WEBVTT - Cooking Meets Stress Relief with Silent Chef

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<v Speaker 1>This is Bloomberg Business Week with Carol Messer and Tim

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<v Speaker 1>Steneveek on Bloomberg Radio. Well, Jess, I love having restaurant

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<v Speaker 1>tours on our program because well I love food, and

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<v Speaker 1>sometimes if they're here in person, they bring us food

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<v Speaker 1>and I can try it. It's really fun. But also

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<v Speaker 1>I love speaking to them about the business, how much

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<v Speaker 1>food costs, how tough or how easy it is to

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<v Speaker 1>get employees, and of course customer demand. It can tell

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<v Speaker 1>us just a lot about how the economy is doing

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<v Speaker 1>in different parts of the country. Very pleased to add

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<v Speaker 1>with us this afternoon. John Fraser. He's a restaurant tour

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<v Speaker 1>of Michelin starred Chef. He's the founder of JF Restaurants.

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<v Speaker 1>They've got fifteen restaurants with more than four hundred employees

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<v Speaker 1>around New York City, Long Island. They're in California, in LA,

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<v Speaker 1>They've got places in Florida as well. He's also got

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<v Speaker 1>a YouTube series called Silent Chef, which we're going to

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<v Speaker 1>talk about as well. Chef John Fraser joins us on

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<v Speaker 1>a zoom from New York City. Chef, how are you great?

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<v Speaker 2>Thank you, thanks for having me.

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<v Speaker 1>Yeah, it's really good to have you with us. First,

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<v Speaker 1>just give us an update on the state of the business.

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<v Speaker 1>What are you seeing right now across your different restaurants

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<v Speaker 1>and concepts.

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<v Speaker 3>Yeah, you know, it's really city by city. New York City,

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<v Speaker 3>as everyone knows, kind of is a really seasonal business.

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<v Speaker 3>Our New York City restaurants who were up from last summer.

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<v Speaker 2>For sure. We'll know in a couple of days kind

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<v Speaker 2>of how much.

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<v Speaker 3>But we do see a bit of a little always

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<v Speaker 3>in the second heading into the third quarter, and then

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<v Speaker 3>we ramp up into the fourth which is by far

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<v Speaker 3>our best around Florida. Also, seasonal dips happen in the summertime,

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<v Speaker 3>and we're seeing really really strong demand across the hotel

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<v Speaker 3>properties out in Long Island, as you know, that's that's

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<v Speaker 3>where it all happens in the summertime.

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<v Speaker 2>It's the seasonal business, and we've been rocking.

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<v Speaker 3>We'll probably go until around the end of October and

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<v Speaker 3>then we'll settle into the winter months.

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<v Speaker 1>Well, what about getting employees. If we were to speak

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<v Speaker 1>to you two years ago, I know you would have

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<v Speaker 1>said it's tough to find employees to uh, to work

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<v Speaker 1>in the restaurants. Tell us about how tough it is

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<v Speaker 1>to get those employees and then also tell us about

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<v Speaker 1>food costs and if they're going done.

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<v Speaker 3>Yeah, Starting with food costs, I mean, we have seen

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<v Speaker 3>a drastic decrease in our food cost as supply chain

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<v Speaker 3>has gotten better.

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<v Speaker 4>You know.

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<v Speaker 3>Thankfully we're able to pass that along to our guests,

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<v Speaker 3>and we're seeing that across all of our restaurants in

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<v Speaker 3>all of our markets. The food hike, the food cost

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<v Speaker 3>hikes were kind of universal. It didn't matter market to market.

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<v Speaker 3>Eggs we're still, you know, wildly expensive. Gas was wildly expensive.

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<v Speaker 3>That was all passed on to us and there for

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<v Speaker 3>our guests. So from a food cost perspective, we're seeing

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<v Speaker 3>things drastically reduced. There still are some pockets, I would say,

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<v Speaker 3>a lot of proteins like beef still very very high.

