June 19, 2025 • 18 mins
In this episode, Mike Zayonc, co-founder at Kodif, shares what finding real traction actually looks like: testing fast, selling early, and staying close to the problem.
If you're still guessing at PMF, this conversation will help you stop guessing and start proving.
Highlights include: Taking a Leap of Faith (01:00), Validating Your Next Role (03:32), AI Agents for Customer Service (06:43), The Best Ways to Find Good Customers (13:05), and more…
Stay updated with our podcast and the latest insights in Outbound Sales and Go-to-Market Strategies!
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome back to the Predictable Revenue Podcast.
(00:01):
I'm your host, Colin Stewart.
Today I'm joined by Mike Zeantz.
He's the co-founder over at Codiff, and we're going to talk product market fit.
Before we jump in, this episode is brought to you by Predictable Revenue's foundercoaching program.
Founders, let's be real.
Scaling a company is tough.
You're juggling product, revenue, hiring, and a million other decisions.
But what if you had a proven framework and expert guidance to help you navigate thatchaos?
(00:22):
That's where a founder coaching program comes in.
Whether you're trying to find your first customers or fine tuning your go-to-marketstrategy, we're here to help.
Now to the episode.
Mike, welcome to the show.
(00:43):
Thanks Colin, excited to be here.
Me too, always happy to have a fellow Canadian on the show.
uh
tell me about how you got involved with Codef, because it sounds like they were in flightalready and you joined as a co-founder.
Help me understand what that looked like.
Yeah, exactly.
So, so after seeing the success of Plug and Play, becoming a partner, working with so manyincredible founders, like every day I was just like investing kind of realized like I
(01:09):
needed to get more experience as like a founder and scaling like a successful technologycompany.
So as an investor, I just kind of realized I needed more operating experience.
And so
I made that decision.
like, I'm young, I should do this.
And so I started thinking about different startup ideas.
I was thinking about founding a company and just going right from the beginning.
(01:30):
And I ended up talking to a lot of my friends who...
You know, some of them went to Stanford and some of them had deep startup experiencetrying to build startups and raising VC funding.
And so I talked to this one guy who He actually worked at Plug and Play.
Then he tried to found, his startup ended up failing, but he basically told me he's like,listen, like
(01:50):
you can found the startup.
I'm sure you'll raise, cause I had a few ideas I was working on, but he's like, I have aclassmate from Stanford He's like, he just started this company.
You should just, you know, just join him.
this, this, they, just got their first few customers.
Like it's already like, has a great idea.
You can get in really early.
(02:13):
And so basically I met my co-founders and basically I was very inspired, one, by the idea,two, behind the opportunity we had to really scale a successful business, and three, just
the credibility of having a co-founder who's an MBA from Stanford who already sold twocompanies.
(02:34):
And then having a CTO who scaled all of Uber's customer support platform from scratch.
so having the ability to like join them right in the beginning and, where the value that Icame was obviously like the deep relationships I built from plug and play with very large
investment firms, very large family offices and fortune 500 companies.
(02:56):
which is something that not everyone can tap into.
So, so yeah, I started kind of.
And really focusing as you described, on the podcast is like helping find product marketfit, helping develop the initial like customers and closing like some of the initial kind
of key accounts.
And so that's what I kind of came on board and did.
And obviously now doing a lot more, but definitely super excited that I joined.
(03:22):
And yeah, we raised a number of kind of VC rounds since then.
I believe we found product market fit.
We're scaling faster than ever.
And we found kind of a product that is in a great market.
Cool.
What kind of validation of the company, of the founders, of the product, or the market didyou do before deciding to, I'm going to go from this great job as a partner at Plug and
(03:47):
Play into being a co-founder at a startup?
As a VC, you meet a bunch of startups and then you decide to invest.
But what I did was like more focused.
So like I decided on one company to really focus on it.
And I think that's what you're doing.
if you're an early employee in
a startup, you have to evaluate the team and think this a company I want to invest in?
