October 27, 2023 • 13 mins
Are you ready for a transformative look at how technology is reshaping the world of recruiting? Our guest Alper Tekin, Chief Product Officer at Findem, takes us through the importance of data in recruiting, ATS rediscovery, and how his platform is mapping an impressive 750 million people data against 4 million companies to reveal the true picture of a candidate's potential.
We then switch gears to examine how artificial intelligence is revolutionizing candidate search, with Fundum's platform taking center stage. Learn how it automates the sourcing process, all while considering specifics such as diversity data, experience, and project-level skills.
Special mini series recorded with Oleeo at HR Tech 2023 with hosts Ryan Leary, Brian Fink, and Shally Steckerl.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:07):
Welcome back party people.
We're coming to.
You live from the talentacquisition content lounge
powered by Olio.
It's recruiting daily source inschool.
It's me, Brian Fink, it's himRyan Leary.
What's up Right?
What's going on?
Not?
Speaker 2 (00:18):
much man.
We've got a what do you call ita concert?
Speaker 1 (00:22):
maybe We've got a woman who is talking.
She's trying to hype everybodyup to believing that Taylor
Swift is coming and just to letyou know, taylor Swift is not at
HR Tech.
Speaker 2 (00:32):
She's not here.
You two is here.
You two is here.
Yeah, and they're playing atthe Sphere.
Speaker 1 (00:35):
That is awesome.
Yeah, speaking of people whoare playing, we are playing the
20 questions game with Alper,who is the Chief Product Officer
at Fundum.
What's going on, alper?
How are?
Speaker 2 (00:47):
you All right, all right.
How's the intro, are you?
Speaker 3 (00:50):
like it?
Yeah, geez okay.
Speaker 1 (00:52):
All right, 20 questions.
Are you ready?
Speaker 2 (00:54):
No, that was question number one.
Question number two Is a hotdog, a sandwich.
Speaker 3 (00:59):
Probably, probably.
Oh, he didn't even think aboutit.
Speaker 1 (01:02):
All right.
Speaker 2 (01:02):
He's thought about that.
Speaker 1 (01:03):
Yeah, he's thought about that.
What's another good?
Speaker 2 (01:05):
one Toilet paper over or under, so hold on.
Speaker 1 (01:08):
Jesus, it's like a mullet or a beard right.
That's what.
That's what I try to teachMaddie.
Speaker 3 (01:13):
All right.
Speaker 1 (01:13):
so, alper, it's a beard.
If it goes over, if it goesunder, it's a mullet.
Yes, okay, that is what I'mteaching my seven-year-old
Alper's looking at me going.
I'm not letting you anywherenear my side right, that's
awesome.
Alper.
You know our last guest thatwas on.
We were talking about thesourcing crisis that's taking
(01:34):
place right now, because a lotof recruiters are having to
source for passive talent asopposed to having a sorcerer to
support them.
I immediately think I find themthat, you guys.
I mean that's what you do.
You turn recruiters, you givethem superpowers, your little
robot that sits on theirshoulder.
What are you seeing coming downthe pipeline for recruiters and
(01:55):
sorcerers?
What's going on there?
Speaker 3 (01:57):
First of all, let me acknowledge that I'm with
experts here.
We're coming from thetechnology side.
In the last three years or so,we've been trying to surround
ourselves with domain expertsjust to understand these pain
points more.
Once you understand the painpoint, like we had the
technology down so we can, likeyou know, create those workflows
et cetera.
And, as you probably know andI'm happy to go into detail we
(02:19):
have this 3D data platform thatwe've been empowering since the
inception of the company, whichis like the core differentiator.
So, basically, for the, whatwe've been doing in the last
year was, like we're more knownas the sort of outbound company.
You know, just if you in thehot market, obviously you need
that and that's sold quite well.
But we also realized that, youknow, as things slowed down,
(02:41):
like you know, 80% of hiringstarted to become from inbound
sources.
So we actually like startedleveraging the platform to
inbound and outbound workflows.
This is, like you know, rateall the candidates I'm like
score candidates, scoring ATS,refresh and Rediscovery.