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<v Speaker 3>I'm not sure that's ever going to get better, to

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<v Speaker 3>be honest with you, It may just be a change

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<v Speaker 3>in the way that we construct our menus, in the

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<v Speaker 3>way that our guests actually eat. From a labor perspective,

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<v Speaker 3>we are starting to see some uptick. We've we've hired

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<v Speaker 3>on let's call re hired a lot of people that

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<v Speaker 3>were pre pandemic hospitality employees. So in other words, we're

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<v Speaker 3>bringing folks in that who have had experience in the past,

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<v Speaker 3>whereas I would send in the last couple of years

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<v Speaker 3>we were training from zero, which is fantastic for us,

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<v Speaker 3>and I think that that guest experience is drastically improved

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<v Speaker 3>in that way. I still think that we you know,

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<v Speaker 3>hot topic. You know, when we figure out a sensible

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<v Speaker 3>kind of reaction to our borders, that will be kind

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<v Speaker 3>of the last domino for us to fall in order

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<v Speaker 3>to equalize and be able to pass as savings on

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<v Speaker 3>to our guests.

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<v Speaker 4>So in your view, do you really think the economy

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<v Speaker 4>is slowing?

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<v Speaker 3>In my view, well, compared to what right compared to

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<v Speaker 3>a year ago, were drastically up from last year compared

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<v Speaker 3>to two years ago. Obviously, What I would say is

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<v Speaker 3>what we're seeing is the sort of bifurcation of spending.

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<v Speaker 3>We'll have some folks that join us who, let's say,

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<v Speaker 3>are not big spenders, they're out for an experience, and

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<v Speaker 3>then we'll have some folks who are clearly out to

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<v Speaker 3>party and have a great time and who will have

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<v Speaker 3>a drastic spend. What I would say is, I'm starting

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<v Speaker 3>to see a split of sort of people that are

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<v Speaker 3>willing to kind of separate with those those dollars and

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<v Speaker 3>people there's that are remaining sort of tight. But we

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<v Speaker 3>have not seen a drop in reservations, which for us

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<v Speaker 3>is kind of the tell.

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<v Speaker 4>And when you're seeing that split there, I mean is

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<v Speaker 4>how do you sort of gauge that as far as

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<v Speaker 4>particular segments where your restaurants are located. Do you see

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<v Speaker 4>that more so in particular parts of the country than others.

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<v Speaker 3>It's across all of our restaurants, So we're in some

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<v Speaker 3>pretty major, pretty major cities, but we're seeing it across

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<v Speaker 3>guest spend, right, So we're able to gather the data

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<v Speaker 3>based on the average guest spend and we say, well,

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<v Speaker 3>wait a second, why is it a few bucks less?

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<v Speaker 3>And then we go into like individual checks and what

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<v Speaker 3>you'll see is people that are celebrating. Our corporate spend

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<v Speaker 3>is drastically higher than it was last year. I think

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<v Speaker 3>that might be a you know, as a response to

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<v Speaker 3>return to work vis a vis couple of days in

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<v Speaker 3>the office those few days, people are heading out to

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<v Speaker 3>spend quite a bit more than they would Let's just

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<v Speaker 3>say if they were in the office five days a

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<v Speaker 3>week and had five days of spend but we're also

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<v Speaker 3>seeing a lot of corporate gatherings small to medium sized

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<v Speaker 3>groups as well. That's sort of like sweet spot between

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<v Speaker 3>eight and eight and twenty. A lot of those types

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<v Speaker 3>of bookings are coming through. And you know, it's not

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<v Speaker 3>just families, it's a lot of corporate and workforce spend.

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<v Speaker 1>Hey, go back to chef what you were saying about

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<v Speaker 1>immigration and border policy here and connect back to what

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<v Speaker 1>you see at restaurant. At the restaurants, well, you know, I.

