Don't see. Let me check my email if

anybody's having problems getting in.

So, I'm not seeing the founder here yet.

Um,

okay.

>> I appreciate it.

>> Um, great. Yeah. So um high level just

start you know just back at the at the

top right of of what we're building uh

here here at Talon. Uh essentially our

uh and and what we set out to build

build versus what we're building today

is obviously a little bit different. The

the the

scope or rather and the the vision has

has has gotten a lot bigger. Um which is

why we decided to then go out and raise

venture capital. um you know when we

initially went out to just solve some

automation based problems in an industry

that we saw was a legacy industry that

wasn't good at automating things um and

and spending a lot of manual time on uh

you know basically the operations of a

of an agency uh but since then I think

we've we've realized uh that the future

of this industry is you know the ability

for firms to scale revenue without

scaling headcount necessarily at the

same at the same pace.

Traditionally, these businesses, it's

they, you know, they operate, I think,

very similarly in their billing models.

Not exactly the the hourly model, but,

you know, similar to kind of like a law

firm, uh, or other traditional uh,

billable type service uh, uh, businesses

where they can only scale revenue by

scaling headcount. It's all about how

much am I billing, you know, per person

uh, per per month or per year uh, uh,

and whatnot. And uh really the we said

there's a lot of system of record type

technologies in this businesses right

applicant tracking systems CRM but there

was nobody uh there was nobody building

the like an operating system designed to

scale revenue specifically for them

right and there's two sides to their

business

uh delivery and then sales right placing

candidates and they get paid almost you

know exactly like right when they get

those candidates placed Um, and then

they need to be bringing new logos in

the door constantly as well in order to

to grow their business and not be

stagnant or even not shrink. Uh, and

it's incredibly competitive in nature

right among amongst one another. Last

thing I'll say on this, the really

unique thing, uh, and why we were really

uniquely positioned, I think, to like

tackle it in the way that we are is that

it's all incredibly uh, outbound based,

right? It's all about very similar to

how like you know an outside sales team

operates in a lot of orgs whether it's

candidates again or you know net new

logos. It's about understanding,

being able to figure out to reach out to

the right company, the right candidate,

the right hiring manager at the right

time with really good research in a

really thoughtful way uh in in

relatively, you know, small medium kind

of batches, right? This is not a spray

and prey doesn't work uh in in this

industry and that's why there's so much

labor involved in doing kind of the

things that we do do correctly. So um

that's what Talon does is being able to

scale delivery and sales for these for

these agencies uh while integrating with

their system of record tech to help them

get more business and and service that

business. So um some notable things kind

of since um uh uh main thing we've been

focusing on as of late um we've closed

uh on the on the round uh about 1.2 2 uh

1.2 million uh Canadian uh and then also

gotten about another 300k in in

non-dilutive funding. So we've gotten

about 1.5 million Canadian in capital in

the last uh about 45 days uh or so. Um

and so we've got the capital that we

need now to start, you know, executing

on on our plan. So we've been in full on

hiring mode. Uh we've gone from five

people and all the metrics that you see,

I think we'll talk we did with a very

lean team of five. uh we're going to

about 11. Um and so that team of 11 uh

being comprised of we just sent out

offers that were accepted uh end of last

week for two salespeople uh senior

account executives uh in uh is is sort

of the profile uh as well as an SDR um

to help kind of support uh top of

funnel. Uh so those three hires uh on

the business side we hired an additional

customer success manager um and we also

hired a founding uh head of UX um very

senior design lead. Last last role we

have to do is we're going to be hiring

another uh AI focused senior senior

engineer uh to cap out our engineering

team will be then three plus our CTO uh

and that's the team that's going to get

us to north. Right now we're on a sprint

to to 2 million uh in ARR. Uh, and so

that's the team that's that's what we've

been really working on is building the

V2 of of our engine, right? Up until

now, we've been able to very reliably

get 30, 40, 50K and net new MR on a on a

monthly basis. But in my background,

it's about engines and we're now taking

a step back, rebuilding the engine so

that we can we need to be doing 150 to

200K a month in in net new ARR. And so

this is the engine that's gonna that's

going to do it for us. And so that's

what that's what we've been focusing on.

And uh yeah, we're just wrapping up now

the last few folks at the table uh to to

kind of get the round closed and then

now it's complete focus on on sales. So

>> great. So uh the you're expecting to

close your round when? You're aiming to

close it when?

>> Uh the goal is to have it wrapped up uh

in the next couple of weeks uh at at the

latest. So uh yeah, kind of that third

or end of April. end of April. Yeah.

>> And do you have money in the bank

already from some of these or?

>> Yep. That all of that capital is in the

bank. That's correct.

>> Sweet. Okay. Tell me what you mean by

rebuilding the engine.

>> Yeah, absolutely. So, this is my

background, right? So, I my for those of

you that that that haven't seen, I spent

the last close to a decade as a growth

growth marketer for VCbacked uh early

stage early stage software, right? Um I

work directly with founders to scale

their go to market right and their kind

of revenue revenue functions. So um a

lot of times like the the act the type

of activity or right the type of whether

it's tech stack right process people the

type of you know if you need to let's

say close five deals a month and then

all of a sudden you have to now go close

20 deals right a month uh in order to

kind of hit your goals. It's not a it

doesn't scale linearly. It almost never

does in a in any kind of revenue

organization. You have to um you know

rebuild a lot of you know sort of how

you how you do things. Um for example

>> scalability rebuilding the scalability

side of the engine.

>> Absolutely right. Like we're really big

on you know for example like meta ads

and search engine marketing. We drive

you know a fair bit of our leads there.

um like as soon as you start to spend

more money, it gets harder to get

conversions at a dollar amount that

makes sense, right? In terms of dollar

in, dollar out. So, you need to now work

on that engine to to maintain efficiency

while you scale your spend to get more

opportunities in the door from that

channel, you know, as an example. So, my

role now is I'm going to be selling. Um,

you know, obviously you don't really

stop selling as a founder, but I go from

being the sole saleserson, right, doing

kind of everything to I essentially now

at this point become a go to market

engineer, which right, that's my sweet

spot to make sure that our sales team is

extremely like well supported. So, I'm

kind of moving into that role now for

the next 8 to 12 months.

>> I hope this isn't rude. Uh, but you we

can see that you are an exceptional

salesperson.

beginning to end. How how do you

replicate you? I know your systems are

there,

>> but how do you bring in that talent that

that that can do that that that that

founder level of of of closing and

charisma?

>> Yeah, that's a that's a great question.

So, and I think it's always about making

moves so that you hedge, right, as as

best you can, right? So there's a few

moves that that I've made with trying to

accomplish that. A really big one is

going senior on the sales side. You

know, the profile I wanted was someone

with with 7 to 10 years of closing

experience specifically in high velocity

SMB SAS, right? Exact type of sales

cycle that we're in. Uh and the two

people that we closed on, really, really

excited about them. I think we got

really strong profiles in and we were

able to attract them because of how

automated we are in terms of our sales

process and they were excited at how

much they could close potentially

because we've automated data entry into

HubSpot research on accounts heading

into demos. Um our outbound is 100%

automated. they won't be doing they

won't be sending emails like we we're

going to be doing that from like you

know what I mean from from essentially

as a marketing function not a a sales

function and so that enabled us to I

think punch above our weight in terms of

sales people we were able to get we also

you know we're the OT for for those

about 225K so we didn't we didn't cheap

out on sales people either um half of

that being being base right the other

piece that we're using to hedge is we've

we've recorded like every demo I've ever

done has been recorded on Fathom and is

now in a data lake. Vast amount of uh

research is being done on figuring out

patterns, demo process. It's if you can

processize the things that work, you

kind of take the guesswork out of the oh

what's that magic what's that magic

thing? It's like we want we don't want

there to be a magic thing. We want there

to be a a plus b plus c, you know,

equals close, right? That's that's kind

of what what we want to try to build. So

a

>> what are you doing for clients as well?