We built our own CRM, all thosethings.
Speaker 1 (03:01):
Why is ATS Rediscovery important?
It just for the people whothink that they need to go and
find a new candidate every time,jump into this yeah.
Speaker 3 (03:09):
I mean again, like they already show the affinity
to your company, they willlisten when you sort of hit them
up.
They will have higherlikelihood of, like you know,
opening that email, opening thatLinkedIn message, et cetera.
And it's much cheaper.
I mean to be honest, like youalready have that data, you
don't have to go like advertiseand all that sort of stuff is
(03:30):
just like nonsense if you havethe data.
Speaker 2 (03:32):
It's faster, it's cheaper.
Speaker 3 (03:34):
Faster, cheaper, opens better and probably you
know someone that's interestedin you and not like someone
you're chasing.
So there's also psychologyattached to it and the issue is
that you know especially largecompanies.
They've been emessing, like youknow.
I don't know like Microsoftexisted for like 30 plus 40
years, like, imagine, like adata asset they have.
Speaker 1 (03:54):
Oh, they've got to have a database for at least a
million people.
Speaker 3 (03:57):
Per month.
I would say Everyone appliesright.
So like, how do you actually?
There is a bunch of issuesthere.
First it gets stale right.
So maybe I applied to I don'tknow a company like 15 years ago
, 10 years ago.
Speaker 1 (04:08):
A lot of things change.
Speaker 3 (04:10):
Even like three months ago, things change.
Number one, number two ATSsdon't necessarily have good
search capabilities to thedegree of finding right.
Tell me more, Okay, so let'snow Keyword searching.
Speaker 1 (04:21):
Keyword searching.
I'm not going to go down thatrabbit hole today.
Go ahead.
I'm sorry.
Speaker 3 (04:25):
There's nothing wrong with keyword Like the concept
we're trying to introduce andhopefully doing an okay job.
Is this 3D data right?
So it's not just about what Itell about myself or what I
don't tell about myself, but dohave.
It's more like what's theobjective truth out there.
So the way to get that in ourmodel is like we have maps 750
(04:47):
million people data against 4million company data over a
timeline.
So why is that interesting?
So I may say I'm a startupperson, I love startups.
I love startups on my LinkedInresume, but I may not have been
on a startup Like and what is astartup?
Right, it's like a LinkedInstartup.
It's like an OIL startup.
It's find them a startup.
What you can do is actuallythere's an objective truth to
(05:09):
that.
Like if a company has receivedseries A, series B, series C,
series like a D, like those areobjective states of a company.
So what you can do if you mapthose data, you can say, hey,
this person existed at thiscompany as a sales person
between series A's and series C,as opposed to this person as a
startup person Opposite example.
(05:32):
I found a couple companies.
You know I still have to work,so like nothing super successful
.
Speaker 1 (05:37):
But like.
Speaker 3 (05:38):
I won't say like I'm a startup model or anything like
that on my resume, like Istarted companies, et cetera.
There's no keyword that saysstartup, startup, startup.
They would differentiate mefrom someone else, but actually
I find them.
System will know this guystarted companies or existed in
companies when they were seriesA and scale them.
So that's one example of, likeyou know, objectifying, like the
(06:01):
data out there, so you can runthings like give me a list of
Python developers who understandthe chaos of a startup, ie,
like they exist in a startup, astartup.
Speaker 1 (06:12):
B series A.
Speaker 3 (06:13):
Same thing with Python or Java developers.
Right, I may say Python, python, python, java, java.
Is it the coffee that I'mtalking about?
Is it like the snake that I'mtalking about or is it like?
So?
What we do in that case is likewe map you to your GitHub
profile or, like you know, yourStack Overflow profile and say,
okay, this person has actuallycommitted like a Python or Java
(06:34):
code.
So we know objectively, thisperson has done this.
So the source doesn't have tohave that paranoia of going back
and forth.
Does he really have startup?
Does he really have like theverified code skills and that
sort of generalizes to like10,000 or so attributes?
No-transcript.
Is this an athlete?
