Wouter Durville, Founder and CEO of Test Gorilla, joins hosts James Mackey and Elijah Elkins to discuss the evolution of skills tests and assessments.
He shares the latest on how Test Gorilla is helping customers screen talent more thoroughly and talks to our hosts about impactful upcoming product releases.
Thank you to our sponsor, SecureVision, for making this show possible!
Our host James Mackey
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hello, welcome to the Breakthrough Hiring Show.
I'm your host, James Mackey.
Welcome back.
We're really happy to be herewith you.
I'm here with Elijah, ourco-host.
Elijah, how you doing.
Speaker 2 (00:09):
Doing well.
Doing well, James, Thanks forhaving me here.
Speaker 1 (00:12):
Yeah, it's great to have you back on the series AF
for Hiring, and we also haveWalter Derville.
Walter is the founder and CEOof Test Gorilla.
Walter, welcome to the show.
How are you doing?
Speaker 3 (00:23):
Yeah, great, great to be on the show.
Thank you, james.
Speaker 1 (00:26):
Yeah, I'm pumped.
I think most people in theindustry have heard of Test
Gorilla very successful companyand I'm really excited to learn
more about you.
I think just off the bat, likejust from the top, let's just
I'd love to learn about you,where you're based, a little bit
about yourself, and then we canget a little bit into your
founding story too.
But I saw on LinkedIn are youin Spain?
Is that right?
Or where are you based out of?
Speaker 3 (00:45):
Yeah, that's right.
Yeah, I am Dutch, as you canprobably hear from the accent,
but I've been here in Barcelonafor over a decade.
So I moved here with my thengirlfriend.
When we had a remote companystarting to grow a little bit,
we were like, hey, we can workanywhere.
This was way before COVID.
Fantastic choice for us.
(01:06):
So when you started, tess-Grilla you were working remote,
just traveling.
Yes, yeah, I was a fan ofanother company and that was
very international and thatstarted to grow a bit.
So we're like hey, wait aminute, we don't have to be in
rainy Amsterdam, we can beanywhere.
We picked Barcelona.
Speaker 1 (01:15):
Yeah, it's funny, when I started Secure Vision I
was actually working remote andit was I had a corporate job
that I worked really hard to getreally hard.
And then I got there and I waslike I think I need to do this
entrepreneurship thing.
I was getting a little bit, Iwas grateful, but I was also
getting bored and I remember Iquit my job, started Secure
(01:37):
Vision and started traveling andfor the first like two to three
years I think closer to threeyears I was essentially
traveling.
I'd lived in like ninedifferent countries and travel.
And then the company wasgrowing and so it started to
settle down a little bit andopened an office and these types
of things.
But the remote work I starteddoing that before COVID as well
and it was a little bit trickierbecause it's harder to find
(01:58):
places with good internet.
But really what opened that upwas Airbnb, for me at least, and
that was incredible because Iwouldn't have been able to
afford traveling to just stayingin hotels or anything.
Speaker 3 (02:07):
No, way, no.
And when did you startSecureVision?
Speaker 1 (02:12):
Technically 2015.
I went full-time in 2016.
I think like February 2016.
Speaker 3 (02:19):
Nice yeah.
So way before COVID, yeah alittle bit.
Speaker 1 (02:23):
Airbnb was out.
I don't know how long it hadbeen out at that point, but it
was definitely and I don't knowif I've been able to start the
business honestly if I didn'thave Airbnb, because there's
times where I found a room in atownhome in Belgrade for $200 a
month, but at the time I didn'thave money saved up, so I don't
know how I would have been ableto do it.
But so, anyway, something moreabout your travels, like where
(02:46):
did you guys try?
Did you go straight toBarcelona or did you go to a
bunch of different places?
Speaker 3 (02:51):
Actually, my favorite country in the world is
Colombia, in South America.
So we went to Colombia for amonth together with my
girlfriend, and then also amonth to Barcelona.
Which city did we pick?
We love the culture and thenature and everything in
colombia.
I'm not sure if you've everbeen, but it's a what a country.
But it's just.
Yeah, it's a bit far away,right from family.
It's a bit less secure, ofcourse it's.
And now, with kids I have threeyoung kids I'm very happy that
(03:13):
we picked barcelona, but we werein doubt between those two yeah
, and then how?
Speaker 1 (03:18):
so how did that come about with the test gorilla?
I'd love to learn more abouthow you had that idea and
decided to take the leap offaith and start the company yeah
, yeah, of course.
Speaker 3 (03:27):
Yes, I've basically been an entrepreneur my whole
life, since I was 10, I think.
I said in candy and organizingparties and god knows what, like
all kinds of startups forprofit, not for profit, and some
of them are very serious, right, like with a business plan and
everything.
But the last company I hadbefore, test girl, I was not
like that.
It was a company that I startedtogether my wife wife, a social
enterprise selling handmadecarpets or rugs online and they
(03:50):
were made by women in developingcountries and this idea was
really born out of the nightwith a glass of wine and, hey,
it would be so nice to havethese rugs and that like really
organic.
And it just kept on growing.
It was still a relatively smallcompany.
It was a nice lifestylebusiness, I'd say, but we would
always have huge numbers ofapplicants if we would have a
vacancy, but literally up to afew thousand, and that's really
a lot, right If you're a smallcompany without even a recruiter
(04:12):
on the payroll.
So we'd get all these resumesfrom around the world and we
would really struggle with it.
And I think it attracted somany people because it was like
design and social enterprise andremote.
So there's a lot of things tothink for people.
So we got all these resumes andwe'd be like, oh my God, like
how on earth do we find the bestperson?
First of all, out ofself-interest, we just want to
(04:32):
hire the best.
But also, how do we make it afair process?
Like we were social enterprise?
We want to give everyone a fairchance, not only the people
with these Chinese brands ontheir CV that we, or CV that we,
or resume that we recognize.
And also, how do we make itefficient?
If you spend 30 seconds onevery resume, it's like days.
So we were really strugglingwith that.
Like CVs clearly didn't workfor us, or resumes, as you say,
in the US.
So we started thinking like, hey, but there must be something
(04:54):
else.
Then we introduced like littletests that we basically stole I
wouldn't say copy, but basicallystealing on the internet, like
from other places so to have alittle bit of a filter at the
beginning, like at the top ofthe recruitment funnel, let's
say.
And that really worked.
So we're like, hey, wow, thisreally works.
But it's just a lot of work andthis is not our profession,
like we don't know what we'redoing really actually.
So then I started looking as apotential customer for what Test
(05:16):
Girl has become right thetalent assessment with different
types of tests, video questions, you name it.
We can talk about it more,maybe later, but I started
searching for the product.
Hey, where is this product?
My intention was not to startthat company.
It was like I just want to buyit to have a better way of
filtering my and creating ashortlist for my candidates,
like in a data-driven way thatpredicts job success.
And it just wasn't there, andthat's how the idea was born.
(05:38):
It's a disaster.
It clearly doesn't work.
Really, has no one developedthis product yet?
So that's how the idea was born, and that was already.
When was this?
Like 2016,.
I think we launched Tesco Relayin 2020.
So that was a couple of yearsbefore that, and this was, I
(05:59):
think, many entrepreneurs.
You might recognize this.
You always have these ideas,right, they have like little
notebooks and you make notes andthey're from the craziest weird
ideas that are mostly very bad,but sometimes, sometimes, you
have a better idea.
This one really stuck.
Every time I was on a holidayor something, I was just like oh
my god, like thinking itthrough and all the things I'd
learned also as an entrepreneur,like it ticked all the boxes in
terms of scalability, solving areal problem, having positive
(06:21):
social impact, being for profitand you name it like a whole
list of things that you likelearned that I thought were
important for to set up a newbusiness in the future.
So I just kept being so excitedand I spoke with otto, my
friend, long-term friend andco-founder about this idea and
he was still a partner at banethe bane and company, the
consulting firm, and he's wow.
(06:42):
I love it so much he got likesuper excited as well, and then,
like on the plane back, he wasleaving Amsterdam.
He's like writing me emails ofmore ideas and then we took it
from there.
So that's how the idea was bornand that's how we decided to go
for it.
That was 2019.
Speaker 1 (06:56):
That's awesome, and I was looking on LinkedIn.
I didn't realize that your teamraised a $70 million series A
that's a large series a yeah,yeah, that's probably one of the
largest in europe, yeah a lotof money
Speaker 3 (07:10):
but there's also huge potential right, for it's not
just desk gorilla, but it's abit bigger than that.
It's skills-based hiring.
Right.
There's a big move away from cvor resume based hiring towards
skills-based hiring and it'saccelerating fast.
During covid, but alsopost-COVID, you really see more
and more companies adopting it.
Many companies still finding itout, finding out how to do this
, but it's a big wave and Ithink these investors also
(07:33):
recognize that.
They're like hey, there will besome winners in this mega shift
and yeah, that's how we managedto raise so much money.
And it's a very fortunateposition to be in, so we still
have a lot of cash and it allowsus to hire amazing people
scientists, you name it to buildproduct to fail, also, to try
stuff fail try again, it's avery fortunate position to be in
to be so well-funded.
Speaker 1 (07:51):
Yeah, that's an incredible success story and
you've developed a very strongbrand as well in a short amount
of time in the industry, whichis really cool.
I would love to learn a littlebit more about the nuance of the
product and what you can offerto customers.
Could you tell us a little bitmore about that?
Let's get a little more productdetail on what you offer.
Speaker 3 (08:14):
Yeah, yeah.
So we see Tasker as a talentdiscovery platform, right.
So we help organizations justto hire better, hire faster and
hire without bias throughskills-based hiring.
Now, the first product welaunched is a talent assessment.
It's our talent assessment,right.
So what you can do is in ourproduct, you can create an
assessment and then you can addup to five different tests.
So we have a very big libraryof over 400 scientifically
(08:38):
validated tests in all kinds ofcategories, like from cognitive
ability to coding tests, topersonality tests you name it.
So you can add up to fivedifferent tests in an assessment
and then add your own questionsto it.
For example, hey, why do youwant to work for our company?
With a video response?
Or here's a customer email.
How would you respond with anessay response?
So you can be quite creativethere, and I would recommend
(08:59):
making it a real life task based.
Right, you can actually see alittle bit of work from talent.
So then you have created yourassessment and then there's
different ways that you caninvite candidates for your
assessment.
So you can either invite themby email in bulk, or one by one,
or you can even put a link onyour vacancy right On the job
post.