(04:10):
And so that's what I think.
Like you're making a decision like at a much higher scale of risk.
But I think it's valuable.
And I always encourage people.
Like I talk to young people all the time.
Like what should I do with my career?
Like, and I always recommend startups because it's like, you're going to learn more.
and you're gonna move faster and there's gonna be more kind of intensity than a lot ofother roles.
(04:33):
but yeah, I I looked at all the traditional things like the market, the team, I excited?
Do I like the culture?
And so those are all things that I made a decision on, but obviously this was a lot moreintense given that I'm now like obviously full-time operating in the business.
Very cool.
And talk to me about where the idea came from.
(04:54):
Yeah.
So it was kind of them coming together.
They wanted to build a startup, cause they had that kind of trust.
They're from the same community.
And so they were poking around with a bunch of ideas.
then Norm basically had this, background from Uber, which was extremely profound, whichthey, didn't even realize in the beginning, but then they're like, wait, like you built
(05:16):
the most automated system for Uber ever created for customer service.
Why can't we pull the company around that?
And so our initial customers, our first customer was actually Good Eggs.
So Good Eggs saw like, you basically can automate like the complex requests, like arefund.
And it's like a huge cost savings.
(05:36):
Cause like, if you're calling in for customer service, it doesn't make sense to repeatedlywait on hold or call in and like, from a business perspective, like,
you lost your banana like en route to a group for a grocery delivery.
Why can't you just do two or three clicks and just get an automated refund?
Like it's, it's going to be a more accurate, it's going to be a better experience and it'sgoing to just make sense all around.
(05:59):
the, even the customer service agent who does these repetitive requests, like they don'twant to, essentially even go through that process of like creating a, a ticket and going
through.
it's, it's a no brainer.
Right.
And so that's what Codiff initially built.
And the reason, the reason they selected us was just because of the Uber background.
So the Uber, and I'd say a lot of the initial customers, like when they're testing andlooking to use Codiff, they saw kind of that Uber background and said, okay, I want to
(06:29):
have that same kind of level automation or experience as Uber.
And they're all kind of digitally native brands that we started working with.
they're quite tech advanced and
They wanted to find a good partner.
that's, that's how the initial ones kind of came in the door.
Just a random sidebar.
But is Clay
I had an experience with their chatbot this weekend.
And basically, I screwed up something on behalf of a client.
(06:50):
And they're like, their automated chatbot refunded me the credits without having to ask.
And I was like, oh, that's, I was not expecting that.
They're like, yeah, you can only do it once.
And I was like, I don't care.
Like, I'm only going to screw it up one time this weekend when there's nobody in house orrunning their support.
So it sounds like you provide that experience, but kind of at scale for like, I lost mybanana.
(07:11):
The driver didn't show.
The food was cold.
That kind of stuff.
Yeah, exactly.
And that's all like algorithms, right?
If you get an immediate refund, some companies try to build that themselves.
Some companies they'll try to find a trusted partner.
So it's kind of all over the maps.
A lot of them have a bad experience or they don't have any automation,
I'm resisting the urge to go into just yammer on mode.
(07:36):
So I'll put a pin in that.
I want to talk about, I want to get your thoughts on the idea came from the genesis of oneof the founders' experiences.
How did you go and validate that?
mean, it's a pretty obvious use case of, hey, he built this for Uber.
that's the leading edge of the space.
How did you prove out the model that, there is a market for this and that even though he'swritten this white paper, there are going to be other companies that want this?
(07:59):
Yeah, so I think like two books that my co-founders were really excited about in thebeginning or two that I recommend to other startup founders are like one is like most
famous is like Four Steps to Epiphany by Steve Blank.
So talks through like how to build a startup, like what, and this is like well known for alot of startup companies, but it's like validating essentially does the idea work and
(08:23):
then, that's great.
Yeah, so.
think this is actually the startup owner's manual, is like the book.
