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Today I'm joined by Eron Wong and John
Wright, the founders of Alex. They just
announced a 17 million series A with Pak
15. Congrats. That's awesome.
>> Thank you. Great to be here.
>> Can you first tell us what Alex does?
>> Alex is your AI recruiting partner that
helps companies and staffing firms
interview everyone and ultimately hire
the best people. Alex has access to all
of your favorite recruiting tools and
can conduct phone screens, video
interviews, scheduling, sourcing, uh,
updating the applicant tracking system
completely autonomously.
>> So, what's wrong with our today? Like,
what are you trying to fix?
>> Sure. So, applicant volume has tripled
in the past 3 years and time to hire is
at an all-time high. It's about 60 days.
I mean, we're here sitting in San
Francisco. You can close a house in San
Francisco faster than you can hire your
next engineer. And so the hiring market
is incredibly inefficient today and it's
never been more efficient in history.
And the main bottleneck is uh one
bandwidth of the recruiters uh and two
uh matching right how can I actually
match the best candidates with the best
opportunities. So, Alex solves both of
those problems by allowing any company
or any employer to interview everybody
at scale, right, and get incredible
insights on these candidates and to be
able to match those candidates to the
right opportunities because we have
access to all that data.
>> Can you give us a sense of like the
scale uh of the operations here? Like,
how many interviews have you performed
so far?
>> We're doing thousands of interviews
across all of our customers every single
day just since we launched last year.
Uh, Alex has helped hire thousands of
people and today Alex is uh filling tens
of thousands of active job roles.
>> Who are you doing that for? Like is it
mostly mostly like companies or staffing
agencies or how does that work?
>> What's really interesting about kind of
the working in the recruiting space is
that uh it's an incredibly large market.
Every employer hires by definition,
right? And so the question is where is
that niche edge of the wedge? Where do
you get started? We found a lot of pull
from the staffing agencies. Our
incentives are highly aligned. If we do
a really great job
>> is that you're more people. Is that the
main
>> Exactly. Right. They concentrate the
pain point. So it's a true hair on fire
problem. If we do a great job, we also
increase revenue for them. That's how
they make money. And so uh staffing is
one of our largest segments. We do also
work with large some of the largest
employers in the world. Um for example,
the largest US supplier of let's say
nucle like nuclear components, right? We
have a huge electron deficiency with
training.
>> But what roles are you hiring for here?
>> Yeah, it's across the board. Everything
from highly technical uh staff software
engineer roles to senior accountants to
for that nuclear company. They're a
publicly traded nuclear company. We help
them hire nuclear welders, right? And
that's much more blue collar.
>> And so the AI do you test the technical
skills too? It's like stays kind of like
first screen kind of interview. Yeah,
that's exactly right. So, it tests for
skills on that first call or that first
interview. What's really interesting is
nuclear welding is very niche uh
discipline, right? But with uh Alex,
right, with an AI, you're able to really
dig deep into that discipline and really
better understand if that particular
candidate is qualified for for that
role.
>> And how do you make sure the
interviewers the interview the AI
interviewer knows about welding?
>> Yeah. So, um, first, Alex has access to
your applicant tracking system or your
HIS. And so, uh, Alex will be able to
see the types of people that you've
hired before. We'll be able to see the
job description and any intake meeting
notes from the hiring manager. And so,
Alex will learn to be the best uh,
employer brand ambassador for your
company. Alex uh can also be fine-tuned
on particular roles like nuclear welding
um such that uh it will find and and and
ultimately qualify the best people for
the job.
>> Let's go back to the early days of Alex.
You're both relatively young, right? I'm
not sure you did a lot of interviews
yourself.
How come you picked that idea?
>> We've been candidates a long longer than
we've been employers actually. Um but I
think that ends up being a really unique
advantage. I think for a lot of uh the
companies that have tried to build in
the recruiting tech space today, I think
where they haven't succeeded is in
building uh a candidate experience uh
that you know really puts job seekers
for first an experience where hey I
applied for this job I want to talk to
you know an AI want to engage with this
employer right and so I think that gives
us a very unique advantage you know John
and I met in undergrad we met at Brown
um we had built a hiring uh tech company
uh before this um and that was a lot of
fun.
>> Learned a lot and yeah um it really kind
of helped us better understand the
market what like the pain points were
for uh for candidates and optimize for
the candidate experience cuz ultimately
um that's kind of what's more most
important uh in what we're building.
>> So how long have you been working on X?
I mean like a years ago like you
couldn't even imagine using AI for that
right? I guess now now you can make that
conversational you can have a real
conversation with an AI. When did that
become possible?
>> It really got started uh in uh the end
of uh 2023 with the launch of G GPT4
Turbo. We ultimately we're building at
the start of 2024, launched towards the
end of the batch. I believe it was in
April of 2024 um and quickly got our
first customer and then uh continued to
to scale from there.
