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Hallo en welkom iedereen bij de Bright
Signal Podcast.

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Ik hier met mijn vriend en co-host
Charlotte.

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We hebben het plezier van het interviewen
Heidi van Dijk van Atena Studio.

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Atena Studio is the first AI native
operating system for concept to market
teams,

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specifically for the fashion industry.

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Let's go to the interview.

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We are here with Heidi, co founder of
Athena Studio Heidi,

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can you introduce yourself?

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~ of course Charlotte. first of all,

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thank you for having me. ~ hi Bart.

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Happy to be here. ~ yeah, so as a short
introduction,

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~ I'm Heidi, I'm co-founder and CEO of
Athena Studio,

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Belgium. And so my background is ~ in
tech.

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I actually started my career

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At different tech startups and scale-ups,

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including VP in Belgium ~ and Rocket
Internet in Southeast Asia.

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Then started my career at McKinsey and
Company as a consultant,

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but in their fashion practice.

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So I've done over six years working with
global fashion brands,

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advising them on digital and technology
topics.

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And that then actually led me to
discovering the challenges that I

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saw that fashion brands have in bringing
collections to market,

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which led me to found Latina Studio.

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Yeah,

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because with the Athena Studio you focus a
lot on the fashion industry,

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right? Like ~ c can you explain a bit what
what it is that the Athena Studio offers?

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Absolutely. So ATINA Studio is an AI
native operating layer for fashion concept

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to market. So what that means,

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very simply explained, the teams in
fashion brands that bring

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new collections to market are the
designers,

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the sourcing teams, the merchandising
teams,

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it's typically hundreds of different
people working on thousands

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of different new styles, but they all work
in different systems,

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files, and different processes.

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And so what Athena Studio does is
basically

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Bringing the information across different
that is currently fragmented together

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so that all teams work at any stage in
time from the same information.

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And the reason that that's really
important is on the one hand,

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~ you know, these teams are currently
spending over half their time moving data
from

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one system to the other, reconciling what
the latest status is,

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which is absolutely not what they signed
up for.

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But more importantly, ~ these brands are
bleeding millions of dollars every season

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~ because they make the wrong decisions.

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And maybe I'm gonna ask a few dumb
questions because this is a this is a

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Yeah, you can go.

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domain w in which I'm definitely not an
expert.

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but if I if I understand you correctly,

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like you're focusing on this let's say
fashion life cycle

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~ process where like like you go from I
don't know I don't look

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choosing the right garments, creating a
design,

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creating a physical sample, like ~
probably a very iterative process,

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a lot of different people involved at
every different stage.

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Yes. So if you think of and I'll go back
to fashion,

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but it's it's actually applicable to every
industry that brings physical products

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to market.

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Mm-hmm.

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You have the whole process of of starting
with indeed planning what do

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you want to bring to markets in the next
season.

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And when I say season, you have to think
about it traditionally this is summer,

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spring, winter, autumn. A big brand
typically has four different seasons per
year.

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Some like HM, Zahra, they will have more.

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Now there are multiple teams with each
their own role.

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Merchandising teams will plan what to
produce based on historic sales

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and trends and future trends data.

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Designers work on designing what that
would look like.

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Product and sourcing teams work with
suppliers to get it actually realized,

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have the physical samples, and based on
those,

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then decide which final items make it to
the collection.

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So all these themes, it sounds sequential,

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but the challenge of this all is that
there's a timeline attached to this,

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and therefore all this work overlaps with
one another in addition to

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all the seasons having overlapping phases.

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So it's a very complicated
cross-functional process that needs yeah,

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that needs a lot of coordination,

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which costs manual time and which
typically results in rework and delays.

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Yeah. And maybe because you mentioned
seasons,

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like a delay is probably also very costly
because they're then not in

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the shop for at the beginning of the
season and like ~

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~ the biggest cost drivers of that would
be on the one hand,

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if you're delayed, you will do everything
you can to speed up.

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So items would be shipped over air instead
of boat,

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Mm. Okay, interesting.

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which is

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a massive cost cost increase. Indeed,

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every time you you land something late in
the market,

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that impacts your your revenue forecast
that we're impacting also your your
margins.

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So that's a big cost. And I think in
addition to that,

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not just the delays, but the fact that you
if you cannot see everything together,

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you cannot opt.

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So you might have some teams that are
using all very similar fabrics that could
have

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been one, but because they didn't see it,

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they're using a hundred, and that way you
you're spending way too much.

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Hmm. Interesting. And like practically for
the user of Athena the Athena platform,

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like

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Yes.

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who who is the user and what do they w
what are they what information do they

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get to better coordinate the

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Yes.

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process?

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So if you the way that it works,

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so we sell to brands, not to one team,

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but typically to all of these teams at
once that are involved in that concept

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to market. So we will say we track the
next collection,

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spring twenty twenty-eight, because this
thing is over twelve months in advance.

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Spring twenty twenty-eight. and we then we
built in the software integrates with

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the tools they already use. So we
integrate with Microsoft SharePoint,

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Teams.

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Outlook, their PLM systems, which is
basically their system of records

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for product data. Their mirror boards,

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because that's typically where designers
collaborate on all

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the different designs and the colors that
they want.

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So basically, ATINA then pulls that data
together,

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and that's where we bring the value.

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We have specialized AI ~ workflows and
agents that can turn that data into

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a unified view.

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And from that unified view, our AI
assistant,

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Athena, who lives in Microsoft Teams,

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basically ~ automates work within
SharePoint,

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PLM, or Miro, sends alerts when things go
off track,

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yeah, and keeps teams on track.

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Okay, okay. So it's a bit like a like a
layer on top of all kind of decess

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Yes.

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decentralized systems. Like probably these
systems are like for a

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for a very specific part of the process
they are an expert,

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but you don't have this overarching view
and that's what you're trying

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to build with with Athena.

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Yes. And it's also

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We we opted to do this rather than
introducing a one-stop shop platform
because that

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was basically what has been done the last
decades.

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we've tried to do that with PLM,

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we've tried to do that with point
solutions.

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The problem is it always benefits one team
and then the other teams maybe

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a little bit. So everyone falls back to
their Excel sheets,

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fall back falls back to emails,

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and we just acknowledge that and said,

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Okay, let people work the way they work.

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We l we build over that. Like we or data
layer.

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lives on top of that, which means that we
are agnostic to what you use.

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We just make sure that the data speaks to
each other and that we

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can automate that data syncing.

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So

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the idea came then specifically from your
experience from at Mackenzie,

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that you saw this was an actual problem
that you had to solve over and over again?

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Or when was the moment you thought,

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let's let's create our own company,

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let's solve this issue?

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~ yeah, so I think when was the moment I
wanted to create my own company

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was when I was already before McKinsey.

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I I actually with my work at VP and Rocket
Internet,

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I loved that so much. I always said one
day I'll build my own company,

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a tech company. I didn't know what to do
yet.

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Within McKinsey, I rolled into fashion.

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I loved the industry for the challenges it
brings along.

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It is a complex

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Industry, it's not rocket science,

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you may say, but there's so many
different,

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you know, global supply chains,

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so many different teams. Everyone wears
clothes,

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you know.

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And I started working in that space.

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So I saw collection development was a very
critical process with that

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was still run on very old school
solutions.

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However, I just started at Tina Studio
first to bring with a different vision,

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to bring sustainability into the
collection development,

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the earlier design stages. That was the
initial goal.

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There was a lot of European regulations
coming up,

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everybody focused on reporting,

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nobody focused on tackling design from the
start.

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~

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We did that for a while. We did some
projects with with enterprise brands too,

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but we realized effectively this the data
that we needed to do this

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was spread out everywhere. And at that
point,

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there was not a clear solution other than
introducing yet another platform.

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But that then collided actually with this
the first wave of AI,

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I would say, that started hitting the
developers first.

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So we were coding, ~ we saw how we could
use AI to completely change the

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way that we were doing work, and that then
actually

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Brought together inspired us to start at
Tina Studio as a data layer,

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as an operating layer.

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So ~ and when did you officially start
Tennis Studio?

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yes, company was registered in twenty
three with the other products.

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We pivoted in twenty twenty five.

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yeah, so.

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was it har was it a difficult pivot to
make?

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yes and no. so I still deeply believe that
~ sustainability is

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an honorable mission and I do think brands
should work on it.

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I also saw that ~ one, there was no budget
for a strategic use for sustainability,

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in all honesty. Two, it wasn't possible
with the data that wasn't there.

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And so I really believe in the mission we
do now.

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So I don't think it was hard. Like I think
it took me a bit longer than

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it should have.

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Because you always you like what you do,

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you see some traction, you say,

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that's good, but then the moment we
changed,

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I was very happy and I never looked back.

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Yeah, interesting. I think ~ for for some
reason the the there was there used

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to be a big hype on sustainability and for
some reason you don't really hear about

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it these days anymore. unless it has a
real impact on on your bottom line.

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And I and and I you could

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Yes.

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argue like with better coordination
through a system like Athena,

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like that there is less waste in the
process,

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right? And this it does have these side
effects on sustainability which

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are very positive.

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Yes, ~ so I'm

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Also happy to say that one of our big
customers that are in the US,

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we did add circular design as a dimension
to what we did because once

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you have that unified data layer,

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they had their own framework of how they
measure circular design

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and what their targets are. And we then
basically translated those calculations.

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~ we we applied them to the unified data
layer and we sent alerts

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to the designers and the product teams to
let them know how compliant they were

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and how they could improve.

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interesting. Yeah.

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that's something I do really love about
what we do today is once you have that
view,

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that single view, we see a lot we we get a
lot of inbound from our

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own users that say, ~ this would be
extremely helpful.

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This costs me so much time. Even things
that the people that like or or

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Clients or buyers, they don't even see
that this is an issue at ~ at

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the lower at the lower scale, I'd say.

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and that's very often the biggest wins.