They're both great.
they're both great books and very famous startup books in Silicon Valley.
and so I think those, those books like have a great methodology, which Codiff hasfollowed.
but in general, like the big thing I think is just getting like in front of a lot ofcustomers and another book that we like is called the mom test.
(08:46):
it's like, asking a bunch of like clarifying questions, like what is the customer reallythink?
Cause the premise is like, a lot of people just tell you over and over again, if you're astartup, like you have a good idea, but like, how can you go in more in depth and like ask
the right questions to understand like what they really think about the product.
And so that's something that we've done a lot of as well as, we started just to grow thebusiness and make sure that we're on the right path.
(09:14):
And we have like a scalable kind of like idea and product.
Cool.
And so where did the first customers, the first paying customers come from?
So I'd say a lot of the very first customers all came from trusted sources within ournetworks.
So the three networks that we have had deep relationships were Uber, people trusted ourCTO from Uber, people trusted our CEO from, my co-founder from Stanford, because he went
(09:46):
to Stanford GSB or they said, okay, like this guy has a good background.
or they trust my kind of like background from plug and play, or they had a relationshiplike, like Dollar Shave Club, for example, like they knew me from plug and play.
so now we're like the AI agent for Dollar Shave Club.
That was one of the first like kind of marquee brands that we kind of brought on.
(10:08):
And so they recognize like, okay, Mike's doing this.
Like we trust Mike, but then we really trust this.
that it's not just Mike has a relationship, but like he has like Uber level engineers inthe background who can create really automated, like deep kind of like personalized like
experiences for customers.
so I think both all, and in all cases, like it required both, it required some likerelationship to get in the door, but then it required a deep level of like validation that
(10:39):
this is good technology, that the product works, et cetera.
So codef, we've been able to provide both consistently.
So first customers came from founder networks.
Where did your next customers come from?
What was that first repeatable channel for you?
Yeah.
So, so I would say like, we, we had a variety of customers.
just met at conferences.
Like one that comes to my mind is Halo caller.
(11:02):
So like, we started like sponsoring like these community events or going to the, theselike CX conferences and
Like I just met this guy and he was in the community and he urgently needed like a, like achat AI experience, from halo caller.
And I remember with never met him before.
And basically he kept complaining.
(11:22):
He's like, my current chat bot is brutal.
And like, he, was very outspoken about it as well.
They're a scaling brand.
uh, Caesar Milan is the co-founder from halo collar.
they basically started blowing up cause they have these like high end, smart dog collars.
And so he needed something.
And he, he told us
(11:43):
I want to test something.
I had one of our engineers build him like an AI chatbot in five minutes.
And I'm like, listen, just test this out.
Tell me how this, what you think of it.
And basically he started playing around.
He's like, this works way better than we have live on our website.
And we built it in five minutes.
(12:03):
And so this kind of like sold him and he sent it to like his leadership.
And they like hardcore like tested the bot like continuously.
and realize like this is a good experience, this makes sense.
So that's, I'd say like the next level of like customer that like we just met kind of likeat a conference and we've had a lot of those since then.
And then in terms of the other channels that have been effective, we've done all theabove, we've done LinkedIn outreach, we've done cold emails, we've done referrals.
(12:32):
I think the referrals have worked really well because
A lot of times people buy from someone they trust or you get a referral from someone that,you know, or there's some sort of like credibility because we're finding there's a lot of
noise in the market.
yeah, going back, we've tested a lot of things, we've tested the AI outbound, but ingeneral, think having some sort of personal referral, whether it's from an investor,
(12:58):
customer,
someone that they trust, those have been the best ways of finding new customers.
Absolutely.
I've rambled on about this quite a bit on the podcast, but I'm a big fan of I thinkreferrals are the biggest indicator of product market fit.
And looking at the percentage of customers that send you a referral is one of thestrongest indicators of like, is this going to be something that you can invest a dollar
(13:23):
in and $10 is going to pour out the other side of your go-to-market engine.
