COSMOFACTORY

Deanna: [00:00:00] This episode is all about virtual skin analysis and product try on technology, about consumer applications of AR and AI, and much more. With me today on the CosmoFactory podcast is Wayne Liu, Chief Growth Officer and President of Perfect Corp.
Deanna: Welcome.
Wayne: Thank you. Hi, Dina. Thank you for having me. So I'm very happy to be in your podcast to share some of our company's development and innovation products.
Deanna: Excellent. I'm so glad you're here as [00:01:00] well. Now for listeners who don't yet know about Perfect Corp, just tell us a bit about the company, will you?
Wayne: Yeah, sure. So Perfect Corp is a technology company, but one thing very special about our company is that we are using technology to create a product to serve the beauty industry. So a company been in the business for about, um, almost 10 years, a little bit more than nine years. So, uh, we're using, um, AI and AR to create technology like, uh, virtual try on on the makeup.
Wayne: For virtual skin analysis, and then probably something like the hair try on. So that's primarily what we've been doing.
Deanna: Excellent. I love that. I didn't realize that. I think I'm perfect. Corp and I have been in the beauty industry about the exact same time. That's very fun. Little fact. I didn't notice. Um, now when you and I spoke last week, you reminded me that the company actually started as an incubator project.
Deanna: I'm wondering if you can help me think about how it grew into what we see today. Why, why are you in beauty? What's going on?
Wayne: [00:02:00] Yeah. So actually that's a very interesting, um, amazing journey. So, um, actually our company was, so I, I also work in, uh, our parent company, supposed to be parent company. Right now it's our largest investor, the company called CyberLink, which is the same company.
Wayne: Alice Chen is our current CEO. She founded. Like 20 something years ago. So that company is purely a technology company. So they are very tech. And doing the video editing, video playback, and all kinds of cool technology. So I think around 2014, Okay, so because of CyberLink's products mainly on the PC, and at that time personal computers sort of like slowing down.
Wayne: So that's why we are thinking about, okay, so what's next? We have such good technology. And then, so that's why we are thinking about, Oh, maybe let's try mobile at that time. Mobile is good thing. And also Alice Chen, our founder, CEO, she's female. She said, why don't we do something fun just for the female audience?
Deanna: So
Wayne: that's why [00:03:00] we do the brainstorming and then, uh, with our technology and then the application on the mobile and the for female. So naturally we come up with a virtual trial because we are really good at all this, the color and then the reflection, the lighting. So that's why we are trying. So we create an app called You Can Make Up and also another app called You Can Perfect.
Wayne: So we start with these two apps. We think CyberLink. Okay, so we are more like a, uh, a project inside CyberLink. And then as we grow, we create this app and put it into the app store. And then, so we see this naturally, like organically, the app grow very fast. We start seeing 1 million downloads, 10 million downloads, and at a point in time where we get 100 million downloads.
Wayne: We feel like, okay, so they can be a real business. And then also we get some, um, uh, the beauty company call us say, can you, um, think about like a trying, uh, can we license your technology? Because it seems like a bump. So at that time, it's about like, um, as I [00:04:00] said, like nine years ago, we started putting everything together and create perfect.
Deanna: That's excellent. That's excellent. I think it's a it's a good example of of technology and action because it clearly was fun to use right to see the user numbers go up. Um, even when it wasn't a product. I think that that's very telling. And it also kind of strikes me. Um. I think it's a good example of, uh, you know, early tech adoption for the companies that reached out to you, but also really the power of partnerships.
Deanna: I know that, uh, so many of your partners now are what we think of as consumer facing companies, um, brands, big and small, but if some of the first companies that had reached out to you had been maybe ingredient companies or contract manufacturers, you and I could have a totally different conversation today.
Deanna: So I think, I just think it's so interesting, you know, who was paying attention, uh, to your technology early on.
Wayne: Yes, yes, that's very interesting because, uh, Of course, because we do a virtual trial, so very naturally, we get the attention from the beauty company, [00:05:00] especially a big beauty brand, and then they are always looking for like a seeking the new innovation to engage with customer.
