Welcome to the Innovation Conversation, a podcast
about innovators, both in business and real life.
Hosted by myself, Ricardo Rescual and Harry McCona.
This podcast is also sponsored by ODEV Tech.
ODEV Tech is your premier software development
partner. Make sure you check them out at odev
.tech. The podcast is also sponsored by Notion
.so. Notion is offering six months for free for
new users. Make sure you check out our website,
thestoreofevents .co .uk to read in the software.
Hi, and welcome to another episode of The Innovation
Conversation. Today we are joined by Luís Valente.
Luís, welcome. It's a pleasure to be here, Hikaru.
Thanks so much for having me. It's a pleasure.
Luís, you have an absolutely fascinating story
behind you, so I'll let you introduce yourself
to the audience. Yeah, so I'm Luís. I serve as
CEO of ILOF, background in engineering, trained
as a computer scientist, but... spent most of
my life working elsewhere. In the last couple
of years, I felt absolutely drawn and passionate
about one of the biggest health care challenges
of our times, which is scaling work -right health
care and effective health care to the masses.
So I've been working on a deep tech health tech
company which is based in the UK, in Portugal
and in the US. And we've been accelerating the
arrival of personalized therapeutics to hopefully
millions of patients around the globe. I was
having a look at ILOF. It's absolutely fascinating
what you guys do. How do you get started? Cause
this is, you know, you have a background in computing
engineering and now you're doing something completely
different. So how did that come to be? So first
of all, I think we fell in love with the problem.
So this is not the case for a lot of entrepreneurs
where they fall in love with the father. That's
not our case. We fell in love with the problem.
We really realized that 10 years ago, 15 years
ago, there were really high expectations for
the impact that personalized medicine would have
on every person's everyday lives. And the reality
is that if you ask a room full of people, like
a hundred people in an auditorium, how many of
them did a genetic test in their lifetime? You'll
realize that it's actually not a lot of people.
And this was not the expectation 10, 15 years
ago. And what we realized is that there were
clear problems. So there are problems around
the way we collect this data. There were problems
around the way that we process this data. And
it's still hard, quite hard to connect personalized
healthcare information at scale. and create models
that can actually change healthcare. So in 2019,
together with a couple other co -founders, I
realized that there was a clear opportunity to
take a technology to market that would tackle
the need for more data sources, for better data
sources. Over the course of around 10 weeks,
we interviewed 100 global experts, stakeholders,
CEOs of biotechs, farmers, feasible investigators.
And after those 10 weeks, we had around 12 letters
of intent that validated the need to work with
us and the need in the market for a product like
ours. And yeah, that's pretty much how ILOF was
born. Uh, we're now a team of close to 30, uh,
balance that in three countries. And, uh, yeah,
we raised around 10 million so far. Did you get
started in Portugal or in the UK? Yeah. So technically
we got started in both cases and I'll tell you
why. So, um, we, uh, our first investor was a
German and, um, and we started our operations
in the UK and Oxford. So we actually started
our operations in the Oxford university accelerator,
which was the Oxford Foundry. But our German
investor didn't really want to take the risk
that Brexit would bring. And they were like,
okay, of course, you're a British company. You're
operating in the UK and all your operations are
there. But I would prefer if the legal structure
is outside of a Brexit location. And so we created
a company in Portugal. Years later, when we closed
our first round and when we brought two American
investors in, we ended up uh, pivoting and bringing
also the legal operation into the UK. So yeah,
that's why I say that technically we kind of
started in the UK and in Portugal at the same
time. So this, this is actually quite interesting
because I talk with a lot of people and they
always have, if they're in the UK, they have
the ambition to go to Europe or the U S and if
they're in Portugal, obviously they want to go
to the UK or the U S. So you've actually been
in all these three different markets. So where
do you think it's best suited to actually start
a company? because you've had this experience
for your life, you raised 10 million, so you
know exactly how to do. So for the scenario where
I might be biased, so I was born in Portugal,
so I'm not already disclosing my vision, but
I was born in Portugal, so I might be biased
towards the European ecosystem to the Portuguese
ecosystem. That's point one, and point two, I
think it really depends. So I don't think the
best place to start an AI company is the best
place to start. A medtech company is the best
place to start. At clinical trials, enabling
companies, it's the best place to start a crypto
or fintech company. It really depends on the
type of business. In our case, we're a deep tech
health tech company and as a deep tech health
tech company, it was important to be number one
close to talent. So again, this is deep tech.