Yeah, exactly.
And another thing I've realized about the referrals, because I've spent a lot of timedoing this and generate doing a lot of our like top of funnel and just generating new
potential partners.
The more like credible or the more like trusted the person is, the better the stickinessof the referral as well.
(13:44):
Like a lot of VCs have made really good referrals, investors.
They've been really good partners for us or just like people with like a deep like
experience or trust in the industry, those people have been like incredible partners forus.
Awesome.
Talk to me about the moment where you realized you had something, you might have productmarket fit.
Like, when did you realize, like, hey, there's something special here?
(14:06):
So I think We tried a lot of things and then just like repeatedly, customers are selectingthe same product, the same AI agent that's easy to deploy.
It's a natural language.
And they just want to go deeper in that.
I'd say it just kind of gradually happens over time where we're seeing more interest inthat product.
(14:27):
But I think that's that's the moment where we're realizing it.
it's a hot market now.
Like people are realizing like it's a no brainer use case and generative AI.
it goes back to that like good eggs example.
If you can get a refund, for like on a banana or whatever that wasn't delivered, it makesno sense from a business perspective to go and contact customer service, speak on the
(14:50):
phone.
It's
board providing, it's better experience, huge cost savings, makes the team happier.
what, and what we're selling, I think is a no brainer.
And now it's that comes back to one of the only challenges.
It's there's, there's a lot of noise in the market.
So it's, have to filter through and just ensure that people recognize that CODIF is areally trusted
(15:13):
proven solution that we're scaling.
It's not just something that people are hyping up or it's in the case of the biggercompanies.
Yeah, it's a real and it's not just like we're saying that we're an AI company because alot of bigger companies just say we're AI just because it's a buzzword.
we're going super deep on those experiences.
Very cool.
I think the referrals and the repeatability of this is customers are asking for the samething.
(15:38):
I love your comment on, you know, they're asking for the same product and they want to godeeper, right?
They're pulling more of the product out of you where they're like, hey, it does thisalready, but we want it to go farther and take another step.
And that to me is sort of like the definition of market poll.
I'm like.
Exactly.
Yeah.
And it's like, we can keep going deeper.
people keep saying okay, you're building AI agents and for chat and email.
(16:04):
There's so many other things you can go, but it's like, in my mind, it's like, there's somuch more we can do and customers are pulling more and more that they want that we can
build on the product.
So if we can just keep building deeper and deeper, then
It differentiates us, right?
Cause we've built out a much more robust developed product compared to other companiesthat they might have something superficial, but if they actually want something proven
(16:27):
that is credible, that, that, we can, we can provide that consistently.
It sounds like a really exciting point in time for Kodof.
What comes next?
Yeah, so we are super excited to work with digitally native brands.
We have a bunch of e-commerce companies, like I mentioned, Dollar Shave Club.
(16:47):
We're now rolling out Liquid IV, so bigger brands, which is in the Unilever portfolio.
We work with a variety of investment firms who are like Just Food for Dogs is part of ElCaterton, right?
we want to continue to grow.
want to focus on those kinds of key markets.
Number one, e-commerce, we go a lot deeper in terms of you have a physical product andyou're selling in a digital channel, and then two,
(17:12):
We have a variety of like fintech and crypto customers, like coin market cap, TrustWallet, like huge kind of like crypto wallets love our product.
And then also like the kind of like health or like the mental health like Alloy or Alma,for example, Alma is like a mental health kind of app that is now using CODIF
Like if they have a lot of customer support tickets, they have a need for a solution likeours.
(17:38):
I think the companies that are like slower to adopt the technology, they will take moretime but we want to work with mid-market companies and eventually enterprise customers.
Cool.
Mike, if somebody's interested in getting in touch with you, asking your advice or they'reinterested in code, if what's the best way for them to get in touch?
Yeah.
So I would say just on LinkedIn, like you can just add me at Mike Zayans on LinkedIn, youcan just email me at mike at codiff.ai.