>> What were the biggest challenges? You
know, like there is that uncanny valley
where it's so weird to speak with an AI.
It looks like we're past that. How did
you get there? Yeah, at the end of 2023
is when this kind of building a voice
agent that was had low enough latency um
where candidates were comfortable
talking with it. Um that's when that
really became possible. Um and there
were no other voice tools out there to
kind of build this like you have today,
the Vappies, the retails. So we really
owned that orchestration platform and
being able to connect all the different
models succinctly to be able to have a
low latency, high performant um reliable
agent. And over time models just kept on
getting better and better. So we made,
you know, incremental progress on
latency, on voice quality, transcription
quality, and that really helped kind of
optimize the candidate experience.
That's actually one reason we named the
AI recruiting partner Alex was because
the transcription models weren't quite
there yet in uh in even in 2024. And so
like Alex was like a it's a very easy
name to understand that the trans that
the transcription models could like
understand is say like Alex okay like
that's very obvious that he's here he
>> text would always get his right
>> exactly right it would always get it
right um and so that's kind of the
origin of the the name
>> the conversation during the interview
can lead the in some kind of like
corners or corner cases but at the end
you want to have some score how do you
make sure you you get back get the
conversation back on track when
necessary.
>> That's just a matter of prompting um and
a lot of kind of um really seeing these
candidates do do these interviews,
evaluating your agents at scale is just
making sure that their guardrails in
place so that kind of um the questions
all questions are asked, follow-ups are
asked appropriately and making sure that
um the yeah, candidate has no opening
for for trying to to take advantage of
of Alex, even though we've had a lot of
candidates try doing that. No successful
attempts.
>> Do you have any good stories of
candidates trying to hack the AI?
>> We've definitely had uh interesting
attempts, you know, candidates,
especially software engineers who are
kind of trying to break and see what
what the um weak points are of of the
agent. Um we've seen, you know, people
try to do that and usually at the very
end we, you know, we ask for, hey, do
you have any feedback? Like what's the
interview? How'd the interview go? And
they're like, yeah, I'm I'm impressed. I
wasn't able to break it. Um so we we've
definitely had a lot of Yeah. people who
applied to for our software engineering
roles to try and
>> so did you score them higher in the
>> we've had candidates like try to talk in
like XML or in markdown to try to prompt
inject unfortunately for them that that
hasn't worked um but it's it's funny an
interesting phenomenon here is that um
you know when you build you know agents
in a particular vertical for this one
where you're in the job market it's a
market right and so one clear dynamic of
a market is that you have uh adversarial
effects right that is you know
candidates are going to do what they can
to try to get the job right and you know
the job is a scarce resource and so
recently we've seen a lot of uptick in
candidates using you know their own AI
to uh mass apply to jobs but also during
the interview have you know transparent
you know overlay like a clue or
something like this to try to cheat on
uh interviews and so that's something
that we've had to build out building
kind of cheat detection to make sure hey
this person is legit right we've had
we've had to models for detecting deep
fakes, right? Because now they're like
live deep fakes that you can stream into
your device.
>> Yeah. Because you see the video of the
candidate or
>> Exactly. Right. Because these interviews
are often times done through like a
video conferencing platform that we
actually built from the ground up.
>> And so like interviews that end up like
being an a candidate agent and then the
recruiter agent and they speak to each
other.
>> Yeah, that's that's it's it's funny. We
get that a lot and we'll see where the
future goes, right? Like that's actually
pretty interesting. um that the the two
sides of the market, the employers and
and job seekers will will always
challenge each other. But ultimately, I
think it's it's it's actually good,
right? Because that means that the
market's evolving, right? You know, we
believe in a world where, you know, jobs
should be much more accessible, right?
Today, you know, we see knowledge is
democratized, right? I can go on Google,
I can go on Chad GBT, education is is uh
a lot of education is democratized,
right? But still like the job market
opportunities are not right. I'll apply
to hundreds of jobs as a candidate and
I'll be lucky if I get a handful of
interviews. We believe in a world where
if you apply for a job, your dream job,
you should be able to get that
interview, right? And it shouldn't be
about if you, you know, went to an Ivy
League or, you know, whatnot. It should
just be, hey, can you tell me about the
skills you bring to the table and the
knowledge that you
at least get that first interview?
every candidate gets a
>> nurse like some overlooked candidates
that would not have had an interview.
>> Yeah.
>> Like do you have like any examples where
you actually ended up helping a company
hiring a candidate they would never have
interviewed?
>> Yeah, there are several examples. I was
actually just on the phone with one of
our customers yesterday. Um so Cobalt is
this very old programming language. Um
it's used by some of the largest banks
and financial institutions in the world.
