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Podcasts


IA40 SPOTLIGHT: CRESTA CO-FOUNDER AND CEO ZAYD ENAM ON USING AI TO EMPOWER
PEOPLE TO BE MORE PRODUCTIVE

--------------------------------------------------------------------------------

August 10, 2022



In this episode of Founded & Funded, Investor Ishani Ummat talks with Zayd Enam,
Co-founder and CEO of Cresta AI, one of our 2021 IA40 winners. The two dive into
the important topic of how AI can be used to help empower people to be more
productive, specifically in the context of call centers — or now more commonly
referred to as contact centers — because they include much more than phones.

We hear the story of why Zayd dropped out of his Ph.D. program to pursue
launching his own company based on what he explains as the “schlep blindness” of
contact centers. He also discusses the unique way he landed his first customer,
which happens to be Intuit, one of the largest financial software companies in
the country, and the benefits of taking a modular approach versus a full rip and
replace of a customer’s entire system.

This episode may make you crave a Costco hot dog, but you’ll have to listen to
find out why.

This transcript was automatically generated and edited for clarity.  

Ishani: Hi, everyone. I’m delighted to be here with Zayd Enam today, the CEO of
Cresta AI. Cresta is a productivity suite for the modern contact center that
leverages artificial intelligence to drive a better customer experience in real
time. We’ve all struggled with contact center experiences and so we’re really
excited to have Zayd here to talk about the unique angle that Cresta takes.

Cresta was selected as a top 40 intelligent application by over 50 judges across
40 venture capital firms in 2021. A quick moment on that. At Madrona, we define
intelligent applications as the next generation of applications that harness the
power of machine and artificial intelligence to create a continuously improving
experience for the end user and solve a business problem better than ever
before.

Zayd, we’re super delighted to have you with us today.

Zayd: Awesome. Thanks so much for having me, Ishani.

Ishani: Why don’t we start it back at the beginning. Cresta was formed out of
Stanford, but I believe you dropped out of your Ph.D. program. Tell us a little
about the work you were doing and how that led to the founding of Cresta.

Zayd: In the Ph.D., I was working on how artificial intelligence can be used to
help empower and augment people and make them more effective in their day-to-day
work? Originally, I was looking at applications of artificial intelligence for
things like helping graders and teachers grade assignments more effectively and
be able to give better feedback to students based on the most effective feedback
and based on common mistakes. It was really a thread of a lot of the work that
was done in the early ’80s at the Stanford AI Lab where this concept of
intelligence augmentation, which is pioneered by a lot of the folks that started
that lab. And it’s a continued thread of work in terms of understanding how
humans can be bicycles of the mind and extensions of bicycles of the mind. What
ended up happening is I built software for teachers and grading assistants, and
I built software for email and both those directions of the project, ultimately,
didn’t lead to success. With email, what happened is at six months after I built
it and I got a bunch of people in the Stanford building using it, Google
released their smart buy project.

The day Google released the project, like 20 people messaged me that link
because they had this big PR announcement. It was clear that Google had a data
advantage there in the sense that they have access to billions of emails to
train these kinds of models on. So, then I pivoted to graders and teachers, and
there, the tricky thing is that a lot of universities and schools aren’t ready
to adopt software. It’s just a slower market to try to get traction in. I ended
up working with a bunch of offices in the Bay Area. I’d go sit down with someone
and just observe the kind of work they do on a day-to-day basis. The goal was to
build small tools to help augment or help automate basic things in the workflow.
I started working with these sales and support teams, and, just as a grad
student, I could sit down with a sales and support team and basically build
these systems that would understand the effective way to resolve an inquiry or
the effective way to have a conversation and provide these real-time prompts
that guides someone through the conversation. At the first company I worked with
while I was a grad student, within a few weeks, we were generating $100,000 more
of sales per month — that’s more than $1 million per year. It was clear that
there was opportunity here from a business perspective. And so, through a whole
process, I decided to drop out. Because there’s something core to this, where
there’s a lot of value to be delivered, it turned into this overall vision of
using artificial intelligence to help empower people to be more productive. And
it felt like a great place to start. And that’s ultimately how I got there.