You know, hey, this is a jobpost, blah, blah, blah.
Instead of, like in the past,people would say, hey, if you're
(09:20):
interested, email us.
We say no, if you're interested, take the assessment right, and
then you just people could takethe assessment and then, as a
customer, you log in toTestGorilla and you see all your
candidates ranked automaticallyfrom the highest scoring to the
lowest scoring, and then youcan click on them, you can see
their video responses or othertypes of questions and maybe you
can take a look at their CV andresume and then take it from
there.
But what you end up doing is youspend your time on the best
(09:42):
candidates and what we reallyrecommend, or preach almost, is
to do a top of funnel to giveevery candidate a fair chance to
stand out and remove that bias,Because it's really hard to
take out bias.
I think most or if not almost,every recruiter almost no
recruiter, let's say wants to bebiased, but you are.
It's really hard to judgepeople on stuff that you've
never heard of universities orcompanies, et cetera, and you
(10:06):
may just make it much moredata-driven this way and you
give everyone a fair chance andyou find those hidden gems right
Like you've got some amazingtalent you would surely have
missed otherwise and save a lotof time, of course.
So that's in a nutshell howthat talent assessment product
works.
And I think two, if I may like,two things that are a little
different from how the industrytraditionally functioned,
because assessments obviouslyare not new, right, they've been
(10:27):
around for a century or so, Ithink, in the Second World War
or something.
They got really big in the armyor something like that.
They've been around forever,but they were typically used
quite late in the process, right.
They're like oh, we have donethis whole funnel recruitment
funnel, right, interviewedpeople with three candidates
left and we sent them to anassessment center or something
like that.
We say, hey, let's turn thataround.
Let's first see if people arehave the skills that you need
(10:49):
for the job and then invest timein people that do so.
We flipped it around.
That was quite different.
And the other big difference isthe that I briefly touched upon
already is like the very biglibrary of tests, over 400
different tests.
And we're doing that for areason because there's academic
research that you might befamiliar with that actually
shows that the most predictivething for job predictiveness is
(11:10):
multi-measure testing.
So that's a very complicatedword, but it basically says test
for different skills inassessment, not just one thing
like only a psychrometric testor only coding skills or
something, but actually combinedifferent things and that makes
it very predictive and in's.
In a way, it's very similar towhat you do in a job interview,
right?
If you're hiring a programmer,let's say like, okay, of course
(11:31):
do you want to know, does thisperson have the JavaScript
skills or whatever coding skills?
But if that person has it, it'snot.
Oh, check, bam, you're hired.
No, of course not.
You're like okay, you're a team, so I also want to see your
management skills or yourcommunication skills and your
culture, your culture at orculture fit with the company.
Speaker 1 (11:51):
So you're also looking for these different
things and that we're mimickingin our talent assessment.
Yeah, it's like verycomprehensive right, and that's
that's exciting too.
This work as well for folks intechnology as, as well as
healthcare, manufacturinganything in between, or is it
dialed into specific industriesor types of roles like what does
that look like?
Speaker 3 (12:11):
yeah, yeah, good question.
So it's very broad.
Speaker 1 (12:14):
So our because of that huge library, you can
create very relevant assessmentsfor a very large range of jobs,
but our focus is on whitecollar jobs, right, so it's less
focused on manufacturing orthose kind of yeah, so I was
curious about my assumption wasmore so on the white collar
space, but I was wonderingbecause I know, just in terms of
expanding and whatnot and allthese different, I was just
wondering, yeah, there's likewhere do you typically see the
(12:36):
most demand?
Speaker 3 (12:37):
so, yeah, no, definitely in, in in white
collar, but then it's a one-stopshop and that's also very
consciously there's been ourpositioning and strategy, right,
right, so for a typical whitecollar company, tech company,
whatever, they can use a testcontroller for almost any role,
right Like, from the programmersto customer success, to admin,
finance.
You name it by combiningdifferent tests.
Speaker 1 (12:57):
Yeah, that's awesome, and I would be curious to get
your thoughts on how yourcompany is thinking about AI
right now.
I think all of us in this spaceand talent acquisition are
thinking about how do weactually implement AI in a
meaningful way.
That's really improving thingsfor our customers, right, and
it's challenging, right, it'schallenging sometimes to find
those use cases that are trulycompelling, that are moving
(13:18):
businesses forward, and I'mcurious to learn more about the
conversations that you might behaving with the leaders within
your organization and otherindustry leaders and investors
or customers and I don't know ifyou're currently implementing
things or you're thinking aboutit, or I'd just love to get a
sense for where your head's aton this topic right now for our
industry.
Speaker 3 (13:37):
Yeah, I find it super exciting, I think it's
fantastic for our industry also,but, yeah, it's clearly
changing.
It's changing the way we workand therefore changing the way
we hire, I would say so.
I would say, in terms ofrecruiting, first of all, the
resume and cover letters aredead right, like these days.
It must be terrible to be arecruiter, right?
You get like all these perfectchat, gpt letters or resume.
(14:00):
That clearly doesn't workanymore.
So I think that is another pushtowards skills-based hiring,
very clearly.
But then, within the skillsthat you're looking for, I think
what we hear from customers andwhat we actually see in our
data, we have around 700,000candidates per month now.
That's over 7 million years.
So we have a lot of data andwe're starting to see those
shifts.
I think away from and that goesvery slowly, I must say, but I
(14:21):
just subtly see it happeningaway from hard skills and
towards more soft skills.
For example, right, so, thetypical stuff like, let's say,
translating or basic accountingor data entry.
So many things can be done bythe AI, right, but there's still
a lot of human skills, right,in terms of leadership and
planning and you name it.
Right so, communication,there's so many that are just
(14:43):
ever more important.
You could say.
So that's one thing we see.
Another thing we see is thatit's just demand for AI tests.
So we have a lot of AI testsnow in the library a deep
learning test or a machinelearning test or a very specific
one like Keras or PyTorch.
There's all kinds of specificsoftware tests language do you
call it Language tests almostfor AI.
So there's a lot of demand forthose things as well.
(15:04):
Right, but you want people whoare efficient, who can use those
new ways of working and use AItools.
Speaker 1 (15:11):
Yeah, yeah, for sure, yeah.
And so I'm curious on the forassessments.
Right now, we're talking withCEOs that are implementing
different solutions at differentparts of the talent acquisition
funnel from everything fromsourcing to screening,
interviewing, to assessments andwhatnot and I'm curious from
(15:31):
feedback from customers or whenyou're thinking about the next
12 to 18 months, whether it bein your own product or maybe
just in talent acquisitionholistically.
Whatever you want to thinkthrough, when do you see the
most disruption or the biggestchanges occurring in terms of
implementing AI?
(15:51):
Do you see anything specific ordoes anything stand out to you?
Speaker 3 (15:56):
Yeah, if I focus on our own product.
So we're now really starting toimplement AI inside the product
.
So one exciting thing we justlaunched is an AI recommender.
Right, we have this big libraryof tests, as I said, but then
people are drowning a little bitsometimes, right, oh, which
test to use?
And I'm not the expert, right,and fair enough, right, so we
can help them manually with ourteam.
But we now have built like an airecommender that, based on all
(16:19):
the experience and scientificknowledge that we have, helps
you with a push of the buttonyou can upload a job description
and bang it gives you arecommendation for the best
assessment.
So those kind of things arevery exciting.
I think they really helpedimprove the our product but
therefore also improve hiringprocesses, I think, and then I
think, in the future, the otherthing for us that where I see a
lot of potential for ai is inreally matching those candidates
(16:42):
directly with jobs.
Right, really find from themega database of millions of
candidates, but now sort of tensof thousands of jobs, like to
say, hey, these are the bestmatch based on all the data
points that we have.
That's very exciting for us aswell.
Speaker 1 (16:56):
Do you see your team ever going in the direction of
actually doing more of thepotentially like AI interviewing
?
I know it's like you're doing alot of the tests and
assessments.
We're seeing some companiesgoing in that direction too,
where there's even like someproducts where it's like the AI
video interviewing.
I know you said there's also anelement to some of your tests
where correct me if I'm wrong,but I think you said like people
are responding via video.
Do you see yourselfincorporating AI into those
(17:20):
types of tests where candidatesare responsive, to have more of
like an interactive experiencethere, or do you feel like
that's really a differentproduct?
That's not where you play, likeyou're going to stay laser
focused on more so just thetesting and eval versus the AI
interviewing.
What are your thoughts on that?
Speaker 3 (17:38):
Yeah, it's not our immediate focus but it's
definitely something that we'rediscussing and looking at
Because indeed, like you said,we call it custom questions, so
you can add these custom alsovideo questions to your
assessment and we have a bigquestion bank for that as well.
To help customers Say, hey,you're hiring for a front-end
developer, here's a set ofquestions that you can really
help to do in a skills-basedkind of way ask questions to
(17:58):
your candidates.
Analyzing those responses wouldbe a very logical thing.
To put ai on top of that.
Say, here's an automatic kindof rating of those responses,
something we're not offering now.
So now it's the customer who'swatching those responses.
But this would be a very kindof logical thing, right where to
apply ai to, and then the nextstep could be to just have an ai
bot there right to do thatthose questions to answer, to
(18:18):
ask and answer those questionsor have the conversation with
with candidates yeah, there's.
Speaker 1 (18:23):
there's a lot of money being poured into into the
space right now.
I know there's a company calledMercor, based out of I think
they're out of San Francisco nowseries.
They just raised a series A for30 million and it's really.
It's really based around likeAI, video interviewing, yeah,
assessment.
So I think they're actuallyconnecting like engineering
talent to companies based inSilicon Valley or primarily like
(18:47):
that tech hyper growth scene,and essentially, like the
candidates are proactive.
It's a different business modelthan what you're doing.
But I'm just saying, like, inthat space of like cancer, going
on doing interviews and thengetting linked up to different
companies, so different kind ofvariations of that business
model, we're seeing a lot ofmoney get poured into right now.
(19:08):
Yeah, so it's just it's a wildtime and I yeah, so I think it's
just it's an interesting usecase and it's yeah, it's uh,
kind of it's definitely not whatyou're doing.
It's like adjacent or it's likesimilar in a sense, because
it's like the eval in a sense,but it's just from a different
angle.
But I don't think they'reinvesting in like the testing or
anything like that.
It's nothing like that.
It's more of just, I assume,matching role requirements that
(19:31):
are provided by a hiring teamand giving the AI instructions
on hey, figure this stuff out.
I want you to ask questionsabout this stuff.