Wayne: So that's how things started. And then, um, you know, of course, I see another company and even L'Oreal, they are interested in our technology. So, as you say, it's very important because we are technology company. We don't know too much about beauty. We have a, uh, uh, we have a tons of technology, but how do we implement into the beauty industry to serve the beauty customer?
Wayne: That's something partnership really fitting to. Okay. So when we talked to Estee Lauder company, they told us Oh, can you do this? Can you do that? Because they may have a wish list. That's, well, actually, we are thinking, well, let me, uh, maybe a good, uh, interesting project. Okay, so use our technology and with their domain knowledge, we implement something together.
Wayne: And also, when we get into the skin analysis, that one is definitely partnership play a [00:06:00] crucial role in that because. The skin analysis is a, it's a machine learning, you know, machine learning, we need lots of data. How can we get all this data, right? So that's why we engage with the early partner like Neutrogena.
Wayne: Yeah, Johnson Johnson, there was a Johnson Johnson. So that's why they have quite a lot of data. And then building on the trust. Okay, so they provide us the know how the data and then we can use our technology to create something benefit for both both sides. So that's right. That's how everything gets started.
Wayne: Yeah. It's a super. It's a critical in our.
Deanna: Yeah, no. And that makes such good sense. Um, that the partnerships really are a big piece of it. Um, and since you mentioned, uh, machine learning, and of course, we're talking about, you know, both AI and AR here, I wanted to think a little bit more deeply about those topics.
Deanna: Um, My understanding, and you can correct me if I'm not right on this, but generative AI, like a lot of what we see sort [00:07:00] of in the consumer, um, or everyday space now, right? With things like chat, GPT and what have you is purely statistical, right? It's trained on a fixed set of data. And then when prompted, it will return information based on what I would understand as statistical probabilities.
Deanna: And so I also kind of want to take our listeners back to one of our very first episodes here on the CosmoFactory podcast. When I spoke with a company called say a vision, um, that they, they use AI for quality control in cosmetic product manufacturing. So this company, um, works with images as their data.
Deanna: And I think perfect corp is doing the same thing. Can you tell me a little bit about. You know, how your technology was trained and what sort of data is tagged and how, how much data is there? What's going on here?
Wayne: Yeah, so, um, AI is a hot topic everybody talk about AI. Yeah, well we are actually using AI since the beginning of our company started, because we do virtual trial, right, but the thing here is that in order to do a.[00:08:00]
Wayne: Also, sort of like a good virtual try on, intelligent virtual try on, you need to have a brain to understand your subject, okay? Because you need to understand who are you trying to put the makeup on, and then what kind of condition they have. So that's why the machine learning plays a very important role here.
Wayne: Um, we, uh, we're using machine learning in many aspects. Of our product, but, uh, one really, um, I would say the permanent one is the skin. As I mentioned earlier, um, we need to skin is more on the diagnostic side. Right? Yeah. So the makeup is try on the skin is diagnosed in order to have an accurate diagnostic result.
Wayne: We need to have a machine to learn. Okay. So far, um, our engine we call engine, but basically it's our skin analysis, the product. So we can, uh, analyze, uh, uh, the, the skin, all kinds of skin condition. We come up with a 14 different concern. Like we just do a quick scan, like a five second, we [00:09:00] tell you how your skin look like, um, in the 14 different category, wrinkle, eye bag.
Wayne: And radians. So that one also, we compare you with your, the peer, and we come up with the score. So, in order to get into that, um, the very accurate and then consistent result, we trend by 70, 000. Different data set. Okay. So 70, 000, our data, as you mentioned, is more like an image.
Deanna: It's a
Wayne: highly, uh, curate tagging image.
Wayne: For example, we, we come out, we have a, we get, uh, the data, which is the raw image and we need to tag it. Cause it's a, for example, this one is a wrinkle. How serious is the wrinkle? We can identify 32 different type of wrinkles and then four different types.