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And what they buy is is a software product
or is more like a layer and advice

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So if you if you look back at how ~
traditional SaaS was very simple per user,

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you know, per month or per year,

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you have a price. For us, it's slightly
different,

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but it is sold as a software product.

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We will basically look at what is the
scope that ATINA is tracking.

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So let's say we start working with you,

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we do the next two seasons. how big is the
company?

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We look at how many styles approximately.

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So we don't price per style, but it gives
us an indication of the volume

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of your collections.

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And then we look, do we plug in
merchandising angle,

234
00:11:53,385 --> 00:11:57,486
design, and sourcing? Okay, those three
elements together,

235
00:11:57,486 --> 00:12:00,227
how many seasons, volume, and which teams,

236
00:12:00,227 --> 00:12:03,848
that drives a price. And typically you
have annual prices,

237
00:12:03,848 --> 00:12:06,428
you say, okay, we want to do all the
seasons,

238
00:12:06,428 --> 00:12:08,569
all the teams, that's then the full scope,

239
00:12:08,569 --> 00:12:12,230
and you can start with a smaller scope to
test before you roll out.

240
00:12:12,730 --> 00:12:17,432
What what are your ~ your typical brands
that you're like are you targeting like

241
00:12:17,432 --> 00:12:19,484
cer certain like large volume brands,

242
00:12:19,484 --> 00:12:24,758
are also niche players? Like what is the
easiest to sell this value proposition to?

243
00:12:25,401 --> 00:12:26,401
So I

244
00:12:26,537 --> 00:12:29,048
Built from my experience at McKinsey's,

245
00:12:29,048 --> 00:12:31,189
so that was enterprise customers,

246
00:12:31,189 --> 00:12:34,170
the multi-billion dollar brands that I
think we all know.

247
00:12:34,170 --> 00:12:36,891
~ and that is still our focus today.

248
00:12:36,891 --> 00:12:40,073
So I I wouldn't say that's the easier.

249
00:12:40,073 --> 00:12:41,924
I think the trade-off is simple.

250
00:12:41,924 --> 00:12:44,625
Like as always, your sales cycles will be
longer,

251
00:12:44,625 --> 00:12:48,048
their budget is also bigger. it's a
trade-off you make,

252
00:12:48,048 --> 00:12:52,129
and we have now also really built a
solution for their pain points,

253
00:12:52,129 --> 00:12:55,551
which is relevant because sometimes I do
speak to smaller brands.

254
00:12:55,771 --> 00:12:59,893
And I see if if your whole team still fits
in a room or even two rooms,

255
00:12:59,893 --> 00:13:04,636
you have less of a challenge than when
part of your design team is in Asia,

256
00:13:04,636 --> 00:13:08,918
and you know, you have that complexity of
distribution of global distribution.

257
00:13:08,918 --> 00:13:11,700
So, yeah, for us the focus today is
enterprise brands.

258
00:13:11,700 --> 00:13:15,141
~ we do all segments. We have a lingerie
player,

259
00:13:15,141 --> 00:13:18,203
~ we have sportswear, we have lifestyle.

260
00:13:18,203 --> 00:13:19,754
So we do the different segments,

261
00:13:19,754 --> 00:13:21,665
but the size is the common denominator.

262
00:13:22,165 --> 00:13:23,165
Okay,

263
00:13:22,605 --> 00:13:24,817
interesting. And very recognizable.

264
00:13:24,817 --> 00:13:30,438
~ I I I do some work some advisory work ~
on around more the let's

265
00:13:30,438 --> 00:13:32,059
say the software development lifecycle,

266
00:13:32,059 --> 00:13:33,801
which I think there are some parallels to.

267
00:13:33,801 --> 00:13:35,162
~ and

268
00:13:34,699 --> 00:13:35,699
Yes.

269
00:13:35,162 --> 00:13:39,918
the advice is more on like how can you go
towards an AI native

270
00:13:39,918 --> 00:13:41,657
~ software development lifecycle.

271
00:13:41,657 --> 00:13:45,010
and you notice like that the bigger the
companies are,

272
00:13:45,010 --> 00:13:46,010
the

273
00:13:46,158 --> 00:13:51,711
harder it becomes for them to like define
~ even like what does a good process look

274
00:13:51,711 --> 00:13:53,322
like, right? And then let

275
00:13:52,440 --> 00:13:53,440
Yes.

276
00:13:53,322 --> 00:13:57,083
alone like how do we speed up this process
or make it more intelligent

277
00:13:57,083 --> 00:13:59,054
by adding adding AI. and

278
00:13:58,788 --> 00:13:59,788
yeah.

279
00:13:59,054 --> 00:14:02,096
indeed like if it's a small team and
everybody's in the same room and someone

280
00:14:02,096 --> 00:14:03,996
has a good idea, then people will follow
the good idea.

281
00:14:03,996 --> 00:14:06,708
But the the the more people you add to a
process,

282
00:14:06,708 --> 00:14:08,439
the hard the harder it gets to coordinate.

283
00:14:09,065 --> 00:14:11,136
And and I'm I'm an honest I'm I'm not

284
00:14:11,369 --> 00:14:13,280
By I'm not by nature a salesperson,

285
00:14:13,280 --> 00:14:16,101
so I mean I love and I'm passionate about
what we do and and that

286
00:14:16,101 --> 00:14:19,503
is what closes deals. but I think I'm an
honest salesperson.

287
00:14:19,503 --> 00:14:22,994
into when I speak to brands and they say
this is our biggest problem and

288
00:14:22,994 --> 00:14:27,046
I see that it's something that you know
CLOT or if they have an open AI contract,

289
00:14:27,046 --> 00:14:30,047
their open AI, the GPT work could fix,

290
00:14:30,047 --> 00:14:34,750
then I also don't sell Athena because then
we're bound to lose anyway.

291
00:14:34,574 --> 00:14:35,661
Hm, interesting.

292
00:14:34,750 --> 00:14:35,860
So I think that's

293
00:14:35,860 --> 00:14:39,572
also a critical thing. ~ I've I've spoken
to smaller brands where I'm like,

294
00:14:39,655 --> 00:14:42,134
~ for this you can perfectly use cloths.

295
00:14:42,134 --> 00:14:43,799
That's not where we would bring value.

296
00:14:44,013 --> 00:14:49,188
Hmm. Yeah. And are you b because it's like
I think that's an evolution that

297
00:14:49,188 --> 00:14:51,811
you see as well. Like and that's an
interesting one.

298
00:14:51,811 --> 00:14:56,886
It's it's a very quickly evolving field
where let's say a year ago

299
00:14:56,947 --> 00:15:01,261
you would have products ~ solving a
challenge that something like Claude
couldn't

300
00:15:01,261 --> 00:15:02,261
do yet.

301
00:15:02,112 --> 00:15:03,112
~

302
00:15:02,123 --> 00:15:03,123
Yes.

303
00:15:02,873 --> 00:15:06,286
let's say for example in software in the
software development lifecycle would

304
00:15:06,286 --> 00:15:11,700
be there is this custom Kanban board to ~
so to help people coordinate,

305
00:15:11,700 --> 00:15:14,002
but you had to have something custom in
place.

306
00:15:14,002 --> 00:15:19,277
But now AI in most cases is that good that
it can just basically orchestrate

307
00:15:19,277 --> 00:15:20,608
existing tools that you already have,

308
00:15:20,608 --> 00:15:21,699
~

309
00:15:20,768 --> 00:15:21,768
Yes.

310
00:15:21,699 --> 00:15:23,621
and you don't need this extra layer on
top.

311
00:15:23,621 --> 00:15:26,023
Like, do you like how how do you look at
this field?

312
00:15:26,023 --> 00:15:27,554
Like it because it's it's it's an

313
00:15:27,949 --> 00:15:29,997
It's challenging that it's evolving so
quickly,

314
00:15:29,997 --> 00:15:30,997
right?

315
00:15:30,779 --> 00:15:32,935
~ it's challeng yeah, I agree.

316
00:15:32,935 --> 00:15:39,029
first of all, I agree. I think how I look
at it is I'm I use a lot of AI.

317
00:15:39,295 --> 00:15:42,947
Personally to manage my work as CEO.

318
00:15:42,947 --> 00:15:45,148
My tech team uses a lot of AI and we,

319
00:15:45,148 --> 00:15:49,750
as you mentioned before, we focus a
hundred percent on having AI native
processes

320
00:15:49,750 --> 00:15:53,031
so that we're not head count growth let
headcount led growth,

321
00:15:53,031 --> 00:15:56,823
but that we can really scale. but that's
also a good exercise for me to see,

322
00:15:56,823 --> 00:16:00,324
okay, wow, Claude got way better in doing
this or that.

323
00:16:00,324 --> 00:16:02,735
so I stay on track of of what I can do,

324
00:16:02,735 --> 00:16:04,917
what and what they can do versus Athena.

325
00:16:04,917 --> 00:16:07,968
So for me, I think the re the the key
difference that

326
00:16:07,998 --> 00:16:11,343
We always focus on, you hear that a lot,

327
00:16:11,343 --> 00:16:14,749
I'm sure, but is really all around
building that correct context layer.

328
00:16:14,749 --> 00:16:15,749
So

329
00:16:15,495 --> 00:16:18,037
We make sure that it's not every time
again,

330
00:16:18,037 --> 00:16:21,099
you know, it's an agent analyzing
something and doing something with

331
00:16:21,099 --> 00:16:25,572
it in a repeated manner. That's not
helpful because that is something any

332
00:16:25,572 --> 00:16:29,184
LLM can do and that Cloud and OpenAI will
be able to offer.

333
00:16:29,184 --> 00:16:31,956
I think for us, we really pre-build,

334
00:16:31,956 --> 00:16:33,788
we do a lot of pre-processing as well,

335
00:16:33,788 --> 00:16:37,580
which d improves accuracy, efficiency,

336
00:16:37,580 --> 00:16:41,412
~ drives down cost as well. and we create
that context layer that is really,

337
00:16:41,412 --> 00:16:42,412
yeah.