This is really damn hard, hard in my French,
but it's really complicated. You need to bring
in profiles. uh, that wouldn't necessarily work
together. Data scientists, physicists, biologists,
and you need to get all of this speaking together
and, and getting something to actually create
value for the third party on a daily basis. And
so you need, you need a pool of talent. Second,
it's important that you are in an ecosystem that
actually values deep tech because the past toolkit
is longer, to be very honest, non dilutive funding.
in terms of either tenders or grants or are important
in deep tech businesses. So that's point number
two. And point number three, just overall, just
regulatory framework. And again, these are just
three points that kind of make the answer to
your question vary. In our specific case, I do
believe that potentially at this point, and even
Portugal might be a good place to be in the sense
of financing wise. So there's a lot of capital
for R &D in Europe right now. There's a lot of
capital for R &D in Portugal right now. Talent
wise, I would say potentially you're better off
in the US or in the UK. And certification wise,
you're certainly better off in the US. No doubt
about it. So I think it's always going to be
a trade off in the end. I mean, as long as you're
able to assemble a group of talented dedicated
people you should be. I'm curious. So you, you
mentioned when you got started, you first interviewed
a couple of people, a hundred people you said,
and then 10 of them actually wrote a letter of
intent afterwards. So I guess what were the biggest
challenges you had in the beginning? Cause this
is a brand new idea and also the application,
this is a, you know, very creative, a brand new
way of doing things. So what were the main challenges
you had to overcome? So various, and again, creating
a startup is hard, creating a deep tech startup
is hard. Creating a deep tech startup in the
health tech space is way harder. Number one,
you need to create goodwill. You need to create
an army of goodwill. One of our investors, when
they invested in Ilove, they were like, you know
what, Luis, you were early at the time. They
were like, okay, you were early. Maybe you don't
have everything figured it out. But every person
that I speak to around you, like partners, customers,
competitors, other VCs, other investors, every
person I speak to, which knows you, they all
say the same thing, which is, we're not sure,
maybe they'll need to pivot, whatever, but I
want to help. They're a smart bunch of people.
They're tackling a big mission. They're pursuing
like a big impactful goal. I want to help. And
this kind of goodwill, creating this kind of
goodwill is hard. I wouldn't say it's a challenge,
it's just hard. And I believe that as founder,
I remember that when we were starting the company
in 2019, we were part of a small bootcamp, small
entrepreneurship bootcamp. And one of the mentors
that were there, he mentioned, okay, when you're
starting, you don't have anything, you don't
have money, you don't have a space, you don't
have an office, you don't have equipment, but
there's one thing that you have. And that thing
is salespeople. And the salespeople is you, right?
The founders, right? That's the only thing you
have, it's salespeople. So I always saw my position
in the company very much as the advocate, the
strongest, biggest, and the more clear advocate
for the solution. And I would say making sure
to create a group of people, either internally
companies or internally talent employees or...
team or externally with partners, with clients,
just creating a group of people that believes
in your vision and that believes you can do it.
I think this is one of the hardest things, but
there's many more. I was wondering, you know,
when you approach all these investors and they
give you that feedback, was it hard to get more
investors on board or was it actually you just
took it as it is and you just say that, you know
what, I'm going to prove you wrong, I'm going
to prove I can do this and I'm just going to
make this company super successful. I think it's
a little bit like dating. It's a numbers game.