Speaker 3 (19:39):
Yeah, but yeah it's hard, I think the investors are
pouring in a lot and I would dothe same.
I guess they're just sprayingmoney a little bit to see, like,
what's going to work.
Speaker 1 (19:46):
Yeah.
Speaker 3 (19:47):
I don't have an opinion on this particular
company, but I think it's a lot.
Of them are a little bitfeature-based right, like
smaller features in the product.
It's hard to more like afeature almost inside the
platform and a big standalonecompany.
Speaker 1 (20:05):
But maybe I'm wrong, oh yeah, for sure, and it's
interesting too, and I think too, it's like some of the more
like well-established companiesthat are like a holistic product
which, at this point, tuscarorais definitely evolving so fast.
Right, it's almost like being anearly adopter, of implementing
(20:25):
this type of stuff into yourproduct isn't necessarily the
idea of just being first.
It is not always ideal, becauseI'm thinking about just like
some of the I've been talkingwith different uh founders and
and ceos of uh like ai hiringproducts and it's they're
telling me the way that we'rebuilding our product now is
completely different than we,how we were thinking about
building it like three monthsago.
It's like things are justmoving so fast and I think it's
(20:46):
some of our more establishedCEOs that I speak with
Greenhouse's CEO and SteveBartel over at Gem and how
they're thinking about AI,they're implementing, they're
doing things, but it's a littlebit more reserved where they're
just sitting back and watching.
They're like, okay, we havethis great product and solution,
we're delivering, but we're notgoing to jump the gun and just
(21:06):
overcommit in one area andreally sit back and say, okay,
what are the use cases that areactually really driving business
forward and looking at whatother people are doing a little
more yeah.
Speaker 3 (21:16):
And you want to prevent introducing bias also in
your product and in our space.
It's so sensitive, right likewith ai, and there's been quite
a few cases where it went wrong,so we definitely want to be on
the sensitive side and that yeah, yeah, for sure, for sure.
Speaker 1 (21:30):
Yeah, that's an interesting one I'm still
waiting to.
I know we brought up on theshow a couple times workday
class action lawsuit we're.
I don't know if you've heard ofthat one.
Yeah, well, yeah yeah, so.
Yeah, so Workday is getting.
It's like a class actionlawsuit.
They're getting sued forpotentially their AI talent
acquisition assistantdiscriminating on candidates
based on race and age andwhatnot.
(21:50):
So it's yet to be seen what'sactually happening there, like
what will happen and how validthe claims are and whatnot.
But it'll be interesting too,because it also set a precedent
for the industry on, I think, alot of the laws and regulations
yeah, yeah, very much yeah.
Speaker 3 (22:06):
And then a bit longer ago we had higher views.
Well, they also had, like withthe video interviewing, quite a
few issues, I think with biasand that creeped in oh really,
which one was?
Speaker 1 (22:15):
yeah, I know higher view.
I'm trying to think.
Speaker 3 (22:18):
I think that was how many five, six ago, maybe a
higher view oh yeah.
Speaker 2 (22:25):
I think what you mentioned earlier about there is
super interesting and isn'tonly specific to the TA space of
what things are going to belike an AI powered feature
within a true solution and atrue platform, versus all of
these, what might be called liketools that are being money is
(22:46):
being thrown at them by VC firmsbut they don't actually have a
clear vision of how they'regoing to become a platform right
, like they're a little toolthat has taken chat, gpt or
maybe some other LLMs andthey're doing just a little bit
more than what you could maybedo on your own right With the
(23:07):
standard version of GPT.
Maybe they have some proprietarydata sources, et cetera.
I think it's the ones who, inmy personal opinion, who will
win are the ones who can buildtrue solutions where you have a
true platform right that isbringing a solution and solving
a problem and are using AIreally well and can continue
(23:31):
architect the platform in a waywhere, as AI develops further,
you can continue to develop yourproduct right, so the product
is very like AI-centric movingforward versus platforms where
the AI is only a feature orwhere it's an AI tool startup
that has no path to become aplatform.
(23:51):
Does that make sense at all?
Speaker 3 (23:53):
Yeah, 100%.
That's a more eloquent way ofputting it.
That's exactly my point.
I very much agree with that.
So I think that's alsosomething we internally always
look at.
Ai is really a means to an end.
Right, it's not the end, it'snot.
Oh, we have AI.
No, this is a very useful toolreally to indeed do stuff better
, to recommend better tests, tomatch people better.
Those are fantasticapplications, but it is a way to
(24:16):
get there, literally.
We had a conversation earlierthis week for a new product.
We're building around talentsourcing and it's like I don't
really care, and it soundsreally bad.
There was people like machinelearning experts and all these
fantastic people in our team.
We're like I don't really carehow you do it, I just want to
see great results, and if it'sAI, fantastic.
If it's Algolia or whateverother tool, it's also great.
I just want to as.
Speaker 1 (24:37):
No, I don't think you do yeah, I don't think so.
I also think it's like thisconcept of I don't know to me
it's every company isessentially going to become.
This idea of an AI company isvery quickly going to become
obsolete.
It's just going to be part ofhow tech stacks are built.
Speaker 2 (24:54):
Yeah, it's like cloud right AWS.
Who's talking about?
We have AWS.
Speaker 1 (25:00):
We are a cloud company, yeah like we're a cloud
company.
Speaker 2 (25:02):
Nobody's saying that anymore right, it's funny.
Speaker 1 (25:05):
My buddy was like it's like saying you have a
refrigerator in the house,you're not a refrigerator
company or house.
Yeah, yeah, that's like stuffyou have in a house.
Yes, obviously, yeah, and soit's.
I think it's it's like theconcept of the tech industry.
I feel like the concept of thetech industry is going to
disappear.
Every industry is going to bethe tech industry.
(25:27):
Every company, every doesn'tmatter industry is going to be
tech.
Speaker 3 (25:29):
Yeah, if I look at our venture capital, we've got
quite a few venture capitalfirms on our cap table, but they
only invest in tech, but yeah.
Speaker 2 (25:41):
Insured tech.
You just throw tech on whateverindustry and that concept is
gonna.
Speaker 1 (25:50):
I think it's cool and it's interesting because I
think some of the we're alreadyseeing a lot of the startups
that maybe names that we thatyou got investment a couple
years ago like you just don'thear of them anymore.
I think a lot of them haveprobably gone out of business.
I think a lot of them haveprobably gone out of business.
I think a lot of companiesstarted Elijah Tell me if I'm
wrong, you probably know moreabout this than I do but a lot
of the co-pilot stuff and likehelp helping with content
(26:11):
generation.
There's just so many tools thatkind of popped up initially
around those and I actuallydon't see quite as many of those
anymore.
I don't think I don't comeacross as I wonder if they just
already gone out of business,but I think so probably right.
Speaker 2 (26:24):
They have to get, yeah, traction and as soon as
some of the bigger players, likeCanva, for example, in content
creation, if you had this littleagain tool where you could
generate, let's say, videos, oryou can make some slides, and
that was like their tool, andthen canvas, like, made it a
feature right, one of their manyfeatures all of a sudden, boom,
(26:48):
you're done.
So I think that's the big risk.
Sometimes, right, with these,what's more of an AI tool than a
true platform is.
The bigger players are going tofigure it out pretty quickly.
If you're a one trick pony, ifyou will, and that's the main
thing and then they integratethat.
Maybe they have more scaleright their pricing.
You're already paying for itand they add it for the marginal
(27:11):
increase.
Let's say, you pay $5 more amonth, but the standalone tools
like 60 bucks a month, likeyou're, it's like a no brainer.
You're going to use canva's newfeature for five bucks versus
that ai slide creation tool for.
So, yeah, I think you're right.
Speaker 3 (27:27):
I think they're falling by the wayside as these
bigger companies add more aipowered features yeah, 100, and
I think the other thing is likethere's a lot of these ai stars.
What I hear from these venturecapital firms is they have quite
a bit of traction, initiallyright, because every cto or cmo
or you name it has budget.
Do I hear ai here's budget?
right so there's a lot ofinitial traction, which is
(27:48):
fantastic to be in, right, as astartup, but then to keep those
customers right, there's a lotof churn.
If I look at myself, I'mchurning a lot like I've tried
quite a few things and I out,out, out and I go, yeah, it's
brutal, right, so you have toopen and, with these new models
coming out and indeed biggercompanies adopting them
themselves or buying them,buying these companies and
incorporating them, it's, it'sbrutal.
It's not easy to build like astandalone company.
Speaker 1 (28:09):
I think yeah, I know definitely, but yeah, it has to
be, like I think, centeredaround the solving the problem.
Like, elijah, what you said,like not looking at ai is like
an end-all be-all.
Like we're here to to solve aproblem and to add as business,
as executives over here, to foropportunity and to create value.
Yeah, we're solving the rightproblem that will create that
(28:31):
type of opportunity, that marketopportunity for investors, if
that's the plan, I think, havingthat vision too, james.
Speaker 2 (28:36):
So maybe you start with this one tool and a lot of
companies I think that's the endof it they almost see that as
the end goal instead of, let'ssay, you start with, I don't
know, ai interviewing or maybeit's AI powered testing or an AI
powered ATS something.
But your vision is to likebecome, let's say, an AI powered
(28:59):
greenhouse or AI agent poweredworkday like.
You've got this kind of like abigger vision of where it can go
and what it can become, even ifyou're only starting with one
tool and I'm just not sureenough of the founders are, you
know, getting further ahead thanthat where they can pitch to
the investors and theircustomers and even their own
(29:21):
team.
We're not just creating thisone tool like we see the future
of hiring in 10 years being, youknow x and the ai is going to
be helping with all thesedifferent things, and we're
building for that day.
We're not building for sixmonths from now.
Only if that makes sense.
Speaker 1 (29:38):
I don't know, it's just my opinion that's the issue
with, I think, a lot of the theco-pilot stuff and the content
creation stuff is it's like what, where do you really build from
from there?
It's like there's no logicallike why is that company well
positioned to build in any?
Why would we back thesefounders?
Like these founders, there's nocompelling story like in terms
(30:02):
of because your customerfeedback is on product roadmap
is just going to be so dispersedbecause it's just such a big
general little feature versussolving really dialing in and
solving like one one important Ileverage problem, like some of
the use cases that we're seeing,whether it's a gorilla or some
of the AI interviewing, which Ithink is a very valuable use
case.
It's like that, if you like, Ilove AI interviewing right.
