oh, it's an absolute pleasure. Look, we've got another conversation on fit today. It's obviously a burning topic. In apparel manufacturing and e-commerce as well. Do you guys— you guys supply over 1,300 patented body scanners to more than 45 countries and growing at a couple per day as well. So it's obviously a big issue for you guys as well.
Yeah, that's right. I mean, I think, you know, the issue of fit has been around for a very, very long time. And I think that there's been a lot of companies that have tried to resolve that problem. And we really took an approach where we saw back in 2012 that e-commerce sales were growing through the roof, I mean, just at an exorbitant pace. We saw returns growing at just as high of a pace, which is really eating into a lot of the profitability on this new channel. And one of the things that we noticed was that there's a lot of companies trying to solve these types of problems, but didn't necessarily have the representative body data to be able to do them. You'd have certain companies that would scan tens of thousands of people, companies would look to match patterns or whatever it may be. And so, we really took a hard-nosed approach and we said, "We need to go first and foremost and collect a massive amount of body information before we can even look to solve the fit problem." And so, to your point, that's exactly what we did. We designed and developed all the R&D for our body scanners. We've deployed over 1,500 of them now into more than 47 countries, and we get a new scan coming in the platform, full 3D scan with automated measurements, about every 30 seconds every single day. So, the dataset is, you know, quickly approaching a million and will far surpass several million even this year. It's just growing so fast.
Wow, and so what do you do with that data then?
Yeah, so, so the primary use for us is really understanding what our customers need, frankly. And so, for example, I can't list off company names because we're under confidentiality, but the 3 largest apparel companies in the world have all enlisted us to do sizing studies for them, right? Where we go back through our dataset, we understand, we work with them to understand their consumer personas, and then we look to match the shape space, like actual body representation shape space associated with that consumer persona. And so then when they're designing their pattern blocks for that, we're basically designing pattern blocks around representative bodies with a significant amount of data science, math, machine learning, and a lot of sort of apparel knowledge behind it to help them even design clothes that are going to fit better. Let's start there. And then we take that many, many steps further where, you know, our goal is to really sort of be the cornerstone of fit for these brands. And then, so we have technologies also that help them distribute the appropriate clothing to the right body, whether that be through scanning, whether that be through our predictive technologies, so on and so forth. And I'm sure we can get deeper into that in the conversation. But the whole basis of it is that we have such a massive data, it's refreshing all the time, it's tagged with user information and users which we could go back and survey. So, we can really solve almost all of the fit needs for every single brand all across the world based on the dataset that we have that's consistently growing.
It's really interesting because, you know, a lot of companies are trying to do this on an individual company basis for the consumer, whereas obviously you're doing this for the company themselves. The kind of priorities are then skewed a little bit, or maybe not the priorities are skewed, but you're sort of— maybe how you're getting to the place you want to be has changed a little bit there. You know, for the consumer, if I want to get something that fits me, then you've got 5, 7 billion people all really with a different and maybe not correct opinion about their own size or fit. You know, the brand and the manufacturer themselves, they're really having to, again, like with design, I guess, tell their consumer what they want and hope that it fits.
Yeah, I mean, I think that's right. I mean, I think that there are, you know, like we talked about earlier in the call and you just brought up, I mean, there's several different ways to attempt to tackle this problem. In our mind, right, consumers are inherently fickle and very much trend-based. And so, trying to create something that's going to create the appropriate amount of consumer engagement that solves this problem is very challenging and it's a consistent moving target. Brands, however, you know, there's a lot of money at stake. Even sometimes there's, you know, total operational amounts of money at stake here. And so they're really going to strategically invest in what they feel is going to be best for their consumers. And I think they've done this in the past, but they've done it with not necessarily the information necessary to do it right. And so, that's where we come into play, where we're sort of bridging that gap between what's good for the consumer and what's good for the brand. And we feel that what's good for the brand is designing and distributing clothing that fits the human body and fits their target audiences much better. And, you know, we sort of help with that, with, again, with the dataset and the set of services that we have at our disposal. But yeah, it's an interesting juxtaposition of, you know, do you go to the consumer, do you go to the brand? I think that both are probably necessary, and our path in that is definitely the brand.