>> There's I mean a lot of this thinking

right is in absolutely right. I think

and we'll kind of get into this I guess

in a bit but like

>> the mo the moat behind something like

talent is the intelligence layer. Uh

automation isn't a moat anymore. They'll

be able to use claude co-work and go

automate some of the stuff that we're

talking about. But they can go in uh to

town and say, "Hey, I want to run this

really specific type of business

development campaign and town will do it

in a way that I I think 99% of

recruitment consultants would not be

able to replicate even with any AI at

their disposal just because we've built

a brain behind our our tool that that

that executes at a higher level than

than the standard." So,

>> okay, we're going to jump into product

in a second, but I just want to make

sure everybody can you just reiterate

the terms of your of your round just to

focus everybody in on the round.

>> Yeah, definitely. So, um yeah, pretty

simple. As as most of you know, right,

Mars, uh led led the round and they they

set the terms uh $8 million USD cap uh

on a safe note uh with no discount. Um

very standard safe. um gave Mars your

basic, you know, side letter things like

observer rights uh and and and things

like that. Uh didn't give up any any

board seats or anything like that at at

this point. Um but yeah, pretty pretty

standard stuff.

>> Any questions so far

before we jump into

>> just just based on that, do you have any

other uh VCs or have you been discussing

with um Canadian or your US ones?

Uh yes, we do have some smaller uh VCs

in uh in the round. Uh we have our

preede uh VC which was out of the US

Cascade seed fund uh also participated

in this round as well. Um and they were

they led our our preed round. Uh we also

have uh Simon Soal of of Relay Ventures.

Uh but we're he's got a a micro fund uh

called Gambit Partners. Uh so Gambit

Partners participated also. um and they

do relatively small uh 50k checks.

Um those are and then DMZ Ventures. So

we're also a DMZ in Toronto for those

you familiar DMZ. We're a DMZ portfolio

company and we're also we did their year

and a halflong incubator and then their

their venture arm invested in us as well

um at both the preede and seed stage

also. So those are the four kind of

institutional checks if you will that we

that we have and the rest is rounded out

by angels.

>> Sorry, go ahead.

>> That's right. I haven't taken a look at

your data room yet. Is the cap table in

there?

>> Yeah, that's right.

>> Perfect. Thank you.

>> You have a minimum check size?

>> Uh minimum check size 25K US.

>> Okay, stand us

>> one quick question here. just uh just

want to double on on your hiring process

to get these uh salespeople on board. I

have seen you use talent as a platform

to find them and to go through that

process. What did you learn during that

process that made you think, oh crap or

things good or not working with the

platform that I have on?

>> I love the question, Gerard. Uh yes, we

used talent to hire all four of the

recent hires uh that we that we recently

made. Um and it was it was great, right?

Because obviously when you dog food your

own product, you you get reminded of the

pieces that you love and then the the

the holes and the gaps they you know

they they make themselves you know very

aware to you um you know upfront. I

think for us it's like um you know we

one thing that was great is we realized

if we didn't have talent how how painful

it would have been. Um so that was a

really nice reminder. um the and then

there's just a very specific features

where we know where we want to get that

feature to. It just we haven't had the

bandwidth or the road map hasn't gotten

there yet for a specific feature in

terms of its of its maturity. Um a

really big just to give you sort of an

example a really big one was our goal

with Talon is to get the the firm to

meeting booked right all the way to

there is a there is a block on my

calendar with the right person. We want

to automate as much of getting them

there as possible. And uh in our uh

we've got a lot of work to do on in our

so we have an inbox right where we we

centralize all the coms that come in

from replies from candidates or from

hiring managers. Um we have a lot of

work to do in making that inbox um uh

essentially automate like the back and

forth with the candidates. So, we

realized what we were then spending the

most time doing was DMing back and forth

with, you know, candidates to to

basically get them get them booked in

when it's like this should just be

either mostly automated or even

potentially fully fully automated. Um,

so that's an example, right, of stuff

that we obviously found uh in in the

road map, but um yeah, great question.

>> Thanks.

talking about the candidate. I just so

I'm clear

your your core value proposition for the

firms

is to book

not recruiting candidates but um

prospects for

like clients. Am I do I am I confused

about that?

>> Yeah, sorry. So the the the core value

prop is is is essentially scaling both

of those things. uh

>> includes includes and then it goes to

the candidate. So it starts with and

then it it's in the same process sort of

>> and this is the unique thing about

staffing and recruitment agencies is and

not all of them operate on this model.

Many many of them do is that your

individual recruiter is responsible for

both of those functions.

>> They're responsible for bringing they

they do the sales and then they actually

make the placement. Uh so that's why

it's this unique problem because they

are constantly juggling uh the two of

those functions

>> bringing bringing them those functions

on the same technology platform.

>> Exactly.

>> What what you want to call it. Um is is

there value in doing a demo? I know

Gerard you can't see. I don't think

you're coming in by phone. Um we we are

recording this. Uh, does that make sense

to do a a free?

>> I love I love a walk.

>> Yeah, let's do that.

>> Can we do that, Julian?

>> Absolutely. Yeah, let's do

>> it. Wasn't on the agenda, but

>> that's that's okay. I've um

>> But it wasn't in the memo either, right?

So, we can read we can read all the

answers in the memo. The demo.

>> The demo. I think the demo is very

important. I think it's a good idea.

>> We like the demo. If uh yeah, if you're

if you're scared of a demo, then there's

probably a problem. Um

>> probably your funnest part, right?

>> For you.

>> Yeah.

>> Yeah. Exactly. All right, let me just

spin up an instance here and then we'll

get uh we'll get rolling. So, there's a

few workflows uh within Talon. I'm gonna

show one in particular uh that um

that was been has been a massive uh like

winner for us just generally speaking.

Um cuz we essentially just we we we

found a way uh and this is part of that

intelligence layer, right? We found a

very specific type of playbook that

worked incredibly well at engaging

hiring managers, which is the hardest

thing to do as a as a recruiter by far

is getting a hiring manager to actually

>> basically have a conversation with you

in a way that's not I'm not using I'm

not using recruiters. Please leave me

alone. Right. Um that's that's

definitely like the hardest thing thing

to do in in recruitment as a whole. So,

>> how many um how many pings would a

hiring manager have from recruiting

firms, do you think?

>> So, the the most the classic one is as

soon as they post a job, right? Then

they just get flooded with DMs from from

recruiters, right?

>> That's why they say no and that's why

they say no recruiters at the bottom of

their posting.