(30:25):
That's a space that I'm reallyexcited about right now and to
me, it's.
I like the idea of some ofthose companies I think will be.
I think a fair amount of themwill be very successful because
it's also very logical.
It's like you identify a nichecustomer base, like ideally,
some of them are sprayed wide.
Very logical.
It's like you identify like aniche customer base like ideally
, some of them are identifiedyour customer base.
They're going to give youproduct feedback that will
logically build out more of aholistic product and it's you're
(30:47):
going to get pulled in a morespecific direction and, like
some of those initial aiproducts were just like these
vague kind of note takers.
Are you like, what are you goingto do?
You're going to go like intocrm space.
That sounds like that's a hugeundertaking.
What are you going to do?
You're gonna go like into crmspace.
That sounds like that's a hugeundertaking.
What are you gonna do?
You're gonna go into, like,sales enablement, like it's just
so.
It is like a very small featureand it's also it's not hard to
(31:11):
build.
Like some of these things too.
It's you get, I don't know,like a full-time, mid junior
level engineer can get prettyfar on on some of this stuff.
Like it's like literally thatsimple.
Like somebody who knows alittle bit of Python can do an
open AI plugin like API.
It's just not that hard to dosome of this stuff.
(31:32):
It's just that's not a, that'snot a product.
Speaker 3 (31:34):
Yeah, I completely agree with that.
For, speaking about test career, we see ourselves as a talent
discovery platform.
We have seen ourselves from thebeginning right Starting with
these tests, but only expandingtowards talent assessment right
and much way more tests, moresophisticated tests, more
science-backed adding like thesevideo questions et cetera and
AI video is a very logical onefor the future as well and then
(31:55):
expanding to talent sourcingright, but it's like a much
bigger vision in it, I thinkthat's a good way to go.
Speaker 1 (32:01):
It's awesome and it's also cool to see.
I think we're seeing some ofthe most successful recruiting
tech products, like expandinginto a lot more product lines,
which has been a huge push overthe last year or two.
Specifically, we had the CEO,nikos of Workable and there
seems, geez, they have a productfor everything.
And then you see, like Gemright, they started out as like
(32:24):
outbound sourcing cadences forcandidate outreach, email data,
that kind of thing, and now theyhave a CRM, they have the
applicant tracking system.
There's other products too andthey're expanding within that,
and so I was actually reallycurious to learn a little more.
We got a few more minutes here.
You're expanding within that,and so I was actually really
curious to learn a little more.
We've got a few more minuteshere.
You're expanding into sourcing.
Could you tell me, could youtell us a little bit about what
(32:46):
went into that decisionstrategically to expand your
product offering into sourcingtechnology?
Speaker 3 (32:52):
Yeah, definitely yeah .
So it goes back a little to ourmission and vision.
So our mission is todemocratize opportunity for
talent from all walks of lifewith a vision of a billion
people in dream jobs.
So it's a kind of an ambitiousvision, you could say.
And this ties into it very well.
So with our assessment product,of course, anyone has all of a
sudden a chance to stand outright and be noticed and picked
(33:13):
up by recruiters, and not justonly people who have a fantastic
CV.
And then the next step basically, I'm looking at our own
databases.
You could say, on the one hand,you have these millions of
candidates who are now takingthese assessments, right, so we
have all that data about thesepeople who are looking for their
dream job.
On the other hand, we havethese tens of thousands of jobs,
right, and they're looking foramazing talent and we're sitting
(33:33):
on both and our mission is likethe vision of a billion people
in dream jobs mission is like tothe vision of a billion people
in dream jobs and we're.
It's like connecting twodatabases.
So for us it was so logical andin line with what we're doing
to start connecting those.
But it's a little bit chickenand egg, right, if you start
with, that's really hard, right,if you have zero, zero
candidates, zero jobs, like it'sa bad place to start.
So we started from theassessment.
But now that we have, yeah,quite a bit of traction and so
(33:56):
many jobs and people, we feelit's a very logical next step to
say, hey, we connected to andalso there's just so much waste
in recruitment.
You guys know very well, Ithink, for every job we have
like hundreds of candidates ordepending on the company of
course, but only one get hired,gets hired, and all these other
candidates have invested so muchin that process as well.
So I think it's also verycandidate friendly.
They kind of say, hey, arethere ways we can still match
(34:17):
them without opportunities?
Speaker 1 (34:18):
so is that like essentially, if a candidate
completes a test for oneorganization, are they able to
use that assessment or test thatthey completed to submit to
other jobs?
Is that essentially how itwould?
Yeah that's right.
Speaker 3 (34:30):
So they can reuse their scores.
It's their scores, right.
It's not a company.
The company doesn't own it.
If you look at, privacy lawsand everything.
It's your cognitive ability,your personality.
So, yeah, the idea is that theycan reuse that right To say,
hey, maybe this was not a greatmatch, but hey, did you think
about these?
Roles Like these would befantastic.
Speaker 1 (34:46):
Yeah, so that's okay.
So then can will the view be, acompany can put in search
criteria for a certain skill setand then basically the profiles
with people with completedassessments they can click
through and then set up like aninterview with or or how does
that?
Speaker 3 (35:01):
that's right yeah that's right, so indeed can in
the future, the customers willbe able to.
It's basically, you could say,close to linkedin.
Linkedin is the biggest uhdatabase of resumes in the world
, but we are taking a differentapproach.
It's skills based.
So this will be we're creatinga huge database of skills.
So if you're saying, hey, Ilook for someone who speaks
Dutch and I'm Dutch and knowsreally well JavaScript and a
(35:22):
great fit with our culture, andwe know who those people are, we
have tested them.
So we have all that data onpeople, so we can tell you, hey,
look at these people and invitethem to your what is?
Speaker 1 (35:31):
that Some platforms like that, like you think about,
it's like limited right, whereit's you're hiring people on a
project basis or something,where it's like it has to flow
through the platform so peopledon't go around it.
Is it going to be like astructured, a little bit like
one of those types ofmarketplaces, or is it going to
be like okay, this is for W2,full-time employment hires.
(35:54):
What does that motion look like?
So, as a company, I'm goingthrough, I'm clicking on a
profile.
Okay, I want to work with Sally.
Speaker 3 (36:05):
What happens from there right now.
It's closer to linkedin and ofcourse you can find people for
any type of role, but I wouldsay it's for permanent roles
just like people are using ouror can, or companies are using
our platform now, use it to findpermanent employees, but now
you can also source them.
So, because, at this moment,like, people bring their own or
they post their own links andvacancies everywhere, but now we
can say, hey, here, actually,our skills tested candidates are
ready so you can actually haveaccess to that database and
(36:26):
invite them for your jobs.
That's the and their skill sets.
That's the big difference,right with existing platforms so
how does that?
Speaker 1 (36:33):
can we get into whatever you're comfortable
sharing in terms of pricing forthat, like how companies will
engage in the pricing model tosource candidates through your
product.
Speaker 3 (36:42):
I can't, because we don't.
It's not live yet.
Speaker 1 (36:44):
So we're building it as we speak.
We don't have the pricing yet.
Speaker 3 (36:48):
There will be a price , but I guess it will be
something similar to somethingaround the number of people that
you invite or something.
That's probably the way weprice it.
Speaker 1 (36:56):
Oh, yeah, that's a cool idea, like I always because
in those business models,that's like always where my head
goes Okay, like how do youstructure that aspect of when
folks actually engage?
Like I'm thinking like from thebusiness perspective, from your
perspective too, but yeah,that's like a ton of value
because, you're right, you'realready sitting on these
(37:36):
databases, so it's like a veryit's not like you, yeah, yeah,
but I think companies, there'slike more interesting
applications happening right nowand I honest I find the test
grill application a lot moreinteresting because the
evaluations you're doing are alot more thorough than the
assessments and whatnot.
And then also it's there's someof these other platforms too
that are doing this marketplaceish.
I don't know if that's theright, if we could call it a
(37:58):
marketplace, but yeah, I justdon't think it's.
There's also limitations interms of how folks can engage
with those talent.
So if you think about somethinglike an Upwork which I know is
not a great analogy because it'sthis is way different and
better in my opinion, but likestill like the concept of
companies searching for profiles, it's like very limited in
scope, like you can't really putsomebody through a full
(38:18):
interview process, like youcan't talk to them outside of
the platform.
You're not hiring them as a w-2right in the us, it's not.
So it's it's very limited andthen you only are typically, at
least in my experience workingwith the tool.
I might be I don't know, maybeI don't know everything about it
, but it's just limited and likelimits this project scope.
But there isn't.
I haven't seen a tool that'slike really effectively doing
(38:39):
that, like on the W2 side, likefull time with this level of
like vetting and assessmentthat's taking place.
And again, this is a lot, Ithink, a lot more valuable than
what AI is doing currently.
It's different but like stillthere's a lot more value in all
these deep technical assessmentsand tests than, I think, just
AI screening for rolerequirements.
Speaker 2 (39:01):
It's a different, it's a for role requirements.
Speaker 1 (39:02):
It's a different.
It's a totally different.
It's a different use case.
But I guess what I'm saying isI see a ton of value in that and
I could see the sourcing piecebeing like this product being
like huge right, like I thinkpulling off that correctly and
just like with the data you have, like that could be a massive
product.
Speaker 3 (39:18):
Yeah that that's what I think too.
It's a lot of work to get itright, but, yeah, we're in a
very unique position to have allthat data, like you said, to
have those millions ofcandidates and then to have all
that data, and then you canapply AI to it.
So to say to indeed find thebest way of matching jobs.
Speaker 1 (39:33):
Yeah, it's exciting.
For sure it's a massiveundertaking, though Do you have
any kind of timeline on whenyour team is thinking about
going live with this product, oris it still in?
Speaker 3 (39:45):
For sure next year, so in 2025, we'll go live.
The exact month I can't tellyou.
I don't know.
We're still working very hard,but a lot of work has been done
already, so it's getting close.
Speaker 1 (39:53):
That's awesome when you're about to go live or when
you're ready.
When you want to, you shouldcome back on the show and we can
talk about it.
I would love to learn about theproduct when it's ready to go
live and continue theconversation.
Speaker 3 (40:06):
I would love that.
Yeah, thanks, james.
Speaker 1 (40:08):
Awesome.
Look, we're coming up on time.
That was a flu.
Bias was a lot of fun.
Walter, thank you so much forjoining us today and sharing
your insights.
I know people are going to geta lot of value out of this
conversation, so thank you somuch for joining us.