So when it comes to sizing, to fit, I mean, these things do not go hand in hand. Do you feel like standard sizes should continue to exist now?
That is a fantastic question. Yeah, I get asked that very often. I do a lot of guest lecturing, and that question comes up every single time that we have these conversations. And you see a lot of intent to drive towards standardization in the industry, but the reality is, is that brands cater to specific consumer segments, and those segments are just inherently different. Their body shapes are inherently different. Their purchasing patterns are different. Their desires for stretchy fabric or tight fabric or whatever it may be, all of it's inherently different. If you were to try to standardize sizing, you would, I think, create, frankly, a bigger mess. Than you have right now. I think what, where we look at, when we look at standardization, is really helping the brand design the clothing for their consumer segment, as well as understand what one of the brands calls the white space, right? The space that they're not necessarily able to tackle because they don't have a good understanding of the body shapes there. And then they can strategically figure out when they want to expand into these consumer segments and these spaces. But yeah, I think that standardization is sort of one of these pretty terms that we've had forever. I'm very uninclined to believe that it will ever materialize, and I think frankly it probably shouldn't. And I think that it doesn't matter. And why I say that is because through some of the technologies that we're creating, right, where we are able to either scan the human body or predict the measurements of the human body with a very high degree of accuracy. And then we can understand how the designer intended that piece of apparel to fit through their technical specifications and easements and all the different fabric patterns and everything else for the garment, the size doesn't really matter anymore, right? Because all you're doing is predicting a body, matching that to the appropriate size in a specific garment, and if it's a size extra large, a size small, or a size, you know, Tweety Bird, it just doesn't matter anymore because the consumer doesn't need to remember their sizes. They just go through these widgets, we predict out the appropriate size for them, and they just take the recommendation, which increases their confidence. Right? So I think the size standardization is a sort of an antiquated topic in a sense.
Brilliant. I mean, because it doesn't necessarily work for everyone, and that's kind of the reason that we're in this situation anyways. Time to get rid of it and move to a new system. So tell me, would it be my height, age, BMI, what kind of stuff am I going to give you so that you can tell me what my right sort of fit size of clothing is?
Yeah, great question. So first and foremost, everybody can try this. If you guys want to go to Rhone.com, it's Rhone Apparel. It's an athletic brand, quickly growing and a great brand, frankly. And they're one of our earlier customers, right? So we've grown a lot with them as they've grown with our technology. So we really like to highlight them. But just click on a product and click on the little Find My Fit button, right? And so what you're gonna do is you're gonna enter in height, weight, age, and normally you would enter in gender, but it's a men's only brand, so we sort of assume gender. But you're gonna enter height, weight, age, and gender. From there, we're gonna go back through the millions of scans that we have, and we're going to choose the bodies that match sort of a bucket, right? A cluster around that information. We're then going to show you segments of bodies, and you're going to start to use your cognition about how your body looks when you look in the mirror. You're going to look at shoulder slope, muscular definition, roundness of belly, love handles, so on and so forth. And you're going to choose 3 upper bodies, and then you're going to choose 1 lower body. And so, what we're allowing to happen here is that the human cognition is really the first form of AI going into this, right? It's not even AI, it's I, it's intelligence, which is pretty cool. And then on the backend, we then go off and we crunch a bunch of numbers through heavy bits of algorithm. And from there, we can then predict out the measurements of that human body. Now, the real kicker here is that because we have deployed these scanners all over the world, and because the people that have scanned on these scanners are users of ours, we validate all of these predictions by asking our user base to take these quizzes. And so, then we can compare their predicted measurements versus their validated scan measurements. And, we can evaluate accuracy and release those types of reports. We've done this ourselves. We do it every time we launch new algorithms. Third parties have done this. Big apparel companies have done this just to validate sort of what's going on in the backside. And, it's pretty amazing, right? So, now where we are is we've predicted the measurements of the human body. You're on the product, so we know exactly what product you're on. We know the technical specifications of that product, again, the easement of that product, and the different measurement areas. Then we can recommend the appropriate size. It's really going— I think there's another company out there that talked about the flaw of averages, and I think they're absolutely right, which is that we've seen a lot of these companies go through and they're like, "Are you a round or a triangle or a pear?" I don't know. I guess I'm an inverted triangle apple. Who cares? But when you're looking at actual bodies and actual representations, your accuracy goes up tremendously. And that's what we found. So anyway, so it's really tackling the problem at the source by, again, building out the quote-unquote shape space for these brands and then helping the consumer feel very confident about their purchase because of the rigor that they've gone through to get their predictions. And it's about a 20-second process, right? It's really— are very easy. So anyway, that's kind of how we tackle it. I went a bit deeper into my propellerhead technology type discussion, but it's kind of fun.