I think it was started in 1959. not a
lot of Cobalt developers left in the
world, right? And so one of our
customers is the staffing firm that
hires for all those Cobalt developers.
When they launched Alex, um Alex found
um looked through their database of
their own app through their own
applicant tracking system, found people
that seemed relevant for this cobalt
developer role that they were hiring
for, automatically reached out to them,
interviewed them, and found 11 people
that they automatically just submitted,
and they got placed to their clients
right away.
>> And they were lost in the
>> they were lost in the database, right?
Whether that database is your own
applicant tracking system or your own
applicant pool or sourced from LinkedIn
or Indeed, you know, these pools are
just getting larger and larger and
larger and much more harder to handle
with, you know, manually. And so our our
aim is to really be that recruiting
partner that says, "Hey, well, you know,
there is that diamond in the rough.
Let's use AI to help you find it."
>> Now, you might have already talked to
the perfect candidate, you know, years
ago, and you know, with the current
technology, it that person is just lost.
But with Alex, you know, you're able to
create opportunities that just
previously, you know, you wouldn't have
been able to.
>> Yeah. Is there any specific metric
you're tracking closely like kind of any
evidence for example that really working
for for your customers?
>> Yeah, there are several. Um, I think one
of them that we typically see in pilots
is can Alex given that they interview
all these folks,
>> Alex stack ranks them, will Alex's stack
rank be similar or even better than the
stack rank that uh a traditional
recruiter would look at? Right? So, you
know, is that best candidate really
better than the second best candidate
that's really better than the third best
candidate, right? And I can say that
because, you know, I have the video
interview. I can actually pull quotes
from that interview and say, "Hey, yeah,
this person scored a 95 on their uh
let's say their Python knowledge
because, you know, they have, you know,
10 years of experience. They said this,
they did that." And I tried to dig
deeper and they, you know, kept pulling
out evidence from their experience and
this person's fantastic. And so that's
one way we look at it. Other things that
we're seeing um are increases in
retention rate, right? So these
candidates that are getting hired are
actually staying longer at their jobs.
And also for a lot of our staffing firm
customers, again, they generate revenue,
right, from placing people. And so if
they the client hires them and then they
don't end up staying, they don't they
don't get any incremental revenue. And
so that's another way that we track
success for for our customers. And
again, it really ties back to actually
the candidates. So the first thing we
really look at is our candidates
enjoying, you know, interviewing with
Alex, meeting Alex.
>> How do they feel? How could they feel
like like I guess the first time was and
still there's a lot of first times
today, right? A lot of people have never
spoken to any.
>> How do they react?
>> I think my favorite part that I hear
from candidates is that we really
eliminate ghosting, right? Because in a
world where, hey, if you apply to this
job, you get that opportunity to at
least be heard, right? You get to be
updated on your your your candidacy,
right?
>> Like versus the company where they never
hear back. At least they get the first
interview even if it's an AI.
>> Exactly. And they can also ask questions
during the interview. They have Alex's
phone number and so they can text Alex
at any time and say, "Hey, you know,
actually I had this question about your
PTO policy or hey, are there any updates
to my application? Has this application
been filled yet?"
>> It's more that interview. It's the whole
candidate experience.
>> That's exactly right. Yeah. but really
supporting the candidate throughout
their entire journey at any particular
recruiting or hiring process that that
they're in.
>> Is there anything that Alex actually is
looking for in candidates that u human
interviewers are overlooking?
>> There's an incredible amount of data in
within a video interview, right? And a
lot of it is is missed, right? If you
think about a video interview, 20 30
minutes, you know, that could be, you
know, gigabytes worth of data. You know,
with a traditional recruiter, that's
oftentimes boiled down to handwritten
notes, right? Um, but with with Alex,
that's all recorded. You know, you have
much more in-depth notes. Um, and Alex
is able to both analyze, hey, your
technical skills, your hard skills,
requirements for the role, but also
things like soft skills, right? If I'm
hiring for, let's say, a salesperson, I
want to make sure that they are a
concise seller and they have concise
communication, right? That's something
Alex able to test for because Alex has
that that video data, that audio data,
and will remember that that interview.
>> Okay. Awesome. Let's talk about the news
you are announcing. So 17 million siz uh
that you you just closed. Uh where will
the capital go? Why raise now? Like tell
us more about the the round.
>> Now is an incredibly exciting time I
think to to build um a company in
particular. Um but we've seen incredible
market pull across all of our customers.
A year ago I think we wouldn't have been
able to say that you know especially you
know hire selling to some of the largest
companies in the world. you know, you
know, HR is one of the areas that is,
you know, you know, they've really been
burned by technology that hasn't worked
for them in in the past. And so, a lot
of what we've been doing recently has
been building that transparency,
building that confidence and building
that relationship with with some of the
largest employers in the world. And
that's really allowed us to um one uh
sell into these organizations and two
continue to build a great product for
them. And so we're going to use this $17
million to really make sure that hey, we
have the best product when it comes to
uh AI and recruiting today.