Ishani: So, in many ways you were able to do the super early stages of customer
discovery while you were in your Ph.D. program, spending time with different
kinds of customers and mitigating a couple of these issues that we see. You
know, you and I were talking about GPT-3 earlier — lots of people have good
applications of big models like that, that feel like solutions in search of a
problem. And the way that you got to spend your time doing this early customer
discovery and building smaller tools for them was starting to figure out, okay,
where is there a problem where I can really apply a solution that I know how to
build, and I can build in a small way and create a wedge. So, a couple of trends
there where you need to figure out 1) what’s a real problem to go solve. And
then 2) where do I have a unique advantage to go do that.

Zayd: Right. And that’s a philosophy of my co-founder and Ph.D. adviser at
Stanford — a gentleman named Sebastian Thrun. He started the Google X project
and Udacity and won the DARPA Grand Challenge in 2006 with the first
self-driving car. And his philosophy and what he teaches in his labs is
basically that in order to go build a self-driving car, you don’t sit in the lab
and research the future of computer vision, you go out in the Mojave Desert and
figure out how to get those kinds of systems to work and then come back to the
lab. And you’ll actually realize that you’ve made some fundamental breakthroughs
in the technology and the engineering systems associated with it. That’s the
same thing that happened with Cresta in terms of working with companies and
identifying how can we make this really work for the customer and then coming
back, understanding what are the fundamental technological breakthroughs that
happened in terms of making it really work for the customer.

Ishani: It feels related to this concept of customer obsession. Madrona was one
of the earliest institutional backers of Amazon. I know you think and talk a lot
about customer obsession. Take us along the journey. You leave the lab. You
start Cresta. What drove the decision to focus on contact centers? Contact
centers are amazing. Notoriously bad experiences, long wait times, low NPS.
You’ve shifted from call center to contact center, and that means that you have
digital as a new modality and email and all these other components, but still,
lots of friction. And everyone interacts with a contact center, right? The vast
majority of businesses have them. The vast majority of customers are talking to
them and interacting with them in some capacity, but it’s not obvious,
necessarily, that augmenting humans who work at contact centers is a business to
then go build. So how did you zero in on that as a customer segment? How did you
obsess over that? And then talk to us a little bit about that first customer
journey you had.

Zayd: I mean, the thing is they’re not particularly sexy, right? So, it’s not
like a Stanford Ph.D. says, ” Hey, I’m going to go focus on contact centers and
understand how these technologies apply and drive tremendous value or
transformation there.” There are a few secular trends happening in general in
the last couple of years where folks are rearchitecting their systems from
on-premise to cloud-based systems. And you have folks adopting these
multi-channel experiences, whether it’s phone, chat, email — it’s a space that’s
going through rapid transformation right now. Because NLP and deep learning have
transformed so much and are progressing so quickly that what’s possible in
software now is just dramatically different than what was possible just a few
years ago. So, you have a lot of change happening in the space and you have a
fundamentally new solution set with deep learning. And it is just an opportunity
there. That’s an analytical answer. I think the more emotional answer actually
just was that, so Paul Graham has this essay in Y Combinator about schlep
blindness. He talks about when Patrick and John started Stripe, everyone was
building these niche social networks and these niche travel websites, and no one
was solving the payments problem on the internet because it felt like a lot of
schlepping around. To do it, you’d have to go deal with legacy APIs of banks and
deal with how to do partnerships with banks and these kinds of things. But every
website in the world needs some way to take payments if they’re doing business
on the internet, and the experience was terrible. And I felt the same way about
contact centers. It felt like a lot of schlepping around. You’ve got to figure
out how you integrate into all these systems. It’s not exactly the most exciting
part of the business for a lot of people. But the thing is, every company in the
world has some way to interact with their customers, and the experiences right
now are terrible, right? Where the average employee NPS within a contact center
is less than zero, average customer experiences are very poor in terms of the
way that they interact with contact centers. So, everyone has something. No,
one’s happy with it. And I have something where I could, within a few weeks,
have an impact on a team. Even though it wasn’t the dream of the Stanford Ph.D.
to go work in the contact center, it felt like a lot of schlep blindness in why
people were avoiding the problem. And so, I was really excited to lean in and
solve that problem.