It's interesting, I mean, does that then carry on? Can you then save your avatar, save your widgets, and then use them again? I mean, does it need sort of brands to all be using a similar tool within their e-commerce platform to then get that across different companies?
That's a great question. Yeah, so our goal is, you know, as a business development side of the house, is to Install this on as many brands as we can, and frankly, we're getting a tremendous amount of uptick. Our integration in February is going to be ridiculous because we're doing it differently, and we have validated results. We are showing anywhere between a 2 and a 3 times increase in conversion to the traditional size chart. Regardless of what we do with returns, just that level of conversion is ridiculous. Secondly, we're also, because of the confidence that people have, and because we're able to actually accurately do these things, we're reducing returns by up to 7%. And these are early tellers with brands that we haven't been able to help them on their sizing yet. So this is, it's great. I mean, it's really moving the needle for these brands. And as a vendor to the space, that's what we want to do. We want to empower our customers to be better, move faster, be stronger, so on and so forth. Yeah, yeah. The second part of this prediction widget, and I think coming back to your question for this segment, is that, yeah, we track all of the predicted body shapes, and we track their purchases, we track their cart adds, who bought, who didn't buy, we track all the returns. And so what that helps us to do for these brands as well is to present a set of reports that helps them again define buying the shape space of their consumer.
Well, that was what I was going to ask is what happens when it doesn't go so well, you know, because nothing's perfect and every now and then it must, uh, must go awry. So, so what happens then?
Yeah, it's a good question. Um, there's, there's sort of a couple things that can help it go awry, if you will. Um, and so one of them is that, you know, brands change their, uh, their offerings like quarterly generally, right? And so sometimes they're a little late getting us their product suite. And so we integrate that back into our platform. So, you know, you may click on this button and it says, oops, we don't have, you know, this pattern defined in our system quite yet. So that's one thing. It's a pretty mundane, benign type of error and easily fixed, which is pretty nice. Another thing, which is sort of an, I guess, an error, but also an opportunity. Is that, you know, there are all sorts of different body shapes out there, right? There's people that are, you know, 6 feet tall at 120 pounds, and there's people that are 7 feet tall and 450 pounds, right? And so I think that, you know, trying to build predictive technology that's entirely inclusive is really, really hard. And the reason for that is that your representation of data falls quite precipitously as you get outside of the norms, normally. And I think that's another area where we have been very diligent and very good, and that's that we have representative bodies for almost everything you could ever imagine, right? So if you start to look at those buckets that we talked about before, age, height, weight, gender, you know, let's say you have a 75-year-old, 7-foot-3, 400-pound person, right, who's looking to shop for something. And I'm just pushing out random numbers here, but numbers that nobody else in the world would have. We have those bodies represented. We can actually predict those body measurements for that human being. And that's, I think, another thing that really differentiates us from other quote-unquote body scanning companies that have tried to capture, you know, scans here and there, is a sheer amount of representative data.