338
00:16:42,643 --> 00:16:45,354
specially built by us for fashion concept
to market.

339
00:16:45,354 --> 00:16:48,955
That means that you have also this data
layer that exists.

340
00:16:48,955 --> 00:16:51,776
It's not something that is how do you say
transient,

341
00:16:51,776 --> 00:16:57,197
like it's there. And that is so far
something yeah we see nobody else can do.

342
00:16:57,197 --> 00:17:00,658
We're also building with every client
Athena gets better because

343
00:17:00,658 --> 00:17:03,039
she learns more exceptions, more
challenges,

344
00:17:03,039 --> 00:17:06,340
more different ways of managing a calendar
for a collection.

345
00:17:06,340 --> 00:17:09,321
So yeah, that's really how we ~ how we
position ourselves.

346
00:17:09,740 --> 00:17:13,464
Yeah, interesting. So you in in a way like
Athena became becomes a

347
00:17:13,464 --> 00:17:18,981
bit like the knowledge fault for a brand
around its like product lifecycle

348
00:17:18,981 --> 00:17:19,981
management.

349
00:17:19,883 --> 00:17:21,704
Yeah, the last brand I showed something
to,

350
00:17:21,704 --> 00:17:24,816
I showed a demo and and and their head of
product said,

351
00:17:24,816 --> 00:17:27,978
wait, you're basically the brain of our
brand then.

352
00:17:27,136 --> 00:17:28,711
Yeah. Yeah.

353
00:17:27,978 --> 00:17:28,978
I said, Yes,

354
00:17:28,789 --> 00:17:30,660
but ~ we don't touch, for instance,

355
00:17:30,660 --> 00:17:32,962
marketing at the moment or sales in your
stores.

356
00:17:32,962 --> 00:17:35,994
We're very much the brain of that new
collection before

357
00:17:35,542 --> 00:17:36,542
Hmm.

358
00:17:35,994 --> 00:17:37,004
it reaches the shelves.

359
00:17:37,375 --> 00:17:39,840
Yeah. And that's also something

360
00:17:38,750 --> 00:17:39,750
Is it then also?

361
00:17:39,840 --> 00:17:44,179
that's t where like typically I think
that's an active like for a

362
00:17:44,179 --> 00:17:45,481
lot of companies that are on

363
00:17:46,486 --> 00:17:47,486
that

364
00:17:46,676 --> 00:17:48,097
are following these evolutions in AI,

365
00:17:48,097 --> 00:17:51,448
like it's an active question. Like how do
we make sure that the knowledge

366
00:17:51,448 --> 00:17:53,709
is that is now in the heads of people,

367
00:17:53,709 --> 00:17:56,991
how do we make sure that we that it
becomes more explicit and and that

368
00:17:57,151 --> 00:18:00,982
a system like has access to that knowledge
and that's to some extent also what

369
00:18:00,982 --> 00:18:03,183
the TNA does, right? Like to make the
knowledge in their

370
00:18:02,721 --> 00:18:03,721
Yeah.

371
00:18:03,183 --> 00:18:04,183
projects explicit.

372
00:18:04,802 --> 00:18:06,208
And and the other thing is

373
00:18:06,449 --> 00:18:09,961
That very often, especially executives,

374
00:18:09,961 --> 00:18:13,172
they will ask us, but then Athena can help
with completely transforming

375
00:18:13,172 --> 00:18:15,833
the way this is done. We wanna have less
Excel,

376
00:18:15,833 --> 00:18:19,275
we wanna have less email reporting and all
of this.

377
00:18:19,275 --> 00:18:22,636
And I always tell them, building from my
consulting background,

378
00:18:22,636 --> 00:18:25,897
I say, let's do the first year or half a
year,

379
00:18:25,897 --> 00:18:30,079
but like let's definitely start with just
Athena plugging in and capturing

380
00:18:30,079 --> 00:18:31,860
the way things work. Because I can tell
you,

381
00:18:31,860 --> 00:18:34,341
you don't even know how your processes
work.

382
00:18:34,341 --> 00:18:36,362
We will discover that by plugging in and

383
00:18:36,402 --> 00:18:39,574
tracking and once you know how everything
works that's when

384
00:18:39,574 --> 00:18:43,275
you can start transforming. But otherwise
you're only fixing if

385
00:18:43,275 --> 00:18:45,646
you know something for 50% and you want to
fix it,

386
00:18:45,646 --> 00:18:47,207
yeah the result will not be there.

387
00:18:48,123 --> 00:18:49,123
Yeah.

388
00:18:48,874 --> 00:18:53,318
~ does ~ Athena then also work with like
the after the production space,

389
00:18:53,318 --> 00:18:55,189
like bas the product lifetime cycle.

390
00:18:55,189 --> 00:18:57,080
There's also big players on that,

391
00:18:57,080 --> 00:19:01,684
~ of software developers. Do they plug in
into that as well or is that

392
00:19:01,924 --> 00:19:03,265
an area you stay away from?

393
00:19:03,765 --> 00:19:06,549
Yes, so f it's it's not a yes no question,

394
00:19:06,549 --> 00:19:07,921
it's a timing question at

395
00:19:07,424 --> 00:19:08,424
Mm-hmm.

396
00:19:07,921 --> 00:19:08,921
the moment.

397
00:19:09,002 --> 00:19:12,143
It's we focus on a concept to market,

398
00:19:12,143 --> 00:19:15,005
which stops basically at the moment you
you have,

399
00:19:15,005 --> 00:19:17,216
you know, the production orders are in,

400
00:19:17,216 --> 00:19:20,747
the production is happening. The reason is
twofold.

401
00:19:20,747 --> 00:19:24,329
One, that is, it's not that that only
takes half a year,

402
00:19:24,329 --> 00:19:25,870
right? Given those four seasons,

403
00:19:25,870 --> 00:19:29,171
it's a full-time job. People are already
on the next two seasons.

404
00:19:29,171 --> 00:19:32,153
~ that's how it works. So that's one one
reason.

405
00:19:32,153 --> 00:19:35,975
The second is that we are open to expand
and we will,

406
00:19:35,975 --> 00:19:38,636
but at the moment that would be an
overload.

407
00:19:39,237 --> 00:19:41,440
This is already the most complicated
process.

408
00:19:41,440 --> 00:19:44,404
We want to win here and then you can
expand further.

409
00:19:44,904 --> 00:19:47,765
And do you see some of the bigger
companies entering your field or

410
00:19:47,765 --> 00:19:49,532
do you see yourself in it?

411
00:19:49,770 --> 00:19:53,752
Yes, ~ every PLM provider or system is now
AI native.

412
00:19:53,752 --> 00:19:59,334
so you would you would say I mean that's
also a question we get.

413
00:19:59,334 --> 00:20:05,207
~ but their their their base the way that
it started is completely the opposite.

414
00:20:05,207 --> 00:20:07,788
So I'm very I'm more I'm not afraid,

415
00:20:07,788 --> 00:20:09,279
but I'm more afraid, let's say,

416
00:20:09,279 --> 00:20:14,601
of of an open AI ~ looking at fashion
versus a traditional PLM system.

417
00:20:15,016 --> 00:20:18,178
Why? Because you had one their traditional
ERP,

418
00:20:18,178 --> 00:20:20,810
they built their whole model on
implementation,

419
00:20:20,810 --> 00:20:24,413
static system of record. Their job is also
to have that system

420
00:20:24,413 --> 00:20:25,744
of record that doesn't, you know,

421
00:20:25,744 --> 00:20:28,056
that stays reliable and everything.

422
00:20:28,840 --> 00:20:31,461
What we do is basically saying there's no
human touch.

423
00:20:31,461 --> 00:20:33,131
Like you change something in Excel,

424
00:20:33,131 --> 00:20:36,022
Atina's already picked it up and it's a
material change,

425
00:20:36,022 --> 00:20:38,323
we make sure the material tracker is
updated.

426
00:20:38,323 --> 00:20:40,744
it needs it impacts something in PLM,

427
00:20:40,744 --> 00:20:43,024
we make sure that update is pushed to PLM.

428
00:20:43,024 --> 00:20:46,124
PLM cannot. They will not be able to do
that.

429
00:20:46,124 --> 00:20:49,786
And even if they would, they would be
completely destroying their

430
00:20:49,786 --> 00:20:53,327
own business model, which relies on
implementation fees,

431
00:20:53,327 --> 00:20:54,327
you know.

432
00:20:53,891 --> 00:20:55,817
the customization that you do per
customer,

433
00:20:55,817 --> 00:20:57,502
which is hard-coded and rigid.

434
00:20:57,502 --> 00:21:01,734
So I think, yeah, that's the only ones
that we see moving into our fields.

435
00:21:02,234 --> 00:21:04,536
And it is like still quite a niche field,

436
00:21:04,536 --> 00:21:06,938
right? You're AI native in a quite a niche
field.

437
00:21:06,938 --> 00:21:09,150
which is it's definitely a differentiator,

438
00:21:09,150 --> 00:21:12,524
and like I think I was talking about
software development earlier,

439
00:21:12,524 --> 00:21:13,524
like it's I think in

440
00:21:12,847 --> 00:21:13,847
Yes.

441
00:21:13,234 --> 00:21:17,528
software development it's even still very
hard to find good AI native people,

442
00:21:17,528 --> 00:21:19,750
let alone ~ in something like fashion.

443
00:21:19,750 --> 00:21:22,533
I can I can imagine like just having the
skills there and being ~

444
00:21:22,533 --> 00:21:25,276
the ability to execute, like it's a big
differentiator.

445
00:21:26,112 --> 00:21:29,023
Yeah, and I even compare it. I like a lot
what you like what

446
00:21:29,023 --> 00:21:32,174
you said on development. If you can almost
think about it as

447
00:21:32,394 --> 00:21:34,334
but that's quite technical comparison.

