Carrie (00:49)
Okay, welcome to Artificial Breakdown. Today we have Courtney Koss, and she's the founder of Prevoir, which is an AI-driven fashion agent. And I'm so interested to find out more about this because I'm not exactly the most fashion-forward person in the whole world, but I think this is super interesting. So welcome to the podcast.

Pete (01:10)
Ha

Courtney (01:13)
Thank you both.

Carrie (01:17)
yeah.

Pete (01:16)
How did you meet,

Carrie? Because this is your guest. how did you... This is CMA, the Calgary Marketing Association.

Carrie (01:25)
Yeah. So I went to the Calgary marketing association. What was the name of it?

Courtney (01:30)
⁓ it was a panel focused on marketing AI tools. Yeah. Back in November.

Carrie (01:35)
Right. Yes.

And Carl Ye was on that podcast or was on that panel as well. And we've actually, we've had him on the podcast already too.

Pete (01:41)
God, where is he not?

Courtney (01:41)
Mm-hmm.

Thank

Pete (01:48)
everywhere.

Carrie (01:48)
Yeah, and at the time you were talking about using this to basically create fashion lines. That's kind of what Prevoire does, but why don't you tell us in your own words what Prevoire does?

Courtney (02:05)
Well, I identified a use case for fashion way before AI was part of my vocabulary. I was back at grad school over five years ago now. I did a lot of research in the intersection of fashion and technology and data analytics. And I realized that there was a real opportunity for a tool to help fashion brands manage their data, improve its accuracy, save their time. I talked with, you know, many different fashion professionals.

Carrie (02:11)
Mm.

Courtney (02:34)
in my academic research as part MBA and really realized that there was a need for some kind of tool. I looked at lot of different types of merchandising tools that were already on the market. A lot of them are not tailored to fashion. They're just tailored to retail in general. like cosmetics, CPG, they'll kind of roll fashion into that, but really not like targeted to fashion brands to help them manage their data.

And then, you know, with the last couple of years with all the advancements of AI, I realized that there was just this incredible technology now that really made my use case much more of a reality than it was before.

Carrie (03:17)
very cool. I do like that you come from the side of, you come more from the side of fashion than from the side of technology, but still.

Courtney (03:18)
Yeah.

Yeah, I have

very little technology background actually. Most of my background is in fashion and in marketing, and sometimes fashion and marketing together. I did some work in technology right after grad school. I was headhunted by a firm down in LA to work on a retail tech platform. So I learned quite a bit quite quickly. But before that, I really had very little exposure to the tech industry.

Carrie (03:52)
So would you have had like a more traditional look at it before that, or I guess more traditional education, I suppose?

Courtney (04:00)
Yeah, I Communications from U of C. After graduating from my undergraduate degree, I had my own store here in Calgary. So I have first-hand knowledge of what it's like to buy for a boutique. I was on sales floor all the time. I was making observations about my own sales. And I mean, already at that time, was over 10 years ago, I starting to ask the question,

Carrie (04:15)
Hmm.

Courtney (04:26)
of like, why are certain products like such a hit and some are not? And you know, there's like that 80-20 rule where it's like 80 % of your sales are coming from 20 % of your products. And I was already asking that question, like how do we buy better and make better decisions so you can really get more products that your customers really want because...

Carrie (04:38)
interesting.

Courtney (04:49)
anyone who's been in retail, whether it's fashion or some other, you know, retail vertical, like what people say they want versus what they end up buying and what they really end up liking can be quite different. And then after having my store, I worked at a marketing firm ⁓ that had offices in Quebec, and it did marketing for most of the big Canadian retailers. So that was a different perspective for the fashion, but that is definitely ⁓ more of a traditional background than I think a lot of tech founders have.

Carrie (05:18)
Yeah.

Courtney (05:19)
Yeah.

Pete (05:21)
you mentioned kind of fashion, first of all, fashion data, as opposed to just any other data, what does that look like? Like, what do you need that other kind of CPG brands don't have or what, you know what I mean?