Deanna: If I can jump in, you've taught the technology then to, I did not only identify a wrinkle, but to determine what, what depth it is or how, that's what you're explaining.
Deanna: Yeah. Okay. Go on. Yeah. So
Wayne: basically it's more like a, [00:10:00] we are training, uh, dermatologists if you like. So we're feeding all the data to train the machine because of the AI machine, they know pretty much nothing. So you start to train them. Okay. So that one we consider as a so called a classic. So right now we call the classic, uh, um, AI.
Wayne: Which machine learning and then they do prediction, okay, based on what you trend. So they do prediction. Okay, so you're what your skin look like. And then, uh, you know, what's, uh, the score, as I said, compared to your peer. And then, however, you know, right now we do generative AI is similar. You have to train them.
Wayne: But the thing is, is, um, we call the unsupervised learning. So you throw all the data to the, to the engine, they learn by themselves. Okay. But, uh, Supervised learning, which we call the classic AI, is, uh, you set a boundary and let it learn, and then they do prediction. So it's more like under control, but the generative AI is different.
Wayne: So they [00:11:00] create something by themselves. Okay. So that's the, the power of a generative AI, but also a little bit scary thing there because they just, uh, train by themselves. Okay. So We are using generative AI to do a simulation. So we train, and then they identify, they diagnose, um, your skin. And then we'll start doing the what if, okay?
Wayne: So, what if you do this? Um, then how your skin will look like, so that's kind of a simulation part and also what if
Deanna: I apply this product? How would it benefit or change my skin? Yes.
Wayne: And also we are getting to a more like a plastic surgeon site. So if you, uh, okay, so if you do this kind of, uh, treatment or routine.
Wayne: what your face will look like. So something like that. So that one is part of a more like a generative AI. So it creates. So the different thing is, uh, uh, on the traditional, we call the, the classic AI, it's more like a prescription. Okay. [00:12:00] So they understand your condition and then they predict, but generative AI is they understand your condition and then they create.
Wayne: So that's a, that's a different in between these two AI, but basically they have to learn by the data.
Deanna: Yes. No, that was very helpful. Thank you so much for that. And I know too, that, um, with all partnerships and, and businesses, um, proprietary data is, is very important. And I think when we spoke earlier, you mentioned that, um, the companies you partnership, uh, partner with rather.
Deanna: you know, of course, sort of own their own customer data. Um, and, and, and that data then is not being used to train the general AI, right? It belongs to the brand partner. Can you help me think about how that works?
Wayne: Yeah. So usually we create a baseline with our information and the data we get, we create a baseline.
Wayne: And then for example, like a brand, if they want us to do something specifically, For [00:13:00] them, and then they create they, they provide us data. So we will use their data to train a set of engine. So that engine primarily will be using in their project. We are not going to, we won't use the brand A to train data to serve to sell to brand B.
Wayne: So that's why that's how we, we call them more like a. A clean room type of practice. Okay, so that's why the brain like to work with us. For example, if we have a clinic, right? So we're using clinical data to train their skin. Um, the, the shape. So we have a more like a, a shape by their sheen matching with this clinic.
Wayne: We won't take a clinic to get another brand. So that's pretty much a, that's the, the, the practice. So that's why that we always follow on that. Yeah. Right.
Deanna: Right. That's so interesting. It learns for each company that you work with. I love that. Help me think a little bit too about how perfect corp works differently with large multinational brands and then with smaller brands, because I believe you serve, you know, beauty, beauty brands of every size.[00:14:00]
Wayne: Yes. So, um, basically the, I'll say the technology is pretty much equal, right? So we train AI, we do the skin analysis, and we do the virtual trial. So the baseline, the technology is It's the same. However, the bigger brand has more variety. They have more, uh, ask to do customization. So, uh, based on their brand DNA.
Wayne: So that's why the final product you see from the big brand will be very different because they are their own, uh, called the ingredient, the color. So based on their, um, how they engage with their customer. So we customize something for them. So usually for the bigger brand, their product is more complicated.