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<v Speaker 3>Can only speak from my experience, and it's anecdotal, right,

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<v Speaker 3>I'm not coming from a place of data, But I

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<v Speaker 3>can tell you who our applicants are, and I can

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<v Speaker 3>tell you where they're from. What we used to have

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<v Speaker 3>is quite a bit of Latino applicants, especially in the

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<v Speaker 3>back of house, and that has changed. We are now

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<v Speaker 3>not as not employing as many, and a lot of

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<v Speaker 3>those that we are employing our first time hospitality employees.

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<v Speaker 3>So I'm not sure if there was a move around

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<v Speaker 3>the United States and move back home to their home countries,

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<v Speaker 3>but I can tell you that the hospitality workforce is

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<v Speaker 3>severely understaffed, and that would make a huge chunk for us.

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<v Speaker 3>It has to be sensible but it has to be

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<v Speaker 3>soon and that would probably mark a drastic change in

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<v Speaker 3>your guest experience, its being better and the price that

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<v Speaker 3>we have to charge.

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<v Speaker 1>That's so interesting to hear, and because I think that

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<v Speaker 1>part of the conversation often gets lost when we talk

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<v Speaker 1>about immigration policy, because it's so polarizing when you have

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<v Speaker 1>Democrats and Republicans fighting about it, and it actually sounds

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<v Speaker 1>very clear from you. I'm wondering if you do any

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<v Speaker 1>work in telling your local legislators about this, if you've

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<v Speaker 1>partnered with any other restaurants to talk about this, because

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<v Speaker 1>they're serious economic implications here.

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<v Speaker 3>It is polarizing, and I think that you know, I'm

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<v Speaker 3>a small business owner and I'm not coming at it

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<v Speaker 3>from a political point of view.

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<v Speaker 2>It's simply spaces that we need filled.

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<v Speaker 3>We do some work with our local folks in each

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<v Speaker 3>one of our markets. We do a lot to work

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<v Speaker 3>also in some of the training and onboarding of folks.

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<v Speaker 2>You know.

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<v Speaker 3>Unfortunately, you know, the hospitality industry is one it is

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<v Speaker 3>the largest employer in America. But our voice is not

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<v Speaker 3>quite as unified as I think it probably could be nationally,

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<v Speaker 3>and that could just be you know, a difference between

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<v Speaker 3>states and and and also the competition in between.

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<v Speaker 2>You know, small businesses.

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<v Speaker 4>Talk to us about your YouTube series.

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<v Speaker 2>Sure, yeah, thank you.

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<v Speaker 3>So, you know, during COVID, for the first time, I

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<v Speaker 3>had some some some some free time in probably twenty years,

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<v Speaker 3>and I was looking around the media landscape of on

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<v Speaker 3>the food scene, what's happening. And what I was finding

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<v Speaker 3>is a lot of what was out there didn't speak

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<v Speaker 3>to me. It was a lot of sort of how to,

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<v Speaker 3>a lot of you know, uh, a lack of why,

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<v Speaker 3>a lack of why cook this way or why why

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<v Speaker 3>this produce? And and I felt like that storytelling it's

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<v Speaker 3>inside of me. It's how we were in our restaurants,

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<v Speaker 3>it's how we construct our menus and our concepts. But

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<v Speaker 3>I didn't see it in the media. And so I

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<v Speaker 3>wrote some short stories and I was lucky enough to

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<v Speaker 3>get linked up with Jose Andres Media who who was

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<v Speaker 3>a co producer and produced these.

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<v Speaker 2>These pieces.

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<v Speaker 3>You know, it's storytelling in the very simplest way. There's

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<v Speaker 3>no talking, there's some music and ambient and it's sort

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<v Speaker 3>of the chef's journey. The chef's journey is about community,

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<v Speaker 3>and it's about bringing people around a table and I'm

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<v Speaker 3>lucky enough to be able to be connected with these

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<v Speaker 3>incredible farmers and fishers and winemakers and cheesemongers, and I

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<v Speaker 3>really wanted to tell their story, but in a way

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<v Speaker 3>that was perhaps not loud and ambasticking in your face,

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<v Speaker 3>but a little bit more subdued. It was about connectivity

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<v Speaker 3>and mindfulness.