448
00:21:34,334 --> 00:21:35,975
I wouldn't make this to a fashion brand.

449
00:21:35,975 --> 00:21:40,136
But you know how GitHub manages all the
people working on different branches

450
00:21:40,136 --> 00:21:43,277
of a repository? Technically that's what
we do.

451
00:21:43,277 --> 00:21:46,958
You have hundreds of people working on
parts of a collection.

452
00:21:46,958 --> 00:21:48,468
Your collection is your repository.

453
00:21:48,468 --> 00:21:51,799
We just make sure that everything connects
and we flag when it doesn't,

454
00:21:51,799 --> 00:21:54,332
when it's not mergeable. So yeah.

455
00:21:52,372 --> 00:21:54,183
Yeah. Yeah.

456
00:21:54,525 --> 00:21:55,810
That's an interesting way to look at it,

457
00:21:55,810 --> 00:21:56,810
yeah.

458
00:21:56,390 --> 00:21:57,390
Yeah.

459
00:21:57,322 --> 00:21:58,685
Fashion wouldn't understand it,

460
00:21:58,685 --> 00:22:00,630
so I'm not I'm switching

461
00:21:59,120 --> 00:22:00,120
Yeah.

462
00:22:00,630 --> 00:22:02,405
it accordingly. But our our engineers,

463
00:22:02,405 --> 00:22:04,730
they they make they draw a lot of
parallels,

464
00:22:04,730 --> 00:22:06,103
to be honest. So yeah.

465
00:22:06,541 --> 00:22:11,653
now because yeah, most of the there are
not a lot of like AI natives ~ in

466
00:22:11,653 --> 00:22:14,224
the in the in the data science space,

467
00:22:14,224 --> 00:22:17,615
let's say, and you're looking for data
engineers within fashion,

468
00:22:17,615 --> 00:22:20,706
which is not a typical connection to make.

469
00:22:21,228 --> 00:22:22,228
So

470
00:22:21,428 --> 00:22:26,834
is do you f do you find these fashion
savvy AI people or are you more

471
00:22:26,834 --> 00:22:31,249
a tech company or do you or in the fashion
field or are you also identifying

472
00:22:31,249 --> 00:22:32,360
a bit of a fashion company?

473
00:22:32,860 --> 00:22:35,252
Yeah. ~ for the first

474
00:22:35,524 --> 00:22:37,665
Wave or let's say of Athena Studios.

475
00:22:37,665 --> 00:22:42,657
So the the past what is it, year and a
half or so building our team,

476
00:22:42,657 --> 00:22:45,699
we've prioritized tech talent.

477
00:22:45,699 --> 00:22:49,761
So we didn't look for people in fashion
and tech.

478
00:22:49,761 --> 00:22:53,182
I mean, that's great, but we moved we
looked for good engineers.

479
00:22:53,182 --> 00:22:54,772
also open engineers, because yes,

480
00:22:54,772 --> 00:22:56,703
you mentioned people can be AI native,

481
00:22:56,703 --> 00:22:59,545
but let's be very honest, everything is so
new.

482
00:22:59,545 --> 00:23:01,806
On a weekly basis, everything changes.

483
00:23:01,806 --> 00:23:04,587
So almost more important than being AI
native is

484
00:23:04,637 --> 00:23:07,838
That openness to okay, let me try
something out.

485
00:23:07,838 --> 00:23:10,329
What seems to work? What worked for you?

486
00:23:10,329 --> 00:23:12,960
How are you doing this? so that was our
focus.

487
00:23:12,960 --> 00:23:15,531
So we have only senior engineers at this
stage.

488
00:23:15,531 --> 00:23:20,842
so all people with 15 years of experience
software programming,

489
00:23:20,842 --> 00:23:23,729
because that also brings that basis that
there was before AI.

490
00:23:23,729 --> 00:23:26,594
I think for me that was really important
to know that people know

491
00:23:26,594 --> 00:23:29,175
how to build secure and scalable ~ tools.

492
00:23:30,156 --> 00:23:33,007
In the second wave, I mean, as a C as a
founder,

493
00:23:33,007 --> 00:23:34,478
you're also always recruiting.

494
00:23:34,478 --> 00:23:39,100
~ so I have a lot of conversation now for
starting or for adding to

495
00:23:39,100 --> 00:23:43,372
the tech team people who effectively have
either a background in fashion

496
00:23:43,372 --> 00:23:47,624
and tech or tech background with an
interest in fashion.

497
00:23:47,624 --> 00:23:50,855
And so you find more of them, and this is
a bit cliche,

498
00:23:50,855 --> 00:23:55,847
but in New York and London, there are more
people that combine these two interests.

499
00:23:55,847 --> 00:23:59,289
~ so we have a lot of conversations
ongoing currently for our

500
00:23:59,350 --> 00:24:00,350
Expansion.

501
00:24:00,932 --> 00:24:03,868
And also the client base, do you find that
they're quite tax heavy or

502
00:24:03,868 --> 00:24:06,604
do you find that they're more yeah,

503
00:24:06,604 --> 00:24:10,191
hold back against AI? ~ or they

504
00:24:10,691 --> 00:24:15,767
Yes. no. Concept to market teams are
generally not very tech savvy.

505
00:24:15,767 --> 00:24:18,040
So I don't wanna generalize across all my
clients,

506
00:24:18,040 --> 00:24:20,733
but in general you can assume they are not
tech savvy.

507
00:24:20,733 --> 00:24:22,875
~ so that is one of the reasons why

508
00:24:23,067 --> 00:24:27,892
I also internally within my team I fight
really hard to not give them

509
00:24:27,892 --> 00:24:31,636
a new platform that maybe would work
better or would be easier for us

510
00:24:31,636 --> 00:24:35,319
to maintain as chatbots that is in
Microsoft Teams.

511
00:24:35,319 --> 00:24:40,284
The reason that I'm so adamant on this is
because it's we see very good adoption

512
00:24:40,284 --> 00:24:42,386
from our users because it's nothing new to
learn.

513
00:24:42,886 --> 00:24:45,379
Even that, even interactions with a chat
bot,

514
00:24:45,379 --> 00:24:48,272
we sometimes see there's a bit of skill
building required.

515
00:24:48,272 --> 00:24:50,114
even though it's natural language,

516
00:24:50,114 --> 00:24:52,717
sometimes people forget they're chatting
to a chat bot.

517
00:24:52,717 --> 00:24:54,900
And and that's super interesting to see,

518
00:24:54,900 --> 00:24:57,743
but we are building for an audience that
is not tech savvy.

519
00:24:57,743 --> 00:24:59,185
That's the starting point.

520
00:24:59,685 --> 00:25:00,685
how

521
00:24:59,905 --> 00:25:03,488
do you see the the future growth ~ of
Athena?

522
00:25:03,488 --> 00:25:08,521
Like I ~ have the feeling that a lot of
companies that that are starting

523
00:25:08,521 --> 00:25:12,824
to get like to product market fit where
you clearly have some customers that that

524
00:25:12,824 --> 00:25:16,366
have that clearly have a need for your for
your product from the moment that there's

525
00:25:16,366 --> 00:25:18,918
product market fit I see a lot of
companies these days like raise a lot

526
00:25:18,918 --> 00:25:22,171
of money to grow to capture the market as
quickly as possible because

527
00:25:22,171 --> 00:25:26,194
I think it's also l linked to AI like it
has become very easy to build

528
00:25:26,812 --> 00:25:28,927
Products, so from the moment you sell
them,

529
00:25:28,927 --> 00:25:31,973
like there is this an incentive to quickly
grab market share.

530
00:25:31,973 --> 00:25:33,335
How do you look at that?

531
00:25:33,835 --> 00:25:38,626
~ so I think until now it's a very it's an
excellent question.

532
00:25:38,626 --> 00:25:42,037
And I think the one that we it's a very
strategic topic we often talk

533
00:25:41,666 --> 00:25:42,666
Mm.

534
00:25:42,037 --> 00:25:44,377
about. of course building a product you as
you know,

535
00:25:44,377 --> 00:25:47,698
distribution is everything. In our case,

536
00:25:47,698 --> 00:25:53,820
particularly knowledge of like ground
knowledge is it is also very,

537
00:25:53,820 --> 00:25:58,301
very important. So you need to know what
you're building for and have

538
00:25:58,665 --> 00:26:02,847
been through it before you can build
something successful in in this very niche

539
00:26:02,847 --> 00:26:04,988
space. That's also the reason why we went
niche.

540
00:26:04,988 --> 00:26:07,270
I mean, I love the industry and I love the
process,

541
00:26:07,270 --> 00:26:08,950
so I wanted to work in that space.

542
00:26:08,950 --> 00:26:14,173
But ~ it gives the one benefit of it it's
a bit harder to to get in.

543
00:26:14,173 --> 00:26:15,954
But of course it's not impossible.

544
00:26:15,954 --> 00:26:17,954
Like we're not naive. It's not impossible.

545
00:26:17,954 --> 00:26:22,227
I think for now we have been bootstrapped
because we could we could

546
00:26:22,491 --> 00:26:24,222
I think that's the main reason we could.

547
00:26:24,222 --> 00:26:28,524
It allowed us to not be distracted by
investor discussions,

548
00:26:28,524 --> 00:26:31,075
ri raising money, which takes a lot of
time,

549
00:26:31,075 --> 00:26:36,384
right? So we allowed to do that through
revenue in combination with non-dilutive

550
00:26:36,384 --> 00:26:38,037
~ grants that we had

551
00:26:37,817 --> 00:26:38,817
yeah.

552
00:26:38,037 --> 00:26:39,388
through Luxembourg and Europe.

553
00:26:39,388 --> 00:26:43,630
and then we for me it was important to
prove that we can build not just

554
00:26:43,630 --> 00:26:47,792
get clients but also charge interesting
amounts.