>> Correct. uh what we we are working on

playbooks that essentially look at other

signals that aren't the obvious ones

that every single recruiter is is doing

in more of a predictive you know sort of

sort of sense

>> and this is yeah and exactly and if when

you look at the two problem sets right

the candidate side versus the the the BD

side the problem with the candidate side

is they're not great at automation so

they're just wasting time like their

time to hire gets hurt by the fact that

they're not great at picking tech

stacks, implementing them, making them

work together, and now with AI tools,

like they're definitely they're not

great at prompting, like they're not

good at injecting their own expertise

into the the the platform. So, that's

what we solve on the candidate side. On

the BD side is they just don't know what

to do. Like, it's just uh you know, very

um uh yeah, they just go I I there it's

a spray and prey approach. It's a oh,

I'm just

>> lean on my network. Those are the

answers there, right? So,

>> all right. Let me share my screen here

>> and then

>> Okay, awesome. Um, okay, great. So, this

is this is our like this is our cockpit,

if you will. This is just our initial

I've got everything in in Talon is

heavily like campaign based, right? We

talked about the concept of sort of of

outbound. Um, but what I'll do is I'll

say, hey, I want to get a net new

campaign off the ground. say I want to

generate I want to generate some

business here. Um and so what I would do

is hop into talent and talent is very

the whole platform is designed to

basically walk it's it's it's part

agentic, right? Part co-pilot cuz with

recruitment you need to give them the

ability to make edits or review work at

every single step or they just simply

won't use it.

>> Um and it's also I think that's the best

practice with AI as well. I think it's

8020 is really the the sweet spot here.

So what we can do is we can say hey I'm

I'm doing a sales campaign. I need to

generate some business. And then we ask

hey what kind of playbook uh do you want

to use? The one uh the one that I'll

show you uh today uh is called the most

placeable candidate. Essentially this is

the idea that we're reverse engineering

the process and instead of and we have a

great candidate that we want to now get

in front of potential hiring managers.

It is one of the most effective ways to

get in front of a hiring manager because

you are coming to them with something

timely, relevant and essentially the

person right is is your product. We then

pick our data source right so right now

we work we can pull in data from

LinkedIn CSV

um we are building um very soon we're

going to be able to do a a deep search

within the actual CRM or ATS uh of the

recruiter as well. Uh, but I'll use I'll

choose LinkedIn Recruiter as my data

source. Um, this is going to pop me

directly. For those of you that don't

know, LinkedIn Recruiter is used by, you

know, 95% of recruiters and it's

basically their, you know, it's their

search tool for for mostly candidates.

We can hop into a project. Uh, project

is basically a list. and I would say to

talent and this is the Talon Chrome

extension. Hey, I want to build out

build out my search. I've got a blank

search here. We've got our most

placeable candidate uh workflow. And all

I really need to do is start from uh the

resume here of a of a candidate. I

actually did. So, I did this demo this

morning. So, I'll use I'll use the

person that I did the demo this morning.

Um and yes, we boo we did book them into

into a pilot. Um, so, uh, this

particular recruiter did, uh, he did

like wealth management, high netw worth

in like Switzerland. And he said his key

problem set, he's I have great

candidates all the time that I don't

have a role for. I'm usually like

ignoring them when he's like, I want to

be using them in this way, right? I want

to be using them as a business

development tool. So, I can basically

upload the resume of the candidate.

Talon's AI is going to completely parse

that resume, understand the person's

background, all of those things. It's

going to translate that into

essentially here. It's going to help me

build my list on LinkedIn Recruiter. And

the idea behind the Chrome extension is

that it's

going to where we're going with the road

map. It should just work anywhere,

right? That a recruiter would be doing

research or or building lists or things

like that. Um,

and you'll see my screen refresh here in

a second. and it will have automatically

gone in. This is if those of you that

have played with like Claude Co-work,

right? There's a little bit of

similarity here uh with with sort of

Claude Co-work and how it operates. Uh

but this is pre-trained, right? Um if

you ask Claude Co-work to do this, it

would um you know uh potentially it

would give you an LLM response that

would be very general, right? Not not

super well trained. So, uh, what it's

done, it's gone and automatically filled

in job titles, locations, industries,

and I can go into the reasoning and kind

of see, you know, why did it do what it

did? And this is what it's trained to

do, right? Lucas is a VP at Julius Bear

in Swiss private banking. The hierarchy

typically runs like so. Um, his hiring

manager would most likely be a managing

director, executive director, head of

private banking, etc. Things like that.

This is helping me dial in on leads,

basically.

What I would do from here is, you know,

I could make uh I could make some edits

myself before I sort of move on. Uh

obviously, like I could narrow things a

little bit um

however I'd like.

And then once I'm happy, let's say I've

got 200 leads that I want to get going,

one click, we'll be able to get these

leads back into Talon. And then Tal's

going to walk us through everything we

need to do to get a clean um very

wellressearched campaign off the ground.

Um the next step here is deep research.

So what Talon will do is it'll basically

we're going to do what's called lead

scoring. We go through these 213 leads

and uh we're going to look at signals

that are available to us online,

behavioral signals, company signals. Uh

what does that hiring manager do? what

what team are they responsible for?

Who's on their team? We're basically

going to take in all the available data

points. Um that is going to allow us to

do two things. One, hone in on the list

and make sure we're reaching out to only

the most relevant people. And number two

is we're going to get the data and the

research to make sure that when we do

this outreach, it is as uh relevant and

personalized as possible. So I got my

confirmation screen there uh for the 213

talent automatically builds scoring

criteria right. So for the lead scoring

these are the five things that talent

says hey these are important if we are

going to map out the quality or the

relevancy of this list and I can change

this right I can move order I can add

things I can remove things etc right

>> we hit we hit start short listing

talon then begins uh its deep research

and now for 213 individual contacts

Talon is going to produce for us a score

in terms of how good a lead they are, a

recommendation if we should reach out to

them or not, and then then essentially a

deep research report on each uh on all

these scoring criteria that that we that

we care about.

Any questions while that's while that's

running,

>> what's the source of that deep research

like primarily the resume, LinkedIn

profile or where do you get the

information? Because if the candidate

has generated his LinkedIn profile and

resume with AI, then it's as good as a

random thing for AI to then

>> Yeah, absolutely. So we use um we we

have built on our back end what's called

like a data waterfall. So we are using

um essentially multiple data providers

as well as our own you know even like

anthropics like web search ability

to pull in data from all all kinds of

sources right so we're omni omni data

source and we're always testing uh

various data sources their quality right

sort of things like that and generally

like what we find is like LinkedIn

profile data it is generally pretty high

quality right cuz that's someone's

public profile in terms of what they

want to share about themselves and it's

self self-reported obviously. So

LinkedIn profile data is definitely it's

a big big part of it uh as well. Um

basically any public like any sourcing

tool or any other kind of search tool

it's it's about what public what public

data is available to us and what's the

quality of that of that data. We when we

initially built this, we evaluated 40

plus data providers and we ended up

choosing five to then have on our back

end to enrich these profiles with with

additional data. And the whole purpose

of that testing was was Q&A, right? Um,

and that's another that's a you know

there's a lot of small problems talent

solves, but that's another problem

talent solves is what data provider do I

use? Are they quality? all of those

things like recruiting I can tell you

recruiting agencies are not well

equipped today to go pick a data

provider and then assess the quality of

that data provider uh in a way that's

statistically significant for example so

yeah that's a big big problem that we

sort of set out to set out to solve

>> quick question for you on LinkedIn um I

guess how reliant are you on the data in

there you know if that was to be

restricted in some way uh how that

impacts you.

>> Yeah, absolutely. Great question. So, we

we did a really to try to again hedge

against that as much as humanly

possible. We did a really specific we

took a really specific way of doing this

and that all we're getting from that

list on LinkedIn is the LinkedIn URL.

That's all we want. We're not scraping

the deep the data in there. We're not

trying to essentially go get proprietary

stuff in LinkedIn Recruiter. All we want

is the LinkedIn profile because then

once we have that, we can then use the

data waterfall I just talked about, we

can use that to then fill in all of the

blanks. Um, so there's very uh

essentially we said, hey, like how do we

make this as as non-reliant as as

possible? Uh, you know, if you will. Um,

does that make sense?