Though I've been with the same partner for most
of my life, certainly most of my adult life,
but a lot of people say, I can't speak firsthand,
I guess, but a lot of people, they compare the
dating game and the Tinder game to fundraising
because it's going to be a numbers game, right?
I mean, you need to scroll, God knows how many
Tinder profiles to get a match. And then out
of these matches, 50 % are going to ghost you.
And then, yes, sorry. No worries, we can stop.
It shows a little bit. It's really a numbers
game. It's going to certainly be a numbers game.
And all you need to do is you need to make sure
that you have a big enough pipeline in the beginning.
You need to make sure that you have a relevant
pipeline, not just a big pipeline, but a relevant
pipeline in the beginning. And then when you
get to the point where you see that you have
a handful of investors, they are a good fit,
you need to know how to close. And you know that
famous phrase, always be closing. So you need
to close and you need to create a sense of urgency.
I was reading your profile and I noticed Forbes
30 in the 30s. So how did that happen? Did they
reach out to you? Did you apply for it? How did
the whole thing work? Someone nominated me. So
I've been nominated by It might've been a jury
member, it might've been someone I know, they
don't really disclosure it, but it was a nice,
a lot of people like that. He helps me with the
goodwill part and he helps open a couple of doors.
Yeah, it's just one of those things that you,
I guess, are happy about and then you continue
with your life. But yeah, thank you for mentioning
it. Do they have like a special badge to give
out or a special card where like you want to
go to a busy restaurant? Hey, I got this business
card. Forbes 30 and the 30, I need to get in.
They do organize a couple of exclusive events.
They do give you a badge, but I don't think I
ever use it. They give you a digital and then
like a real badge. I don't think I've ever used
that. So they give you like a, not a badge, but
they give you like a diploma of some sort, certification
or something like that. Yeah, but I don't think
so. I do mention it on social media. That's mostly
because again, it helps creating this goodwill
because basically what he said is that the problem
I was working with was big enough and hopefully
I was doing a decent job, but more like the problem
I was trying to tackle was big enough that it
deserved to be noticed by Forbes. That's what
he means to me. It's a huge problem. So how does
the whole thing work exactly? So you kind of
mentioned briefly, I'm very curious to fully
understand the process. So we know we're an AI
first company. We collect very large amounts
of data. We train AI models and then we deliver
and deploy solutions that help select the right
patients for each clinical study. The way it
works is that we have a physical and digital
component on our solution. Physical is basically
sensors that allow us to collect data on biological
fluids and that can be easily deployed. And then
the platform, which is basically where the most
of the value is, is where we integrate really,
really very large quantities of data, not just
from our own sensors, but also from other sensors.
Yeah. And then create models that can identify
the patient that is more likely to respond to
a certain treatment. So you don't actually make,
obviously, I'm just trying to explain this to
the audience. So you select what treatment they
got a better response to respond to. And then
based on that, you tell the doctors, look, this
is what we think that you should give them as
treatment because they're going to have. Well,
we do it mostly within the scope of clinical
trials right now. So we do it mostly within the
scope of what is the patient that is going to
be more prone to react well to this treatment,
try to get these treatments approved, try to
get them in the market and then. Once they are
in the market, of course, the aim is to be able
to then tell the right or tell to the patient,
which is the right treatment for him. I mean,
is the future something that, or in the future,
will we have something where you go to the doctor,
the doctor tests, there's a simple blood test
using your solution. And then they realized,
actually, I shouldn't really prescribe you, I'm
going to give a very basic example. I shouldn't
really prescribe you paracetamol because you
don't respond well to it. to a year semester
is like, is that going to be in the future? Absolutely.