Speaker 3 (40:20):
Great, I love being here.
Thank you so much.
Speaker 1 (40:23):
Awesome, all right.
Thanks for tuning in everyone.
We'll talk to you soon, takecare.
Hello, welcome to the Breakthrough Hiring Show.
I'm your host, James Mackey.
Welcome back.
We're really happy to be herewith you.
I'm here with Elijah, ourco-host.
Elijah, how you doing.
Speaker 2 (00:09):
Doing well.
Doing well, James, Thanks forhaving me here.
Speaker 1 (00:12):
Yeah, it's great to have you back on the series AF
for Hiring, and we also haveWalter Derville.
Walter is the founder and CEOof Test Gorilla.
Walter, welcome to the show.
How are you doing?
Speaker 3 (00:23):
Yeah, great, great to be on the show.
Thank you, james.
Speaker 1 (00:26):
Yeah, I'm pumped.
I think most people in theindustry have heard of Test
Gorilla very successful companyand I'm really excited to learn
more about you.
I think just off the bat, likejust from the top, let's just
I'd love to learn about you,where you're based, a little bit
about yourself, and then we canget a little bit into your
founding story too.
But I saw on LinkedIn are youin Spain?
Is that right?
Or where are you based out of?
Speaker 3 (00:45):
Yeah, that's right.
Yeah, I am Dutch, as you canprobably hear from the accent,
but I've been here in Barcelonafor over a decade.
So I moved here with my thengirlfriend.
When we had a remote companystarting to grow a little bit,
we were like, hey, we can workanywhere.
This was way before COVID.
Fantastic choice for us.
(01:06):
So when you started, tess-Grilla you were working remote,
just traveling.
Yes, yeah, I was a fan ofanother company and that was
very international and thatstarted to grow a bit.
So we're like hey, wait aminute, we don't have to be in
rainy Amsterdam, we can beanywhere.
We picked Barcelona.
Speaker 1 (01:15):
Yeah, it's funny, when I started Secure Vision I
was actually working remote andit was I had a corporate job
that I worked really hard to getreally hard.
And then I got there and I waslike I think I need to do this
entrepreneurship thing.
I was getting a little bit, Iwas grateful, but I was also
getting bored and I remember Iquit my job, started Secure
(01:37):
Vision and started traveling andfor the first like two to three
years I think closer to threeyears I was essentially
traveling.
I'd lived in like ninedifferent countries and travel.
And then the company wasgrowing and so it started to
settle down a little bit andopened an office and these types
of things.
But the remote work I starteddoing that before COVID as well
and it was a little bit trickierbecause it's harder to find
(01:58):
places with good internet.
But really what opened that upwas Airbnb, for me at least, and
that was incredible because Iwouldn't have been able to
afford traveling to just stayingin hotels or anything.
Speaker 3 (02:07):
No, way, no.
And when did you startSecureVision?
Speaker 1 (02:12):
Technically 2015.
I went full-time in 2016.
I think like February 2016.
Speaker 3 (02:19):
Nice yeah.
So way before COVID, yeah alittle bit.
Speaker 1 (02:23):
Airbnb was out.
I don't know how long it hadbeen out at that point, but it
was definitely and I don't knowif I've been able to start the
business honestly if I didn'thave Airbnb, because there's
times where I found a room in atownhome in Belgrade for $200 a
month, but at the time I didn'thave money saved up, so I don't
know how I would have been ableto do it.
But so, anyway, something moreabout your travels, like where
(02:46):
did you guys try?
Did you go straight toBarcelona or did you go to a
bunch of different places?
Speaker 3 (02:51):
Actually, my favorite country in the world is
Colombia, in South America.
So we went to Colombia for amonth together with my
girlfriend, and then also amonth to Barcelona.
Which city did we pick?
We love the culture and thenature and everything in
colombia.
I'm not sure if you've everbeen, but it's a what a country.
But it's just.
Yeah, it's a bit far away,right from family.
It's a bit less secure, ofcourse it's.
And now, with kids I have threeyoung kids I'm very happy that
(03:13):
we picked barcelona, but we werein doubt between those two yeah
, and then how?
Speaker 1 (03:18):
so how did that come about with the test gorilla?
I'd love to learn more abouthow you had that idea and
decided to take the leap offaith and start the company yeah
, yeah, of course.
Speaker 3 (03:27):
Yes, I've basically been an entrepreneur my whole
life, since I was 10, I think.
I said in candy and organizingparties and god knows what, like
all kinds of startups forprofit, not for profit, and some
of them are very serious, right, like with a business plan and
everything.
But the last company I hadbefore, test girl, I was not
like that.
It was a company that I startedtogether my wife wife, a social
enterprise selling handmadecarpets or rugs online and they
(03:50):
were made by women in developingcountries and this idea was
really born out of the nightwith a glass of wine and, hey,
it would be so nice to havethese rugs and that like really
organic.
And it just kept on growing.
It was still a relatively smallcompany.
It was a nice lifestylebusiness, I'd say, but we would
always have huge numbers ofapplicants if we would have a
vacancy, but literally up to afew thousand, and that's really
a lot, right If you're a smallcompany without even a recruiter
(04:12):
on the payroll.
So we'd get all these resumesfrom around the world and we
would really struggle with it.
And I think it attracted somany people because it was like
design and social enterprise andremote.
So there's a lot of things tothink for people.
So we got all these resumes andwe'd be like, oh my God, like
how on earth do we find the bestperson?
First of all, out ofself-interest, we just want to
(04:32):
hire the best.
But also, how do we make it afair process?
Like we were social enterprise?
We want to give everyone a fairchance, not only the people
with these Chinese brands ontheir CV that we, or CV that we,
or resume that we recognize.
And also, how do we make itefficient?
If you spend 30 seconds onevery resume, it's like days.
So we were really strugglingwith that.
Like CVs clearly didn't workfor us, or resumes, as you say,
in the US.
So we started thinking like, hey, but there must be something
(04:54):
else.
Then we introduced like littletests that we basically stole I
wouldn't say copy, but basicallystealing on the internet, like
from other places so to have alittle bit of a filter at the
beginning, like at the top ofthe recruitment funnel, let's
say.
And that really worked.
So we're like, hey, wow, thisreally works.
But it's just a lot of work andthis is not our profession,
like we don't know what we'redoing really actually.
So then I started looking as apotential customer for what Test
(05:16):
Girl has become right thetalent assessment with different
types of tests, video questions, you name it.
We can talk about it more,maybe later, but I started
searching for the product.
Hey, where is this product?
My intention was not to startthat company.
It was like I just want to buyit to have a better way of
filtering my and creating ashortlist for my candidates,
like in a data-driven way thatpredicts job success.
And it just wasn't there, andthat's how the idea was born.
(05:38):
It's a disaster.
It clearly doesn't work.
Really, has no one developedthis product yet?
So that's how the idea was born, and that was already.
When was this?
Like 2016,.
I think we launched Tesco Relayin 2020.
So that was a couple of yearsbefore that, and this was, I
(05:59):
think, many entrepreneurs.
You might recognize this.
You always have these ideas,right, they have like little
notebooks and you make notes andthey're from the craziest weird
ideas that are mostly very bad,but sometimes, sometimes, you
have a better idea.
This one really stuck.
Every time I was on a holidayor something, I was just like oh
my god, like thinking itthrough and all the things I'd
learned also as an entrepreneur,like it ticked all the boxes in
terms of scalability, solving areal problem, having positive
(06:21):
social impact, being for profitand you name it like a whole
list of things that you likelearned that I thought were
important for to set up a newbusiness in the future.
So I just kept being so excitedand I spoke with otto, my
friend, long-term friend andco-founder about this idea and
he was still a partner at banethe bane and company, the
consulting firm, and he's wow.
(06:42):
I love it so much he got likesuper excited as well, and then,
like on the plane back, he wasleaving Amsterdam.
He's like writing me emails ofmore ideas and then we took it
from there.
So that's how the idea was bornand that's how we decided to go
for it.
That was 2019.
Speaker 1 (06:56):
That's awesome, and I was looking on LinkedIn.
I didn't realize that your teamraised a $70 million series A
that's a large series a yeah,yeah, that's probably one of the
largest in europe, yeah a lotof money
Speaker 3 (07:10):
but there's also huge potential right, for it's not
just desk gorilla, but it's abit bigger than that.
It's skills-based hiring.
Right.
There's a big move away from cvor resume based hiring towards
skills-based hiring and it'saccelerating fast.
During covid, but alsopost-COVID, you really see more
and more companies adopting it.
Many companies still finding itout, finding out how to do this
, but it's a big wave and Ithink these investors also
(07:33):
recognize that.
They're like hey, there will besome winners in this mega shift
and yeah, that's how we managedto raise so much money.
And it's a very fortunateposition to be in, so we still
have a lot of cash and it allowsus to hire amazing people
scientists, you name it to buildproduct to fail, also, to try
stuff fail try again, it's avery fortunate position to be in
to be so well-funded.
Speaker 1 (07:51):
Yeah, that's an incredible success story and
you've developed a very strongbrand as well in a short amount
of time in the industry, whichis really cool.
I would love to learn a littlebit more about the nuance of the
product and what you can offerto customers.
Could you tell us a little bitmore about that?
Let's get a little more productdetail on what you offer.
Speaker 3 (08:14):
Yeah, yeah.
So we see Tasker as a talentdiscovery platform, right.
So we help organizations justto hire better, hire faster and
hire without bias throughskills-based hiring.
Now, the first product welaunched is a talent assessment.
It's our talent assessment,right.
So what you can do is in ourproduct, you can create an
assessment and then you can addup to five different tests.
So we have a very big libraryof over 400 scientifically
(08:38):
validated tests in all kinds ofcategories, like from cognitive
ability to coding tests, topersonality tests you name it.
So you can add up to fivedifferent tests in an assessment
and then add your own questionsto it.
For example, hey, why do youwant to work for our company?
With a video response?
Or here's a customer email.
How would you respond with anessay response?
So you can be quite creativethere, and I would recommend
(08:59):
making it a real life task based.
Right, you can actually see alittle bit of work from talent.
So then you have created yourassessment and then there's
different ways that you caninvite candidates for your
assessment.
So you can either invite themby email in bulk, or one by one,
or you can even put a link onyour vacancy right On the job
post.
You know, hey, this is a jobpost, blah, blah, blah.
Instead of, like in the past,people would say, hey, if you're
(09:20):
interested, email us.