Courtney (05:35)
Well, there's a lot of sales data which any retail vertical is going to have. And merchandisers are already analyzing that mostly in traditional ways like in Excel. But what is more difficult to analyze without AI is the pictures of your products. And that includes analyzing your products with AI from fabric to the silhouette to the color.

Pete (05:40)
Mm-hmm.

Courtney (06:02)
prints or patterns if they have any, any embellishments if they have any. And really what a lot of merchandisers get is they get, you know, a list of analytics and SKUs, but they have no images for one, they have no images next to the products that they're analyzing. So a lot of times they're

looking for the images to go with these products in some analog way, whether it's on their website or literally walking around their store. Sometimes we've heard fashion merchandisers at really big, you know, well known companies. That's how they try to find the product that they're looking at. ⁓ And they're also

Carrie (06:37)
wow.

Courtney (06:39)
not only that, but they don't have that reference, but they also are not pulling all of those attributes and being able to combine the attributes with their sales. It's more so just going, okay, like what are my 20 bestselling products and going and looking at them and then trying to decide for themselves why something sold well or why it didn't without any real analytics behind it.

Carrie (07:02)
Right, and I guess not being able to see them all at once either, because you're kind of just going around looking at this article, looking at this piece. It's like, they both have buttons, I guess.

Pete (07:12)
Hahaha

Courtney (07:14)
Yeah, and like right now we're in our beta phase right now. We have our initial users that are using Purvoir. They're giving us a lot of feedback. And it's shocking to me that just having pictures of their products next to analytics and a mood board type of user interface where they can get that overall look and start to kind of slice and dice.

their products, something that they haven't really had. So it's kind of what we like to call like our wedge product where we can give them something that like isn't super, super hard to do, but really gives them value before we really start building out some of those more complicated features.

Carrie (07:52)
I love that. Just that kind of stepping stone of like, look what technology can do. It can really simply make things a lot easier for you.

Courtney (08:00)
Yeah. And they're so busy. Like they don't want to spend months and months learning a new tool. And we've heard that from so many competitors that it can take anywhere from three to six months to onboard a brand. Whereas, you know, it takes us 20 minutes to get a user onto Provoire. There might be some bugs right now, but they can start using it right away. And we watch them use it and we're like, okay, they get it. They don't have to tell them, you know, you know, where to go, which is really great feedback. But yeah.

Carrie (08:17)
I said they're always.

So then what is, who is your clientele? Who's using Prévoir?

Courtney (08:33)
Brands, could be, well, some of them are designers, so they just have like one brand.

Carrie (08:37)
Hmm.

Courtney (08:38)
⁓ then some are multi brands. So it's going to be interesting to see if it's more useful for companies, like for example, Holt Renfrew or Sachs or Nordstrom that have many, many brands and they're going out and buying products. They don't get a say in what's getting designed versus. You know, a brand like, I don't know, Calvin Klein that's designing all of their products. It's there's a lot of overlap in their work functions and their job functions, but some things are unique. So yeah, I think that's going

be really interesting to see if one is a more useful use case than the other. But right now we cater to both and we have both types of companies using it.

Pete (09:19)
So just so I can get a handle on Prévoir, is it a marketing CRM, like an AI CRM tool? Is it there to help organize and match up your data set, or does it generate photos on the fly for you? What exactly is it, just from a high level?

Courtney (09:36)
Right now, we only accommodate brands on Shopify, which is a massive amount of the market. It's about half. We have the data to back that up. So we connect to the Shopify API. We import all of their sales and transaction data and images from Shopify. And so that's where we have our own dashboard where they can log in and they can see all of their products and they can see their sales data combined with their images and they can.