Does this person have a blog?
Does this person have like a?
Did this person finish theirPhD quickly, you know?
(06:56):
Are they?
Do they have publications inlike molecular biology?
Do they?
Are they sighted?
Have they built diverse teams?
Like this is all data that'sout there.
Speaker 1 (07:07):
This is all within the final.
Speaker 2 (07:09):
Yes, well, the data is out there.
Well, the data is out there,but they're making sense of it.
Yeah, yeah, yeah.
Speaker 1 (07:14):
I want to dial down on a dichotomy that you created.
You did the specificity in thetopography for engineers and you
also mentioned salespeople.
Those are two diametricallyopposed groups of people.
How does FINDOM know thedifference?
Or can you spill that secretsauce?
Speaker 3 (07:34):
Can you tip the tea Without sounding too
controversial, like we don'tnecessarily go after a keyword
when we scrape the public data,like there is a layer that
collects the data and then youderive meaning from it.
For instance, I don't say likego get me Enterprise Account
Execs.
I say, get me all titles thatyou can publicly find in the
(07:55):
world, and then you may come andbe interested in account
executives, you may beinterested in developers,
someone else may be interestedin HR people, etc.
So we have curated hundreds andthousands of titles, obviously,
and all that sort of stuff.
And in the case of EnterpriseAccount Execs, for instance, you
(08:15):
can look up things like havethey been a president's club
person?
Have they been groomed under areally good CRO?
Have they worked at, say, a fewstartups, then a big company
and then a startup?
Like you can set all thepatterns like a CRO would hire
and then express those Wow,express those in, actual, our
GenAI interface.
(08:36):
Like you can just say what youwant.
Speaker 1 (08:37):
Sure.
Speaker 3 (08:38):
Literally say or speak.
Speaker 1 (08:40):
And then we'll generate MIT engineers in Boston
who went to, who haveexperience in data science, that
weighted tables at a pizzarestaurant.
Speaker 3 (08:48):
The last bit is a bit tricky.
Speaker 1 (08:50):
Okay, all right, I just, I, just, I just see how
far we can go you got 85% of thedata, though.
Speaker 2 (08:54):
Right.
So you're the.
You know the.
Something you said, though,albert.
What was really interesting tome is when, so we're able to
search for a manager, saymanager-level individual, who
you know, based on informationand or charts.
In the company, they've builtdiverse teams, so we can say I
(09:17):
want a middle manager, softwaredeveloper, whatever it's going
to be, who has experience, whohas built a diverse team, and
then you can, you can pull thatinformation in and make that.
I can break it down.
I think you like that, yeah.
Speaker 3 (09:30):
I mean just to demonstrate the point.
If you enter an organizationand then the the breakdown of
the organization, we havediversity data as well.
Well, let's say 50-50.
And like you know, you're in thetenure for another like three
years and it's like now, youknow, I don't know like a more
equal, like a better sort ofteam.
You know that data, the data isout there but, like it's very,
(09:51):
very hard to mine that data asan individual Because, like you
know, as a source you have like15, 20, and 200, 200, 300
candidates each.
The complexity is huge, right,so you need the automation there
to serve you up and the hiringmanager, like, will tell you
like one line like hey, I needlike B2B salespeople.
Like you know the experience.
(10:12):
you work for this guy and thisguy was the CRO there in Seattle
.
That's it.
It's like a sentence or two.
Speaker 1 (10:18):
Yeah.
Speaker 3 (10:19):
You express that and the the source can just see like
the candidate that exactly meetthose criteria.
Speaker 2 (10:24):
Now can you get down to project level, meaning a
developer at a company worked onthis particular specific
project at that competitor?
Speaker 3 (10:35):
If it's publicly available, you will have that
against the candidate.
And what we recently have doneis the following Like let's get
into AI a little bit.
Let me actually like follow upon the ATS.
Why is that importantconversation?
If you have this data and werefresh your ATS data, which we
do, meaning like we look ateveryone we refresh them, like
(10:55):
make them current and make itsearchable, now you can search
with this kind of depth thatwe're talking about not just in
the outbound.