We say no, if you're interested, take the assessment right, and
then you just people could takethe assessment and then, as a
customer, you log in toTestGorilla and you see all your
candidates ranked automaticallyfrom the highest scoring to the
lowest scoring, and then youcan click on them, you can see
their video responses or othertypes of questions and maybe you
can take a look at their CV andresume and then take it from
there.
But what you end up doing is youspend your time on the best
(09:42):
candidates and what we reallyrecommend, or preach almost, is
to do a top of funnel to giveevery candidate a fair chance to
stand out and remove that bias,Because it's really hard to
take out bias.
I think most or if not almost,every recruiter almost no
recruiter, let's say wants to bebiased, but you are.
It's really hard to judgepeople on stuff that you've
never heard of universities orcompanies, et cetera, and you
(10:06):
may just make it much moredata-driven this way and you
give everyone a fair chance andyou find those hidden gems right
Like you've got some amazingtalent you would surely have
missed otherwise and save a lotof time, of course.
So that's in a nutshell howthat talent assessment product
works.
And I think two, if I may like,two things that are a little
different from how the industrytraditionally functioned,
because assessments obviouslyare not new, right, they've been
(10:27):
around for a century or so, Ithink, in the Second World War
or something.
They got really big in the armyor something like that.
They've been around forever,but they were typically used
quite late in the process, right.
They're like oh, we have donethis whole funnel recruitment
funnel, right, interviewedpeople with three candidates
left and we sent them to anassessment center or something
like that.
We say, hey, let's turn thataround.
Let's first see if people arehave the skills that you need
(10:49):
for the job and then invest timein people that do so.
We flipped it around.
That was quite different.
And the other big difference isthe that I briefly touched upon
already is like the very biglibrary of tests, over 400
different tests.
And we're doing that for areason because there's academic
research that you might befamiliar with that actually
shows that the most predictivething for job predictiveness is
(11:10):
multi-measure testing.
So that's a very complicatedword, but it basically says test
for different skills inassessment, not just one thing
like only a psychrometric testor only coding skills or
something, but actually combinedifferent things and that makes
it very predictive and in's.
In a way, it's very similar towhat you do in a job interview,
right?
If you're hiring a programmer,let's say like, okay, of course
(11:31):
do you want to know, does thisperson have the JavaScript
skills or whatever coding skills?
But if that person has it, it'snot.
Oh, check, bam, you're hired.
No, of course not.
You're like okay, you're a team, so I also want to see your
management skills or yourcommunication skills and your
culture, your culture at orculture fit with the company.
Speaker 1 (11:51):
So you're also looking for these different
things and that we're mimickingin our talent assessment.
Yeah, it's like verycomprehensive right, and that's
that's exciting too.
This work as well for folks intechnology as, as well as
healthcare, manufacturinganything in between, or is it
dialed into specific industriesor types of roles like what does
that look like?
Speaker 3 (12:11):
yeah, yeah, good question.
So it's very broad.
Speaker 1 (12:14):
So our because of that huge library, you can
create very relevant assessmentsfor a very large range of jobs,
but our focus is on whitecollar jobs, right, so it's less
focused on manufacturing orthose kind of yeah, so I was
curious about my assumption wasmore so on the white collar
space, but I was wonderingbecause I know, just in terms of
expanding and whatnot and allthese different, I was just
wondering, yeah, there's likewhere do you typically see the
(12:36):
most demand?
Speaker 3 (12:37):
so, yeah, no, definitely in, in in white
collar, but then it's a one-stopshop and that's also very
consciously there's been ourpositioning and strategy, right,
right, so for a typical whitecollar company, tech company,
whatever, they can use a testcontroller for almost any role,
right Like, from the programmersto customer success, to admin,
finance.
You name it by combiningdifferent tests.
Speaker 1 (12:57):
Yeah, that's awesome, and I would be curious to get
your thoughts on how yourcompany is thinking about AI
right now.
I think all of us in this spaceand talent acquisition are
thinking about how do weactually implement AI in a
meaningful way.
That's really improving thingsfor our customers, right, and
it's challenging, right, it'schallenging sometimes to find
those use cases that are trulycompelling, that are moving
(13:18):
businesses forward, and I'mcurious to learn more about the
conversations that you might behaving with the leaders within
your organization and otherindustry leaders and investors
or customers and I don't know ifyou're currently implementing
things or you're thinking aboutit, or I'd just love to get a
sense for where your head's aton this topic right now for our
industry.
Speaker 3 (13:37):
Yeah, I find it super exciting, I think it's
fantastic for our industry also,but, yeah, it's clearly
changing.
It's changing the way we workand therefore changing the way
we hire, I would say so.
I would say, in terms ofrecruiting, first of all, the
resume and cover letters aredead right, like these days.
It must be terrible to be arecruiter, right?
You get like all these perfectchat, gpt letters or resume.
(14:00):
That clearly doesn't workanymore.
So I think that is another pushtowards skills-based hiring,
very clearly.
But then, within the skillsthat you're looking for, I think
what we hear from customers andwhat we actually see in our
data, we have around 700,000candidates per month now.
That's over 7 million years.
So we have a lot of data andwe're starting to see those
shifts.
I think away from and that goesvery slowly, I must say, but I
(14:21):
just subtly see it happeningaway from hard skills and
towards more soft skills.
For example, right, so, thetypical stuff like, let's say,
translating or basic accountingor data entry.
So many things can be done bythe AI, right, but there's still
a lot of human skills, right,in terms of leadership and
planning and you name it.
Right so, communication,there's so many that are just
(14:43):
ever more important.
You could say.
So that's one thing we see.
Another thing we see is thatit's just demand for AI tests.
So we have a lot of AI testsnow in the library a deep
learning test or a machinelearning test or a very specific
one like Keras or PyTorch.
There's all kinds of specificsoftware tests language do you
call it Language tests almostfor AI.
So there's a lot of demand forthose things as well.
(15:04):
Right, but you want people whoare efficient, who can use those
new ways of working and use AItools.
Speaker 1 (15:11):
Yeah, yeah, for sure, yeah.
And so I'm curious on the forassessments.
Right now, we're talking withCEOs that are implementing
different solutions at differentparts of the talent acquisition
funnel from everything fromsourcing to screening,
interviewing, to assessments andwhatnot and I'm curious from
(15:31):
feedback from customers or whenyou're thinking about the next
12 to 18 months, whether it bein your own product or maybe
just in talent acquisitionholistically.
Whatever you want to thinkthrough, when do you see the
most disruption or the biggestchanges occurring in terms of
implementing AI?
(15:51):
Do you see anything specific ordoes anything stand out to you?
Speaker 3 (15:56):
Yeah, if I focus on our own product.
So we're now really starting toimplement AI inside the product
.
So one exciting thing we justlaunched is an AI recommender.
Right, we have this big libraryof tests, as I said, but then
people are drowning a little bitsometimes, right, oh, which
test to use?
And I'm not the expert, right,and fair enough, right, so we
can help them manually with ourteam.
But we now have built like an airecommender that, based on all
(16:19):
the experience and scientificknowledge that we have, helps
you with a push of the buttonyou can upload a job description
and bang it gives you arecommendation for the best
assessment.
So those kind of things arevery exciting.
I think they really helpedimprove the our product but
therefore also improve hiringprocesses, I think, and then I
think, in the future, the otherthing for us that where I see a
lot of potential for ai is inreally matching those candidates
(16:42):
directly with jobs.
Right, really find from themega database of millions of
candidates, but now sort of tensof thousands of jobs, like to
say, hey, these are the bestmatch based on all the data
points that we have.
That's very exciting for us aswell.
Speaker 1 (16:56):
Do you see your team ever going in the direction of
actually doing more of thepotentially like AI interviewing
?
I know it's like you're doing alot of the tests and
assessments.
We're seeing some companiesgoing in that direction too,
where there's even like someproducts where it's like the AI
video interviewing.
I know you said there's also anelement to some of your tests
where correct me if I'm wrong,but I think you said like people
are responding via video.
Do you see yourselfincorporating AI into those
(17:20):
types of tests where candidatesare responsive, to have more of
like an interactive experiencethere, or do you feel like
that's really a differentproduct?
That's not where you play, likeyou're going to stay laser
focused on more so just thetesting and eval versus the AI
interviewing.
What are your thoughts on that?
Speaker 3 (17:38):
Yeah, it's not our immediate focus but it's
definitely something that we'rediscussing and looking at
Because indeed, like you said,we call it custom questions, so
you can add these custom alsovideo questions to your
assessment and we have a bigquestion bank for that as well.
To help customers Say, hey,you're hiring for a front-end
developer, here's a set ofquestions that you can really
help to do in a skills-basedkind of way ask questions to
(17:58):
your candidates.
Analyzing those responses wouldbe a very logical thing.
To put ai on top of that.
Say, here's an automatic kindof rating of those responses,
something we're not offering now.
So now it's the customer who'swatching those responses.
But this would be a very kindof logical thing, right where to
apply ai to, and then the nextstep could be to just have an ai
bot there right to do thatthose questions to answer, to
(18:18):
ask and answer those questionsor have the conversation with
with candidates yeah, there's.
Speaker 1 (18:23):
there's a lot of money being poured into into the
space right now.
I know there's a company calledMercor, based out of I think
they're out of San Francisco nowseries.
They just raised a series A for30 million and it's really.
It's really based around likeAI, video interviewing, yeah,
assessment.
So I think they're actuallyconnecting like engineering
talent to companies based inSilicon Valley or primarily like
(18:47):
that tech hyper growth scene,and essentially, like the
candidates are proactive.
It's a different business modelthan what you're doing.
But I'm just saying, like, inthat space of like cancer, going
on doing interviews and thengetting linked up to different
companies, so different kind ofvariations of that business
model, we're seeing a lot ofmoney get poured into right now.
(19:08):
Yeah, so it's just it's a wildtime and I yeah, so I think it's
just it's an interesting usecase and it's yeah, it's uh,
kind of it's definitely not whatyou're doing.
It's like adjacent or it's likesimilar in a sense, because
it's like the eval in a sense,but it's just from a different
angle.
But I don't think they'reinvesting in like the testing or
anything like that.
It's nothing like that.
It's more of just, I assume,matching role requirements that
(19:31):
are provided by a hiring teamand giving the AI instructions
on hey, figure this stuff out.
I want you to ask questionsabout this stuff.
Speaker 3 (19:39):
Yeah, but yeah it's hard, I think the investors are
pouring in a lot and I would dothe same.
I guess they're just sprayingmoney a little bit to see, like,
what's going to work.