For example, they have a dashboard that shows like, you can select a date range, you can select a brand, you can say, I'm gonna look at tops, you can see what were the best performing colors in the last week, or what were the best performing fabrics from last season, for example. there's major decisions that merchandisers are making on an ongoing basis. It's allocations, like having the right products in the right stores.

from a small store that maybe just has e-commerce and one physical store to one of our clients in New York that has about seven stores and then e-commerce, they're constantly looking at this data to go, how should we be reallocating our products? How should we be reordering? And also like, how are we planning for the future? Like if we know that this is what did well in fall of 2024, how can we plan for fall of 2025? I think one of the things that isn't surprising to

Fashion professionals was surprising to people outside of fashion is a lot of what they're planning for is based on historical data and that's a good thing because Like they get to know their clientele and their clientele typically wants just more of the same thing You do need some level of newness. You might want to like pull in some trend type products, but generally speaking you're planning based on what's done well in the past and

forecasting in the future and like take into account other things like maybe the economy and different things. ⁓ But yeah, that's how they use it.

Carrie (11:36)
That's very cool.

Courtney (11:38)
Thank you.

Pete (11:38)
Hahaha

Courtney (11:40)
I should send you a demo after. Can I screen share?

Pete (11:42)
Yeah,

Carrie (11:43)
Please.

Pete (11:44)
so it's interesting because it allows you, so I'm assuming that it's indexing all your products, but you're not going in there and tagging, hey, this is a wool sweater, this is a red sweater, this has a V neck. Obviously, I'm picking on your sweater right now. But it can pull all that out by just looking at and analyzing images. So you can get a lot more data without trying to sit there and...

Carrie (11:59)
haha

Courtney (12:01)
This the thing.

Pete (12:09)
index everything manually is just going through and giving you so many different ways to look at stuff.

Courtney (12:14)
We're generating about 20 tags per product. So you're describing my sweater and like one of the things that is surprising to some people who log in the first time, they're like, well, where'd all these tags come from? I didn't put these in there. And I'm like, no, AI did it. You didn't have to do it.

Pete (12:18)
cool.

⁓ love it.

Carrie (12:31)
Whoa, do

you remember when tags were the future? Like you had to tag everything and you had to tag every single thing in every photo in your whole photo library and that was how you were gonna, and now classic, just another way that AI's come in and been like, I'm gonna do this a thousand times easier.

Courtney (12:36)
Yeah.

Pete (12:46)
Yeah,

Courtney (12:46)
No,

no.

Pete (12:46)
one more summer student job done. Right? ⁓

Carrie (12:49)
yeah.

Courtney (12:52)
Accuracy is when you're doing it manually, right? Like you call this radical pink, you know, like AI is not perfect, makes mistakes as well. But we spent a lot of time testing the accuracy as well. We've really been able to improve that.

Pete (13:05)
What about generate it like I've seen a lot of like, for instance, This kind of like flow through one where you can buy in demand. That's what I'm talking about. you're you can generate like a t shirt, put up your design and

And I saw in real time it generated about 30 different images with the t-shirt on a model and here's the front, here's the back, and here's it in gray and here's it in blue. And all of that was generated in about five seconds, which is pretty cool because in the past, well, we know we have to go out and shoot all that stuff or use mockups and stuff like that. Is that part of this?

Courtney (13:38)
We haven't gotten into that realm. We're really just focused on helping brands and designers empower their collection planning and make better decisions, but we're not looking into kind of becoming the designer for them. There are quite a few tools out there, like as you described, one that comes top of mind is Raspberry. I think they're American.

That's probably the most prominent one that does that type of prototyping and I'm sure you are aware of all the types of ways that AI has infiltrated marketing, especially with fashion photography, AI models, all those types of things. But no, we're not going into that realm. Yeah, we're really trying. Yeah, go ahead.

Carrie (14:21)
But that's

kind of nice too though, because it's, you you're not saying, we're gonna use AI to tell you, the designer, what's in fashion. You know, it's more like, would you say it's more of a accent to their kind of creativity?

Courtney (14:40)
Yes, trying to really help them save time in the data analysis and the decision making, like the less fun decisions, like the allocation, like how much should I order? How should I price something? Like they need all that data when they decide to go buying for a season or they're going to design for.

season, like those types are very time consuming and not very fun. I think that like the image product prototyping that you're describing would be probably more applicable to fast fashion. We've developed Prevore with mid to more luxury fashion in mind just because they don't make products within two weeks to a month. They're having to plan so far ahead and that makes it more tricky. And they're also not

Carrie (15:14)
Hmm.