555
00:26:48,064 --> 00:26:49,885
Because we want to build a profitable
business.

556
00:26:49,885 --> 00:26:52,366
AI, as we all know, is not cheap.

557
00:26:52,366 --> 00:26:54,788
Like it's not magic. It costs actual
money.

558
00:26:54,788 --> 00:27:00,291
So, how do we build a model and a and a
and a go-to-market model that brands

559
00:27:00,291 --> 00:27:03,132
are willing to pay for that and we can
make money on?

560
00:27:03,132 --> 00:27:09,716
So I think we're there. ~ I'm not gonna
say we're we're ready for like

561
00:27:09,716 --> 00:27:10,937
and there's nothing more to learn,

562
00:27:10,937 --> 00:27:15,159
but we've proven that. And I think for now
it's a much more interesting situation

563
00:27:15,159 --> 00:27:16,280
to look at fundraising.

564
00:27:16,640 --> 00:27:20,179
And definitely an an avenue we're looking
at in the in the short term.

565
00:27:20,950 --> 00:27:26,854
Is is token consumption is it is it ~ a
reason to look for funding or not?

566
00:27:26,854 --> 00:27:31,138
Because I can imagine like like in the
product that you're offering that that's

567
00:27:31,138 --> 00:27:38,003
you have quite a bit of token spent and
that it's also hard to match correct
pricing

568
00:27:38,103 --> 00:27:40,715
with correct token performance like

569
00:27:41,215 --> 00:27:42,215
Okay, like I I it

570
00:27:41,767 --> 00:27:42,767
Mm.

571
00:27:42,365 --> 00:27:45,848
the the challenge I think to these days
with the LMs that we have is like

572
00:27:45,848 --> 00:27:47,989
the more you pay for the LM the better the
performance is,

573
00:27:47,989 --> 00:27:51,831
right? But at some but but if you sell
product to a customer,

574
00:27:51,831 --> 00:27:55,382
you can to some extent sell like the
performance will be better,

575
00:27:55,382 --> 00:27:57,884
so you will be you will you will pay for
consumption,

576
00:27:57,884 --> 00:27:59,825
but produ customers don't really like
that.

577
00:27:59,825 --> 00:28:03,937
They want to know they want to know either
like some something like that they

578
00:28:03,937 --> 00:28:07,549
can calculate, like it's per user or per
month or per module or whatever,

579
00:28:07,549 --> 00:28:08,549
right?

580
00:28:08,065 --> 00:28:09,467
And there's like this disjoint,

581
00:28:09,467 --> 00:28:14,385
like it becomes easier if you sell per
user or per module or whatever.

582
00:28:14,385 --> 00:28:18,170
~ v but in the end like your your cost
basis,

583
00:28:18,170 --> 00:28:22,137
like a lot of it is per token and the b
the more your customer uses it,

584
00:28:22,137 --> 00:28:23,669
the more expensive it gets for you.

585
00:28:24,169 --> 00:28:27,802
~ so also this we're very important topic,

586
00:28:27,802 --> 00:28:30,214
and I have many discussions with other
founders about this.

587
00:28:30,214 --> 00:28:32,156
The way we decided to proceed,

588
00:28:32,156 --> 00:28:34,278
and that is because it's enterprise sales.

589
00:28:34,278 --> 00:28:37,501
I we do fixed pricing, like we mentioned
earlier.

590
00:28:37,501 --> 00:28:40,223
We don't price for extra credits,

591
00:28:40,223 --> 00:28:43,285
or or going overboard in in the usage of
it.

592
00:28:43,285 --> 00:28:45,337
The reason for that is

593
00:28:45,899 --> 00:28:47,501
And we're continuously measuring,

594
00:28:47,501 --> 00:28:50,164
so ~ think that's an important note to
make.

595
00:28:50,164 --> 00:28:55,070
But we have a fairly solid understanding
right now of what

596
00:28:55,884 --> 00:28:58,565
What will be involved in a collection?

597
00:28:58,565 --> 00:29:01,507
When you say, okay, I want you to track
these and these teams,

598
00:29:01,507 --> 00:29:04,988
our collection is approximately a thousand
styles per season.

599
00:29:04,988 --> 00:29:06,378
we already know, for instance,

600
00:29:06,378 --> 00:29:10,050
we add a bit of margin because you're
gonna overproduce and you're gonna
overdevelop

601
00:29:10,050 --> 00:29:12,311
things, so it will be a thousand five
hundred probably,

602
00:29:12,311 --> 00:29:16,203
but fine. And we take that into
consideration and we do a prediction

603
00:29:16,203 --> 00:29:20,775
of what we think the scope of the project
will be in token counts

604
00:29:20,775 --> 00:29:23,776
in token consumption, in addition to also
the usual.

605
00:29:23,866 --> 00:29:26,107
The workload, etc., of people to build it,

606
00:29:26,107 --> 00:29:29,187
but mostly token consumption. And then we
put a buff,

607
00:29:29,187 --> 00:29:30,460
yeah, of course, a buffer to that.

608
00:29:30,460 --> 00:29:33,822
That's how we price. but that allowed us
so far to actually,

609
00:29:33,822 --> 00:29:35,563
and that's going back to what I said
earlier.

610
00:29:35,563 --> 00:29:39,906
I think that's important. I want to prove
that we can build a profitable business.

611
00:29:39,906 --> 00:29:42,928
In addition to just grabbing, how should I
say,

612
00:29:42,928 --> 00:29:47,190
rather than quickly getting a lot of
brands,

613
00:29:47,190 --> 00:29:49,012
like small brands, for us also,

614
00:29:49,012 --> 00:29:50,472
if we work with

615
00:29:50,972 --> 00:29:57,446
10 between 10 and 15 brands, we look at an
annual revenue of over 10 million.

616
00:29:57,446 --> 00:29:58,446
So

617
00:29:57,937 --> 00:29:58,937
Yeah, no.

618
00:29:59,007 --> 00:30:01,028
it's a different game than saying,

619
00:30:01,028 --> 00:30:04,290
~ I want to go very quickly with a lot of
different brands.

620
00:30:04,290 --> 00:30:07,772
For us, the key thing is to grow within a
brand,

621
00:30:07,772 --> 00:30:10,753
expense to all their seasons, all their
teams,

622
00:30:10,753 --> 00:30:13,695
and together with them identify new ways
to add value.

623
00:30:13,695 --> 00:30:17,717
And then add others as we go. But yeah.

624
00:30:18,217 --> 00:30:21,540
Yeah, I think that's a probably a very
very sensible approach.

625
00:30:21,540 --> 00:30:25,323
in a in a in a world where where a lot of
a lot of companies,

626
00:30:25,323 --> 00:30:26,704
especially that have a lot of funding,

627
00:30:26,704 --> 00:30:29,326
are today basically subsidizing token
usage.

628
00:30:29,326 --> 00:30:33,620
Yeah. That is and a bit betting on ~ on
the price coming down in in

629
00:30:33,620 --> 00:30:35,734
the coming two years. And it's ~

630
00:30:35,232 --> 00:30:36,232
Yeah.

631
00:30:36,173 --> 00:30:38,996
Which I mean is not wrong, it's a
different approach.

632
00:30:38,996 --> 00:30:41,759
And so we might talk again in five years
and I can tell you,

633
00:30:41,759 --> 00:30:43,060
that's what we should have done.

634
00:30:43,060 --> 00:30:45,032
But I think right now I'm yeah,

635
00:30:45,032 --> 00:30:46,763
I'm confident in the approach we're
taking.

636
00:30:46,305 --> 00:30:47,305
Yeah.

637
00:30:46,825 --> 00:30:51,748
Well, I d I guess it makes sense in the in
the when you're selecting like very like

638
00:30:51,908 --> 00:30:53,339
you call an enterprise customers,

639
00:30:53,339 --> 00:30:54,339
you could you call it

640
00:30:53,580 --> 00:30:54,580
Yes.

641
00:30:53,887 --> 00:30:56,030
like the the high end customers.

642
00:30:56,030 --> 00:30:59,001
I think it will probably work.

643
00:30:59,001 --> 00:31:01,693
I mean they will definitely have the
willingness to pay two years from now.

644
00:31:01,693 --> 00:31:05,275
If you have a very wide range of customers
and two years from now

645
00:31:05,275 --> 00:31:06,935
it didn't become the as cheap as you
hoped,

646
00:31:06,935 --> 00:31:10,677
like then who is then still willing to pay
if you're not no longer subsidizing,

647
00:31:10,677 --> 00:31:12,337
right? It's yeah, interesting.

648
00:31:12,357 --> 00:31:15,681
maybe on the grants you talked that you
had the Luxembourg grant

649
00:31:15,681 --> 00:31:19,926
and the European grant. which one which
specific one did you apply to

650
00:31:19,926 --> 00:31:21,428
and how did you find the voices?

651
00:31:21,428 --> 00:31:23,350
Was it easy, was it hard to combine?

652
00:31:23,350 --> 00:31:24,350
~

653
00:31:24,698 --> 00:31:25,698
~ yes, I think

654
00:31:26,154 --> 00:31:28,646
Maybe the one I can high there were a few.

655
00:31:28,646 --> 00:31:31,879
I think the ones I'll highlight mostly is
is from Luxembourg,

656
00:31:31,879 --> 00:31:33,830
because I I do think that's worth the
mention.

657
00:31:33,830 --> 00:31:38,775
Luxembourg has prioritized building their
tech ecosystem and helping startups

658
00:31:38,775 --> 00:31:42,758
in scaling rapidly. so we did ~ a range of
things.

659
00:31:42,758 --> 00:31:46,611
We were selected as one of their one of
the startups for their one of their

660
00:31:46,611 --> 00:31:49,343
fit for start cohorts. So every year they
select,

661
00:31:49,343 --> 00:31:52,476
I think it's 20 startups across different
fields,

662
00:31:52,476 --> 00:31:53,476
but in tech.