>> Yeah, absolutely. And has there been any

terms of service flags or enforcement

actions that you've seen so far?

>> No. So we haven't we haven't gotten

anything like that. Uh we have like a

lot of guard rails built in to not allow

the user to go, you know, do anything

sort of too uh uh too crazy, uh if you

will. Um we have there's versions of our

of our of individual feature sets that

can be, you know, dialed back in terms

of what they automate if if need be. Um,

we've actually we've also entertained

uh building out our own entire search

function as well. You'll see, but Talon

does a ton on the piece after this and

because we knew recruiters all already

had this tool and they were already

pretty reliant on it. That's why we kind

of decided to build over top of it as

opposed to take the mammoth undertaking

of trying to rebuild it. Um, but roadmap

wise like that's absolutely uh an option

too, right? is to basically, you know,

build that out and then uh have

virtually zero LinkedIn involvement,

right, in it in it as well. And there's

lots and that's becoming a like search

APIs are now becoming a commodity as

well, like to basically get, you know,

uh versions of LinkedIn's data set that

you can then just purchase um and then

and then use on on your side. So um

>> Got it. Thanks.

>> No problem. Awesome. So this is about

done doing its uh its deep research.

I'll just give you an example of of kind

of what this looks like, right? So, we

have right now our best lead right now

is Nadine. Uh she's the managing

director and team leader at uh this

Swiss company. Um she directly leads uh

this is ultra high net worth is what

that stands for. She she leads that team

meaning we've met criterion one uh DAC

focused ultra high netw worth private

banking. Um and then gives like some

background as to like key areas that

that's happening in. So that me matches

criterion 2 family office focus 20 plus

years exclusively in ultra high net

worth.

This is basically a deep research. This

is justifying why did we give Nadine a

93. The second part and this is one of

my favorites and you'll see where this

comes in after is this does deep re this

also makes a suggestion if we were to

reach out to Nadine regarding the

candidate that we have here how do we

position our candidate to Nadine right

it's saying hey we should lead with this

DAC fit essentially this crossber

structuring expertise maps directly to

Naen's uh to what her and her team focus

on so there's a direct match between the

candidate and then the function area. We

want that to come through in our actual

outbound uh in in the messaging.

And kind of what I'll stress here is

like if a recruiter really wanted to

automate this using like claude, could

they? Potentially. It'd be pretty hard

to do it for all 213 people at once.

That would require like deep

architecture experience. Let's assume

that they figured that out. if they did

figure that out. The amount this is

powered by over 50 proprietary prompts

that are happening on the back end. It's

not uh hey go do this deep research and

come back with the best stuff like this

is very complex uh prompt engineering

and it's really it's not prompt

engineering it's context engineering

happening on the back end for them to be

a I strongly believe for them to be able

to replicate this would be at this level

of quality would be near impossible and

then you have to ask is it worth it for

them to try to do that for 150 bucks a

month per person and that's kind of the

way I look at like defensive ibility and

mode.

So once we're done, we have 92 fantastic

leads out of our 213, which is great.

I'm going to hit save and complete. The

good leads get saved. Everyone else gets

removed.

And now it's build our own journey. So

now we actually need to do the outreach.

And this is part of our thing. We're

covering a pretty wide swath here. Not

just the research, not just the scoring,

but then now the actual doing the work

as well, automating the tasks. And I can

build this out however I like. I could

say, "Hey, I want to do an email on day

one." If I don't have their email, I

want to do an inmail.

I want to schedule a phone call after

that. It's really whatever uh the

recruiter wants. Roadmapwise, this is

like immediate.

Um this is going to be built out for you

by AI there. One thing we've learned,

this is like a skill that recruiters um

don't necessarily have is, hey, what's

the structure that makes the most sense

based on what I'm trying to do? This

will all just be built out. Step one, we

recommend AB D right through and then

all the copy written for me uh as well.

Today we can use AI to obviously craft

like copy like that's in the box or

that's in our in our first message.

Um, it's funny. This was a feature that

we thought was, you know, this is

obviously a bit of a a commoditized

feature like structuring and, you know,

outreach with logic and branches and

stuff like that. But this is one of

those features that honestly like users

like love um just cuz they they they

typically um do this stuff completely

manually and don't use anything to

really do do stuff like this. This has

been built out. This copyrightiting is

trained on thousands of campaigns we've

run like of this specific type. So, we

know what to say, how to say it to get

the best response uh from from hiring

managers. And if uh and if you read

this, I don't think it sounds like AI.

That's and that's kind of how uh we

spent a lot of time really training it

and fine-tuning it to get that. To give

you an idea, average outbound campaign

produces like a 1 to 2% reply rate. And

that's across like all industries. This

campaign that we're going to do uh will

produce a 5 to 10% uh reply rate in one

of the hardest industries

around to do BDN. And that's a big value

prop for for our for our user base.

Once I'm done, we hit continue. Second

last step, we want to get this now

personalized. So, we've got our template

here. We've got Edgar here. Uh we have

the deep research that we did right

there. One click. I'm going to be able

to I could personalize it for my entire

list. I'll just preview this one one

contact here. You'll notice a lot of RAI

you we're not asking for very much from

the user and we built this this way very

specifically because kind of like I

mentioned we actually don't want to

leave it up to the user. Our goal is to

say hey just do what the AI is doing.

check it to make sure you're happy with

it and you can make tweaks manually, but

ultimately like the the the quality of

what you will get here is better than

anything your recruitment consultants

will come up with individually cuz this

is not necessarily

this is not like their strength is, you

know, being able to do this. We want

them on the phone. We want them closing.

And so here it'll do things like, "Hi

Edgar, right? I was looking at uh it

private bank Zurich's desk. thought of a

senior private banker with a portable

CHF 380 million book whose DAC and

Nordic crossber expertise could add

European ultra high net worth coverage

alongside your lat platform.

It's incredibly specific research uh and

it's incredibly tailored to Edgar and

it's driving relevancy. It's not, hey, I

noticed we went to the same school,

which is what half of the AI SDR type

platforms out there kind of do.

Once I'm happy, hit save and continue.

We've built a list. We've dialed it in.

Um, we need their data. So, this we

replace, one of the things we do is we

get rid of Zoom info contracts, for

example, uh, which has been really

successful for us. So, if you have

Talon, you don't need a third party data

provider. the data is in the workflow

provided for you. So I will go ahead and

I'll say hey I need a work email for

this particular campaign or I need work

email and phone number. I hit enrich.

Great. Talon's off. It's going to our

again our five different data providers

and it is grabbing uh you know sort of

those those contact details. Um I'm

doing a BD campaign for Lucas in ultra

high net worth. I can do some settings,

uh, basic settings in config, right, for

how I want my campaign to behave, time

zone, stuff like that. I could launch

this immediately or I could say, you

know what, let me, uh, save this and

launch it later. I've got my full

campaign in here. I can quickly hop into

anybody I want and and go see what are

we going to say to them, what was the

deep research uh, that was done on them,

and I'm pretty much ready to go. when I

I I would turn on this campaign and then

all of my replies and leads and things

like that uh would pop up in here. And

this is sort of I was mentioning this

before. This is my inbox for kind of

everything uh where I would basically

get the person to uh booked uh booked

lead.

And so the work what we just did

together in the last 15ish minutes

traditionally this would take a

recruiter three to five hours to do

this. Um

and the the output that they would get

at the end of it would probably wouldn't

be you know something that uh it

wouldn't be at the same quality that we

were able to to do here in a in a very

very uh automated automated way. Um

yeah and actually just so you can just

so you can all see it, I will show you

this is the campaign we did for

our um founding account executives.