Courtney (15:29)
you know, copying other brands, like they're trying to be the ones that are developing the new products and setting the trends more so. So I wouldn't be surprised if, you know, really big fast fashion companies are going to want to do that like rapid prototyping because, you know, the design element is pretty data driven anyway. But yeah, if you're ahead of a luxury fashion house, we know they don't want to be told how to design your clothes, at least not right now.

Pete (15:56)
Mm.

Carrie (15:58)
Yeah.

Courtney (15:58)
Yeah, and that's like the best job for, you know, humans to be able to do, It's the best for Yeah, exactly. We're trying to help you eliminate the things that are not fun.

Pete (15:59)
I'll bet.

Carrie (16:04)
Totally. I mean, that's the stuff that people want to do. That's the fun part of work.

Pete (16:12)
Yeah, not me. prefer tagging things.

Carrie (16:13)
Right.

Pete (16:19)
That's super cool. So Courtney, do you have a sense of it's a good application of AI getting in here? Do you have a sense of where it might go? are there, do you see a bit of a roadmap where AI might be taking fashion?

Courtney (16:19)
you

There's just like so much influx, just like probably every industry right now, there's so much influx of ⁓ AI startups for fashion. When I was first scoping out my idea, that was about three years ago now, I think I found like five companies, pioneer companies. Now I have a list of well over a hundred and I probably haven't even found all of them. There's a use case for everything. I know there's a lot of focus on

sustainability, trying to help brands produce less. There's a lot of focus on sustainable fabrics. There's a lot of focus on the secondhand market, which is still a very small part of the market, but it's a growing market. And there's a lot of more growing interest on people in like buying secondhand products. So I think that the sustainability element is a really big thing. Fashion brands, you

The two probably biggest ways that they can help their sustainability is producing less, which is what we ultimately would love to help them do. It's going to take time for us to get those KPIs to be like, okay, we helped you increase your profitability, increase your sell through, shrink your dead stock, because every brand has dead stock at the end of the season. I'm sure you've heard like the horror stories of big brands like burning their dead stock or whatever, because they don't want people to have it, but they know that they can't sell it.

We would love to be able to show brands that we can shrink that at the end of the season. yeah, sustainability is definitely a really big focus.

Carrie (18:10)
That makes, that was actually going to be one of my questions. Like I, I imagine this would lead, especially if you have that 80, 20, you said 80 % of sales are 20 % of your.

Courtney (18:20)
have your stock. Yeah.

Carrie (18:22)
of your stock so then what happens

is that other 80 % of your stock that isn't selling. And that's very cool that that could help probably significantly reduce that.

Courtney (18:33)
Yeah, and it's tough because, you know, one of my professors, I did part of my school in Milan and he's been with Zegna for like 20 years and he's like, well, you're always going to have some dead stock because if you were producing only exactly what everybody wants, like they want this perception of ⁓ selection and choice.

So you do have to have other products, but there are definitely ways to be able to shrink that down. And then there's also some really interesting startups that take dead stock and do other things with it, like make new products. I know Coach has started an entire second brand, I think it's called Coach Topia, and they use only their own dead stock to make new products, which is pretty cool. Yeah.

Carrie (19:15)
that is very cool.

Pete (19:17)
I had no idea that dead stock existed. This is fascinating to me. But I do remember a little, I remember being at Cole's bookstore, which I can talk about now because they don't exist. at the end of whenever it was, one of my jobs as in the back was to tear off all the jackets and tear the books in half and throw them in a can. Yeah, kind of. Yeah.

Carrie (19:21)
What?

Courtney (19:29)
Thank

Carrie (19:39)
You were a book burner.

Thank you.

Courtney (19:44)
And they wouldn't want to just give them to like a secondhand store or anything.

Pete (19:48)
No. Yeah, into the garbage they went. And I just had to, we returned the jackets back to the publishers, but the rest of it just went into a bin, I'm assuming for maybe recycling. I don't know. This is a long time ago. Probably was book burning parties.

Courtney (20:02)
Yeah.