663
00:31:53,879 --> 00:31:58,721
~ to basically ~ support them with support
them support them financially

664
00:31:58,751 --> 00:32:03,463
and help them with introductions for those
who seek funding etc which

665
00:32:03,463 --> 00:32:07,045
is a very international pro project which
I think many people don't know

666
00:32:07,245 --> 00:32:10,617
but I think at least half of that program
are companies that

667
00:32:10,617 --> 00:32:15,198
are not from Luxembourg. So it's really an
idea to to to bring startups Europe

668
00:32:15,198 --> 00:32:19,990
but also Asia as a global to set them to
to have them have an office in Luxembourg.

669
00:32:19,990 --> 00:32:21,151
And so that was a very

670
00:32:21,231 --> 00:32:25,053
helpful ~ program which also helped us to
understand then others

671
00:32:25,133 --> 00:32:29,836
~ research and development grants so where
we can we first need to prove that

672
00:32:29,836 --> 00:32:32,687
we can build something and then once
you've proven that you get part

673
00:32:32,687 --> 00:32:34,918
of your investment that you made yourself
back.

674
00:32:34,918 --> 00:32:39,721
So we did a mix of those things some
pre-financing some some without

675
00:32:40,622 --> 00:32:43,465
Interesting. Did not know the Luxembourg
one was worldwide.

676
00:32:43,465 --> 00:32:47,750
So ~ worldwide. So the really good and
really good concept to attract talent into

677
00:32:47,850 --> 00:32:49,993
Luxembourg. So nice, nice

678
00:32:49,183 --> 00:32:50,183
Yes.

679
00:32:49,993 --> 00:32:51,374
brands. And the European ones,

680
00:32:51,374 --> 00:32:55,739
you there were more smaller ones that were
just the research and development brands.

681
00:32:54,913 --> 00:32:57,479
~ yes, they were smaller.

682
00:32:55,739 --> 00:32:57,281
Yes.

683
00:32:57,762 --> 00:32:58,762
Okay.

684
00:32:58,682 --> 00:33:00,744
Maybe talking about ~ geography,

685
00:33:00,744 --> 00:33:04,357
you you already already talked about ~ a
vibrant scene in in New York.

686
00:33:04,357 --> 00:33:07,370
how do you see that evolving? Like I can
also imagine that a lot of these

687
00:33:07,370 --> 00:33:09,541
big brands are actually in the US,

688
00:33:09,541 --> 00:33:14,245
their headquarters? Like do you do you
need to be in the US at some point

689
00:33:14,245 --> 00:33:18,628
or or is is it possible to really build
this out from you from Luxembourg?

690
00:33:19,719 --> 00:33:24,651
So ~ at the moment our our our only ~
headquarter,

691
00:33:24,651 --> 00:33:26,815
our only office is in Luxembourg.

692
00:33:26,815 --> 00:33:29,638
~ I do travel a lot personally,

693
00:33:29,638 --> 00:33:34,502
both to for instance the UK but on New
York I probably spent most of my time
abroad.

694
00:33:35,223 --> 00:33:38,275
It's on our agenda in any case to open an
office in the US.

695
00:33:38,275 --> 00:33:41,188
And the timeline is also in the short
term.

696
00:33:41,188 --> 00:33:42,979
So we're already working on that.

697
00:33:42,979 --> 00:33:47,322
And that would be really to grow the US
market even better and also make sure

698
00:33:47,322 --> 00:33:50,244
we can hire people there on the commercial
side.

699
00:33:50,244 --> 00:33:53,647
Because so far our sales have been
founderless.

700
00:33:53,647 --> 00:33:57,560
So that's me. And I think that's that's
gonna be a really,

701
00:33:57,560 --> 00:34:01,063
a really big next step, in addition to
just spending time there,

702
00:34:01,063 --> 00:34:02,994
having also people ~ on the ground.

703
00:34:03,494 --> 00:34:06,956
I do believe you shouldn't underestimate
Europe in terms of size.

704
00:34:06,956 --> 00:34:10,187
So we have on the one hand the luxury
brands here and the groups.

705
00:34:10,187 --> 00:34:14,709
On the other hand, we have many of the big
sportswear sportswear brands

706
00:34:14,769 --> 00:34:19,221
and companies. I think the difference that
we have ~ encountered is just that

707
00:34:19,221 --> 00:34:22,193
the US is more open for innovation,

708
00:34:22,193 --> 00:34:25,134
working with startups, doing pilots,

709
00:34:25,134 --> 00:34:28,995
and their budgets are bigger. So we have
just decided to really focus

710
00:34:28,995 --> 00:34:32,557
on that market first in order to use it
then to also open up the

711
00:34:32,557 --> 00:34:33,557
Yeah.

712
00:34:33,642 --> 00:34:34,642
Yeah,

713
00:34:34,062 --> 00:34:38,624
see, see. And do you do you notice when
you're ~ when you're in the US

714
00:34:38,624 --> 00:34:40,994
or in New York or when you're talking to a
brand,

715
00:34:40,994 --> 00:34:42,305
when you're trying to sell to a brand,

716
00:34:42,305 --> 00:34:49,157
like does it make the discussion more
difficult if they know it's not a US
company?

717
00:34:49,157 --> 00:34:51,655
Does it does it ease the or let me put it
the other way,

718
00:34:51,655 --> 00:34:54,509
would it ease the discussion if you would
be US based?

719
00:34:54,889 --> 00:34:56,649
from my experience I don't think so.

720
00:34:56,649 --> 00:34:57,649
I'm pretty

721
00:34:57,131 --> 00:34:58,131
Okay.

722
00:34:57,620 --> 00:34:59,150
I'm actually even convinced not.

723
00:34:59,150 --> 00:35:02,662
~ because I they all know I'm from Europe.

724
00:35:02,662 --> 00:35:07,474
~ I do think because they see me so often
they they they don't care so much.

725
00:35:07,432 --> 00:35:08,432
Yeah yeah, I see.

726
00:35:07,474 --> 00:35:08,565
~ we

727
00:35:08,565 --> 00:35:11,236
I I do I do make it a rule ~

728
00:35:11,518 --> 00:35:13,569
every time I'm in the US I meet everyone
again.

729
00:35:13,569 --> 00:35:16,716
~ it's very much in person. I think that's
super important.

730
00:35:16,716 --> 00:35:19,120
I think it's more for for newer brands.

731
00:35:19,120 --> 00:35:22,525
I mean I already and I already have listed
a few people that

732
00:35:23,025 --> 00:35:24,346
I have become friends that say,

733
00:35:24,346 --> 00:35:26,877
but with a shared background in fashion
tech,

734
00:35:26,877 --> 00:35:29,856
~ that have a good network, that I think
that would ease up things,

735
00:35:29,856 --> 00:35:32,539
people who have worked for a long time in
the US.

736
00:35:32,539 --> 00:35:37,202
I do think what is important, I started
going to the US since 23,

737
00:35:37,202 --> 00:35:41,854
when I started with the initial
sustainability ID and I was testing this
with some

738
00:35:41,854 --> 00:35:45,136
brands there. And I've learned rather
quickly,

739
00:35:45,136 --> 00:35:48,367
but it's important, that there's obviously
different ways of doing business

740
00:35:48,367 --> 00:35:51,672
in the US versus here. And that I do think
made a difference.

741
00:35:51,809 --> 00:35:54,841
difference because I do speak to European
founders who say it's impossible

742
00:35:54,841 --> 00:35:56,640
to sell in the US and then I'd say,

743
00:35:56,640 --> 00:35:59,903
okay, I think it's because you're behaving
too European,

744
00:35:59,903 --> 00:36:01,544
so to speak, ~ that

745
00:36:00,513 --> 00:36:01,513
Okay, interesting. Yeah.

746
00:36:01,544 --> 00:36:03,415
might make things different, difficult.

747
00:36:04,053 --> 00:36:05,053
And

748
00:36:04,173 --> 00:36:08,810
do you have ~ pointers for people like the
things that w that maybe work

749
00:36:08,487 --> 00:36:11,688
~

750
00:36:08,810 --> 00:36:10,172
in Europe and don't work in the US,

751
00:36:10,172 --> 00:36:11,814
like things they should be aware of?

752
00:36:12,188 --> 00:36:16,070
yeah, I think honestly, I think and it
depends on the European culture.

753
00:36:16,070 --> 00:36:17,490
Like it depends on where you're from.

754
00:36:17,490 --> 00:36:21,032
Okay. So that's of course also I think the
US is just very direct.

755
00:36:21,032 --> 00:36:23,292
Like relationships matter, yes,

756
00:36:23,292 --> 00:36:25,693
a lot, but also we're very transactional.

757
00:36:25,693 --> 00:36:28,995
So I think getting to the point is super
important.

758
00:36:29,314 --> 00:36:33,236
Not pricing too low is also very important
because they might literally

759
00:36:33,236 --> 00:36:34,926
not take you serious if you say,

760
00:36:34,883 --> 00:36:35,883
Yeah.

761
00:36:34,926 --> 00:36:35,926
~

762
00:36:35,327 --> 00:36:37,307
this is five thousand or something.

763
00:36:37,307 --> 00:36:39,708
They'll be like, I'm sorry, what?

764
00:36:39,708 --> 00:36:44,512
and I think for me that was the the the
biggest thing to be much more okay,

765
00:36:44,512 --> 00:36:47,757
we're here to do business. I'm not like
I'm not ashamed to say that,

766
00:36:47,757 --> 00:36:50,298
right? Like we're having this dinner
because I want to do a deal with you.

767
00:36:50,298 --> 00:36:53,219
And also enjoy ~ whereas like I in Europe

768
00:36:52,640 --> 00:36:53,640
Yeah.

769
00:36:53,219 --> 00:36:54,410
I would be much more like

770
00:36:54,970 --> 00:36:57,381
let's build the relationship. It's not
about doing deals.