You can search for thesecandidates in your inbound
applicants as they come in andsay, hey, this is a good fit,
this is a great fit, this is nota fit In your ATS.
You can sort of search theentire, refresh the ATS and,
like, bring candidates to thefront.
Speaker 1 (11:17):
Yeah.
Speaker 3 (11:17):
In your CRM anyone that was in our talent community
that fits the bill in youralumni.
Meaning did we ever have peoplethat fit the bill that used to
work for us in your own company?
Like, do we have people thatfit the bill in my company?
As we speak, All that getsserved up in one flow.
That's the beautiful thingabout the consolidation that the
(11:37):
platform enables you, Right.
Speaker 1 (11:40):
Interesting.
That is food for thought.
All right, so we're kind ofwrapping up on time here.
You lead product and find them.
Can you give us an idea ofwhat's going to?
I'm asking you to spill the teayou know, I know and we're, you
know.
What's happening in the nextsix months?
What's the roadmap?
Speaker 3 (12:02):
Yeah, big surprise coming right.
Ok, big surprise For which UK?
Yeah, ok, I'll remiss when Isay yeah, I don't.
Speaker 1 (12:11):
Yeah, ok if you can't .
You can't, and I understandthat.
No, no, no, but you've got topromise me that you'll come back
on and you'll do the show againwith Ryan and I when it's time
to make that announcement.
Speaker 3 (12:21):
Yeah, we'll make.
We're getting into workflow andhow to think about workflows,
like more superpowers for thesources, basically Because we
sort of mapped out 25 flows thatthey do day to day and parts of
that we can just automate, sothey don't have to do this data
piping, just backgroundengineering type of stuff.
Speaker 1 (12:41):
Oh OK, cool, I got you.
Speaker 3 (12:42):
So that stuff can be automated.
And then what we're trying todo is really, really lessen the
load on them so they can reallyfocus on the candidate
experience, and the way we dothis is through AI-powered
interfaces, et cetera.
Speaker 1 (12:57):
OK, we'll talk more again.
There's so much for having Sixmonths.
We'll have you on the big show.
All right, it's Ryan.
It's Ryan.
We are the talent acquisitioncontent lounge that is powered
by Olio, with Recruiting Daily,sourcing School, and it has been
my pleasure to have the ChiefProduct Officer from Findem, mr
Alper.
Thanks for joining us, sir.
Welcome back party people.
We're coming to.
You live from the talentacquisition content lounge
powered by Olio.
It's recruiting daily source inschool.
It's me, Brian Fink, it's himRyan Leary.
What's up Right?
What's going on?
Not?
Speaker 2 (00:18):
much man.
We've got a what do you call ita concert?
Speaker 1 (00:22):
maybe We've got a woman who is talking.
She's trying to hype everybodyup to believing that Taylor
Swift is coming and just to letyou know, taylor Swift is not at
HR Tech.
Speaker 2 (00:32):
She's not here.
You two is here.
You two is here.
Yeah, and they're playing atthe Sphere.
Speaker 1 (00:35):
That is awesome.
Yeah, speaking of people whoare playing, we are playing the
20 questions game with Alper,who is the Chief Product Officer
at Fundum.
What's going on, alper?
How are?
Speaker 2 (00:47):
you All right, all right.
How's the intro, are you?
Speaker 3 (00:50):
like it?
Yeah, geez okay.
Speaker 1 (00:52):
All right, 20 questions.
Are you ready?
Speaker 2 (00:54):
No, that was question number one.
Question number two Is a hotdog, a sandwich.
Speaker 3 (00:59):
Probably, probably.
Oh, he didn't even think aboutit.
Speaker 1 (01:02):
All right.
Speaker 2 (01:02):
He's thought about that.
Speaker 1 (01:03):
Yeah, he's thought about that.
What's another good?
Speaker 2 (01:05):
one Toilet paper over or under, so hold on.
Speaker 1 (01:08):
Jesus, it's like a mullet or a beard right.
That's what.
That's what I try to teachMaddie.
Speaker 3 (01:13):
All right.