Carrie (20:02)
Wait, can you,

can you rip a book in half? Are you that strong?

Pete (20:07)
Yeah, if you've never seen me tear like yellow pages in half ⁓ Yeah Craziness, okay. Well, that's really interesting. What about like ⁓ It's interesting because that's kind of the data the marketing side of fashion. What about like fashion itself? Have you seen anything? I know for a while there's it was big into wearables like light up dresses that could change colors or have rotating patterns and stuff like that is there

Carrie (20:14)
Maybe today's Yellow Pages. It's like four pages.

Pete (20:37)
I haven't heard anything about it since, like this is, I'm not in this industry. So I have no idea if it's like firing like crazy or if it's kind of going through a phase. have any idea Courtney?

Courtney (20:48)
⁓ Based on what I've observed, what I've read, also just my own shopping habits. I love shopping. People are going a little more traditional now. They want to buy pieces that are going to last a really long time and not go out of style. There's definitely more of a priority on comfort, but it's almost like two ends of the spectrum. want, there's my hand, that on one end. And on the other end, you also want to buy like really cool

avant-garde things for those odd times that you go out because a lot of people aren't really leaving their houses as much still right there's still that post-covid like I don't know about you two but you know I go to an office one day a week so and I kind of have my uniform during the day and then if I'm going out with my friends or something I want to wear something really interesting because I feel a little cooped up and I think that's I think a lot of people are feeling that way in fashion so you're buying two very opposite

types of things and the really avant-garde stuff they want it to be like really unique for themselves like there's a brand called Golden Goose an Italian sneaker brand and they have like a whole section in their stores that is just customizable sneakers they'll do like fun graphics on them some of their sneakers are like really distressed on purpose so they're very very unique and so that's kind of where i see a lot of the taste going right now

but it can change quickly too.

Carrie (22:17)
Yeah, somebody just told me about, I feel like I've been exploring my fashion in the last few years. And I'm a big thrifter too, I'm a big second-hander. But somebody was like, well, you need your capsule wardrobe. And I was like, what the heck is a capsule wardrobe?

Pete (22:30)
You

Carrie (22:34)
And now I just want AI to make me a capsule wardrobe.

Courtney (22:37)
don't need anything, first of all. yeah, there are brands that are founded on the capsule wardrobe philosophy. So if there's something that is helpful for you, you're like, I want to look good, but I don't really like shopping, then that's a great choice for you.

Carrie (22:40)
Yeah

That is literally me. You just described me.

Courtney (22:55)
Yeah,

and that's great.

Pete (22:56)
can, so sorry,

⁓ what is a capsule wardrobe?

Courtney (23:02)
It's it's a theory or philosophy that you can buy maybe 10 pieces of clothing and they're interchangeable. You wear those 90 % of the time and you would only update them like if I guess if something wears out or maybe you incorporate a couple pieces every few years, but you're pretty much wearing the same thing all the time and it looks good. Steve Jobs was, didn't even have.

Carrie (23:02)
You'll probably explain it better than I will.

Pete (23:29)
Yes.

Courtney (23:30)
We just had one outfit, but we had a few that would have been considered a capsule wardrobe. Yeah.

Carrie (23:32)
Yeah.

Pete (23:32)
Hehehehehe

Yeah, he had just like

Batman outfit, like seven of them in the wardrobe.

Carrie (23:40)
You

Courtney (23:41)
Yeah.

Carrie (23:42)
just so interesting to me, and I don't know, this is coming from somebody who's not in the industry, that fashion just seems not at all entwined with technology. So I'm so interested in how you, what made you think, clearly there was a way that this was all done and it wasn't entirely efficient and people didn't have images with their lists of items.

Like how did you start thinking, let's add technology to this and see what happens.