So we did a couple of rounds founding

account executives

and this is what it this is what it

looked like,

right? We used a email and inmail kind

of combined strategy. We built out the

entire campaign. AI generated

essentially everything about this

campaign. The list, the copy, uh all of

it. We had uh on this campaign, 24% of

all candidates got back to us. 36%

on this other campaign. We talked to

over 200 account executives to to fill

these roles. Um, and we did that using

Talon. So,

yeah. And last thing I'll say, this was

a very specific playbook. Talon, we're

just developing essentially more and

more playbooks, right, for for the user.

Um, our other one is uh our other

general or our other generalized um or

rather our other business development

type campaign is they can just describe

their target market and in natural

language, we reach out to their target

market. We tell them how to approach

them, what to say, clean the list, all

of the things you just saw. We do it

that way. And if I'm recruiting, this

process works very similar, just on the

candidate side. And instead of a, you

know, a candidate or a market, I'm I'm

just putting in the job description here

to sort of to sort of do that.

I ask a question about uh so you talked

about uh the outreach the the the the

difference in the the the data for the

outreach and getting a response. Do you

have the data in the comparative sort of

close rates with and without talent?

uh like on their actual like how many

for if they meet a hiring manager like

what's their likelihood of like closing

>> like what yeah how how how from the data

that you have from I don't sure if you

have this sort of from

>> before talent and with talent what is

the expected difference in final close

like where you get the cash where the

recruiter is is where the hiring manager

has their their person

>> yeah I Um,

so I would say on the candidate side,

it's not so much about if you ask a

recruiting firm like are you struggling

with like recruiting? They'll never say

yes, right? So they'll never say that we

have a problem when it comes to like

recruiting candidates.

What their problem generally is is not

necessarily

um like their close rate. So that's why

we don't track it very much. Their

problem is velocity is time time to

hire.

>> So you have two closes. You've got the

closing in with the hiring manager and

presumably it's not always exclusive.

There's two ways of operating. There's

exclusivity and then there's not or or

are all of your companies doing

exclusive deals?

>> They'll there'll be a pretty big mix. I

think now because of where the market is

at, it's it's a lot of it is

non-exclusive.

>> Okay.

>> Uh because is going to help them close

faster. So we know that they're getting

Okay. So they're getting

responses. You gave us the data for

responses. Do you know what the

comparative data is? Re getting that

agreement to work with hiring manager.

>> Yeah. So truth we don't track that

>> too much just because of the main reason

is because it's so difficult on the BD

side.

>> We don't have the data. It's not with

you.

>> It's not with us. We could collect it on

a survey basis or something like that,

but it's more like if we like let's put

it this way. If I can convince a firm

that each of their reps can book one

meeting a week with talent, they would

write me a blank check to to

>> that is that is what they that is

valued. That is

>> that is it is so hard to do that in

recruitment that that alone that gets us

close. Yeah. to give you. So, I'll give

you I have some really good data I just

got from a pilot that we're doing on the

enterprise side. I'm sure some of you

are familiar with um Drake International

um which is one of the yeah enterprise

uh recruiting firms here in obviously

across Canada. They have US offices,

European offices. Um we are on a pilot

90-day pilot with them with five of

their salespeople

uh in it's day 70 roughly right now. So,

we're on month three. In 70 days, their

five salespeople have closed over

$100,000 in contracts since using Talon.

>> But what's that compared to though?

>> That's so it's like directly because of

Talon. They closed like an additional

100,000.

>> Okay.

>> Yeah. Then then they based on like their

other like normal sort of

>> over and above. Gotcha. Um, and to to

give you an idea, those five talent

licenses over, you know, three three

months, let's say, that that would cost

them, you know, uh, you know, you know,

approximately $150, right, per per

person per month, you know, so you're

looking at maybe a cost of around two

two thou, just over 2K, you know, for

those. So, we basically got them a 50x

return on their pilot. Um,

>> so so curious. I have so many more

questions, but I want to wrap up the

product.

>> That's okay. Yeah,

>> talk to me about uh what's coming up.

What's this elev evolution of the

engine? What's the timeline your path to

development your roll out?

>> Yeah, absolutely. So, we have all the

major pillars built out. It's now I

think we're in a game of just making the

pillars that we've built out uh just

continuing to push the boundary of like

what we can do with them. We know that

they drive value. We have right you know

150 customers over 500 active users that

are using this on a on a weekly basis.

We know that the we're not in

exploratory mode in terms of like what

do they care about? We know that now

it's just saying how you know what can

we do to make this to make the

experience incredible. Um so one of the

the thing that we're ultimately working

into at the end of it is what we we're

essentially calling like autopilot mode.

um which will be an experience where we

can basically very similar to what you

would see now. We just collect some

basic info um and talent essentially

just is able to run from end to end um

and then get them to that hey this

campaign is ready to launch um without

them needing to really do a lot or make

very very minor tweaks and it's able to

do that faster and and essentially

better um than and obviously within that

you know we're talking about a couple

dozen uh you know feature improvements

at least based on to like actually like

make that happen. Um, but that's that's

the goal is to be able to take it and

allow talent to go sort of completely uh

uh from from end to end uh in terms of

being able to to to build this out. Um,

for example, like what I was talking

about one example in that when you're

building out your actual sequence,

the ability to craft the entire

sequence, write the copy for the entire

sequence. That's a pretty important

part. We don't want them to have to say,

"Okay, what am I going to do on day one

and then day three and then day five?"

Stuff like that. We should just be

basically prescribing uh uh to them. And

the more and this is a big part of our

network effect, right, of our tool. The

more recruitment firms that use this,

the more data that we have on what works

and what's driving conversions and reply

rate and all these metrics we're talking

about, the more confidently we will be

able to just make those prescriptions,

if you will, for each stage here. and

we'll be able to dial it down to this is

an accounting firm in Arizona that

specializes in automotive and being able

to make the decisions because we have,

you know, we have 350 firms that match

and, you know, 100,000 data points uh uh

monthly, right? That that that that help

us do that in that specific sub area.

>> So, that's the long term that's the

long-term vision. Within that,

>> lots of little things need to happen,

but yeah. Okay. And what's the timeline

for your long-term vision?

>> Yeah, absolutely. So, the goal being

able to get that's what the from now

until the end of the year uh is goal of

getting as as as close as possible to uh

to autopilot. Realistically, I think

that's an 8 that's 8 to 12 months away

from it being um you know, quite exactly

uh where we want it with speed, scale,

all all of those uh sort of things. So,

>> okay, we're on we're on number two, so

I'm going to have to speed it up, but I

think we we've covered a lot of ground.

Any questions about product before we

move forward?

>> Okay, innovation and IP. I think you've

answered most of the questions. I want

to talk to you about uh you spoke about

the moat, how it just wouldn't be worth

it for the user to flip over and do all

of the work themselves. But what about a

competitor? How what what

I mean I know that you've got the

context but and how are you going to

sort of hold your space as as uh as you

scale?

>> Yeah. No, absolutely. Great question.