(18:07):
Thank you so much for coming on the show and sharing your story.
Yeah, thanks.
It was a lot of fun calling.
Great to be here.
I enjoyed our conversation for sure.
And thanks to everybody for listening.
We'll catch y'all next time.
Welcome back to the Predictable Revenue Podcast.
(00:01):
I'm your host, Colin Stewart.
Today I'm joined by Mike Zeantz.
He's the co-founder over at Codiff, and we're going to talk product market fit.
Before we jump in, this episode is brought to you by Predictable Revenue's foundercoaching program.
Founders, let's be real.
Scaling a company is tough.
You're juggling product, revenue, hiring, and a million other decisions.
But what if you had a proven framework and expert guidance to help you navigate thatchaos?
(00:22):
That's where a founder coaching program comes in.
Whether you're trying to find your first customers or fine tuning your go-to-marketstrategy, we're here to help.
Now to the episode.
Mike, welcome to the show.
(00:43):
Thanks Colin, excited to be here.
Me too, always happy to have a fellow Canadian on the show.
uh
tell me about how you got involved with Codef, because it sounds like they were in flightalready and you joined as a co-founder.
Help me understand what that looked like.
Yeah, exactly.
So, so after seeing the success of Plug and Play, becoming a partner, working with so manyincredible founders, like every day I was just like investing kind of realized like I
(01:09):
needed to get more experience as like a founder and scaling like a successful technologycompany.
So as an investor, I just kind of realized I needed more operating experience.
And so
I made that decision.
like, I'm young, I should do this.
And so I started thinking about different startup ideas.
I was thinking about founding a company and just going right from the beginning.
(01:30):
And I ended up talking to a lot of my friends who...
You know, some of them went to Stanford and some of them had deep startup experiencetrying to build startups and raising VC funding.
And so I talked to this one guy who He actually worked at Plug and Play.
Then he tried to found, his startup ended up failing, but he basically told me he's like,listen, like
(01:50):
you can found the startup.
I'm sure you'll raise, cause I had a few ideas I was working on, but he's like, I have aclassmate from Stanford He's like, he just started this company.
You should just, you know, just join him.
this, this, they, just got their first few customers.
Like it's already like, has a great idea.
You can get in really early.
(02:13):
And so basically I met my co-founders and basically I was very inspired, one, by the idea,two, behind the opportunity we had to really scale a successful business, and three, just
the credibility of having a co-founder who's an MBA from Stanford who already sold twocompanies.
(02:34):
And then having a CTO who scaled all of Uber's customer support platform from scratch.
so having the ability to like join them right in the beginning and, where the value that Icame was obviously like the deep relationships I built from plug and play with very large
investment firms, very large family offices and fortune 500 companies.
(02:56):
which is something that not everyone can tap into.
So, so yeah, I started kind of.
And really focusing as you described, on the podcast is like helping find product marketfit, helping develop the initial like customers and closing like some of the initial kind
of key accounts.
And so that's what I kind of came on board and did.
And obviously now doing a lot more, but definitely super excited that I joined.
(03:22):
And yeah, we raised a number of kind of VC rounds since then.
I believe we found product market fit.
We're scaling faster than ever.
And we found kind of a product that is in a great market.
Cool.
What kind of validation of the company, of the founders, of the product, or the market didyou do before deciding to, I'm going to go from this great job as a partner at Plug and
(03:47):
Play into being a co-founder at a startup?
As a VC, you meet a bunch of startups and then you decide to invest.
But what I did was like more focused.
So like I decided on one company to really focus on it.
And I think that's what you're doing.
if you're an early employee in
a startup, you have to evaluate the team and think this a company I want to invest in?
(04:10):
And so that's what I think.
Like you're making a decision like at a much higher scale of risk.
But I think it's valuable.
And I always encourage people.
Like I talk to young people all the time.
Like what should I do with my career?