Ishani: Yeah. And I mean, by the same token of Paul Graham and the Y Combinator
reference, you know, every incubator will tell you to stay away from the
enterprise. Right? No one will tell you to go and drop out of your Ph.D., start
a company, and then go sell to the big enterprises at the get-go. How did you
even begin that early customer journey of getting someone to sign on?

Zayd: Yeah. Another Y Combinator advice, which is, don’t do big deals. But I
think one of the most important skill sets is figuring out what advice applies
to your situation and what advice does not apply to your situation. So that one,
I don’t think applies. For Cresta, the market for artificial intelligence makes
a lot of sense to start an enterprise because that’s where you have these teams
of contact center agents with more than 250 people. And you’re able to use
artificial intelligence in a way that can capture repetitive patterns across the
conversations that multiple people have, and that’s where you can provide the
most value and the largest segment of the market on which to make an impact.

Usually, it’s hard to go to enterprise because they have a bunch of requirements
to go engage in that business and have an impact there. So, people often start
mid-market or SMB segments of the market. This is where I was slightly
unconventional — I tried cold calling and prospecting in terms of getting our
first customer, and it wasn’t getting much traction. But then, through Scott
Cook, I cold-emailed him after a presentation, and I got this meeting with the
CIO of Intuit. And I went down there to present Cresta and the results that we
had in terms of my Ph.D. work and asked him, “Hey, I’m starting a company in
this space, and I’d love to work with you as our first client.” He said, “This
really ties into the Intuit strategy. This is what we’re trying to do from an AI
perspective, and this is a really great project, but we can’t work with you
because you’re a one-person company, and we’re Intuit, we’re the nation’s
largest financial software company, And we can’t really work with a company like
yourself. But if you want to sign up as an intern to my group, you can sign up
as an intern for the summer. I took him up on the offer. Once I got in, I got
access to their data systems and basically worked with them on the technology
and deployed the first software to their group in Tempe, Arizona. And the first
person to use it was actually one of the top salespeople there, and he loved it
so much that he got the rest of the team to start using it. The whole team’s
performance doubled in a few weeks. And so that’s when they were looking at it
and saying, okay, now we want to take that team and expand it to this other
bigger team in Virginia. That’s when I went back to them and said, “Hey, this is
now becoming a really serious project, and you probably want some sort of
enterprise agreements around this. We came to a standard SaaS agreement, the
only addendum being that this clause hereby terminates Zayd Enam’s internship.
Then I had to sign as the CEO of Cresta and then as a former intern at Intuit,
which is a fun reminder of that contract every time it comes up again. But yeah,
that’s how we got our first customer. And then, once we had that case study, we
were able to go to more enterprises with that credibility.

Ishani: That’s an incredible story. And I think YC needs to write that into
their playbook around how you go build a first enterprise customer. First of
all, most startups don’t go to enterprise, but if you’re going to go to
enterprise, follow the playbook that Cresta lays out.

Zayd: Yeah, I think you learn a lot, and I think you do things that don’t scale,
right? So, take an internship at a company — that’s something that doesn’t
scale.

Ishani: No, definitely not. But I think it really does take customer obsession
to a whole other level — around being able to go in and understand and see how
these data are collected and see how people are using them. And then how they’re
able to use something that you can build in the duration of an internship, even
to increase performance and double it in this case, then actually take that out
into the real world and say, okay, that’s great validation. Now I have the
comfort to go build a company around it. And the proof points, right? And then,
by the way, you also start with a great contract.

Zayd: Yeah.