Yeah, absolutely. Me and my colleague both ordered a certain suit. Someone, another company that's trying to take a lot of Body Data, and I guess do similar kind of things to you guys. I don't know whether you're necessarily competitors with Zozo or not, but anyway, we both ordered this suit. Now, my clothes came in a few days. I'm quite a skinny guy, but apparently I, you know, fitted a fairly normal, normal size for them very easy. I think they just shipped me something that they had already made, you know. I don't think they necessarily tailored it to my size at all. Sure. For him, it took him 6 weeks. You know, to get his, and even then, I think it didn't even fit right. So, it's quite a difficult thing to get right on this individual basis. And I think that they've actually stopped doing the suits now, haven't they?
That company. That's right. From what I've heard, you know, sort of two things, and I, you know, I can only speak to what I've heard third party, but yeah, it sounds like they stopped doing it. I mean, they made quite an investment in in the dozens of millions of dollars to try to collect this data. But I don't think it turned out exactly the way they wanted. I don't think the data was very clean coming back, I would assume, based on all the different types of body scanning apparatuses that we've built in the past.
Yeah, well, this is the thing, and I wonder if it's a case to do with how you scan a body, because when you're doing it yourself, you know people are gonna do it wrong. And there has to be the standardized process, and I think people probably didn't all adhere to the same kind of thing. So, um, that brings me back to you guys, and when you're actually taking these scans from all of your body scanners over all these countries in the world, um, do you have to adhere to a pretty strict process in how you take the scans?
Yeah, that's a, that's a great question. Um, so I'm going to answer that twofold. Um, one, to take a valid scan, yes. You need to wear form-fitting clothing, you know, hair needs to be tied up above the neckline, and you have to, you know, hold onto these handles and stay very still during the process. However, we don't control that, right? Because we sell these systems to businesses and they can kind of use them as they will. And so what that's forced us to do is to build out a tremendous amount of AI technology on the backside to auto-validate all of these scans autonomously. Right? We don't want people looking at this stuff. So we want to be able to build out algorithms that can go through and say, yep, that one's valid. Yep, that one's valid. Nope, that person's wearing a watch, or nope, that person's wearing shoes, or nope, that person's wearing pants, take that out of the dataset. So yeah, big piece of our, our business is, is really sort of under the surface. It's sort of like the iceberg, right? It's all the things that you have to do to clean and mature and heal the data as it's coming in. And it's not necessarily an easy process, but it's a process that over 5 years we've been, we've become very, very good at. And our AI capabilities are quite substantial, where the data that we're using for these brands and for these retailers is absolutely crystal clean. And one of the things they love about us is that we'll show them, right? Here's our data, here are representative scans, here's what they look like, here's what was captured. And I think that, that level of Transparency has also been something that's been a big challenge for brands in the past.
Yeah, you know, and when it comes to transparency, that also leads me into a conversation about sustainability and how technology can help that. And do you feel like getting the right size will reduce waste? I mean, it might be an obvious answer to that question, but I'll ask it anyway.