Courtney (24:20)
It's kind of just a classic trying to look where there's business opportunities. It was, you know, in grad school being told from my professors, like, you need to do a research dissertation. You need to pick something that you like, but something that you think could be applicable to your job in the future. And I was just like, I like fashion. I don't know anything about technology. And I just started researching those two things together, you know, top of the funnel.

like what companies are doing things with fashion technology. And then I just identified an opportunity. Yeah, and I think that's where AI can be probably the most disruptive is just finding like those.

those business functions, whether it's fashion or something else where it's just very analog and it's very time consuming. Fashion is a sexy industry, but I don't know if data analytics and saving time and tagging clothes is very sexy. It's not, but it's super, super useful. So that's really how I came to that conclusion.

Carrie (25:24)
Yeah, well, I love that because it can be easy to just disregard all the non-sexy stuff. But in reality, that is going to make this a whole lot sexier in the long run and so much easier. you get a client who... I'm just imagining this client who suddenly has all this extra time to create more or to do their own...

even if they just go like walk around in nice clothes more. Like I don't know what they would do if they weren't taking photos, but. ⁓

Courtney (25:56)
Well, it's an industry where people

get really burnt out because the expectations for the amount of time that you put in, especially when they're at like a peak of a season, like I don't want name any companies, but I've talked to a lot of companies and it's very normal to expect your buyers to be working around the clock for weeks on end during fashion week.

Carrie (26:00)
Hmm.

Hmm.

Courtney (26:21)
typically not paid very well either they burn out quickly like a buyer at a really big brand usually only lasts a few years you can only do that for so long right so so yeah

Carrie (26:28)
really?

Yeah, I just, love this idea of...

Obviously you started looking into technology with fashion far before this like chat GPT AI explosion. And, just knowing that like keeping your eye out for an opportunity. And then when it shows up, you were able to jump on it because you were already thinking about it you were already like, there's something here and ⁓ now this thing's here to help accelerate that instead of, you know.

Some people who might be like, wait, what is any of this? Even though they're like, I think AI can help me, but I don't know how. Whereas since you were already kind of looking into it and thinking about it, you were like, I know exactly how this can help.

Courtney (27:09)
Thank

Yeah, and I actually, I was very disappointed at the time, but three years ago, I was looking at doing my own startup, but I was also interviewing with some very pioneering fashion tech companies, some over in Europe, some in the US. And I learned a lot about their technology, gave me a case study. I talked to like all their entire staff and plan A was really like just to go work for one of these companies. I'm like, I don't have to be a founder. I'm not the person who has to do this, right? ⁓

Pete (27:48)
Hehehe.

Carrie (27:48)
Right.

Courtney (27:49)
But you know, as I said, there are very few companies doing it. I didn't get any of those jobs, but it's kind of one of those things where you're very disappointed at the time. And then now you're like, ⁓ like maybe this was a way better path. Cause like, you know, maybe I'd be living somewhere cool probably, but maybe it wouldn't have been that interesting. And now I get to do it myself. Yeah.

Carrie (28:09)
Well, yeah,

and now you get to work with, it's the same as us at marketing. You get to work with a bunch of different companies, a bunch of different brands instead of one.

Courtney (28:17)
Yes, exactly.

Carrie (28:19)
Yeah, that's the rush, the marketing rush.

Courtney (28:21)
Yeah.

Pete (28:22)
What are you most excited for over the next little while here with AI growing the way it is?

Courtney (28:25)
you

It's overwhelming. Every day I'm just like, no, we've got some big competitor. You're always monitoring your competition and new products and everything like that. What am I most excited for? I'm just most excited for our progress because we're really getting to the point where it's like, we are really understanding what exactly we need to do to get that product market fit and being able to...

Carrie (28:37)
You

Courtney (28:59)
mass onboard brands to using Prevore. Really excited about getting into the Shopify app store. It's just from a technological perspective, it's a real pain in the ass not being in the school. It's just, I probably don't even know the half of it, the engineers do, but that's going to be really exciting. And that has a whole marketing component to it as well, an exposure to our ICP. So yeah, I'm really excited on about what we're working on. Yeah.

Carrie (29:05)
Ooh, that is exciting.

Pete (29:10)
Mm-hmm. Yeah.

speaking of which you're doing you're doing a bit of a raise at the moment is that right?