Wayne: More complicated, especially in the engagement side. And then also the UI UX, the final presentation will be very different. But for the, um, for the smaller brand, so they can use our more like a standard solution. [00:15:00] They take this, uh, Usually it's API and plug into their website. Okay. So they still can do a virtual trial.
Wayne: They can still do a skin analysis. So that's why the baseline technology is very similar,
Deanna: but
Wayne: the bigger brand, especially the multinational brand, they have a different concern. Um, the consideration on the different area, right? So because they do a different region, uh, they may need to fit into different region because the customer behavior is different.
Wayne: So it tends to be. Um, the larger scale and then also more complicated product project.
Deanna: Yeah. Oh, that's so interesting. Thank you. Um, and I know too, that perfect corp technology is being used for what I would describe as consumer education. Um, one of the examples you shared with me in an earlier conversation was about, um, oral care.
Deanna: Um, yeah. Can you, can you tell us a little bit about that concept?
Wayne: Yeah. So that one basically, uh, we work with, so oral is now our, But it does cost [00:16:00] intermuting more and more now. That's why it's interesting is that the brand, when the brand sees technology, they have a different idea. Yeah. So we reach out by a Colgate, because they know we are very good at the color.
Wayne: We do different color try on and then the different, all type of different, um, the, um, But we call the skin, the shade, binding. So that's why they know we are very good at coloring.
Deanna: Yeah. So
Wayne: they, they approach to, they approach us and they say, can you do something about teeth? Uh, brightening because they have a tool to using the different not these to show customer.
Wayne: So that's why we work with the cocaine. We have a, um, activation. So now they put this into the, their package is a QR code. So, the customer can scan the QR code from the package, and then they can start to try using to try as a part of the education process. So, we believe, you know, especially the trial.
Wayne: Yeah. Can be a pretty significant part, especially on the education side.
Deanna: [00:17:00] That's helpful. Thank you. Wonderful. You've mentioned the shade finder technology a couple of times. And one thing we like to talk about here on the CosmoFactory podcast is how ideas become innovations. Can you tell us more about the concept for shade finder and then what it looks like today?
Wayne: Yeah. So as I said, our roadmap usually impact by our customer. Okay. So that one is a, is a great example. So. First, we do virtual trial, right? So we do all the color. And then one time we, we regularly do this business update with the executive on the, on the, this brand. So one time, one of the president of the brand, he asked us.
Wayne: Okay. So, because one of their challenges really get the right shape. foundation, especially for the e commerce, right? You, because you cannot try. If you go to a store, you may try, but the, the shape finding become an issue. And then the problem here is it create lots of a return.
Deanna: Yes.
Wayne: If you buy the different shape, usually you buy the same shape.
Wayne: [00:18:00] Over and over, but actually your skin tone change, especially like during the summer, you went to a vacation, come back, your skin get a little bit darker. So, that's why, um, if you, you get the wrong shade, and then you, you return. So that's why he asked us, can you do this, uh, with the shade binding. We'll say, well, let's see, let's try.
Wayne: And then that's how we start, you know, again, using the AI to learn your different shade and also not just, uh, you know, Um, your skin tone, but also the variation, especially in some of because, you know, we need to be inclusive. We need to cover many different aspects of the shade. So some of the darker skin, they tend to reflect the light a lot.
Wayne: So that's why that's a process of the machine learning to refine the algorithm. And also some of them we have a situation where I have an even skin. Okay. So, because originally we sample once. Like a different part of the, the skin, the face, but now you have the uneven skin tone. So you [00:19:00] need to be careful.
Wayne: So understand your customer's need, getting right data, refine your algorithm. So that's how we come up with this, uh, the ShadeBinder. Now, now this is much easier. The product become pretty mature. Lots of brands using that. They are so happy because you can get the right shade. And then you don't really need to go through that process to return.
Wayne: Yeah. Create some of the environmental challenge, right?
Deanna: Yeah. Yeah. No. And that's helpful that you explain some of those challenges in terms of light reflection, in terms of shade variation across any individual's face. Um, I'm wondering How significant, like camera technology is to what you do, um, of course, lighting matters as you suggested.