Ishani: One of the things you mentioned is this concept of building on existing
infrastructure versus designing an entirely new user interface. And what I mean
by that is you talk about integrating with all these systems that already exist
in contact centers, right? They have agent-facing software. It seems like you’ve
taken the approach of building on top and integrating with that rather than
having contact center agents focus on learning something new. From a product
perspective and from a utility perspective, tell us about that decision and how
it’s worked out so far.

Zayd: Right. Our approach is to peacefully coexist with existing systems and
infrastructure in the contact center. And that strategy really is just, there is
a lot of different underlying systems and topographies in the contact center, a
lot of different C cast platforms, a lot of different CRMs and knowledge bases
of these kinds of things. Some folks take the approach of a full rip and replace
where they recommend that they come in and replace your entire system. And our
belief is that’s likely not the right approach. What we have is a modular
approach where we can come in and integrate with your existing systems and take
you to the future state of the contact center piece by piece by one module when
you adopt another module will get more value from the second module because they
amplify each other. But you don’t need to rip and replace your entire system to
do it. It’s a lot of pain, a lot of implementations. You have to build more
integrations. You have to spend more time on some of these edge cases and these
kinds of things. It’s more effective and efficient to start with one piece and
see the value from that and then expand over time.

Ishani: Especially when you think about going to the enterprise, right? It’s
unlikely that you get a Fortune 100 company that’s going to rip and replace
their entire existing system for you if you’re even a 50-person company. I
really love how you lay that out and saying, “Hey, actually, this is worth a lot
of investment from your end because there’s clear ROI.” And in your case,
doubling performance and being able to demonstrate that feels like a very clear
path to demonstrate for an enterprise buyer that there’s a specific return for
buying Cresta.

You’ve alluded to this a couple of times in terms of different modules of the
product, maybe just contextualize. Cresta started as this sort of augmentative
tool for contact center agents. Where’s the product today? If I’m an end user at
a big enterprise, how have I adopted it? And what exactly does Cresta do for me?

Zayd: Yeah, so we started as a real-time agent coaching and assist for chat —
sort of, how do we help chat agents more effectively and more efficiently handle
sales and support conversations? Over time, we built a full platform. It Really
pieces together all the different components of what an intelligence layer and
an intelligence suite on a contact center should do. The way Cresta works is we
have an insights product that is understanding your conversations, understanding
what makes things effective. Why are your top performers performing better? And
what behaviors do they do that make them effective at that? And that’s giving
you insights on how that’s changing over time. And then, we are able to take
that and have a set of actions through real-time coaching and post-call coaching
that help democratize those behaviors across your entire team. So, we figure out
what makes your top performers really good. And then, we’re coaching everyone
across the team to help them be as good as the best. And then, we take those top
performers and we’re able to make them superhuman because we have stuff like
automation and efficiencies like summarization and these features that drive
these kinds of superhuman efficiencies of the team, because all of a sudden you
can summarize a call, or you can automate a call or automate a workflow that
takes less than a second when it used to take a long time. That’s the core loop
of Cresta. And then, we go back to insights. We identify what’s working, what’s
not working, how customer sentiment trends market’s changing. And then go back
through the loop again in terms of democratizing the behaviors that lead to
better results and driving automation that lead to superhuman efficiencies.

And it’s that virtuous cycle that goes to the contact center and it’s a platform
that each piece can be adopted in a modular way. But then, as you adopt each
piece, it adds value to the other piece. And when they put all the pieces
together, it becomes a powerful engine that drives compounding benefit for the
business.

Ishani: Absolutely. We would call that a flywheel effect. That the more you use
Cresta, the better it gets. And in many ways, this is perfect for our core
concept of what an intelligent application is. The idea that you’re building a
core machine learning-based product that gets better the more that you use it.
Versus, I think a lot of companies out there today are building AI and ML, but
they’re kind of retrofitting to a product and using AI and ML to optimize it
overall and over time. Whereas the concept of starting with a core machine
learning-based and intelligence product that enables your users to get better,
but then also actually becomes better as a product because of that feedback loop
and iterative cycle, is really, I think — The next generation of companies are
going to have to be built that way.