Courtney (29:31)
Yeah, we're looking to close our pre-seed round. We've raised $750,000 from AltaML and we want to raise another quarter million to close a pre-seed round of $1 million.

Pete (29:42)
very cool.

Carrie (29:42)
Congrats,

AltML is pretty great, hey?

Courtney (29:44)
Yeah, I should have talked about them more. They've really been, I mean, if I hadn't met them, I probably wouldn't be sitting here talking to you guys. They were really a game changer for me. And they've, I feel like they've taken a chance on me. Like we started working together ⁓ last January. So it's been a little over a year now. And that whole process has been really incredible just being part of their venture studio, their team and

their software developers all are all just really, really wonderful. And I mean, they're, they are a co-founder. It can be a little confusing. I think sometimes from the outside looking in there, technically like my technical co-founder, but instead of person, I have access to this entire business that the both CEOs have built out. And yeah, it's just been really, really wonderful. At the time that I met them, I really wasn't looking for investment. I kind of.

gotten sick of talking to investors and I just didn't think that there was much interest there. But yeah, it was a really good fit because I happened to have a use case that is exactly what they're looking for. Like they want to be in B2B, high vertical or vertical high growth industries. And I checked a lot of boxes. So it's been a really great relationship.

Carrie (31:02)
Yeah, that just gives that Alberta give each other a chance, let us help each other out story, which I love.

Pete (31:12)
Yeah, we worked with AltML a little bit too. They're great. Absolutely great.

Courtney (31:15)
Okay,

Pete (31:16)
thanks so much for spending some time with us. I've learned a lot, mostly ⁓ capsule, capsule, I can't even remember, but I am going to go, when I first heard it, I'm like, is that what you bury yourself in when you're, but no, it's radically different than what I thought. ⁓ So I'm gonna.

Carrie (31:24)
wardrobe.

Courtney (31:33)
Yeah.

Pete (31:35)
I'm gonna take a close look at my capsule wardrobe right now.

Courtney (31:38)
mean, no, you need any

help.

Carrie (31:41)
We do. We both need help.

Pete (31:42)
Yeah.

Courtney (31:44)
it sends you recommendations.

Pete (31:45)
But thank you so much. It was really, really great to have you on. again, such a neat area that AI is kind of helping out with or changing in an interesting way. So yeah, it was great.

Carrie (31:57)
Yeah, and I just, love seeing women in tech. I just think this is so awesome that you took a bit of a gamble and took a bit of a risk and tried something that you love. And I just think it's great.

Courtney (32:08)
Well, thank you and thanks so much for having me. This was fun.

Carrie (32:18)
I would like to hire her and AI to dress me. Like that just seems, I know that's not what they do.

Pete (32:28)
I thought that was great. Like you said, super informative. had no idea. Well, mostly about fashion, but then AI and fashion is a lot.

Carrie (32:36)
Well yeah, and it's funny too, because I think a lot of people don't know about dead stock, and it's like a problem. Like you have, like the textile, the amount of textile waste from fashion, especially fast fashion, is like a world problem. Like there's some documentary about it that I've watched, and it's, it's disgusting. Like how much of this stuff just ends up in a landfill, or burned, or however else to get rid of it.

Pete (33:06)
I had no idea. just kind of thought that went to like secondary markets and whatnot, but it's just

Carrie (33:10)
I'm going to send

you some links. This is why I'm a big second-hander.

Pete (33:13)
I was in maybe, like I was in the grocery store yesterday, as we are like every day, it seems, but I was at the grocery store. And I'm like, what happens just sitting in all the produce? I'm like, what happens a week later? With all the stuff? There's no way it's sold, right? So in all the milk and everything that goes on, it's like, where does all this go? don't even know if I want to know.

Carrie (33:35)
And I think there's,

I don't, food waste is, okay, well here's another thing. Did you get Odd Bunch? This is gonna be a commercial for Odd Bunch, not paid for by Odd Bunch, but I'm a big fan. But Odd Bunch collects all the, cause there's also all the food that the grocery stores just don't buy. So where does that go? Cause they can say no to a lot of stuff. They'll say no to things that are too big, too small, too ugly, too bruised, too whatever. So Odd Bunch actually collects all of that.