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<v Speaker 1>How do you do this in a way that without

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<v Speaker 1>without talking?

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<v Speaker 2>Well, I think the visual is is quite impactful.

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<v Speaker 3>We really spent a lot of time thinking about the

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<v Speaker 3>best way to tell a story without words, and hopefully

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<v Speaker 3>what that does is it brings a viewer in and

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<v Speaker 3>they fill in the story. I mean, I would throw

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<v Speaker 3>the question back to you, have you ever made a

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<v Speaker 3>recipe off of something that you saw on television?

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<v Speaker 1>I have, yeah, but it always requires me to go

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<v Speaker 1>back and like google it because it's just the way

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<v Speaker 1>my brain works, like inspired by it, you know, right?

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<v Speaker 3>And I think that that's kind of what this series

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<v Speaker 3>is supposed to do. It's supposed to inspire you to

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<v Speaker 3>lean into where did those tomatoes come from? And how

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<v Speaker 3>are they being used? Not necessarily how to treat the

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<v Speaker 3>tomato and cut one inch and that sort of thing,

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<v Speaker 3>because I frankly quite boring. I want to know where

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<v Speaker 3>it comes from and what was the life cycle of

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<v Speaker 3>the tomato, not necessarily one inch or two inches. I

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<v Speaker 3>find that actually most people would would say no to

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<v Speaker 3>that answer that they don't cook from what they see

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<v Speaker 3>on television.

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<v Speaker 2>It's it's entertainment.

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<v Speaker 3>And the style of entertainment that we're hoping to bring

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<v Speaker 3>here with our partnership with Calm is one of mindfulness

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<v Speaker 3>and connectivity.

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<v Speaker 4>Yeah, once I get my cooking back caps back on,

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<v Speaker 4>I will currently be doing this. I'm curious how you

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<v Speaker 4>pick a particular food for each segment.

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<v Speaker 3>Right, So what this series does is it focuses on

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<v Speaker 3>a very small radius of locality. So within a couple

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<v Speaker 3>of miles in Long Island, there's a small town called

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<v Speaker 3>Southoldt and inside of that there's a biodynamic form called KK.

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<v Speaker 3>There is an oyster fishery called Little Ram, and there's

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<v Speaker 3>a cheesemonger called Coptapano. And what I really wanted to

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<v Speaker 3>do is sort of bring you into this place it's

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<v Speaker 3>very special and dear to me, and show you kind

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<v Speaker 3>of what happens within a very small radius and a

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<v Speaker 3>very intense kind of time as well. Summer of one

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<v Speaker 3>year ago, basically summer of twenty twenty two.

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<v Speaker 1>Hey, John, before we let you go, I am not

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<v Speaker 1>very good in the kitchen, I gotta be honest. However,

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<v Speaker 1>I loved the Bear, and I'm wondering if the popularity

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<v Speaker 1>around the FX series The Bear has led to an

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<v Speaker 1>increase in people just like wondering what happens in the kitchen,

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<v Speaker 1>you know.

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<v Speaker 3>I think I think there's a lot of conversation around

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<v Speaker 3>also not only sort of the personality of a cook

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<v Speaker 3>or what happens during a service. Yeah, so it's beyond

0:11:13.280 --> 0:11:16.200
<v Speaker 3>the kind of recipes and sort of the action, but

0:11:16.280 --> 0:11:18.640
<v Speaker 3>also the kind of the people that are producing those

0:11:18.679 --> 0:11:20.200
<v Speaker 3>things and the kind of pressures that they're in.

0:11:21.000 --> 0:11:23.080
<v Speaker 2>I'm thankful for that story to be out there because

0:11:23.120 --> 0:11:25.520
<v Speaker 2>I think it's one that's it needed to be told.