Like, and I always recommend startups because it's like, you're going to learn more.
and you're gonna move faster and there's gonna be more kind of intensity than a lot ofother roles.
(04:33):
but yeah, I I looked at all the traditional things like the market, the team, I excited?
Do I like the culture?
And so those are all things that I made a decision on, but obviously this was a lot moreintense given that I'm now like obviously full-time operating in the business.
Very cool.
And talk to me about where the idea came from.
(04:54):
Yeah.
So it was kind of them coming together.
They wanted to build a startup, cause they had that kind of trust.
They're from the same community.
And so they were poking around with a bunch of ideas.
then Norm basically had this, background from Uber, which was extremely profound, whichthey, didn't even realize in the beginning, but then they're like, wait, like you built
(05:16):
the most automated system for Uber ever created for customer service.
Why can't we pull the company around that?
And so our initial customers, our first customer was actually Good Eggs.
So Good Eggs saw like, you basically can automate like the complex requests, like arefund.
And it's like a huge cost savings.
(05:36):
Cause like, if you're calling in for customer service, it doesn't make sense to repeatedlywait on hold or call in and like, from a business perspective, like,
you lost your banana like en route to a group for a grocery delivery.
Why can't you just do two or three clicks and just get an automated refund?
Like it's, it's going to be a more accurate, it's going to be a better experience and it'sgoing to just make sense all around.
(05:59):
the, even the customer service agent who does these repetitive requests, like they don'twant to, essentially even go through that process of like creating a, a ticket and going
through.
it's, it's a no brainer.
Right.
And so that's what Codiff initially built.
And the reason, the reason they selected us was just because of the Uber background.
So the Uber, and I'd say a lot of the initial customers, like when they're testing andlooking to use Codiff, they saw kind of that Uber background and said, okay, I want to
(06:29):
have that same kind of level automation or experience as Uber.
And they're all kind of digitally native brands that we started working with.
they're quite tech advanced and
They wanted to find a good partner.
that's, that's how the initial ones kind of came in the door.
Just a random sidebar.
But is Clay
I had an experience with their chatbot this weekend.
And basically, I screwed up something on behalf of a client.
(06:50):
And they're like, their automated chatbot refunded me the credits without having to ask.
And I was like, oh, that's, I was not expecting that.
They're like, yeah, you can only do it once.
And I was like, I don't care.
Like, I'm only going to screw it up one time this weekend when there's nobody in house orrunning their support.
So it sounds like you provide that experience, but kind of at scale for like, I lost mybanana.
(07:11):
The driver didn't show.
The food was cold.
That kind of stuff.
Yeah, exactly.
And that's all like algorithms, right?
If you get an immediate refund, some companies try to build that themselves.
Some companies they'll try to find a trusted partner.
So it's kind of all over the maps.
A lot of them have a bad experience or they don't have any automation,
I'm resisting the urge to go into just yammer on mode.
(07:36):
So I'll put a pin in that.
I want to talk about, I want to get your thoughts on the idea came from the genesis of oneof the founders' experiences.
How did you go and validate that?
mean, it's a pretty obvious use case of, hey, he built this for Uber.
that's the leading edge of the space.
How did you prove out the model that, there is a market for this and that even though he'swritten this white paper, there are going to be other companies that want this?
(07:59):
Yeah, so I think like two books that my co-founders were really excited about in thebeginning or two that I recommend to other startup founders are like one is like most
famous is like Four Steps to Epiphany by Steve Blank.
So talks through like how to build a startup, like what, and this is like well known for alot of startup companies, but it's like validating essentially does the idea work and
(08:23):
then, that's great.
Yeah, so.
think this is actually the startup owner's manual, is like the book.
They're both great.
they're both great books and very famous startup books in Silicon Valley.
and so I think those, those books like have a great methodology, which Codiff hasfollowed.
but in general, like the big thing I think is just getting like in front of a lot ofcustomers and another book that we like is called the mom test.