Deanna: But I'm imagining that, you know, the smartphone cameras and laptop cameras have really helped support, um, sort of the more sophisticated solutions you can offer. Is that true?
Wayne: Yes. Yes. So, um, the hardware, the advancement of hardware will definitely help software. [00:20:00] So when we start, like, uh, nine years ago, at that time, uh, some of the cameras, especially not, not necessarily just a camera, but the, the smartphone, it's not that powerful.
Wayne: So that's why, uh, we need to do lots of, um, you know, uh, optimization on our code. But when we write the code, when we create the product. But now because of the advance of this, uh, the, this, the hardware, especially the camera, the resolution getting very high. So we will help us. So as long as we can develop some algorithm to catch all the detail, so then the product will get more sophisticated.
Wayne: So that's why the hardware advancement definitely will help a lot.
Deanna: Yeah. Interesting. Interesting. I wanted to touch on another, uh, one of your newer technologies. Um, this, um, I believe it's called the makeup transfer. Uh, can you explain what that does and, and how you developed it?
Wayne: Yes. So, um, that concept has been there for many years, as I say, but there is a challenge.
Wayne: So that's [00:21:00] why eventually we, we launched, uh, uh, you know, just, uh, A couple of months ago in May and June, we, we announced in the VivaTech. So basically the concept is a pretty simple, okay. So if you see a makeup, a look, which you like, you want to try it. Right. Maybe I see it in a magazine
Deanna: or a vision program or something
Wayne: or in the red copy.
Wayne: Right. So every time it goes through red copy or the, the, you know, kind of a ceremony, you have a, some really good look you want to try, but how can you try it? Okay, so, you know, it's simple, right? So, ideally, you take a picture and then virtually try on your faces. But this, uh, transfer process can be pretty difficult because in the, in several forms.
Wayne: First is, uh, You don't know how they do this, especially for the eyes makeup.
Deanna: My
Wayne: eyes makeup usually is a layer.
Deanna: Yes.
Wayne: You see the final finish, but you don't know how they layer it, right? And then also, they are using multiple [00:22:00] different products. So you don't know that. So that's why they become, you are guessing.
Wayne: Lips, probably is easier. It's a, it's, yeah, because it's just a color, so you just understand the color, but for the eye makeup and also some of the, like, uh, blushes, you know, you have to do the guessing game.
Deanna: Yeah,
Wayne: so that's why, as I say, as advanced of the AI, uh, we start using machine that, you know, of course, it's a, it's a machine learning and then lots of different algorithms.
Wayne: We try to understand, we try to decompose and then guess what's the best one. It all happened in second. Okay. So complicated process. It happened very soon. So now we can actually transfer the makeup, the look, which you like onto your faces. You can try it like a virtually.
Deanna: Yeah. It's so interesting to think about technology being able to sort of decompose, almost take away a layer at a time to understand, um, or help us imagine.
Deanna: Um, that makes me wonder, and maybe this isn't that [00:23:00] complicated, but I want to ask anyway, um, can you do the skin analysis? Technology applications with people in full face makeup, are you able to sort of remove the makeup and do accurate skin analysis? Or is that sort of,
Wayne: yeah, so I guess ideally, yeah, we want to do the face without makeup.
Deanna: Right? Right. None of the decomposing pieces.
Wayne: Yeah. But the thing here is that in some aspect of the scheme, because we identify 14 different concern, some of the concern can really be done, even you have a makeup. But some of them, it will be a little bit challenging. Okay, especially say for poor right. So if you do a heavy makeup, you cover everything.
Wayne: We won't see it. So if you won't see it, you won't be able to identify.
Deanna: Right. Yeah. But
Wayne: some of them like, uh, moisture, uh, actually, even you have a makeup, we can sort of understand your skin moisture level level. Yeah.
Deanna: Yeah. Oh, that's so [00:24:00] interesting. I love it. I love it. Um, so I know that, um, perfect Corp has participated in several editions of Cosmoprof and including Cosmoprof worldwide Bologna.