That's the vision. The vision is that the same
way that when you go to a clothes shop, you ask
for a t -shirt, they're going to ask you your
size, right? Are you an M size? Are you an XL
size? And if you are like giving a t -shirt at
random, let's say you're an M size and you're
giving an XL, you're going to put it on and it's
not going to fit you, but you're not going to
say it's broken. You're not going to say it's
weird. You're just going to say, no, I like t
-shirts, just not my size. But to date, this
is not what happens in medicine. You go to the
doctor or you go on a clinical trial and then
you test the medication. And then if it doesn't
work, they'll just assume the medication is worthless
and it just doesn't work. And in other cases,
that's not the case. It works. It just doesn't
work for everyone. It just doesn't work for everyone
that has the same disease. And what we're fighting
is that there's going to be more and more treatments
that will only work on a subset of the population.
And this subset of the population is going to
have to be defined via phenotyping and triaging
tools such as... Interesting. It's a good example
where that could actually work saying, hey, this
medicine does not work for you at all, so then
you can try. And you have perhaps an even stronger
example with COVID, right? I mean, people reacting
very differently to the disease, but people also
reacting very differently to the vaccine, right?
Some people got fully immune and then the vaccine
worked wonders and with some others they just...
It did almost nothing to increase that immunity.
And in others, it was even causing very serious
side effects. And we saw that at scale with more
or less the same vaccines. And again, people
having very, very strong and different reactions.
This already happens in various other diseases,
including Alzheimer's. So in Alzheimer's in the
last 20 years, there's been potentially over
400 clinical trials that failed. And they didn't
fail because all of these medications were worthless.
They failed because the disease is very heterogeneous
and the way that the patient reacts to a certain
treatment is very different from the way that
another Alzheimer's patient reacts to the same
treatment. And if you don't have tools that can
select your right shirt size, then these drugs
are never going to be approved. And that's what
I love is taking the mark. Is there any limitations
in terms of your solution? Is there any case
where you say, actually we can all work with
this type of drugs or that type of disease, or
it actually works for everything? So first of
all, the big limitation is data, right? So again,
everyone used, so normally it's said in the AI,
right? And in data science, which is garbage
in, garbage out, right? So you need to have data
sets, enough well -connectedized data sets that
you teach your algorithms how to look for specific
profiles, specific features within these profiles.
If you can't acquire good data sets, then you're
doomed. Then you're never going to get there.
Then of course, there's going to be diseases
that are more... So we look at biological fluids
and there's going to be diseases that are more
easy to detect in biological fluids than others.
So for example, one of the reasons why we're
working in neurogenetic diseases is exactly because
it's hard. It's a lot easier to detect traces
of infectious diseases in biological fluids than
to detect traces of Alzheimer's or even depression.
Yeah, but at this point, it's a path. So I can't
tell you exactly which ones we are competent
and which ones we perform less. I can tell you
that we currently are developing on a couple
of areas, including oncology with ovarian cancer,
neuro generative with Alzheimer's and a couple
others. And we believe that tools like ILOF and
others are actually going to make an impact on
quite an array of diseases and are going to be
making personalized medicine available for the
masses. You think, so you operate in quite a
lot of markets. I know you're expanding to the
U .S. Do you think it's, it's not that it's easy.
Do you think there's less regulation or they're
more open to implementing this type of technology
in one market compared with the other? Or it's
all pretty much the same? No, there is a cultural
aspect and there are cultural barriers. I think
it goes without saying that U .S. is certainly
a good place for innovation. It's especially
a good place to sell innovation. Either it's
a good place to develop innovation or not. There's
conflicting opinions, but I don't think no one
disagrees that it's the best place in the world
to sell and deploy innovation. And people, institutions
are very open to try out. The percentage of early
adopters is a lot higher. And one of the things
that you feel in the US is the tolerance to mistakes,
the tolerance to failure. So the tolerance for
failure in the US is a lot higher than in the
UK, right? Even in the UK, and certainly mainland
Europe. So I'll give you an example. If you fail
in Germany, you're a disgrace. You don't know
what you're doing. You should go home and cry
and rethink. If you bankrupt the company, then
you're absolutely a disgrace. If you bankrupt
the company in the US, you learned something.