(08:46):
it's like, asking a bunch of like clarifying questions, like what is the customer reallythink?
Cause the premise is like, a lot of people just tell you over and over again, if you're astartup, like you have a good idea, but like, how can you go in more in depth and like ask
the right questions to understand like what they really think about the product.
And so that's something that we've done a lot of as well as, we started just to grow thebusiness and make sure that we're on the right path.
(09:14):
And we have like a scalable kind of like idea and product.
Cool.
And so where did the first customers, the first paying customers come from?
So I'd say a lot of the very first customers all came from trusted sources within ournetworks.
So the three networks that we have had deep relationships were Uber, people trusted ourCTO from Uber, people trusted our CEO from, my co-founder from Stanford, because he went
(09:46):
to Stanford GSB or they said, okay, like this guy has a good background.
or they trust my kind of like background from plug and play, or they had a relationshiplike, like Dollar Shave Club, for example, like they knew me from plug and play.
so now we're like the AI agent for Dollar Shave Club.
That was one of the first like kind of marquee brands that we kind of brought on.
(10:08):
And so they recognize like, okay, Mike's doing this.
Like we trust Mike, but then we really trust this.
that it's not just Mike has a relationship, but like he has like Uber level engineers inthe background who can create really automated, like deep kind of like personalized like
experiences for customers.
so I think both all, and in all cases, like it required both, it required some likerelationship to get in the door, but then it required a deep level of like validation that
(10:39):
this is good technology, that the product works, et cetera.
So codef, we've been able to provide both consistently.
So first customers came from founder networks.
Where did your next customers come from?
What was that first repeatable channel for you?
Yeah.
So, so I would say like, we, we had a variety of customers.
just met at conferences.
Like one that comes to my mind is Halo caller.
(11:02):
So like, we started like sponsoring like these community events or going to the, theselike CX conferences and
Like I just met this guy and he was in the community and he urgently needed like a, like achat AI experience, from halo caller.
And I remember with never met him before.
And basically he kept complaining.
(11:22):
He's like, my current chat bot is brutal.
And like, he, was very outspoken about it as well.
They're a scaling brand.
uh, Caesar Milan is the co-founder from halo collar.
they basically started blowing up cause they have these like high end, smart dog collars.
And so he needed something.
And he, he told us
(11:43):
I want to test something.
I had one of our engineers build him like an AI chatbot in five minutes.
And I'm like, listen, just test this out.
Tell me how this, what you think of it.
And basically he started playing around.
He's like, this works way better than we have live on our website.
And we built it in five minutes.
(12:03):
And so this kind of like sold him and he sent it to like his leadership.
And they like hardcore like tested the bot like continuously.
and realize like this is a good experience, this makes sense.
So that's, I'd say like the next level of like customer that like we just met kind of likeat a conference and we've had a lot of those since then.
And then in terms of the other channels that have been effective, we've done all theabove, we've done LinkedIn outreach, we've done cold emails, we've done referrals.
(12:32):
I think the referrals have worked really well because
A lot of times people buy from someone they trust or you get a referral from someone that,you know, or there's some sort of like credibility because we're finding there's a lot of
noise in the market.
yeah, going back, we've tested a lot of things, we've tested the AI outbound, but ingeneral, think having some sort of personal referral, whether it's from an investor,
(12:58):
customer,
someone that they trust, those have been the best ways of finding new customers.
Absolutely.
I've rambled on about this quite a bit on the podcast, but I'm a big fan of I thinkreferrals are the biggest indicator of product market fit.
And looking at the percentage of customers that send you a referral is one of thestrongest indicators of like, is this going to be something that you can invest a dollar
(13:23):
in and $10 is going to pour out the other side of your go-to-market engine.
Yeah, exactly.
And another thing I've realized about the referrals, because I've spent a lot of timedoing this and generate doing a lot of our like top of funnel and just generating new
potential partners.
The more like credible or the more like trusted the person is, the better the stickinessof the referral as well.