Deanna: Um, I believe, um, You've spoken at Cosmo Talks Educational Programming as well as exhibited at the show. I'm just wondering if you can tell me sort of what the experience has been like, why a company like yours would show up there, um, what, what, what's going on for you?
Wayne: Yeah. So, um, yeah, so we'll, we'll Actually, I still remember when the company started the 2015.
Deanna: Yeah.
Wayne: The very first show we went because we started the June 1st. And then Cosmopolitan Las Vegas happened in usually July. So that's probably the very first show we attended. So as the company grow, Um, so the goal for attending the Cosmoprof will have a, it also change, it change, um, as we grow. But basically what we really like to see, of course, is the networking, you know, we are in the beauty industry.
Wayne: It's a good [00:25:00] place to network, to see the friends. And then the second one, of course, education, especially some of the, the Cosmoprof talk, right? Education series. We sit there, we listen. We, of course, we get lots of understanding of the trend. And then the third one is, uh, we see lots of, uh, people, vendors, potential customer.
Wayne: That's a good place to meet all these different people. And then during the conversation, communication, we start learning and understand the trend of the, you know, the beauty industry. So that's definitely as a good place to be educated, to meet people, networking, and also to develop your business.
Deanna: Yeah.
Deanna: Yeah. No, I love that because it really is such an immersive experience. You cannot help but learn. And with, you know, it's just such a, um, a diversity of the industry in terms of the sectors that come together, the countries, um, you know, that show up it's, yeah, I, I appreciate what [00:26:00] you've shared there. Thank you.
Deanna: Um, I, I did, um, you know, have some wishful thinking there at the beginning when I said, if, you know, supply side companies had reached out to perfect corp 9 years ago, we might, uh, have a very different conversation, but I'm wondering if you, um, you know, see any potential to work with, um, you know, ingredient companies or manufacturers or, or, um, or maybe.
Deanna: If it's not something that perfect corp might do, what are you seeing in that space? Do you pay attention to how AR and AI are being used elsewhere in the industry?
Wayne: Yeah. So, um, we, we've been approached by the, uh, contract manufacturer and ingredient company. They have a different idea. So, as I said, uh, because of some of the resource constraint, we may not be able to do this now, but potentially we, we could get some idea, for example, um, virtual prototyping.
Wayne: Okay. Because, uh, uh, sometimes you get a color, right? So it's on the color makeup side, you have a color, but you really don't know if that color is the [00:27:00] right. On some of the person and then so what, how they the feedback on that. So that's why if you do a virtual prototyping, you create some virtual color.
Wayne: And you can do a, you know, probably do a, like a beta testing with a selected customer and see how the feedback, instead of create a batch of the real product. And then you find out the color wasn't the right one. So that's a virtual prototyping. That's the one idea. Yeah. And also some of the skincare ingredient company.
Wayne: They approach to us. So they, uh, you know, sometimes they want to, they, they need to do some kind of, uh, uh, the focus group, right. To get them understanding, to get to understand the skincare product and also sort of like a clinical trial type of thing.
Deanna: And so
Wayne: they said, Oh, because the Kandaga clinical trial or panel can be pretty, uh, time consuming and resource consumption.
Wayne: So that's why they said, maybe we can do this virtually, right. Instead of, uh, uh, get people together. In the place, [00:28:00] we probably can have them do the testing at home and then come up with a different kind of a result. So that can be a potential, uh, application from our, um, skincare analysis tool.
Deanna: Right. Right.
Deanna: I'm reminded as you, as you say, their skincare analysis, I think earlier you mentioned hair as well. Do you want to say anything about what your technology is doing with hair?
Wayne: Yeah. So in the hair, we have a different function for the hair. The very first one is, of course, the virtual tryout. Okay. So, yeah, actually the main, the product, the contract manufacturer company, they can use this one to see if the color is right.