Pete (33:42)
No.

Right.

Carrie (34:04)
and sells it for cheap and you get it delivered to your I get a delivery once a week with fresh fruits and vegetables for $25. I'll send you a link to that too. It's great. It's I'm obsessed with it.

Pete (34:12)
No way. ⁓ cool. Yeah, that sounds amazing. Yeah, we.

That's oddbunch.ca. No, don't know what it is. but... No, very cool.

Carrie (34:26)
I think it is. Just anything

sustainability-wise that AI can help with, and I like what she was saying too about...

you know, this can help us create less waste. I'm like, then yeah, let's go.

Pete (34:44)
yeah, no, it was really good. I don't, I don't know. Like I, it's funny because I, ⁓ I didn't know what to expect. I had no idea. It's one of those things. It'd be like hairdressing. I'm like, I don't know how AI can get in there, but it probably has a role to play in all these different areas. And you just never really know what or how, but the data, didn't even think about like the, the sales data side of fashion is kind of like the boring side of managing a fashion business.

course it makes sense.

Carrie (35:12)
But then that's, you know, those are the numbers you need in order to be successful. And then mix that with the creativity of somebody deciding a brand.

Pete (35:16)
And product?

Right. When product data is such a necessary evil, the more you have of it, the better and the more organized it is, the better, but the bigger you get and the more products you get and all the skews and stuff, it's so unwieldy. Like there's, gets out of hand so much. And if you want to make a change now, you got to change like 50,000 skews. Like there's, can understand why it gets into the States that they get. having AI helping you organize and sort and tag and discard. Yeah.

Carrie (35:49)
the tagging.

I used to work for a disc golf company and we used Shopify and we had to put every individual disc into our catalog and tag every single disc with every single thing about it. So that when the client, when a customer goes out of the website, they can search. it was so like, was into it because I thought it was cool that now we can search and you get all these things, but it was just excruciating tagging stuff.

Pete (36:03)
Yes.

Yeah, like I said, it was a student job. ⁓ any, like any like non-for-profit or organization or anything that has like, just like 15 years of videos on CDs and it's got all these photography and documents and PDFs and news articles. Like it was a miserable job just sitting, going through them, trying to be like, okay, we need to tag with a date and maybe who's there and all this stuff. And you're missing a million other tags that you could potentially be doing, but like who has time for it?

Carrie (36:47)
Yeah, but now you can get that summer student and tell them, Hey, can you shove this all into this AI tool?

Pete (36:48)
Yeah.

Yeah, totally. Okay, that took you an hour. Okay, you're fired. ⁓

Carrie (36:55)
You

my god.

Pete (37:06)
But yeah, there's another, there's another job that was a bit of an entry job that's gone. So that's one of the, like, one of the things I worry about a little bit is just like, how do you start? Because the jobs that you do when you first start out are the jobs that are the easiest, less risky, the most susceptible, I think to AI, at least in our industry.

Carrie (37:28)
Yeah, yeah, I feel like you're definitely right because we keep talking about this too, how like, if you're the expert in your field and you bring AI on to help you out, then you're fine. But what if you're not an expert in your field yet and you need to get into it? How do you get into it?

Pete (37:42)
Mm-hmm.

Right, right. Well, you said the jobs probably just change, right? Like they're just, that job's not a thing anymore, whatever that is.

Carrie (37:53)
Yeah, I think you

become the... you run this custom chatbot that helps us do stuff. Yeah. That still needs some kind of human oversight. Yeah.

Pete (37:59)
Right. Yeah, you get paired with an agent.

We're like just lean over let's plug this into the back of your neck. Okay

Carrie (38:10)
Yeah, you become, we have summer cyborg positions opening.

Pete (38:15)
Hahaha

Carrie (38:19)
Alright, I think we got her. Yeah, I think so.

Pete (38:19)
I think we're good, hey?

Okay, well, good job, as always.

Carrie (38:27)
You too.

Sweet. Bye.

Pete (38:29)
Okay, thanks. Bye.