Speaker 1 (19:46):
Yeah.
Speaker 3 (19:47):
I don't have an opinion on this particular
company, but I think it's a lot.
Of them are a little bitfeature-based right, like
smaller features in the product.
It's hard to more like afeature almost inside the
platform and a big standalonecompany.
Speaker 1 (20:05):
But maybe I'm wrong, oh yeah, for sure, and it's
interesting too, and I think too, it's like some of the more
like well-established companiesthat are like a holistic product
which, at this point, tuscarorais definitely evolving so fast.
Right, it's almost like being anearly adopter, of implementing
(20:25):
this type of stuff into yourproduct isn't necessarily the
idea of just being first.
It is not always ideal, becauseI'm thinking about just like
some of the I've been talkingwith different uh founders and
and ceos of uh like ai hiringproducts and it's they're
telling me the way that we'rebuilding our product now is
completely different than we,how we were thinking about
building it like three monthsago.
It's like things are justmoving so fast and I think it's
(20:46):
some of our more establishedCEOs that I speak with
Greenhouse's CEO and SteveBartel over at Gem and how
they're thinking about AI,they're implementing, they're
doing things, but it's a littlebit more reserved where they're
just sitting back and watching.
They're like, okay, we havethis great product and solution,
we're delivering, but we're notgoing to jump the gun and just
(21:06):
overcommit in one area andreally sit back and say, okay,
what are the use cases that areactually really driving business
forward and looking at whatother people are doing a little
more yeah.
Speaker 3 (21:16):
And you want to prevent introducing bias also in
your product and in our space.
It's so sensitive, right likewith ai, and there's been quite
a few cases where it went wrong,so we definitely want to be on
the sensitive side and that yeah, yeah, for sure, for sure.
Speaker 1 (21:30):
Yeah, that's an interesting one I'm still
waiting to.
I know we brought up on theshow a couple times workday
class action lawsuit we're.
I don't know if you've heard ofthat one.
Yeah, well, yeah yeah, so.
Yeah, so Workday is getting.
It's like a class actionlawsuit.
They're getting sued forpotentially their AI talent
acquisition assistantdiscriminating on candidates
based on race and age andwhatnot.
(21:50):
So it's yet to be seen what'sactually happening there, like
what will happen and how validthe claims are and whatnot.
But it'll be interesting too,because it also set a precedent
for the industry on, I think, alot of the laws and regulations
yeah, yeah, very much yeah.
Speaker 3 (22:06):
And then a bit longer ago we had higher views.
Well, they also had, like withthe video interviewing, quite a
few issues, I think with biasand that creeped in oh really,
which one was?
Speaker 1 (22:15):
yeah, I know higher view.
I'm trying to think.
Speaker 3 (22:18):
I think that was how many five, six ago, maybe a
higher view oh yeah.
Speaker 2 (22:25):
I think what you mentioned earlier about there is
super interesting and isn'tonly specific to the TA space of
what things are going to belike an AI powered feature
within a true solution and atrue platform, versus all of
these, what might be called liketools that are being money is
(22:46):
being thrown at them by VC firmsbut they don't actually have a
clear vision of how they'regoing to become a platform right
, like they're a little toolthat has taken chat, gpt or
maybe some other LLMs andthey're doing just a little bit
more than what you could maybedo on your own right With the
(23:07):
standard version of GPT.
Maybe they have some proprietarydata sources, et cetera.
I think it's the ones who, inmy personal opinion, who will
win are the ones who can buildtrue solutions where you have a
true platform right that isbringing a solution and solving
a problem and are using AIreally well and can continue
(23:31):
architect the platform in a waywhere, as AI develops further,
you can continue to develop yourproduct right, so the product
is very like AI-centric movingforward versus platforms where
the AI is only a feature orwhere it's an AI tool startup
that has no path to become aplatform.
(23:51):
Does that make sense at all?
Speaker 3 (23:53):
Yeah, 100%.
That's a more eloquent way ofputting it.
That's exactly my point.
I very much agree with that.
So I think that's alsosomething we internally always
look at.
Ai is really a means to an end.
Right, it's not the end, it'snot.
Oh, we have AI.
No, this is a very useful toolreally to indeed do stuff better
, to recommend better tests, tomatch people better.
Those are fantasticapplications, but it is a way to
(24:16):
get there, literally.
We had a conversation earlierthis week for a new product.
We're building around talentsourcing and it's like I don't
really care, and it soundsreally bad.
There was people like machinelearning experts and all these
fantastic people in our team.
We're like I don't really carehow you do it, I just want to
see great results, and if it'sAI, fantastic.
If it's Algolia or whateverother tool, it's also great.
I just want to as.
Speaker 1 (24:37):
No, I don't think you do yeah, I don't think so.
I also think it's like thisconcept of I don't know to me
it's every company isessentially going to become.
This idea of an AI company isvery quickly going to become
obsolete.
It's just going to be part ofhow tech stacks are built.
Speaker 2 (24:54):
Yeah, it's like cloud right AWS.
Who's talking about?
We have AWS.
Speaker 1 (25:00):
We are a cloud company, yeah like we're a cloud
company.
Speaker 2 (25:02):
Nobody's saying that anymore right, it's funny.
Speaker 1 (25:05):
My buddy was like it's like saying you have a
refrigerator in the house,you're not a refrigerator
company or house.
Yeah, yeah, that's like stuffyou have in a house.
Yes, obviously, yeah, and soit's.
I think it's it's like theconcept of the tech industry.
I feel like the concept of thetech industry is going to
disappear.
Every industry is going to bethe tech industry.
(25:27):
Every company, every doesn'tmatter industry is going to be
tech.
Speaker 3 (25:29):
Yeah, if I look at our venture capital, we've got
quite a few venture capitalfirms on our cap table, but they
only invest in tech, but yeah.
Speaker 2 (25:41):
Insured tech.
You just throw tech on whateverindustry and that concept is
gonna.
Speaker 1 (25:50):
I think it's cool and it's interesting because I
think some of the we're alreadyseeing a lot of the startups
that maybe names that we thatyou got investment a couple
years ago like you just don'thear of them anymore.
I think a lot of them haveprobably gone out of business.
I think a lot of them haveprobably gone out of business.
I think a lot of companiesstarted Elijah Tell me if I'm
wrong, you probably know moreabout this than I do but a lot
of the co-pilot stuff and likehelp helping with content
(26:11):
generation.
There's just so many tools thatkind of popped up initially
around those and I actuallydon't see quite as many of those
anymore.
I don't think I don't comeacross as I wonder if they just
already gone out of business,but I think so probably right.
Speaker 2 (26:24):
They have to get, yeah, traction and as soon as
some of the bigger players, likeCanva, for example, in content
creation, if you had this littleagain tool where you could
generate, let's say, videos, oryou can make some slides, and
that was like their tool, andthen canvas, like, made it a
feature right, one of their manyfeatures all of a sudden, boom,
(26:48):
you're done.
So I think that's the big risk.
Sometimes, right, with these,what's more of an AI tool than a
true platform is.
The bigger players are going tofigure it out pretty quickly.
If you're a one trick pony, ifyou will, and that's the main
thing and then they integratethat.
Maybe they have more scaleright their pricing.
You're already paying for itand they add it for the marginal
(27:11):
increase.
Let's say, you pay $5 more amonth, but the standalone tools
like 60 bucks a month, likeyou're, it's like a no brainer.
You're going to use canva's newfeature for five bucks versus
that ai slide creation tool for.
So, yeah, I think you're right.
Speaker 3 (27:27):
I think they're falling by the wayside as these
bigger companies add more aipowered features yeah, 100, and
I think the other thing is likethere's a lot of these ai stars.
What I hear from these venturecapital firms is they have quite
a bit of traction, initiallyright, because every cto or cmo
or you name it has budget.
Do I hear ai here's budget?
right so there's a lot ofinitial traction, which is
(27:48):
fantastic to be in, right, as astartup, but then to keep those
customers right, there's a lotof churn.
If I look at myself, I'mchurning a lot like I've tried
quite a few things and I out,out, out and I go, yeah, it's
brutal, right, so you have toopen and, with these new models
coming out and indeed biggercompanies adopting them
themselves or buying them,buying these companies and
incorporating them, it's, it'sbrutal.
It's not easy to build like astandalone company.
Speaker 1 (28:09):
I think yeah, I know definitely, but yeah, it has to
be, like I think, centeredaround the solving the problem.
Like, elijah, what you said,like not looking at ai is like
an end-all be-all.
Like we're here to to solve aproblem and to add as business,
as executives over here, to foropportunity and to create value.
Yeah, we're solving the rightproblem that will create that
(28:31):
type of opportunity, that marketopportunity for investors, if
that's the plan, I think, havingthat vision too, james.
Speaker 2 (28:36):
So maybe you start with this one tool and a lot of
companies I think that's the endof it they almost see that as
the end goal instead of, let'ssay, you start with, I don't
know, ai interviewing or maybeit's AI powered testing or an AI
powered ATS something.
But your vision is to likebecome, let's say, an AI powered
(28:59):
greenhouse or AI agent poweredworkday like.
You've got this kind of like abigger vision of where it can go
and what it can become, even ifyou're only starting with one
tool and I'm just not sureenough of the founders are, you
know, getting further ahead thanthat where they can pitch to
the investors and theircustomers and even their own
(29:21):
team.
We're not just creating thisone tool like we see the future
of hiring in 10 years being, youknow x and the ai is going to
be helping with all thesedifferent things, and we're
building for that day.
We're not building for sixmonths from now.
Only if that makes sense.
Speaker 1 (29:38):
I don't know, it's just my opinion that's the issue
with, I think, a lot of the theco-pilot stuff and the content
creation stuff is it's like what, where do you really build from
from there?
It's like there's no logicallike why is that company well
positioned to build in any?
Why would we back thesefounders?
Like these founders, there's nocompelling story like in terms
(30:02):
of because your customerfeedback is on product roadmap
is just going to be so dispersedbecause it's just such a big
general little feature versussolving really dialing in and
solving like one one important Ileverage problem, like some of
the use cases that we're seeing,whether it's a gorilla or some
of the AI interviewing, which Ithink is a very valuable use
case.
It's like that, if you like, Ilove AI interviewing right.
(30:25):
That's a space that I'm reallyexcited about right now and to
me, it's.
I like the idea of some ofthose companies I think will be.
I think a fair amount of themwill be very successful because
it's also very logical.