Zayd: It’s certainly not easy, but it’s something that compounds over time. Then
it becomes more and more powerful for a company. And the folks that adopt it end
up outcompeting the folks that don’t, so you’ll see teams and individuals that
are empowered with these kinds of systems are going to produce dramatically
more, and they’re going to outcompete teams that don’t, and over time, we’re
just going to see this play out where teams that do this versus teams that don’t
do this and it’s a competition that’s already getting started.

Ishani: That’s a good insight actually, Zayd — that not just is the next
generation of companies, intelligent applications versus predictable SaaS
applications, but rather, also customers that are using intelligent applications
have a wedge and a differentiation component over their counterparts that are
just using SaaS tools.

Zayd: Yeah, that’s what makes it exciting. I think it’s a fun time for the
industry. It’s a fun time for technology. And it’s one of the things that just
makes it fun to build at this time.

Ishani: Agreed. And let’s get a little bit more specific than just around how
you build this intelligence layer. What’s the approach to data, for example?
There are lots of customer conversations available to you, once you’re in a
customer and you get maybe a transcript or set of data that they have on
existing customer conversations. How do you start? How do you continuously train
that model, learn from each of those conversations? And actually make sure
you’re delivering an accurate, great result to the end customer and surfacing
the right level of insights of what good looks like.

Zayd: One piece to that is basically we get the conversation transcripts, the
audio, and then we tie specific outcomes to each conversation — was this a
positive outcome in terms of, if it’s a sales use case, was it a close one or
what was order value? What was the upsell rate? These kinds of things. And if
it’s support, is it a first call resolution? What was the average channel time?
Was it high CSAT, high transactional NPS. So, depending on the outcome the
business is looking to optimize for, we’re looking at those outcomes and tying
it to those transcripts. And then we’re training models. And so that then gets
to one of the core differentiabilities of Cresta, which is that we’re building
infrastructure to make it possible to train tens of thousands of custom models
for many enterprise customers where our vision is Cresta provides the Costco hot
dog, which is like a factory to build these custom models for many customers and
the infrastructure internally in the tooling, internally around conversation
designers and labelers and machine learning engineers that produce high-quality
models trained for that customer and keep them up to date regularly. That’s a
hard challenge, but that’s something that we are specifically investing in from
a tooling perspective. And we’re seeing the results of that payout because we’re
able to deliver a model that understands a customer’s conversations. So, we can
get to the specificity of why a customer doesn’t or does buy from Verizon in
terms of they’re having an effective conversation and up-sell. Versus something
for a different customer. They’re able to train a specific model that helps that
specific company become more effective in their sales conversations.

Ishani: Super interesting. How do you view the role of these increasingly new
and increasingly powerful pre-trained models? When you go to do that. For
example, I could imagine you taking a pre-trained model that’s a, maybe more
rules-based type of engine when you go into a customer initially. And then, of
course, creating all these derivative models on top of that, maybe for specific
customers, but also maybe even for specific use cases within customers.

Zayd: So, pre-train models are powerful, and we’ve known that for a long time —
more than probably I’d say a decade now. The thing, though, is that when you
take a pre-train model and you’ll fine-tune it for a particular application,
what ends up mattering more than the amount of data that you fine-tuned on is
the quality of that data. So, are you training on high-quality examples? And are
you training on high-quality labels that truly get to the root of what you’re
trying to train the model on, and that’s where you need the right infrastructure
and tooling to label and design effectively. And make sure you’re encoding best
practices effectively into the model.

So, pre-train models give a boost across everything really, but by themselves, I
think they’re useful as toys in which, and toys soon become very serious
business applications, but to really leverage them as business applications, you
need to have a set of infrastructure around it to really control and fine-tune
it with specifics of what you’re trying to do. And that needs to be high-quality
and effective data.