Speaker 1 (01:13):
so, alper, it's a beard.
If it goes over, if it goesunder, it's a mullet.
Yes, okay, that is what I'mteaching my seven-year-old
Alper's looking at me going.
I'm not letting you anywherenear my side right, that's
awesome.
Alper.
You know our last guest thatwas on.
We were talking about thesourcing crisis that's taking
(01:34):
place right now, because a lotof recruiters are having to
source for passive talent asopposed to having a sorcerer to
support them.
I immediately think I find themthat, you guys.
I mean that's what you do.
You turn recruiters, you givethem superpowers, your little
robot that sits on theirshoulder.
What are you seeing coming downthe pipeline for recruiters and
(01:55):
sorcerers?
What's going on there?
Speaker 3 (01:57):
First of all, let me acknowledge that I'm with
experts here.
We're coming from thetechnology side.
In the last three years or so,we've been trying to surround
ourselves with domain expertsjust to understand these pain
points more.
Once you understand the painpoint, like we had the
technology down so we can, likeyou know, create those workflows
et cetera.
And, as you probably know andI'm happy to go into detail we
(02:19):
have this 3D data platform thatwe've been empowering since the
inception of the company, whichis like the core differentiator.
So, basically, for the, whatwe've been doing in the last
year was, like we're more knownas the sort of outbound company.
You know, just if you in thehot market, obviously you need
that and that's sold quite well.
But we also realized that, youknow, as things slowed down,
(02:41):
like you know, 80% of hiringstarted to become from inbound
sources.
So we actually like startedleveraging the platform to
inbound and outbound workflows.
This is, like you know, rateall the candidates I'm like
score candidates, scoring ATS,refresh and Rediscovery.
We built our own CRM, all thosethings.
Speaker 1 (03:01):
Why is ATS Rediscovery important?
It just for the people whothink that they need to go and
find a new candidate every time,jump into this yeah.
Speaker 3 (03:09):
I mean again, like they already show the affinity
to your company, they willlisten when you sort of hit them
up.
They will have higherlikelihood of, like you know,
opening that email, opening thatLinkedIn message, et cetera.
And it's much cheaper.
I mean to be honest, like youalready have that data, you
don't have to go like advertiseand all that sort of stuff is
(03:30):
just like nonsense if you havethe data.
Speaker 2 (03:32):
It's faster, it's cheaper.
Speaker 3 (03:34):
Faster, cheaper, opens better and probably you
know someone that's interestedin you and not like someone
you're chasing.
So there's also psychologyattached to it and the issue is
that you know especially largecompanies.
They've been emessing, like youknow.
I don't know like Microsoftexisted for like 30 plus 40
years, like, imagine, like adata asset they have.
Speaker 1 (03:54):
Oh, they've got to have a database for at least a
million people.
Speaker 3 (03:57):
Per month.
I would say Everyone appliesright.
So like, how do you actually?
There is a bunch of issuesthere.
First it gets stale right.
So maybe I applied to I don'tknow a company like 15 years ago
, 10 years ago.
Speaker 1 (04:08):
A lot of things change.
Speaker 3 (04:10):
Even like three months ago, things change.
Number one, number two ATSsdon't necessarily have good
search capabilities to thedegree of finding right.
Tell me more, Okay, so let'snow Keyword searching.
Speaker 1 (04:21):
Keyword searching.
I'm not going to go down thatrabbit hole today.
Go ahead.
I'm sorry.
Speaker 3 (04:25):
There's nothing wrong with keyword Like the concept
we're trying to introduce andhopefully doing an okay job.
Is this 3D data right?
So it's not just about what Itell about myself or what I
don't tell about myself, but dohave.
It's more like what's theobjective truth out there.
So the way to get that in ourmodel is like we have maps 750
(04:47):
million people data against 4million company data over a
timeline.
So why is that interesting?
So I may say I'm a startupperson, I love startups.
I love startups on my LinkedInresume, but I may not have been
on a startup Like and what is astartup?
Right, it's like a LinkedInstartup.
It's like an OIL startup.