So, one of the I mean one of the things

that we kind of have is like here is

like an early early mover advantage,

right? That we need to for lack of a

better term that we need to really take

advantage of. Um we you'll and you'll

see this in the competition uh like

analysis. Um but we really have one

competitor globally that's trying to do

a similar thing to what we're trying to

do in in the space. That's Source Whale

UK based company. Um they're a little

older. They're a little bit more of a

legacy uh you know type type SAS

company. Uh we compete with them right

on a number of deals. We've won and

taken some customers from them as well.

um

we need to we need to move quickly

because if if we're able if we're able

to capture a large percentage of the

market share, you have to think like why

would someone switch away from something

that's working and generally speaking

and they people talk about this all the

time of entering new markets, right? You

have to build something that is either a

fundamentally new way of doing something

or is 10 times better than the existing

way of of doing something. Um, I think

that would be once we've been able to

capture a more meaningful percentage of

the market. You know, I think that

that's going to be an incredibly

difficult thing to do for a a new

entrant. And couple two couple core

reasons I think that is number one is

like and this is part of the story,

right? But like I'm I feel like my you

know our founder fit if you will for

like this market is pretty spot-on

because I have this growth background

and I've been doing sales enablement and

scaling revenue engines right and you

you're seeing a lot of that in our

product obviously I had that plus deep

understanding of agency operations. So

someone with both of those things would

need to come together and decide to

compete with us. I think that's a pretty

tiny market of people. The second thing

is when they do if they did try to enter

and actually compete with us, they would

again need to compete with our data

right at that we already have 150 firms.

If we have a thousand plus firms on this

platform,

you know, um and we have and we're

powering like an output that is like

driven on this proprietary data, right?

Right. And that's I think that's the

moat right now for vertical vertical SAS

in general is is building those those

data sets. The code base, you know,

isn't really the mode anymore. I think

everyone kind of knows that. Um yeah, I

think I think that would be an incred I

don't think it's impossible to see a

competitor. So I don't think we have a

necessarily

>> never is impossible.

>> No, it's it's not a it's not a hard I

mean are there any hard modes left? I

mean if you're not in like I don't know,

you know, I don't know. But do we have a

pretty good soft mode? I think we have a

pretty a pretty good soft mode from

those two things.

>> Any any questions about in Go ahead. No,

you're on mute.

>> He's got I think he messaged and he's

got a he's got a hop. Um

>> Oh, he's going. Oh, he's waving at us.

Okay.

>> Sorry. I Yeah, I'm not reading while I'm

talking. Okay, so uh cool. Any other

questions around the remote IP

innovation side of things? I think we've

gone pretty deep there. uh switching to

market business opportunity. Uh one of

the questions that came up at the member

meeting was that

um was that um

let me see um was oh sorry just making

sure record was on. Um, yeah, one of the

one of the questions that came up is you

were talking about how your technology

was going to allow for less less people

having the same outcome with a fewer

number of people because AI was going to

be doing all that

>> in between stuff massively shrink the

size of the team you're p you're billing

at per seat currently. So are you kind

of how what's your growth trajectory

when you're you're also eating your your

seats?

>> Yeah, absolutely. Great question. So

right now our billing is actually it's a

hybrid of per seat and then usage as

well.

>> Um so they have a li like all the all

the the functions that I showed showed

you today use up essentially what are

Talon credits.

>> Ah okay. So, for example, we we

shortlisted about 213

people. That would have used up 213

credits on our on our platform, right?

>> Um, to give you an idea, like right now,

if if on our on our starter plan, which

like 150 US a month, you get 1,500

talent credits per month. Um, and then

we used another to find their email

after, we used another credit, for

example. So, I used about 300 350

credits to do the campaign that we just

did. So, I could do four of these

campaigns reliably in a month. If I'm a

power user and I'm doing which would be

like a couple of Canada campaigns and a

couple of BD campaigns a week, right,

I'd be on our 250 a month plan easily,

right, for like 3500 credits. And so our

idea is uncapped ACV because I think to

your point it's like if a fiveperson

firm can do the labor of 15 20 25 people

in the future then they'll need to be

spending $150,000 a year on software and

if we can be that enablement layer

that's that we need that.

>> So it's revenue it's revenue growth

actually um through the effectiveness is

the um Darl does that make sense to you?

Yeah. So, I'm just reading your face.

Um, you might be reading something else.

Uh, so, so, um, yeah, I wanted to ask

you a question. Um, and it's kind of

gluing me here for a second.

Um, oh yeah, in the in in the in the

Mars investment memo, they talked about

the potential

uh around sort of capturing and being

like taking over verticals. Is is that

something that is on in your plan? They

identified it as an opportunity. Is is

tell us where you're going with that?

>> Yeah. No, absolutely. So, there there

I don't know if this is a good thing or

this is a potential distraction, right?

But there's we have multiple options and

this is something I'm really careful

about. Uh because there's, you know,

it's easy as a founder to to fall to

shiny object syndrome. Yes. and you do

it too early and you miscalculate and

then you end up you don't m you don't

get traction in that new vertical or

that new industry and then the and then

new people come and start to eat you uh

in your current industry because you

weren't innovating at the right pace. So

that's my caveat. However,

um there are two really interesting

opportunities that we've uh identified

um both of which are are are quite

large. Uh the first and kind of I guess

more obvious one that uh we've been told

before is uh basically supplying talent

specifically to SMBs

um that like you know kind of like

talent but I I would actually go into

maybe more traditional industries that

aren't doing a a good deal of or not

doing a ton of outbound candidate

marketing uh and basically offering a

version of talent as their candidate

generation engine, Right. I think that's

a saturated space specifically for tech

startups. I think you're seeing a lot of

AI sourcing tools and AI recruitment

tools and they're all marketing to the

same people, which is other tech

startups. I I'm sure you're all familiar

Juicebox just raised like $80 million.

Juicebox is hyperfocused on other other

tech startups. I think the real not the

real money, but I think the the the

underserved but large pile of money is

how do you help a uh an automotive plant

in Detroit find a plant manager, right?

Um Meteaview is doing this. Uh there

there's a bunch of companies obviously

trying to do it, but that space is so

massive that you don't need to win that

space. You could be the the eighth

biggest player in that space and you're

100 million ARR, right? So that's one

area. Another area that is becoming um

really hot right now is AI enabled

services and the idea of selling the

outcome as opposed to selling the

platform. Right? A lot of the tech we're

building um you know can be utilized to

simply produce the candidate for either

recruitment firm or actually directly to

internal companies.

uh themselves. Um and at that point

you're charging service level revenue.

And the idea is you'd be getting uh

software level margins, right? Um that's

a massive market. That's a that is the

$700 billion, right? Staffing market.

You're basically eating into Robert Half

or Manpowers, you know, instead of

trying to sell to them, you're saying

I'm going to replace them. Massive

undertaking, massive market. We're not

committed at like to either of those.

It's important that we're very aware of

them. We're just building some unique

tech that would enable us to do it. I

think specifically like if you look at

what I demoed today, that's a very

specific motion that there's not any

there's not really any tech out there

that does what I showed you there today.

That would be,

you know, a core core flow in being able

to, you know, sell outcomes to internal

companies. So things that we're

obviously keeping track of, but um but

yeah, those are the two things we've

identified as the the the natural places

we could we could go in the future.

>> Can you speak to a little bit you spoke

about pilots? Can you speak a little bit

to how your customers are converting to

you? What role the pilots play? What the

track record's been in converting your

pilots to customers?

Yeah, absolutely. Um, and there is good

data on if you want to like see the

numbers behind what I'm going to say.

There's the D. We we map this out in our

we use like our HubSpot uh kind of

changes in in stage and advancement

through the pipeline to kind of back

these uh these numbers uh these numbers

up. Um but we basically have a

um from a uh salesqualified opportunity

which is not necessarily a pilot yet but

it's anyone that we've met with and we

say hey like this is a good fit for us

our tech workflow wise we have a 36%

conversion rate of them becoming a

customer.