>> Uh and we want to make sure that we
continue to be best-in-class and
supporting uh our customers in in that
way.
>> All right. So just having a better team,
>> growing the team.
>> That's right.
>> Okay. Anything non obvious?
>> Anything not obvious? You know, I think
a lot of it actually is relatively
obvious. I think something that is less
obvious is being in San Francisco,
you're around a lot of companies that uh
hire a lot of engineering and technical
talent and will oftent times like
outweigh the goto market side of things.
I think for us um go to market is
extremely important. You know, things
like marketing, design end up being
incredibly important because again, you
know, of course you need to have a great
product, right? You need to create value
for your customers, right? You need to
make something people want. But in
addition to that, when you have
enterprise sales, right, relationships
also matter, right? Building a brand
also matters. Go to market at the end of
the day matters a lot. We'll likely see
kind of more go to market hiring than
probably the traditional uh Silicon
Valley company.
>> You're also announcing a a rebranding,
right? uh your name was not Alex. Alex
was the name of the interviewer. Like
was that creating some confusion or why
did you choose to go from a prior to
Alex?
>> Yeah, Alex was uh the name of the an AI
recruiting partner and a lot of our
customers were already calling us Alex.
Um and so it made a lot of sense to
reduce any type of confusion and just
rename uh the company to to Alex. Um we
had already seen companies do a great
job with this anyways. You see saw this
with a kodium right turning into wind
surf and the particular naming the
company actual name made a lot of sense.
You're seeing a lot of success with that
with like Harvey for instance. We want
Alex we want the company we want AI to
be approachable and and grounded right
it's not just another piece of abstract
AI software. It's really you know your
partner.
>> Have you seen people react differently
to that that new name?
>> Yeah. Well, they can certainly, you
know, pronounce it a lot easier uh and
share it a lot easier, but uh we've
we've had nothing but, you know, great
things or heard nothing but great things
from our from our customers and uh so
far. And so we're we're really excited
about the name change.
>> Looking ahead, like what do you think
will still be uh I don't know, five
years from now, will there still be
human interviews? Like where are we
going?
>> We're not looking to replace recruiters.
Um and recruiters won't be replaced.
They're going to be supercharged, right?
a lot of their time today when it comes
to interviewing is you look you're
asking the same you know five questions
to this these you know hundreds of
people there's a lot of scheduling
you're updating your pieces of software
right adding notes into your applicant
tracking system um and we want to give
that time back right a lot of those
administrative tasks can be uh pulled
out and you can reinvest that time into
more strategic roles for instance uh
building a relationship with your hiring
manager helping close that candidate
that's on the fence right or spending
more time with the candidates that you
know are qualified and you really want
them to join the team right those are
things that I don't think AI will be
able to replace right those human
elements and good they shouldn't but
those more administrative tasks we
certainly want to be able to pull them
out of that and and help them do more
>> and speaking of hiring so you are
expanding the team like any role you
want to uh to pitch to to the audience
>> yeah uh we're hiring I mean on every
front so engineers um both full stack
front end backend designers uh PM M um
go to launch to go go to market as well.
>> Yeah. If you want to join a company
that's not replacing people but helping
hire them, right? I think that's like
very exciting and that's the future that
we believe with AI and it's the future
that we want to build towards.
>> That's awesome. Hey, before to conclude,
is there anything you you wish you knew
when you started looking back in time
two years ago?
>> You know, we get this question a lot,
especially when we come back to YC and
and talk with the current badges. things
that stick out to me are I think at some
point you need to you know put your foot
down and have really high conviction in
what you're building. there's a delicate
balance between you know listening to
the market and and and make something
people want and saying hey look we know
that this is going to exist in five
years question is like in what form we
came out of the batch we launched um we
knew that there was going to be some use
case for an AI recruiting partner we
weren't sure if that was going to start
with employers with tech companies with
you know the you know fortune 100s you
know there are a few of our customers
today with staffing agencies and so I
think you need to be really obstinate
about that vision that you have but kind
of flexible on on actually how you're
going to get there
>> as some usual advice for founders from
your journey.
>> Yeah, I mean you just need to be
extremely versatile and be able to grow
uh kind of we live in an age where
technology is changing and maturing so
quickly and you you know there's new
models, new new use cases coming out.
So, just being able to really grow with
the technology and say, yeah, just kind
of have a clear long long-term vision
goal, but be flexible on how you achieve
it and kind of what um what tools you
use. You can really learn anything in a
very short amount of time. So,
possibilities are endless and you just
need to be super super um excited to
build.
>> Eron John, thank you so much for joining
us today. It was awesome to catch up.
So, thank you.
[Music]
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