It's find them a startup.
What you can do is actuallythere's an objective truth to
(05:09):
that.
Like if a company has receivedseries A, series B, series C,
series like a D, like those areobjective states of a company.
So what you can do if you mapthose data, you can say, hey,
this person existed at thiscompany as a sales person
between series A's and series C,as opposed to this person as a
startup person Opposite example.
(05:32):
I found a couple companies.
You know I still have to work,so like nothing super successful
.
Speaker 1 (05:37):
But like.
Speaker 3 (05:38):
I won't say like I'm a startup model or anything like
that on my resume, like Istarted companies, et cetera.
There's no keyword that saysstartup, startup, startup.
They would differentiate mefrom someone else, but actually
I find them.
System will know this guystarted companies or existed in
companies when they were seriesA and scale them.
So that's one example of, likeyou know, objectifying, like the
(06:01):
data out there, so you can runthings like give me a list of
Python developers who understandthe chaos of a startup, ie,
like they exist in a startup, astartup.
Speaker 1 (06:12):
B series A.
Speaker 3 (06:13):
Same thing with Python or Java developers.
Right, I may say Python, python, python, java, java.
Is it the coffee that I'mtalking about?
Is it like the snake that I'mtalking about or is it like?
So?
What we do in that case is likewe map you to your GitHub
profile or, like you know, yourStack Overflow profile and say,
okay, this person has actuallycommitted like a Python or Java
(06:34):
code.
So we know objectively, thisperson has done this.
So the source doesn't have tohave that paranoia of going back
and forth.
Does he really have startup?
Does he really have like theverified code skills and that
sort of generalizes to like10,000 or so attributes?
No-transcript.
Is this an athlete?
Does this person have a blog?
Does this person have like a?
Did this person finish theirPhD quickly, you know?
(06:56):
Are they?
Do they have publications inlike molecular biology?
Do they?
Are they sighted?
Have they built diverse teams?
Like this is all data that'sout there.
Speaker 1 (07:07):
This is all within the final.
Speaker 2 (07:09):
Yes, well, the data is out there.
Well, the data is out there,but they're making sense of it.
Yeah, yeah, yeah.
Speaker 1 (07:14):
I want to dial down on a dichotomy that you created.
You did the specificity in thetopography for engineers and you
also mentioned salespeople.
Those are two diametricallyopposed groups of people.
How does FINDOM know thedifference?
Or can you spill that secretsauce?
Speaker 3 (07:34):
Can you tip the tea Without sounding too
controversial, like we don'tnecessarily go after a keyword
when we scrape the public data,like there is a layer that
collects the data and then youderive meaning from it.
For instance, I don't say likego get me Enterprise Account
Execs.
I say, get me all titles thatyou can publicly find in the
(07:55):
world, and then you may come andbe interested in account
executives, you may beinterested in developers,
someone else may be interestedin HR people, etc.
So we have curated hundreds andthousands of titles, obviously,
and all that sort of stuff.
And in the case of EnterpriseAccount Execs, for instance, you
(08:15):
can look up things like havethey been a president's club
person?
Have they been groomed under areally good CRO?
Have they worked at, say, a fewstartups, then a big company
and then a startup?
Like you can set all thepatterns like a CRO would hire
and then express those Wow,express those in, actual, our
GenAI interface.
(08:36):
Like you can just say what youwant.
Speaker 1 (08:37):
Sure.
Speaker 3 (08:38):
Literally say or speak.
Speaker 1 (08:40):
And then we'll generate MIT engineers in Boston
who went to, who haveexperience in data science, that
weighted tables at a pizzarestaurant.
Speaker 3 (08:48):
The last bit is a bit tricky.
Speaker 1 (08:50):
Okay, all right, I just, I, just, I just see how
far we can go you got 85% of thedata, though.
Speaker 2 (08:54):
Right.
So you're the.
You know the.
Something you said, though,albert.
What was really interesting tome is when, so we're able to
search for a manager, saymanager-level individual, who
you know, based on informationand or charts.