0:11:26.360 --> 0:11:28.280
<v Speaker 3>But also when you've got like a good look in

0:11:28.360 --> 0:11:32.080
<v Speaker 3>dude cooking and screaming and yelling, there's like great inspiration

0:11:32.200 --> 0:11:34.800
<v Speaker 3>that hopefully will come to more folks joining us.

0:11:34.960 --> 0:11:36.960
<v Speaker 1>John Fraser restaurant ur and chef. He's the founder. If

0:11:37.000 --> 0:11:39.000
<v Speaker 1>you have restaurants, we love it when you join us.

0:11:39.080 --> 0:11:39.720
<v Speaker 1>Thanks so much.

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<v Speaker 4>This is Blueberg, all right, Tim, So the hype in

0:11:44.360 --> 0:11:48.040
<v Speaker 4>artificial intelligence, as we know, is everywhere. But what about

0:11:48.120 --> 0:11:53.080
<v Speaker 4>when it comes to the intersection of AI and retailers. Well,

0:11:53.120 --> 0:11:55.760
<v Speaker 4>there is a company that is powered by AI that

0:11:55.840 --> 0:11:58.640
<v Speaker 4>is aiming to bridge that gap and makes selling easier

0:11:58.679 --> 0:12:01.920
<v Speaker 4>for companies, especially when you're talking about Bloomingdale's, Macy's, the

0:12:01.960 --> 0:12:05.920
<v Speaker 4>gap back to school purchases obviously starting up right now

0:12:06.200 --> 0:12:09.439
<v Speaker 4>and then also ahead of the holiday shopping season. Joining

0:12:09.480 --> 0:12:12.520
<v Speaker 4>us now is Perva Gupta, co founder and chief executive

0:12:12.559 --> 0:12:17.559
<v Speaker 4>officer at lily AI, joining us on zoom from Palo Alto, California.

0:12:17.679 --> 0:12:20.679
<v Speaker 4>Thank you so much for joining us. Talk to us

0:12:20.720 --> 0:12:23.880
<v Speaker 4>about what your company is and what you do, just

0:12:23.920 --> 0:12:24.880
<v Speaker 4>to set the scene here.

0:12:26.559 --> 0:12:28.280
<v Speaker 5>Thank you so much for inviting me. Jess, It's so

0:12:28.360 --> 0:12:29.079
<v Speaker 5>nice to be here.

0:12:30.000 --> 0:12:30.320
<v Speaker 2>Yeah.

0:12:30.360 --> 0:12:34.960
<v Speaker 5>So my company is, it's called lily AI. We are

0:12:35.200 --> 0:12:37.520
<v Speaker 5>working with some of the largest brands and retailers. But

0:12:37.600 --> 0:12:40.760
<v Speaker 5>let me ask you a question. I'm sure you shop

0:12:40.880 --> 0:12:44.280
<v Speaker 5>online all the time, and I do.

0:12:44.360 --> 0:12:45.120
<v Speaker 4>I've gone through this.

0:12:46.800 --> 0:12:48.720
<v Speaker 5>Have you gone through an experience when you're looking for

0:12:48.760 --> 0:12:51.440
<v Speaker 5>something as simple as a navy blue sweatshirt and you

0:12:51.760 --> 0:12:55.760
<v Speaker 5>are getting no results or you're getting even worse. Adalypsticks

0:12:55.840 --> 0:13:02.000
<v Speaker 5>and slippers, and so it's not that they don't have

0:13:02.200 --> 0:13:05.679
<v Speaker 5>navy blue sweatshirts. That's a basic search. But the reason

0:13:05.679 --> 0:13:10.520
<v Speaker 5>why that's happening today is because the way that the product,

0:13:11.160 --> 0:13:13.400
<v Speaker 5>the way this product or the way the products are