Um in my background that's super it's

healthy right if you can get kind of

above 25% you're in a

>> any background that's healthy. Yes.

It's it's healthy, right? Um, and we

took a cohort of like an entire quarter,

all the basically all the demo all the

all the all the customers we met in that

quarter and you can see it in the data

room of their movement sort of along

along the pipeline. Um, once they get to

pilot, then the conversion rate jumps.

It's roughly about 50%. So about 50% of

all pilots uh convert into

uh paying customers. So generally

speaking, like the value is pretty

obvious to them. the the really the the

main reason someone like wouldn't

convert is, you know, and and this is

it's it is a a legacy industry. We meet

recruiting firms that are they're not

like a few years behind. They're like

two decades behind in terms of their

actual tech adoption. I don't think

they'll be around very much longer uh

cuz they simply like won't be able to

compete. But we do see firms that and I

I mean I showed you the platform today,

right? I think it's pretty easy to use.

We hired this head of UX to make it even

easier to use for that time to value to

truncate that as much as humanly

possible. We haven't talked about it,

but productled growth is a really big

part of our strategy for acquisition.

Um, yeah, I think adoption just like

like like is the number one reason we

see you know firms not using it and it's

not 100%

>> it's market it's it's market readiness

adoption gen more generally.

>> Absolutely. Absolutely. Um it's already

like in the LA like now from a year ago

it's completely changed already. Um

which is kind of why I think our timing

is good. Like the velocity here for us

is really really important you know. Um

yeah. So I think uh you know I a year

ago I don't think we would have been

able to close the amount of customers

we're closing now. Yeah.

>> Yeah.

>> Can you talk to your uh customer

pipeline?

>> Yeah absolutely. So um we generate uh we

generate the majority of our we we just

we generate our pipeline through

multiple multiple channels. Uh we like I

mentioned meta ads uh Google ads are are

pretty core uh to how we uh get net new

customers outbound. Um we've done a lot

of good experimenting with cold calling

uh you know sort of sort of things like

that. Um right now with like in terms of

like founder sales um at any at any

given time we'll have generally between

you know 150 to 200k uh in like pipeline

uh that's that that basically would

convert in the next um you know

>> these are all fast move pretty these are

all fastmoving deals right

>> three to five weeks is our typical sales

cycle right now we do get people who say

you know not now they come back six

months later and then They're they start

a sort of a new sales cycle that's you

know quick 3 4 weeks. We have that

happens all the time. But yeah,

typically it's uh demo the goal of the

demo is to convert them into a pilot and

the goal of the pilot is that's 14 days

to 21 days depending on the customer

type unless it's enterprise. If it's

enterprise 90-day pilot and usually the

pilot's paid for enterprise. Um

>> okay.

>> So yeah, the big one that we have right

now.

>> Yeah. Uh you you talked about your very

impressive metrics, but I think there's

great curiosity in the group about how

you achieve them. Um Gerard, did you

have a question about this?

>> I just want to just just one thing

before that. Uh you said your ads are

mainly um from you said Google and Meta.

>> I'm actually surprised why LinkedIn ads

wouldn't be high on your list because

that's where your target audience is

hiding.

>> Yeah. So, I've got a a pretty specific

answer for you. One, LinkedIn ads are

the most expensive ads in the entire

market. So, do I think LinkedIn LinkedIn

ads could convert? Absolutely. Do I

think that it would s based on where

we're at today, would it significantly

for the cohort we got from LinkedIn,

I've projected our customer acquisition

cost would probably be close to double

of what it is today. Um, one, and kind

of this is one of the things I've

learned in my background in B2B SAS, the

big underutilized channel that actually

works incredibly well and is incredibly

scalable is Meta Ads. Uh, of all the

major uh, ad platforms, Meta has by far

the best developed algorithm to simply

allow the platform to to target your

user base for you. So, it looks at your

content, the the the pixel, right, the

the the the the p the data, the tracker

that that learns from the activity

around your ads. Um, it very quickly is

able to dial in on your audience. And we

get free trial signups from Meta. Um,

typically between, depending on the

week, between $50 and $75

uh Canadian. Um, and so we and our

budgets here are small, right? up to

date. We've been spending about 4 4K

Canadian a month on ads alto together.

Now we're going into sort of a scaled up

motion. Uh and we will be adding

LinkedIn ads uh to to the stack moving

moving forward. Uh it just has to be

done in a very careful way because it's

very easy to burn a lot of money and not

get anything for it on on on LinkedIn.

But yeah,

>> just on that point, uh what about

LinkedIn like content marketing?

Yeah. So that's a that's a part of our

our stack bit. So the thing that works

really well on LinkedIn is content

specifically from founders. Uh the

algorithm doesn't do much for company

pages on LinkedIn. Um it's very very

hard to get uh engagement that way. I

post on LinkedIn between one and one and

two times a week. And it you can go on

LinkedIn by the way. You can look at my

post history. It's incredibly tactical

and it's very very targeted to hiring to

uh to agencies, right? I talk about and

I have, you know, five, six kind of core

themes that I'll talk about. Um, one of

the demos I had, I had two demos this

morning before we met. One of them was a

response to one of my posts. Hey, you've

popped up and usually how market how

good growth marketing works, it's about

a web. It's about, hey, I saw your post.

Hey, I I go on podcasts, right? Hey, I

saw you on a podcast and then I finally

saw your meta ad. Okay, I got to see

what you're about. I'll do a demo. And

so that's the engine we're trying to

trying to build. So

>> David,

>> yeah, a quick question. Uh, one before I

100% agree on meta absolutely superior

kind of on my own experience, but one

question. You've talked a lot about paid

channels, bit of LinkedIn. Any other

channels you guys are looking at for

lead genen, sales gen uh as such that's

outside of particular paid? Yeah,

absolutely. So, um one area that has

worked really well for us um and we have

we have a good data set around it now is

actually cold calling. Um

uh in terms of channel I'm so I channel

market fit is my you know define it as

like how how well does that channel

perform right with your with the market

that you're your ICP and recruiters pick

up the phone. It could be a candidate.

It could be a client. Uh they're uh

Apple obviously recently released their

screener right for recruiters don't have

it turned on because that's the last

thing they want is a a brand new client

calls them and then the the and Apple's

asking hey can you verify who you are?

So it's a our connect rate uh from from

call to conversations 13%.

>> Which is wild. If you've ever looked at

an SDR program, it's normally like two

to 4%.

>> So, and that's why we hired an SDR. They

start May 4th.

>> What's an SDR? What What's that stand

for?

>> Uh sales development rep.

>> Oh, okay. Right.

>> Yep.

>> Yeah. Or BDR, SDR, they're

interchangeable, right?

>> So, cold calling. Absolutely. I'm also

uh I've been like cold email uh in

general. Like my philosophy on it is

like your sales people shouldn't be

doing cold email uh because we can

essentially automate it at scale in an

incredibly personalized way that is more

effective than they would be able to do

it at the ground floor. So right now I'm

building out like a very comprehensive

uh automated uh cold email program

that'll basically route leads

automatically to our team based on

replies that come in. One thing I will

say, we've seen the efficacy of cold

email. Um uh it's it's definitely

getting uh harder.

>> Um but uh

>> right.

>> Yeah, exactly. Um people are, you know,

but again, that's our sweet spot. Like

we we know what we're we're doing there,

so we're kind of able to to kind of cut

through uh a lot of a lot of that noise.