And you might be even more likely to get hired
again because you learned how to lower company
enough that you actually then throw it to the
ground. And hopefully they'll think, okay, you
bankrupt the company. Have you learned something?
Okay, perfect. Don't do it again. And they're
different in the shift. And that's why you got
the best market for selling innovation in the
world. And even regarding venture capital, I
think it goes without saying that it's by far
the biggest venture capital market in the world.
When you moved to the US, did you find that the
size of the checks of the investors were actually
getting a lot bigger? Oh, they are. Oh, we have
a channel. So a civilian portion of our cap table,
Ricardo, is US investors. So we kind of felt
that already. And yeah, the scale is different.
It's just bigger because again, people are willing
to make more risks, not because private partners,
and we're speaking pension funds, but not only,
they're more willing to take risks. What are
pension schemes in the US? They're mostly stocks.
They're mostly SP500 and index funds and that's
it. Then you have an allocation for this, an
allocation for bonds, an allocation for whatever,
now even crypto. This I'm sure you've heard,
there's a big discussion because in the UK, most
of the pension funds, they don't have an allocation
to venture capital. They want outsized returns.
They want to return and to assure that the pension
money grows with inflation, but they're not willing
to take risks. And that makes that in the last
20 years, if you look at the unicorns and if
you look at the exits and if you look at the
IPOs in Europe and you look at the IPOs in the
US, there's no comparison. Because there's just
a lot more money and it starts with the big money.
And that's a big difference. Would you tell someone
who's just getting started actually go to the
US? spend some money opening up a company in
Delaware or something like that and just go there
because there's more money? Or would we actually
say, you know what, start in the UK and then
eventually when you get to, you know, Series
A, then start going for the U .S. for some capital.
A company in the U .S. just for the sake of opening
in the U .S. and then be based in the UK. Don't
do it. Investors are pretty sophisticated and
everyone will notice if you are local or not,
if you belong or not. Opening a Delaware C Corp
is not a silver bullet. If the question is like,
should you move to the US and build in the US
and create your team in the US? I'll tell you
my opinion. I think raising a seed round is as
hard in the US as it is in Europe, especially
if you're European. So maybe if you're in the
US, if you're American, it's a lot, maybe it's
easier for an American to raise in the US. versus
an European raising in Europe. But I believe
that an European raising in the U .S. will have
such a hard time as an European raising in Europe.
The difference is going to be valuation and the
difference is going to be size of the round.
But I have a strong conviction that if you're
not an European, then you have no reason to move
to the U .S. and you don't have U .S. investors
and you don't have U .S. customers. And you don't
have a US team and you don't have a US co -founder.
And for some reason, there's no very strong reason
why you should build that company in the US.
Then don't build it. Then stay in Europe until
Series A and then move to the US when you reach
Series A stage. All right. That makes sense.
Cause everyone's like always dreaming about the
US. Oh, but it's such a huge market. We should
go there. Well, actually, you know, hold on.
Once you have product market fit. But not before.
Luis, I know we're running out of time. If people
want to reach out to you, how can you do so?
I'm quite active and LinkedIn, so do feel free
to reach out to Luis Valen to look me up. I'm
normally quite responsive. Hopefully if you ping
me, I'll respond very happy to connect with any
like -minded entrepreneur, wannabe or current
founder or just anyone on the tech. or a health
tech ecosystem. Sounds good. Luis, thank you
so much for your time. I really appreciate it.
Thank you so much, Rico. It was really a pleasure.
The podcast is also sponsored by Notion .so.
Notion is offering six months for free for new
users. Make sure you check out our website, thestarupevents
.co .uk to read them this offer. This podcast
is also sponsored by startupnetworks .co .uk.
Startup Networks, it's an online forum where
you can find all the resources you need to run
your startup. from grants to investors to tips
and tricks on how to be successful in your startup.
This podcast is also sponsored by ODEV Tech.
ODEV Tech is your premier software development
partner. Make sure you check them out at odev
.tech.