0:13:13.400 --> 0:13:17.640
<v Speaker 5>described at retailers are in the language of midnight French

0:13:17.760 --> 0:13:22.000
<v Speaker 5>terry at leisure. So like, that's why you're not finding

0:13:22.000 --> 0:13:25.080
<v Speaker 5>it because it's not called navy blue sweatshirt. It's not

0:13:25.200 --> 0:13:29.160
<v Speaker 5>labeled in the language that us as consumers use. And

0:13:29.240 --> 0:13:31.920
<v Speaker 5>so that's what Lily AI does. We are using AI

0:13:32.040 --> 0:13:36.280
<v Speaker 5>to bridge the gap between retailers speak and consumers speak

0:13:36.360 --> 0:13:41.079
<v Speaker 5>in retail and this is the perfect type of problem

0:13:41.240 --> 0:13:44.480
<v Speaker 5>that AI can come in and help solve. To understand

0:13:44.480 --> 0:13:48.040
<v Speaker 5>the language of the consumer, use AI to extract those

0:13:48.080 --> 0:13:51.640
<v Speaker 5>attributes out of the images of the product catalog and

0:13:51.679 --> 0:13:54.360
<v Speaker 5>then help the consumer experiences.

0:13:54.480 --> 0:13:56.920
<v Speaker 1>Okay, so you got to explain to me, like I'm

0:13:56.920 --> 0:13:59.720
<v Speaker 1>a five year old, how AI is being used here

0:13:59.760 --> 0:14:03.600
<v Speaker 1>and how it's training itself. It's retraining itself. Take us

0:14:03.600 --> 0:14:04.440
<v Speaker 1>through the basics.

0:14:05.400 --> 0:14:09.320
<v Speaker 5>Absolutely. I'm also happy to go a little bit further

0:14:09.440 --> 0:14:12.400
<v Speaker 5>up here with all the hype in AI to sort

0:14:12.400 --> 0:14:15.200
<v Speaker 5>of also a sept the context. I mean AI is

0:14:15.240 --> 0:14:19.920
<v Speaker 5>being used for years now. It literally started in nineteen fifties,

0:14:20.800 --> 0:14:26.000
<v Speaker 5>and fundamentally, AI is when machines are mimicking human intelligence, right,

0:14:26.480 --> 0:14:29.960
<v Speaker 5>and recently, generative AI is all the hype everybody's out

0:14:30.000 --> 0:14:33.200
<v Speaker 5>of chat GPD. That's one type of AI. There are

0:14:33.200 --> 0:14:36.320
<v Speaker 5>so many other types of AI, like computer vision, natural

0:14:36.360 --> 0:14:39.320
<v Speaker 5>language processing, all of those types of things. And so

0:14:39.560 --> 0:14:43.120
<v Speaker 5>now coming back to your question around how we use AI,

0:14:43.720 --> 0:14:46.920
<v Speaker 5>we have basically built we use a type of AI

0:14:46.960 --> 0:14:50.760
<v Speaker 5>called discriminative AI where we have created a lot of

0:14:50.880 --> 0:14:54.280
<v Speaker 5>clean training data where merchants on our team over the

0:14:54.360 --> 0:14:58.560
<v Speaker 5>last few years have manually labeled all of the products

0:14:58.560 --> 0:15:01.000
<v Speaker 5>and added a lot of the language of the consumer

0:15:01.400 --> 0:15:04.160
<v Speaker 5>on every single product.

0:15:04.200 --> 0:15:06.800
<v Speaker 4>To do that kind of process seems very patient.