771
00:36:57,381 --> 00:37:00,183
Like maybe maybe later if we if if we get
to that point.

772
00:37:00,183 --> 00:37:03,424
And I feel I had multiple people in my
first years telling me,

773
00:37:03,424 --> 00:37:05,936
Heidi, I like you, so I'm gonna tell you
this.

774
00:37:05,936 --> 00:37:07,437
I feel like you're wasting my time.

775
00:37:07,437 --> 00:37:10,388
You either tell me what you need from me
or how I can help you,

776
00:37:10,388 --> 00:37:12,949
or otherwise I'm like, Why did we even
have this lunch?

777
00:37:12,949 --> 00:37:16,751
So I think that just it's like a mindset
shift.

778
00:37:13,563 --> 00:37:14,784
Okay, that's interesting, yeah.

779
00:37:14,784 --> 00:37:16,586
Yeah.

780
00:37:18,048 --> 00:37:23,874
So maybe in European terms it's closer to
Dutch than it is to French culture.

781
00:37:23,073 --> 00:37:24,073
Yes, maybe.

782
00:37:23,964 --> 00:37:26,266
and then the other thing though that's
also important,

783
00:37:26,266 --> 00:37:31,249
like you always so what I've learned is in
the US by default when

784
00:37:31,249 --> 00:37:34,251
a startup founder or so they tell you
something about their company,

785
00:37:34,251 --> 00:37:36,012
they already divide it by ten.

786
00:37:36,012 --> 00:37:39,995
Because they assume because they
themselves they ~ they they they always

787
00:37:39,995 --> 00:37:43,537
go much bigger. Like ~ so there is this
natural,

788
00:37:43,537 --> 00:37:47,920
okay, we'll divide it by ten. Which means
that if you're like an honest and modest

789
00:37:49,129 --> 00:37:51,331
I'm gonna say European, but I don't want
to speak bad about us.

790
00:37:51,331 --> 00:37:54,514
But if you if you say something and
they're like divided by ten,

791
00:37:54,514 --> 00:37:56,546
oof, they're really small. Whereas you're
just like,

792
00:37:56,546 --> 00:37:59,038
no, that that's who we are, that's our
size exactly.

793
00:37:58,867 --> 00:38:00,236
Don't be too humble. It's a bit

794
00:37:59,038 --> 00:38:00,439
Yes,

795
00:38:00,480 --> 00:38:02,882
~ just know you do with it what you want,

796
00:38:02,882 --> 00:38:05,864
but know that the person in front of you
is already discounting what

797
00:38:05,864 --> 00:38:08,687
you say because you're a startup and
because they they're used

798
00:38:08,687 --> 00:38:10,799
to discounting what Americans tell them.

799
00:38:11,299 --> 00:38:12,299
Interesting.

800
00:38:11,362 --> 00:38:12,362
Was it

801
00:38:11,712 --> 00:38:14,976
hard to adjust your approach of sales to
those companies,

802
00:38:14,976 --> 00:38:17,488
or was it a learning lesson, or you made a
click?

803
00:38:16,969 --> 00:38:18,786
It's just a learning lesson. And

804
00:38:17,488 --> 00:38:18,689
Yeah.

805
00:38:18,786 --> 00:38:20,893
you really wanna sell to this company so
you adapt.

806
00:38:21,790 --> 00:38:22,790
Okay.

807
00:38:22,653 --> 00:38:26,236
maybe like ~ to start rounding up with the
interview,

808
00:38:26,236 --> 00:38:29,899
like ~ you're at a certain stage now with
with Athena Studio.

809
00:38:29,899 --> 00:38:36,203
Like what is the biggest thing in your
eyes to unlock going to the next stage?

810
00:38:36,203 --> 00:38:40,887
Like really like trying to unlock like the
the if if if you're talking about like

811
00:38:40,887 --> 00:38:43,609
if we if we do 10 enterprise c 10
enterprise customers,

812
00:38:43,609 --> 00:38:46,231
like that's very significant. Like how do
you get to the next five?

813
00:38:46,231 --> 00:38:48,092
Like what is the challenge that you have
in front of you?

814
00:38:48,447 --> 00:38:51,779
So it's very clear. It's also a
straightforward answer.

815
00:38:51,779 --> 00:38:54,840
I've so far built a tech team.

816
00:38:54,840 --> 00:38:57,742
And excellent tech team. I love I love
them.

817
00:38:57,742 --> 00:38:59,962
I've never built a commercial team.

818
00:38:59,962 --> 00:39:05,685
So at the moment, both customer success
and sales is done by me.

819
00:39:05,685 --> 00:39:09,016
That's a clear gap into going to 10
customers.

820
00:39:09,016 --> 00:39:13,257
That doesn't work. And so ~ that is ~ for
the second half of this year,

821
00:39:13,257 --> 00:39:14,378
and we're already working

822
00:39:14,720 --> 00:39:20,503
Hard on this is focus number one is
building on the one hand having customer

823
00:39:20,503 --> 00:39:22,544
success. I'm not sure if that's going to
be the name,

824
00:39:22,544 --> 00:39:25,352
but basically the teams that are really
closely working.

825
00:39:25,352 --> 00:39:27,206
I like the forward deployed engineer,

826
00:39:27,206 --> 00:39:29,966
but for us, it doesn't need to be an
engineering role.

827
00:39:29,966 --> 00:39:33,578
It needs to be a person who gets so close
to the customer into seeing

828
00:39:33,578 --> 00:39:36,059
how the processes work, what are the
learnings,

829
00:39:36,059 --> 00:39:39,451
how does that influence or roadmap for
that client and in general.

830
00:39:39,451 --> 00:39:41,812
So that is that we need as a next step.

831
00:39:42,294 --> 00:39:45,019
And in parallel it's and that's US
focused,

832
00:39:45,019 --> 00:39:49,951
having starting with one, then growing ~
sales team that can also help bringing

833
00:39:49,951 --> 00:39:50,951
in those new customers.

834
00:39:51,377 --> 00:39:56,051
Yeah. I can I can imagine it's har hard to
find these people.

835
00:39:56,051 --> 00:40:02,266
in a sense that it like you probably need
people that are like very into this domain

836
00:40:02,266 --> 00:40:04,378
and like they can brainstorm along with
the customer,

837
00:40:04,378 --> 00:40:05,949
like understand what the challenges are,

838
00:40:05,949 --> 00:40:09,993
can can like translate to this is a
solution that Athena can bring

839
00:40:09,993 --> 00:40:11,064
to these challenges, like

840
00:40:11,564 --> 00:40:16,508
Yeah, so for sales, I'm extremely strict
on what you just said.

841
00:40:16,508 --> 00:40:21,442
So ~ the people ~ and and the people I'm
in discussion with are people

842
00:40:21,442 --> 00:40:24,574
who have sold technology into fashion
before,

843
00:40:24,574 --> 00:40:27,086
have a fashion background on top of that.

844
00:40:27,086 --> 00:40:30,158
So not just people who rolled into fashion
tech but never set foot

845
00:40:30,158 --> 00:40:33,520
in a fashion company. So these are
specific profiles,

846
00:40:33,520 --> 00:40:35,802
but you know, they exist and they're

847
00:40:35,357 --> 00:40:36,357
Texas.

848
00:40:35,802 --> 00:40:36,802
the ones I go after.

849
00:40:37,095 --> 00:40:39,136
Also importantly, enterprise sales.

850
00:40:39,136 --> 00:40:42,457
As Bert, you probably also know from your
past experiences,

851
00:40:42,457 --> 00:40:46,578
a salesperson who is used to selling 10K,

852
00:40:46,578 --> 00:40:49,759
50K projects is not the person,

853
00:40:49,759 --> 00:40:53,320
or not necessarily the person who can sell
200k,

854
00:40:53,320 --> 00:40:56,581
500K tickets. So tickets,

855
00:40:56,581 --> 00:41:00,782
contracts. So ~ that is the perfect
profile,

856
00:41:00,782 --> 00:41:02,823
right? So for sales.

857
00:41:03,527 --> 00:41:07,210
For customer success and and more also
chief of staff role.

858
00:41:07,210 --> 00:41:09,693
the background in fashion is not as
relevant.

859
00:41:09,693 --> 00:41:15,519
The interest is relevant, but it's more
there looking at someone that yeah,

860
00:41:15,519 --> 00:41:20,624
background in in in in one of the top
consulting firms or at other startups,

861
00:41:20,624 --> 00:41:23,046
someone who's like, you know, ~ who's
smart,

862
00:41:23,046 --> 00:41:25,659
proactive. ~ that's different.

863
00:41:25,659 --> 00:41:26,710
That's a different kind of

864
00:41:27,210 --> 00:41:29,362
But it's it's a recognizable challenge
indeed.

865
00:41:29,362 --> 00:41:33,295
~ and of course tech it's sort of from a
completely different point of view,

866
00:41:33,295 --> 00:41:38,439
a letter services company. ~ but like
going from founder led sales,

867
00:41:38,439 --> 00:41:42,732
where you're I think often good at sales
simply because you're very passionate
about

868
00:41:42,732 --> 00:41:43,732
what you're doing,

869
00:41:42,966 --> 00:41:43,966
Yeah.

870
00:41:43,583 --> 00:41:47,306
right? Like and that lets that radiates
off to other people and people trust

871
00:41:47,306 --> 00:41:49,347
you because you're very passionate about
about helping them.

872
00:41:49,347 --> 00:41:50,889
And then getting to that

873
00:41:51,843 --> 00:41:54,645
first version of your sales engine that
you can then scale.

874
00:41:54,645 --> 00:41:55,985
Like the first version is hard,

875
00:41:55,985 --> 00:41:58,757
right? Scaling them further is it becomes
easier.

876
00:41:58,757 --> 00:42:02,679
but it is getting to that like that first
next step in the in that

877
00:42:02,679 --> 00:42:03,840
in that sales maturity indeed.