Yeah, yeah, it's a very, very good question. I don't know. It should, by all means. I think the challenge is human behavior, right? And the question I think then becomes, are people buying more stuff because it doesn't fit them, or are they buying more stuff because they're conditioned to buy more stuff because of all the marketing dollars that we put into the world? So I think it's a complicated question. I do think, however, that One of the things that we're really focused on is the new consumer to a new brand. That experience is normally pretty full of peril and pretty full of waste. If I'm going to a new brand and I don't know their sizes, I don't know how they're going to fit me, I have no understanding really unless I've seen it on somebody else that I can visually match myself to. Especially from an e-commerce standpoint, right? And there's a lot of these direct-to-consumer e-commerce brands popping up. And so I think that the thing that you sort of do to get around that is, one, you look at the return policy, and if it's free returns, then sure, you buy a couple sizes and you see which one fits and you send the rest of it back, right? And so there's a tremendous amount of logistics waste in that. I think that the apparel industries have figured out how to get clothing back, ensure that it's sort of "refurbished" and new, and then they can resell that. But I think in the logistics side of the house, like, yeah, I mean, you're shipping stuff, you're wasting CO2, the emissions are through the roof, you're wasting packaging. And so I think when we look at sustainability, I think a lot of times we look at it through the lens of just direct apparel returns, and we think about the fabrics and the dyes and all the stuff that went into building that material and the fact that that may be lost. But I think focusing on the logistics infrastructure, the people needing to process, the people that have to process all those returns and shipments and everything else, and I mean, there's just a lot of waste in the industry when it comes to fit. And I think that where we look to really, really tackle and make a big difference is helping the first-time consumer feel very comfortable with the sizing of your brand, such that they don't need to go down their traditional path of buying multiple sizes and then returning back the ones that don't fit. That's a big impact, I think, and that's something that we're sort of acutely focused on.
Yeah. Do you think that some brands have a propensity for bad fit?
I think some brands don't know. You know, I think propensity is an interesting way to put it.
I mean, like, you know, do people talk about, like, I don't buy stuff from them anymore, you know, it just never seems to fit?
Yeah, I think that's correct, right? And I think, you know, one of the stories that always sort of flabbergasted me was we went into a huge company, which I obviously cannot say the name here, and we were with all their executive staff and we looked around and we asked them like, "Oh, okay, can you tell us a little bit about the body shapes that are buying your clothing?" And everyone looked around the room and they're like, "We have no idea the body shapes that are buying our clothing." I said, "So how in the world do you design your fit blocks? How do you design your pattern blocks such that they fit your consumers?" And they're like, "We sort of just trust what company X tells us." And I was like, "Okay. Have you ever validated that or looked at it?" And they're like, "I mean, we have purchases and returns data, but that's about it." I was like, "All right. Well, how do you get your models?" They're like, "Well, we just get the fit model we think looks the best." And not necessarily from a beauty standpoint, but looks the best. Visually matches what we think the shape space of our consumers are. And so I think that brands are conditioned to sort of wave their hands and sort of say, "This is a really hard problem and we don't necessarily know how to fix it even though we're spending billions of dollars on it, and so we're just gonna guess. We're gonna focus on other things that make our brand move better." And even self-admittedly, some of these bigger brands are saying, "Our clothing has horrible fit and that's something that we really need to work on." to work on. So, you know, it's sort of eye-opening then when we come in the room and we sort of do our pitch and we talk about our abilities to resolve those problems and to validate the resolution of those problems, especially with a lot of the 3D design technology that's out there where you can physically develop and design right around these avatars that are representative of your sizing. It's a big change, and I think the industry is They're ready for it, they need it, and I think that they're really beginning to adapt to it very quickly, which is a lot of fun to see. But people are taking note for sure.
I mean, that's the thing as well, it's this size versus fit versus style kind of differentiator, you know, all three kind of dip into one another, but they are quite different things really.
Yeah, I think that's right. You know, I think fit is sort of, you can have two definitions for fit, right? One is, and this is what we always sort of cater to, we always say fit as designer intended. Right? And so from that perspective, the designer is going to build a pattern and then, you know, build the product and design that product, and they're going to have an intended fit of that product. Now that fit may not, you know, that fit preference, which is the other side of that, may not match what the consumer wants out of that brand. But it's a really very different question. And then it even gets further complicated. Yeah, of course, with style and even fabric and, you know, tensile strength and stretchiness and whatever else. So I think this is really complicated. And, you know, that's, that's again why we look at this thing from a body-first perspective, right? And if all of this was designed around the human form, and you took a lot of data to develop that human form to ensure consistency and validity of fit across that entire cluster, you have started off at a better place. I think that's what we're missing. I think a lot of times we're just not putting our customers and their body shapes in the middle of the process. Again, that's sort of what we're looking to bring to the table and how we're looking to sort of revolutionize the industry in a sense.