0:15:08.440 --> 0:15:11.920
<v Speaker 5>Yes, it's a very very difficult process where you almost

0:15:11.960 --> 0:15:15.280
<v Speaker 5>have to create the definitions in fashion in a very

0:15:15.360 --> 0:15:19.880
<v Speaker 5>very clean mathematical way, where you know our merchants know

0:15:20.080 --> 0:15:23.640
<v Speaker 5>the difference between boho and Boho chic, for example. Right,

0:15:24.520 --> 0:15:27.680
<v Speaker 5>it needs to be really really clean data for AI

0:15:27.760 --> 0:15:31.320
<v Speaker 5>to understand all of the different types of language that

0:15:31.440 --> 0:15:35.600
<v Speaker 5>our consumers use or just overall consumers use. And so

0:15:35.920 --> 0:15:38.480
<v Speaker 5>it's it's it's not an easy thing. It's a very

0:15:38.560 --> 0:15:41.000
<v Speaker 5>very difficult labors process that we have gone through to

0:15:41.040 --> 0:15:44.440
<v Speaker 5>be able to create all this clean training data which

0:15:44.520 --> 0:15:47.560
<v Speaker 5>we then fed to our AI engines which now do

0:15:48.000 --> 0:15:50.840
<v Speaker 5>which are now doing a really good job of predicting

0:15:51.760 --> 0:15:55.760
<v Speaker 5>all of these attributes on these products, which is basically

0:15:55.840 --> 0:15:59.040
<v Speaker 5>the images. So in short, we have built the technology

0:15:59.120 --> 0:16:03.400
<v Speaker 5>that can extract attributes in the language that consumers understand

0:16:03.480 --> 0:16:04.320
<v Speaker 5>out of images.

0:16:05.080 --> 0:16:07.280
<v Speaker 1>So how do you how do you build these relationships

0:16:07.360 --> 0:16:10.400
<v Speaker 1>with the biggest retailers out there, including Bloomingdale's, Thread up

0:16:10.440 --> 0:16:11.480
<v Speaker 1>the Gap? How do you do that?

0:16:13.000 --> 0:16:16.240
<v Speaker 5>Yeah, it's it's been a super exciting journey for us

0:16:16.320 --> 0:16:19.080
<v Speaker 5>where we've had the we've had the pleasure of working

0:16:19.120 --> 0:16:22.200
<v Speaker 5>with some of the most iconic brands and retailers in

0:16:22.240 --> 0:16:28.840
<v Speaker 5>the world. It's the The overall pitch of Lily is

0:16:28.880 --> 0:16:31.160
<v Speaker 5>as simple as you know, going to any of these

0:16:31.200 --> 0:16:33.600
<v Speaker 5>sites where we are able to show in the search bar,

0:16:34.200 --> 0:16:36.760
<v Speaker 5>do some searches and show to anybody that there's so

0:16:37.000 --> 0:16:40.200
<v Speaker 5>much that is there's there's so much money being left

0:16:40.200 --> 0:16:43.560
<v Speaker 5>on the table, which is a like lots of revenue

0:16:43.560 --> 0:16:46.560
<v Speaker 5>for the business, but also from a consumer experience, perspective,

0:16:46.560 --> 0:16:49.960
<v Speaker 5>there's so much that can be done to to make

0:16:49.960 --> 0:16:52.920
<v Speaker 5>sure that your consumers have a really good shopping experience.

0:16:53.000 --> 0:16:56.400
<v Speaker 5>And so it's not that difficult to sort of explain

0:16:56.440 --> 0:17:00.000
<v Speaker 5>to some of these large brands and retailers the opportunity here,

0:17:00.080 --> 0:17:04.920
<v Speaker 5>because this is like a search example like this for example,

0:17:05.040 --> 0:17:07.800
<v Speaker 5>is very easy to understand. I think the bigger picture

0:17:07.880 --> 0:17:10.800
<v Speaker 5>for all of these brands and retailers is also and

0:17:11.240 --> 0:17:13.480
<v Speaker 5>they get it, is that this kind of a gap

0:17:13.640 --> 0:17:17.119
<v Speaker 5>between the merchant's speak and the consumer speak not just

0:17:17.200 --> 0:17:21.320
<v Speaker 5>impacts their on site search, but actually impacts their entire business.