878
00:42:04,340 --> 00:42:08,360
And are you also thinking of expanding to
other industries or are you just seeing

879
00:42:08,360 --> 00:42:11,459
to fashion and maybe looking at like for
instance bad fashion,

880
00:42:11,459 --> 00:42:12,459
so to say?

881
00:42:12,722 --> 00:42:13,722
Yeah, so

882
00:42:14,317 --> 00:42:16,658
The same as you say never never say never.

883
00:42:16,658 --> 00:42:22,551
So I think for us it's gonna be we're
gonna have to explore how would

884
00:42:22,551 --> 00:42:24,761
I say two there's different options.

885
00:42:24,761 --> 00:42:28,023
One is we stick to fashion, but back to
your earlier point,

886
00:42:28,023 --> 00:42:31,364
Charlotte, we continuously grow further in
that life cycle.

887
00:42:31,364 --> 00:42:33,836
We start adding marketing, sales,

888
00:42:33,836 --> 00:42:35,426
like we we do the full roundup.

889
00:42:35,426 --> 00:42:39,378
and then later you can always still expand
to other other industries and

890
00:42:39,378 --> 00:42:41,419
do the same thing. Or we go earlier.

891
00:42:41,653 --> 00:42:42,653
And I think there's a

892
00:42:42,968 --> 00:42:45,289
My hunch is that that's going to be
opportunity driven.

893
00:42:45,289 --> 00:42:49,601
So if an opportunity comes up where an
FMCG company,

894
00:42:49,601 --> 00:42:52,322
a beauty company says we have exactly the
same problem,

895
00:42:52,322 --> 00:42:55,224
we're interested to explore if Athena
could help us,

896
00:42:55,224 --> 00:42:57,245
then we might do that in parallel.

897
00:42:57,245 --> 00:43:00,816
But it changes the way you build your team
and it changes the way you grow.

898
00:43:00,816 --> 00:43:03,668
So I think for us, it's not our focus.

899
00:43:03,668 --> 00:43:07,359
We don't go out ourselves to other
industries at this stage,

900
00:43:07,359 --> 00:43:11,161
or I think in the short term. But it's
definitely a growth a growth path

901
00:43:11,161 --> 00:43:12,372
for the other.

902
00:43:12,712 --> 00:43:14,382
Because technically every product,

903
00:43:14,382 --> 00:43:17,213
every company, sorry, that brings a
physical product to market,

904
00:43:17,213 --> 00:43:21,094
it will be a different process than
fashion with different challenges,

905
00:43:21,094 --> 00:43:24,906
but it's similar. Different teams work
from different systems

906
00:43:24,906 --> 00:43:28,837
and different files and whatever and need
to coordinate and work together.

907
00:43:29,337 --> 00:43:31,809
Yeah, indeed, I think there probably have
a lot of parallels if you looked

908
00:43:31,809 --> 00:43:34,691
at ~ to the fast move consumer goods,

909
00:43:34,691 --> 00:43:38,634
like a lot of a lot of different ~
verticals that you could potentially

910
00:43:38,634 --> 00:43:40,266
~ tackle with this

911
00:43:38,817 --> 00:43:39,817
Yes, yes.

912
00:43:40,446 --> 00:43:44,179
with this approach. Yeah. And that's an
that's an interesting disco

913
00:43:44,179 --> 00:43:49,056
d this discussion as well. like do you go
further in where in the domain that

914
00:43:49,056 --> 00:43:52,348
you know very well and go a bit earlier or
later there or go

915
00:43:52,348 --> 00:43:54,290
to a different vertical? I think that's ~

916
00:43:54,790 --> 00:43:55,790
But that's probably something

917
00:43:54,982 --> 00:43:55,982
Yeah.

918
00:43:55,634 --> 00:43:58,929
also that that customer demand will will
help solve a bit.

919
00:43:58,929 --> 00:43:59,929
~

920
00:43:59,485 --> 00:44:00,485
But I I

921
00:43:59,935 --> 00:44:02,027
think the question is also how big you
want to go.

922
00:44:02,027 --> 00:44:04,410
And I think for us, we do want to go big.

923
00:44:04,410 --> 00:44:07,903
Like I do believe and I have the ambition
to build a Tina Studio into

924
00:44:07,903 --> 00:44:12,227
a billion dollar company. You don't get
there by only staying niche,

925
00:44:12,227 --> 00:44:16,952
right? But I'm also very focused on what
are the priorities first?

926
00:44:16,952 --> 00:44:18,493
Where do we need to win first?

927
00:44:18,493 --> 00:44:21,156
and and yeah, those are more on the focus
sides.

928
00:44:21,814 --> 00:44:25,347
Yeah, interesting. Because I w I was
thinking about like ~ like you're you're
very

929
00:44:25,347 --> 00:44:27,750
into the nitty gritty of what is fashion
and how does this work.

930
00:44:27,750 --> 00:44:30,663
And you could like have arguments to say,

931
00:44:30,663 --> 00:44:34,607
Okay, let's maybe go into more towards
customer intimacy to really want

932
00:44:34,607 --> 00:44:38,511
to to really understand like what do these
customers want to really like help guide

933
00:44:38,511 --> 00:44:39,852
our next collection, for example,

934
00:44:39,852 --> 00:44:41,394
to be a bit more customer informed,

935
00:44:41,394 --> 00:44:42,394
like

936
00:44:41,778 --> 00:44:43,431
can imagine that you go into something
like that.

937
00:44:43,431 --> 00:44:47,629
But at the same time you are also like you
have a very strong knowledge

938
00:44:47,629 --> 00:44:49,102
on how do you bring a product to market.

939
00:44:49,102 --> 00:44:52,989
And that's something that a lot of other
outside of fashion also struggle with,

940
00:44:52,989 --> 00:44:54,051
right? That's

941
00:44:53,744 --> 00:44:54,744
No, and

942
00:44:54,925 --> 00:44:57,117
maybe ~ and I know we're wrapping up.

943
00:44:57,117 --> 00:45:00,641
It's it's just a thought because last week
I happened to have discussions with

944
00:45:00,641 --> 00:45:03,994
a few operators in the fashion and and in
the tech industry,

945
00:45:03,994 --> 00:45:06,517
and two of them asked exactly the same
question to me.

946
00:45:06,517 --> 00:45:11,422
and they said, ~ you s so one of the
biggest changes in the industry right now,

947
00:45:11,422 --> 00:45:12,422
fashion.

948
00:45:12,453 --> 00:45:15,205
Is ~ companies like Xi in the last years,

949
00:45:15,205 --> 00:45:18,528
so the Chinese player that and Temu that
we're all familiar with,

950
00:45:18,528 --> 00:45:20,570
he they were like, How do you compare to
them?

951
00:45:20,570 --> 00:45:22,972
Why don't you allow brands to be more like
them,

952
00:45:22,972 --> 00:45:26,024
which is more competitive? And I think
from there,

953
00:45:26,024 --> 00:45:29,336
an interesting way of thinking about it is
they copy,

954
00:45:29,336 --> 00:45:31,763
right? And to be honest, there's nothing
wrong with it.

955
00:45:31,763 --> 00:45:32,763
Fast fashion.

956
00:45:32,936 --> 00:45:37,188
Also copies. And then it's a matter of how
do you as rapidly

957
00:45:37,188 --> 00:45:41,940
as possible get something that's trending
and get it on your own on your own shelf,

958
00:45:41,940 --> 00:45:45,222
on your own shop. So I think that's one
way to do fashion.

959
00:45:45,222 --> 00:45:49,363
The other way to do fashion is the brands
that have always existed,

960
00:45:49,363 --> 00:45:52,685
technically they don't listen to what you
tell them you want.

961
00:45:52,685 --> 00:45:54,106
Technically, they set trends.

962
00:45:54,690 --> 00:45:55,690
Yeah.

963
00:45:54,786 --> 00:45:55,786
It's a bit

964
00:45:55,056 --> 00:45:58,059
like Steve Jobs said, you don't wait for
the customer to say what they want.

965
00:45:58,059 --> 00:45:59,591
They don't know. We tell them.

966
00:45:59,591 --> 00:46:03,604
And so we focus very much at the moment on
those customers.

967
00:46:03,604 --> 00:46:09,540
Like, okay, how do we ensure that that
whole ideation process down to like

968
00:46:09,540 --> 00:46:12,663
how does that work? How can we massively
improve that?

969
00:46:12,663 --> 00:46:13,663
but yeah.

970
00:46:14,144 --> 00:46:17,969
Interesting, yeah. Yeah. And you could
argue that that it's more like

971
00:46:17,969 --> 00:46:21,826
~ the more creative brands. because they
indeed they they do shape

972
00:46:21,946 --> 00:46:24,030
the trends of tomorrow. That's

973
00:46:24,530 --> 00:46:27,592
Okay, that was a very very interesting
very interesting discussion,

974
00:46:27,592 --> 00:46:28,592
Heidi.

975
00:46:28,619 --> 00:46:29,619
Thank you.

976
00:46:29,613 --> 00:46:34,175
thanks a lot for your time. and we ~ wish
you all the best with Athena Studio.

977
00:46:34,175 --> 00:46:35,175
w w what

978
00:46:34,193 --> 00:46:35,193
Thanks so much.

979
00:46:35,036 --> 00:46:39,859
is what is the easiest way for people to
follow both you and ~ Athena Studio?

980
00:46:40,735 --> 00:46:43,601
~ I'm still the most active on LinkedIn,

981
00:46:43,601 --> 00:46:44,601
so I would

982
00:46:44,241 --> 00:46:45,241
Okay.

983
00:46:44,564 --> 00:46:49,074
just tell people to follow Athena Studio
on myself on on LinkedIn.

984
00:46:49,895 --> 00:46:51,678
Okay, good. Thanks a lot, Heidi,

985
00:46:51,678 --> 00:46:56,507
and ~ thanks a lot everybody for listening
and we'll see you all next time.