Yeah, yeah, you're not starting off from a guesswork perspective, it's real actual data. I want to finish off to maybe talk about some, uh, more blue sky thinking. When it comes to clothing fitting people better, do you think this would make sort of the fashion industry a sort of much more respected industry? You know, it's getting a bit of a hard time in the press right now, you know, a lot of people are getting a bit more clued up as to the pollution side of the industry, especially here in UK. We've had a couple of big exposés in the last few months about this. Do you think having clothes fit better and having this process could help sort of turn that around a little bit?
Yeah, I do think so. I also think that there's an entire ecosystem that needs to be sort of revamped. And if we're looking, you know, blue skies, I mean, The fabric that gets wasted when we go through cuts, right, when we're cutting fabric out, I mean, there's a tremendous amount of waste because most of it's subtractive manufacturing. You're starting with a roll, you're cutting stuff out, you're throwing the rest of it away. So there's a lot of technologies that are out there that are looking to utilize additive manufacturing. You know, how do you sort of build up a tool for this thing and then build it so there's no waste? So, I mean, I think that's a big deal. It's a bit off, but it's— there's some companies working on it that are doing some really amazing things. I think that fit obviously plays a role in that, a big role in that, especially with respect to the logistics cost of shipping back and forth returns, especially because people are doing a lot more sort of direct-to-consumer sales through e-commerce channels. I think dyes have a huge impact on that, and water, and pollutants in oceans, and in lakes and rivers. You may say that we clean those things up, but we try, but I'm sure there's some runoff that's not so good there. I think when you look at the industry as a whole, it's really ripe for a pretty big change towards sustainability. We see our path into that as helping those consumers be more confident in their fit, helping the brands ensure that they're building clothing to better fit their target audiences. We're going to put the dent into sustainability the best we can as it associates with fit. I think that we're looking for other companies like us that are continuously moving the needle on some of the other bits of the ecosystem to ensure that the fashion industry as a whole just becomes more sustainable, because I think there's a lot of opportunity there.
Yeah. There's some people moving into on-demand manufacturing as well, which is sort of they can have an opportunity to kind of sell a product before they even make it as well.
That's right. I mean, I think that's amazing, and blue sky absolutely is really cool. I think a big part of the challenge there, though, is that the majority of fashion is still, I'd say, to like, probably sort of to the middle tier of consumer, like not necessarily looking at class system, but you know, you pay a premium for that, and a lot of what we do in this world is fast fashion, It's the people that are buying the most amount of clothing are generally sort of a medium income, if you will. And so, you know, I think that those types of initiatives, as they become more cost efficient, can really put a big dent in the industry. I know Amazon's filed several patents on that already, which is, you know, if anyone's gonna move the needle quickly and invoke competition, it's gonna be them. But I think there's a lot of companies like Kirby Best and Matthew Cochran with OnPoint Manufacturing are doing a great job with this. And they're really working to move the needle towards this sort of personalized manufacturing, if you will. And great opportunity. I mean, a lot of the generalized manufacturing industry has gone to just-in-time delivery, and they build to order. And we're getting so quick at being able to build stuff that it makes a lot of sense. And I think apparel's— not too far to follow, but I also think that we can't forget that the majority of clothing is still going to the mass market, and I don't know that that is essentially a mass market initiative for some time.
No, no. I guess you guys will be ready when it is, but for now— That's right.
That's right. Yeah, we want to see it succeed, believe you me, because, you know, you need measurements and you need body avatars in order to do that. Exactly. Yeah, the faster that goes, you know, the better. So Kirby, let's keep going.
Absolutely. Yeah, well, we're all hoping for that. Well, Greg, look, it's really, really good to chat to you today. And yeah, thank you for coming on Fashion Made. It's been very insightful.
Of course. Yeah, David, thanks so much for your time and I really appreciate being here.
Excellent. Cheers.
All right. Have a good day.