Um so yeah, cold email, SDR, uh uh phone

calling, absolutely. uh the content side

the most underdeveloped area that as

soon as we have a lot of these things

working the next thing we'll go is

probably like more we haven't done a ton

on SEO and now SEO is obviously all

about it's LLM based SEO right how do

you recruiters go into Chad GP and

claude and they go hey I'm one I I need

to automate my BD better what should I

do you want to get recommended by the

LLMs and um yeah so that that'll be

another piece that we'll do and if you

can get that going that's your lowest

your lowest customer acquisition cost

channel. It's just that's a that's a

that's more like art part art part

science. So, um

>> yeah, you're not necessarily on the

list. That's

>> I I Yeah, I'm battling with that little

one for our little tiny group here as

well.

Anything anything else? Um

>> anything else around metrics that you

want to talk to uh in the group here?

Um, you know, I think I think we got the

only

>> any other Oh, David, sorry.

>> Well, I was gonna jump after that on the

last question. Um, metrics as well. Can

you tell us about your CAC, but also I

think everyone here is interested in a

17 to1 CAC LTV ratio, which

>> yes,

>> um, is something I don't think I've ever

seen. So, we'd love to know how are you

getting these and how can you scale

that?

>> Absolutely. Absolutely. So um and in the

data room uh there's a really good it

shows our model right of like how do you

like come up with uh sort of 17 17 to1.

So what we did is we um we've got two

kind of key cohorts of customers. We

have our self-s serve customers which is

a below 5k ACV. That's one and two

licensed type deals. Those are your solo

recruiters to maybe two partners running

a small shop. Generally speaking, we

don't want to be demoing them. We want

the product to do the heavy work and we

want them to be converting essentially

on their own. Um, three users and above

means above a 5k ACV for us. That's when

we will put them in our uh sales

process. Basically, what we did is we

took our that 17 to1 LTV to CAC ratio is

specifically with that 5K ACV and above

uh target group. And that's why you'll

notice in the data room in the memo, all

of our strategy is really about hitting

that like mid-market as our sweet spot.

Three recruiters to 20 and then we have

enterprise which is something else we're

working on. But 3 to 20 is the sweet

spot where all the stuff I'm talking

about. 3 to 5 week sales cycles, all

that kind of stuff. So um in that CAC

model, like we tried to be as honest as

possible, right? We we worked in like

50% of my salary, the cost of all of our

ad channels. Um we contracted out uh

some SDR work as well to test out like

the cold calling uh channel. Uh so all

of those costs basically broken down uh

you know based on and this hey this this

is kind of how many customers we closed

right in that cohort based on that spend

breakdown. the thing that is driving

like as a really core part of of CAC is

like what's their churn right like

what's their lifetime what's their

lifetime value and for us like the one

to two users they churn at kind of a

high rate right they churn on a you know

uh on any given month it could be five

between like five and 8% of of them can

churn which is a little bit high of the

5k plus ACV they churn at half a percent

in terms of their rate and they're on

monthly contract this is not I've seen

founders do this too. Everyone's on an

annual and they go, "Oh, nobody turned

last month because they're all on annual

deals." They're all they can all a good

chunk of them can leave like whenever

they want and they're they're not. It's

incredibly sticky. So, I think it's a

that 70 to1 is a really nice mix of like

I'm lucky in that I kind of have this

expertise, right, that you just don't

see I think a lot in early stage

building a meta ads program to get cheap

conversions. uh you know we've built a

really good sales process all of that

kind of stuff combined with this cohort

that has super low super low churn now

to the second part of your question how

do you scale it I don't think we'll be

able to scale 17 to1 uh and I think

that's the right it's like that's that's

kind of unrealistic what that what the

17 to1 is supposed to mean is that we

need to start spending money and capital

on acquiring that cohort tomorrow

because uh uh that's generally what uh

if you have it's you know 5:1 is

healthy. Um anything above 5:1 is great.

So we can be le we can spend more money

to be less efficient to acquire to move

quicker in in exchange for velocity. So

that's that's really like the core plan

there is to is to is to be able to to do

that. phone.

>> Oh, I think you're muted, Suzanne.

>> Correct. Okay. I think any other

questions about metrics? We move on to

competition. I believe we spoke about

competition. Any questions from the

group here on competition? Do you want

any deeper insights to anything there?

Covered going once. Okay. Uh you also

talked about team. Let's take a look at

um can you pull up your cap table?

>> Yeah, absolutely.

>> Who on your team is on your cap table

and talk about um yeah, your key your

key your key leaders as well there.

>> Yeah, absolutely. Um let me just pull

up. So uh

>> yeah and I was noticing the one in the

data room is sort of from the last

round. Um do you have it a proforma

including this round by chance as well?

>> Um yes I can we I can share that right

after this call one that's updated with

existing investors. Um but uh but yeah I

can talk kind of like high level now uh

to the cap like on our team obviously we

have like our standard ESOP right where

everyone on our team uh has has equity

uh generally that ranges uh between from

junior people like.1%

to more senior people uh uh 4%

is uh we we've allocated pay our whole

ESOP from the beginning was about about

15% don't we haven't we haven't even

used a third of it at um and so we don't

want to right but we're preserving that

for hey we got to sign on a really heavy

leader in the future obviously want to

make sure everyone's has a bit so that

standard ESOP type stuff um the the only

the team member we have that has that's

not myself and then my co-founder uh

Trevor which is when we started the

business day one I was 66% of the

business Trevor was 33%

um because I put a lot of my own capital

into the business uh and had started it

a little bit before uh Trevor had joined

uh but he's for all intended purposes

co-founder um board board seat uh member

all that kind of stuff. Um only team

member that we have that has a

significant amount of uh equity is uh

Shawn uh Shawn Young uh who uh for first

he worked with us for the first 6 months

paid uh entirely on equity uh and he was

he was in essence like an investor in

our preede round. So at our $5 million

cap in our preede, we basically paid

Sean for six months in equity. So he's

at two points and change uh for for that

essentially investment. Um obviously at

the preede we gave up about 12%. Um and

then DMZ is also a holder with their the

DMZ incubator program was about uh 2.5%.

Um and then we have just one adviser uh

that we gave uh equity to um who's been

an adviser since like the formation of

the of the company and at that point we

had given 4%. Um so that's pretty much

uh everyone

>> he's on uh reverse vesting.

>> Uh we are not we are not on reverse

vesting. No.

>> Okay. So everybody has that already like

what's the terms of that?

>> Sorry. Uh, with our team, I thought you

meant sorry, Trevor and I. So, no, for

our our team, everyone's on a standard

Yeah. four-year vest with a with a

one-year with a one-year cliff.

>> Yeah.

>> Okay. And, uh, all the assignments of IP

are signed for anybody working in

technology.

>> Um, okay. And how many people currently

working full-time with you?

>> Yep. So, we are seven full-time. Uh,

those two salespeople start in two

weeks. So there will be nine by the end

of April. Uh sorry and the week after

that the SDR starts. So we'll be 10 uh

by early May. And then we just have one

more hire to make with the hiring plan

on the engineering side.

>> How many of your team were there with

you from the beginning that are still

working

>> from the ve? So for it was Trevor and I

for

about six months just us two and Trevor

and I have been uh kind of yeah

co-founders since day one.

Um and then Sean Young joined us. Uh he

was like our earliest first employee.

He's still with us. Um and then we hired

uh yeah since then the only um

yeah the only people who

um yeah all our f and then five team

members was really like our early core

team uh which was myself Trevor Sean

Juan on the engineering side and then

Sanjoli who's our head of customer

experience those five got us from they

preede essentially up to up to now and

all all five people are are still with

the

You're really in a fastmoving sales

customerfocused