(13:44):
Like a lot of VCs have made really good referrals, investors.
They've been really good partners for us or just like people with like a deep like
experience or trust in the industry, those people have been like incredible partners forus.
Awesome.
Talk to me about the moment where you realized you had something, you might have productmarket fit.
Like, when did you realize, like, hey, there's something special here?
(14:06):
So I think We tried a lot of things and then just like repeatedly, customers are selectingthe same product, the same AI agent that's easy to deploy.
It's a natural language.
And they just want to go deeper in that.
I'd say it just kind of gradually happens over time where we're seeing more interest inthat product.
(14:27):
But I think that's that's the moment where we're realizing it.
it's a hot market now.
Like people are realizing like it's a no brainer use case and generative AI.
it goes back to that like good eggs example.
If you can get a refund, for like on a banana or whatever that wasn't delivered, it makesno sense from a business perspective to go and contact customer service, speak on the
(14:50):
phone.
It's
board providing, it's better experience, huge cost savings, makes the team happier.
what, and what we're selling, I think is a no brainer.
And now it's that comes back to one of the only challenges.
It's there's, there's a lot of noise in the market.
So it's, have to filter through and just ensure that people recognize that CODIF is areally trusted
(15:13):
proven solution that we're scaling.
It's not just something that people are hyping up or it's in the case of the biggercompanies.
Yeah, it's a real and it's not just like we're saying that we're an AI company because alot of bigger companies just say we're AI just because it's a buzzword.
we're going super deep on those experiences.
Very cool.
I think the referrals and the repeatability of this is customers are asking for the samething.
(15:38):
I love your comment on, you know, they're asking for the same product and they want to godeeper, right?
They're pulling more of the product out of you where they're like, hey, it does thisalready, but we want it to go farther and take another step.
And that to me is sort of like the definition of market poll.
I'm like.
Exactly.
Yeah.
And it's like, we can keep going deeper.
people keep saying okay, you're building AI agents and for chat and email.
(16:04):
There's so many other things you can go, but it's like, in my mind, it's like, there's somuch more we can do and customers are pulling more and more that they want that we can
build on the product.
So if we can just keep building deeper and deeper, then
It differentiates us, right?
Cause we've built out a much more robust developed product compared to other companiesthat they might have something superficial, but if they actually want something proven
(16:27):
that is credible, that, that, we can, we can provide that consistently.
It sounds like a really exciting point in time for Kodof.
What comes next?
Yeah, so we are super excited to work with digitally native brands.
We have a bunch of e-commerce companies, like I mentioned, Dollar Shave Club.
(16:47):
We're now rolling out Liquid IV, so bigger brands, which is in the Unilever portfolio.
We work with a variety of investment firms who are like Just Food for Dogs is part of ElCaterton, right?
we want to continue to grow.
want to focus on those kinds of key markets.
Number one, e-commerce, we go a lot deeper in terms of you have a physical product andyou're selling in a digital channel, and then two,
(17:12):
We have a variety of like fintech and crypto customers, like coin market cap, TrustWallet, like huge kind of like crypto wallets love our product.
And then also like the kind of like health or like the mental health like Alloy or Alma,for example, Alma is like a mental health kind of app that is now using CODIF
Like if they have a lot of customer support tickets, they have a need for a solution likeours.
(17:38):
I think the companies that are like slower to adopt the technology, they will take moretime but we want to work with mid-market companies and eventually enterprise customers.
Cool.
Mike, if somebody's interested in getting in touch with you, asking your advice or they'reinterested in code, if what's the best way for them to get in touch?
Yeah.
So I would say just on LinkedIn, like you can just add me at Mike Zayans on LinkedIn, youcan just email me at mike at codiff.ai.
(18:07):
Thank you so much for coming on the show and sharing your story.
Yeah, thanks.
It was a lot of fun calling.
Great to be here.
I enjoyed our conversation for sure.
And thanks to everybody for listening.
We'll catch y'all next time.
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