Wayne: So that, of course, that's the right thing. So we do color trial. And we also can do a hair type analysis. We can identify different type of hair. And then the recommended right now is that we do a recommendation on the different product for them to use and then also we are Working with some of the the hair brand to do hair Style because some of them are doing a hair [00:29:00] styling tool and then also forming like the product So that's why they want to educate.
Wayne: So how their Product can help the customer to create different hair type. So that one is 100 percent like a generative AI. So we look at your face, we look at your hair color, you know, the color, your original color, and then the hairstyle which you want to be. And then we create a hairstyle just like yours, like naturally.
Deanna: Sure, sure. It's so interesting. It's, it's, you know, the virtual try on with the makeup with the hair as you're describing it. It really is creating sort of the before and after experience for the consumer, right? Um, in, in a very real way. That's, that's so exciting. Is there anything else? Low risk. Yes. Right.
Deanna: I did not just dye my hair purple, right? I can, I can experiment. That's so true. Low risk and low cost, as you were saying earlier, which helps the consumer and the brand. Um, you mentioned the, um, shade finder, um, helping with product returns. Um, so I, I think [00:30:00] that's, that's really significant. Um, I'm wondering if there are other sorts of AR, AI and beauty, anything we need to be aware of.
Deanna: Um, from your company or elsewhere in the. Yeah. So, uh,
Wayne: we recently announced, uh, the beauty GPT concept. And then I guess we also have some, uh, um, demo early prototype. So basically, so we have all the components, right? So we do, um, we do hair, we just mentioned, and we can do virtual trial and then we, uh, uh, uh, diagnostic your skin condition and we understand your shape.
Wayne: So at the end of day. We are dealing with a human being,
Deanna: so
Wayne: human being is not only hair is not only skin is not only eye or lips. They are holistic, a human being. So that's why what we've been doing here is that we provide a very powerful, um, platform. So integrate everything together to serve the customer.
Wayne: Okay. So, for example, customers simply ask. What's the best look for me today? [00:31:00] Okay, I'm going to a party. So then, the, with all the components we have, the AI engine, we can crank in the engine and then come up with the ideal solution for them. And then they may ask, okay, so wearing all this makeup, and then, uh, how about my hair, I mean, the skin condition today?
Wayne: And then would that any of this make up enhance the skin condition, something like that so they may have all kinds of different questions. So that's why we are looking at holistic as a human as a beauty lover, we try to using the power of AI to empower. The beauty lover. Okay, so they are more like they can constantly doing the consultation and do the trial.
Wayne: They do the, uh, the check a diagnostic and come up with the most beautiful thing they define. It's not like people tell you what beauty is. You define your own beauty with all the AI tools. So that's why we are trying to come out, uh, we, we actually come out with a platform. [00:32:00] Okay. So that's why we put everything together and then empower the beauty lover, also empower the beauty brand.
Wayne: Because in that case, it is, um, if we trend by their own data, they can specifically serve their customer. Sure. So that's the, uh, a platform which, uh, serve all the beauty lover. That's just something we actually, we believe that's a combination of all the technology together. That's eventually come up with this product.
Deanna: Yeah. Oh, that's fantastic. Cause it's, you know, what you're describing to me is partly what I would call like a holistic look, but it's also, You're thinking about treatment or, or care routines, you know, based on, um, like you said, skin condition that day, based on sort of each consumer's expectations of what they want their skin or hair to look like, or what they think, um, you know, makeup, uh, can help them express.
Deanna: So I think that's, yeah, that's very interesting. You wouldn't need to mix and match, um, one celebrity's hairstyle with another celebrity's makeup. You can really build it, [00:33:00] um, for yourself. That's really compelling. So interesting. There's a
Wayne: true personalization. Okay. Look at yourself. You're as the, as the, the main, uh, the topic.
Wayne: And then we look at you, we analyze all the condition and understand your preference. We come up with the best look for you.
Deanna: Right. Right. Oh, that's fantastic. I love it. I love it. Well, this was a super informative podcast. Thanks so much.
Wayne: You're welcome. Thank you again. Thank you.
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