It's like you identify a nichecustomer base, like ideally,
some of them are sprayed wide.
Very logical.
It's like you identify like aniche customer base like ideally
, some of them are identifiedyour customer base.
They're going to give youproduct feedback that will
logically build out more of aholistic product and it's you're
(30:47):
going to get pulled in a morespecific direction and, like
some of those initial aiproducts were just like these
vague kind of note takers.
Are you like, what are you goingto do?
You're going to go like intocrm space.
That sounds like that's a hugeundertaking.
What are you going to do?
You're gonna go like into crmspace.
That sounds like that's a hugeundertaking.
What are you gonna do?
You're gonna go into, like,sales enablement, like it's just
so.
It is like a very small featureand it's also it's not hard to
(31:11):
build.
Like some of these things too.
It's you get, I don't know,like a full-time, mid junior
level engineer can get prettyfar on on some of this stuff.
Like it's like literally thatsimple.
Like somebody who knows alittle bit of Python can do an
open AI plugin like API.
It's just not that hard to dosome of this stuff.
(31:32):
It's just that's not a, that'snot a product.
Speaker 3 (31:34):
Yeah, I completely agree with that.
For, speaking about test career, we see ourselves as a talent
discovery platform.
We have seen ourselves from thebeginning right Starting with
these tests, but only expandingtowards talent assessment right
and much way more tests, moresophisticated tests, more
science-backed adding like thesevideo questions et cetera and
AI video is a very logical onefor the future as well and then
(31:55):
expanding to talent sourcingright, but it's like a much
bigger vision in it, I thinkthat's a good way to go.
Speaker 1 (32:01):
It's awesome and it's also cool to see.
I think we're seeing some ofthe most successful recruiting
tech products, like expandinginto a lot more product lines,
which has been a huge push overthe last year or two.
Specifically, we had the CEO,nikos of Workable and there
seems, geez, they have a productfor everything.
And then you see, like Gemright, they started out as like
(32:24):
outbound sourcing cadences forcandidate outreach, email data,
that kind of thing, and now theyhave a CRM, they have the
applicant tracking system.
There's other products too andthey're expanding within that,
and so I was actually reallycurious to learn a little more.
We got a few more minutes here.
You're expanding within that,and so I was actually really
curious to learn a little more.
We've got a few more minuteshere.
You're expanding into sourcing.
Could you tell me, could youtell us a little bit about what
(32:46):
went into that decisionstrategically to expand your
product offering into sourcingtechnology?
Speaker 3 (32:52):
Yeah, definitely yeah .
So it goes back a little to ourmission and vision.
So our mission is todemocratize opportunity for
talent from all walks of lifewith a vision of a billion
people in dream jobs.
So it's a kind of an ambitiousvision, you could say.
And this ties into it very well.
So with our assessment product,of course, anyone has all of a
sudden a chance to stand outright and be noticed and picked
(33:13):
up by recruiters, and not justonly people who have a fantastic
CV.
And then the next step basically, I'm looking at our own
databases.
You could say, on the one hand,you have these millions of
candidates who are now takingthese assessments, right, so we
have all that data about thesepeople who are looking for their
dream job.
On the other hand, we havethese tens of thousands of jobs,
right, and they're looking foramazing talent and we're sitting
(33:33):
on both and our mission is likethe vision of a billion people
in dream jobs mission is like tothe vision of a billion people
in dream jobs and we're.
It's like connecting twodatabases.
So for us it was so logical andin line with what we're doing
to start connecting those.
But it's a little bit chickenand egg, right, if you start
with, that's really hard, right,if you have zero, zero
candidates, zero jobs, like it'sa bad place to start.
So we started from theassessment.
But now that we have, yeah,quite a bit of traction and so
(33:56):
many jobs and people, we feelit's a very logical next step to
say, hey, we connected to andalso there's just so much waste
in recruitment.
You guys know very well, Ithink, for every job we have
like hundreds of candidates ordepending on the company of
course, but only one get hired,gets hired, and all these other
candidates have invested so muchin that process as well.
So I think it's also verycandidate friendly.
They kind of say, hey, arethere ways we can still match
(34:17):
them without opportunities?
Speaker 1 (34:18):
so is that like essentially, if a candidate
completes a test for oneorganization, are they able to
use that assessment or test thatthey completed to submit to
other jobs?
Is that essentially how itwould?
Yeah that's right.
Speaker 3 (34:30):
So they can reuse their scores.
It's their scores, right.
It's not a company.
The company doesn't own it.
If you look at, privacy lawsand everything.
It's your cognitive ability,your personality.
So, yeah, the idea is that theycan reuse that right To say,
hey, maybe this was not a greatmatch, but hey, did you think
about these?
Roles Like these would befantastic.
Speaker 1 (34:46):
Yeah, so that's okay.
So then can will the view be, acompany can put in search
criteria for a certain skill setand then basically the profiles
with people with completedassessments they can click
through and then set up like aninterview with or or how does
that?
Speaker 3 (35:01):
that's right yeah that's right, so indeed can in
the future, the customers willbe able to.
It's basically, you could say,close to linkedin.
Linkedin is the biggest uhdatabase of resumes in the world
, but we are taking a differentapproach.
It's skills based.
So this will be we're creatinga huge database of skills.
So if you're saying, hey, Ilook for someone who speaks
Dutch and I'm Dutch and knowsreally well JavaScript and a
(35:22):
great fit with our culture, andwe know who those people are, we
have tested them.
So we have all that data onpeople, so we can tell you, hey,
look at these people and invitethem to your what is?
Speaker 1 (35:31):
that Some platforms like that, like you think about,
it's like limited right, whereit's you're hiring people on a
project basis or something,where it's like it has to flow
through the platform so peopledon't go around it.
Is it going to be like astructured, a little bit like
one of those types ofmarketplaces, or is it going to
be like okay, this is for W2,full-time employment hires.
(35:54):
What does that motion look like?
So, as a company, I'm goingthrough, I'm clicking on a
profile.
Okay, I want to work with Sally.
Speaker 3 (36:05):
What happens from there right now.
It's closer to linkedin and ofcourse you can find people for
any type of role, but I wouldsay it's for permanent roles
just like people are using ouror can, or companies are using
our platform now, use it to findpermanent employees, but now
you can also source them.
So, because, at this moment,like, people bring their own or
they post their own links andvacancies everywhere, but now we
can say, hey, here, actually,our skills tested candidates are
ready so you can actually haveaccess to that database and
(36:26):
invite them for your jobs.
That's the and their skill sets.
That's the big difference,right with existing platforms so
how does that?
Speaker 1 (36:33):
can we get into whatever you're comfortable
sharing in terms of pricing forthat, like how companies will
engage in the pricing model tosource candidates through your
product.
Speaker 3 (36:42):
I can't, because we don't.
It's not live yet.
Speaker 1 (36:44):
So we're building it as we speak.
We don't have the pricing yet.
Speaker 3 (36:48):
There will be a price , but I guess it will be
something similar to somethingaround the number of people that
you invite or something.
That's probably the way weprice it.
Speaker 1 (36:56):
Oh, yeah, that's a cool idea, like I always because
in those business models,that's like always where my head
goes Okay, like how do youstructure that aspect of when
folks actually engage?
Like I'm thinking like from thebusiness perspective, from your
perspective too, but yeah,that's like a ton of value
because, you're right, you'realready sitting on these
(37:36):
databases, so it's like a veryit's not like you, yeah, yeah,
but I think companies, there'slike more interesting
applications happening right nowand I honest I find the test
grill application a lot moreinteresting because the
evaluations you're doing are alot more thorough than the
assessments and whatnot.
And then also it's there's someof these other platforms too
that are doing this marketplaceish.
I don't know if that's theright, if we could call it a
(37:58):
marketplace, but yeah, I justdon't think it's.
There's also limitations interms of how folks can engage
with those talent.
So if you think about somethinglike an Upwork which I know is
not a great analogy because it'sthis is way different and
better in my opinion, but likestill like the concept of
companies searching for profiles, it's like very limited in
scope, like you can't really putsomebody through a full
(38:18):
interview process, like youcan't talk to them outside of
the platform.
You're not hiring them as a w-2right in the us, it's not.
So it's it's very limited andthen you only are typically, at
least in my experience workingwith the tool.
I might be I don't know, maybeI don't know everything about it
, but it's just limited and likelimits this project scope.
But there isn't.
I haven't seen a tool that'slike really effectively doing
(38:39):
that, like on the W2 side, likefull time with this level of
like vetting and assessmentthat's taking place.
And again, this is a lot, Ithink, a lot more valuable than
what AI is doing currently.
It's different but like stillthere's a lot more value in all
these deep technical assessmentsand tests than, I think, just
AI screening for rolerequirements.
Speaker 2 (39:01):
It's a different, it's a for role requirements.
Speaker 1 (39:02):
It's a different.
It's a totally different.
It's a different use case.
But I guess what I'm saying isI see a ton of value in that and
I could see the sourcing piecebeing like this product being
like huge right, like I thinkpulling off that correctly and
just like with the data you have, like that could be a massive
product.
Speaker 3 (39:18):
Yeah that that's what I think too.
It's a lot of work to get itright, but, yeah, we're in a
very unique position to have allthat data, like you said, to
have those millions ofcandidates and then to have all
that data, and then you canapply AI to it.
So to say to indeed find thebest way of matching jobs.
Speaker 1 (39:33):
Yeah, it's exciting.
For sure it's a massiveundertaking, though Do you have
any kind of timeline on whenyour team is thinking about
going live with this product, oris it still in?
Speaker 3 (39:45):
For sure next year, so in 2025, we'll go live.
The exact month I can't tellyou.
I don't know.
We're still working very hard,but a lot of work has been done
already, so it's getting close.
Speaker 1 (39:53):
That's awesome when you're about to go live or when
you're ready.
When you want to, you shouldcome back on the show and we can
talk about it.
I would love to learn about theproduct when it's ready to go
live and continue theconversation.
Speaker 3 (40:06):
I would love that.
Yeah, thanks, james.
Speaker 1 (40:08):
Awesome.
Look, we're coming up on time.
That was a flu.
Bias was a lot of fun.
Walter, thank you so much forjoining us today and sharing
your insights.
I know people are going to geta lot of value out of this
conversation, so thank you somuch for joining us.
Speaker 3 (40:20):
Great, I love being here.
Thank you so much.
Speaker 1 (40:23):
Awesome, all right.
Thanks for tuning in everyone.
We'll talk to you soon, takecare.
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