0:17:21.400 --> 0:17:23.600
<v Speaker 5>So all the way from when they are forecasting demand

0:17:23.640 --> 0:17:26.560
<v Speaker 5>about a product to when they are doing all the advertising,

0:17:28.320 --> 0:17:31.680
<v Speaker 5>you know, for their entire catalog, to when they're selling

0:17:31.720 --> 0:17:34.960
<v Speaker 5>the product on their e commerce store, throughout the entire

0:17:35.040 --> 0:17:38.240
<v Speaker 5>retail value chain. Because the fact that that navy blue

0:17:38.280 --> 0:17:41.840
<v Speaker 5>sweatshirt is is not called that way, is not understood

0:17:41.840 --> 0:17:44.640
<v Speaker 5>that way, and it's understood in different ways, it has

0:17:44.680 --> 0:17:48.840
<v Speaker 5>a massive impact on the entire business. And so that's

0:17:48.880 --> 0:17:51.040
<v Speaker 5>the other thing we are able to drive and make

0:17:51.080 --> 0:17:53.359
<v Speaker 5>all of these brands and retailers understand, which makes our

0:17:53.400 --> 0:17:54.920
<v Speaker 5>job a little bit easier are.

0:17:54.920 --> 0:17:56.960
<v Speaker 4>You able to gauge shopping trends?

0:17:58.359 --> 0:18:02.760
<v Speaker 5>Absolutely? You know, that's that's that's a super exciting part

0:18:02.760 --> 0:18:05.880
<v Speaker 5>of our business. You know, trends, especially in the lifestyle category,

0:18:06.000 --> 0:18:08.960
<v Speaker 5>make a big part of what what keeps the consumer,

0:18:09.560 --> 0:18:13.560
<v Speaker 5>you know, going back to to just shopping. And like

0:18:13.720 --> 0:18:18.080
<v Speaker 5>this summer, Barbiecore was a massive trend. Before that, there

0:18:18.160 --> 0:18:21.800
<v Speaker 5>was quiet luxury, and as we are heading into the

0:18:21.800 --> 0:18:25.159
<v Speaker 5>New York Fashion Week, there is going to be like, uh,

0:18:25.200 --> 0:18:28.920
<v Speaker 5>there's going to be sturdy comfort. Uh, there's there's all

0:18:28.960 --> 0:18:31.320
<v Speaker 5>sorts of trends, and we are in the business of

0:18:31.480 --> 0:18:35.720
<v Speaker 5>operationalizing these trends, and so like if if we take

0:18:35.760 --> 0:18:40.000
<v Speaker 5>Barbiicore as an example, that trend became a trend, you know,

0:18:40.119 --> 0:18:42.639
<v Speaker 5>even before the movie came out, and so like feb

0:18:42.720 --> 0:18:46.000
<v Speaker 5>March is when that start, when when that term started

0:18:46.040 --> 0:18:48.800
<v Speaker 5>becoming a massive trend. And so we were able to

0:18:48.840 --> 0:18:53.159
<v Speaker 5>help all of our customers attribute their entire catalog appropriately

0:18:53.680 --> 0:18:57.280
<v Speaker 5>with Barbiecore, so that when consumers were searching for these things,

0:18:58.160 --> 0:19:01.400
<v Speaker 5>they were absolutely able to find thing that they liked.

0:19:02.040 --> 0:19:03.639
<v Speaker 4>Well, thank you so much for your time. We'll have

0:19:03.640 --> 0:19:05.760
<v Speaker 4>to have you back again to talk about the shopping

0:19:05.800 --> 0:19:09.480
<v Speaker 4>trends and the consumer. Pervagupta, co founder and chief executive

0:19:09.520 --> 0:19:11.640
<v Speaker 4>officer at lily AI.

0:19:12.080 --> 0:19:12.960
<v Speaker 5>We'll have more coming up.

0:19:13.160 --> 0:19:14.159
<v Speaker 4>This is Bloomberg