In the company, they've builtdiverse teams, so we can say I
(09:17):
want a middle manager, softwaredeveloper, whatever it's going
to be, who has experience, whohas built a diverse team, and
then you can, you can pull thatinformation in and make that.
I can break it down.
I think you like that, yeah.
Speaker 3 (09:30):
I mean just to demonstrate the point.
If you enter an organizationand then the the breakdown of
the organization, we havediversity data as well.
Well, let's say 50-50.
And like you know, you're in thetenure for another like three
years and it's like now, youknow, I don't know like a more
equal, like a better sort ofteam.
You know that data, the data isout there but, like it's very,
(09:51):
very hard to mine that data asan individual Because, like you
know, as a source you have like15, 20, and 200, 200, 300
candidates each.
The complexity is huge, right,so you need the automation there
to serve you up and the hiringmanager, like, will tell you
like one line like hey, I needlike B2B salespeople.
Like you know the experience.
(10:12):
you work for this guy and thisguy was the CRO there in Seattle
.
That's it.
It's like a sentence or two.
Speaker 1 (10:18):
Yeah.
Speaker 3 (10:19):
You express that and the the source can just see like
the candidate that exactly meetthose criteria.
Speaker 2 (10:24):
Now can you get down to project level, meaning a
developer at a company worked onthis particular specific
project at that competitor?
Speaker 3 (10:35):
If it's publicly available, you will have that
against the candidate.
And what we recently have doneis the following Like let's get
into AI a little bit.
Let me actually like follow upon the ATS.
Why is that importantconversation?
If you have this data and werefresh your ATS data, which we
do, meaning like we look ateveryone we refresh them, like
(10:55):
make them current and make itsearchable, now you can search
with this kind of depth thatwe're talking about not just in
the outbound.
You can search for thesecandidates in your inbound
applicants as they come in andsay, hey, this is a good fit,
this is a great fit, this is nota fit In your ATS.
You can sort of search theentire, refresh the ATS and,
like, bring candidates to thefront.
Speaker 1 (11:17):
Yeah.
Speaker 3 (11:17):
In your CRM anyone that was in our talent community
that fits the bill in youralumni.
Meaning did we ever have peoplethat fit the bill that used to
work for us in your own company?
Like, do we have people thatfit the bill in my company?
As we speak, All that getsserved up in one flow.
That's the beautiful thingabout the consolidation that the
(11:37):
platform enables you, Right.
Speaker 1 (11:40):
Interesting.
That is food for thought.
All right, so we're kind ofwrapping up on time here.
You lead product and find them.
Can you give us an idea ofwhat's going to?
I'm asking you to spill the teayou know, I know and we're, you
know.
What's happening in the nextsix months?
What's the roadmap?
Speaker 3 (12:02):
Yeah, big surprise coming right.
Ok, big surprise For which UK?
Yeah, ok, I'll remiss when Isay yeah, I don't.
Speaker 1 (12:11):
Yeah, ok if you can't .
You can't, and I understandthat.
No, no, no, but you've got topromise me that you'll come back
on and you'll do the show againwith Ryan and I when it's time
to make that announcement.
Speaker 3 (12:21):
Yeah, we'll make.
We're getting into workflow andhow to think about workflows,
like more superpowers for thesources, basically Because we
sort of mapped out 25 flows thatthey do day to day and parts of
that we can just automate, sothey don't have to do this data
piping, just backgroundengineering type of stuff.
Speaker 1 (12:41):
Oh OK, cool, I got you.
Speaker 3 (12:42):
So that stuff can be automated.
And then what we're trying todo is really, really lessen the
load on them so they can reallyfocus on the candidate
experience, and the way we dothis is through AI-powered
interfaces, et cetera.
Speaker 1 (12:57):
OK, we'll talk more again.
There's so much for having Sixmonths.
We'll have you on the big show.
All right, it's Ryan.
It's Ryan.
We are the talent acquisitioncontent lounge that is powered
by Olio, with Recruiting Daily,sourcing School, and it has been
my pleasure to have the ChiefProduct Officer from Findem, mr
Alper.
Thanks for joining us, sir.
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