Ishani: Yeah, you’re giving an example to this concept of garbage and garbage
out, right? That if you’re training your model on, in this case, Cresta’s model,
on bad customer interactions, of course, that’s the guidance that Cresta is
going to give you is, is bad. But if you figured out a way to scalably and
effectively label customer conversations that are really good and train Cresta’s
model on that, then Cresta gives you really good recommendations. So, it’s just
a good way to then think about, okay, you have to have a data strategy and the
infrastructure and tooling, as you say around it, that is really robust. Even if
you already have the machine learning components, the data really does matter as
a differentiator here.

Zayd: Yeah. That becomes fundamental to this Costco hot dog approach.

Ishani: I think if I take away nothing else, there will be two analogies here.
One is to go intern at a company to get a customer. And two is create a Costco
hot dog.

Zayd: Yeah. Our head of engineering — this gentleman named Ping Wu — he’s very
passionate about Costco hot dogs and how their inflation uh, resistant. And so,
that’s the vision for Cresta.

Ishani: Real-world analogies help a lot.

Zayd: Yes.

Ishani: Tell me on this tooling, an infrastructure component, what’s out there
that you’ve been able to leverage in terms of software and structures that exist
versus some of the things you’ve had to create that are big hurdles for you to
get data to, the right insights for Cresta.

Zayd: I think right now we’re still very nascent in terms of the whole stack for
data and machine learning operations. Because there are no best practices really
established, and it’s tricky, right? Because it’s not as mature as other
industries in terms of like, this is how you build this type of application. For
us, we’ve built, I would say, almost everything in-house, just because of the
specifics of our application and the relative latency of tooling and
infrastructure in the ML space. That makes it an interesting problem. I think
over time, it will settle, and the space will mature, but right now, there’s
gaps in open-source tooling. I mean, there’s a lot of great stuff happening, but
I think it’s just a while before that stuff matures to the level that we can
standardize on it.

Ishani: It’s a little bit surprising that you say that. Lots of companies I know
are building off some of these open-source projects, but as you say, there’s
gaps. And so, is the gap in stitching it all together or is it just that they
don’t serve enough function? They’re emerging and nascent and exciting, but
they’re features and not platforms.

Zayd: Yeah. Our data model in terms of conversations and outcomes, and these
kinds of things, let us build in specific ways. We leverage in our stack,
everything from the cloud providers to Kafka and Kubernetes and all these
things. But as part of our stack approaches the machine learning aspect to it,
and the process of labeling and training and regression testing and automating
machine learning delivery, and production, all those things. Those are things
that we’ve built in-house. I think over time, we’ll see more and more of that
probably get adopted across many companies, but that’s the state of where we
haven’t quite found something that truly hits the nail in terms of what we need.

Ishani: That’s awesome. Maybe there’s more opportunity, right? For all the folks
listening that work on MLops, that work on building the infrastructure to get to
an endpoint iterative machine learning-enabled application, it isn’t quite there
yet. The industry hasn’t converted on something, so it feels like a good
opportunity to go and understand, and obsess about com companies like Cresta,
right? If the next generation of end customers may also be intelligent
applications, then we should go be building for those intelligent applications
too.

Zayd: Yeah. I think the promise is there, and I think there’s a major
opportunity here to standardize and build this part of the stack. I just think
it takes a while to have code bases that become mature and solve the problem and
the right approach to it. And so, whenever we’ve looked at these things, there’s
always something missing. But I think the opportunity is there. There’s
definitely a market opportunity there for sure.

Ishani: Well, and in the interim, you’ve been able to build the infrastructure
internally and then also create this company that is an intelligent application.
But then also part of the core of what you talk about is enabling humans to work
alongside technology really well, harnessing the power of automation. It seems
like a deliberate choice by Cresta. You know, people talk a lot about AI
automating away jobs, but in this case, I think you view it as strongly humans
and technology working together side by side. Where does that come from? How
much of that is the idea that we’re not just quite there yet on technology and
in terms of conversational intelligence side, and it is just not quite good
enough. And how much of that is a deliberate design choice working with your end
customer and those partners?

Zayd: I think it’s a fundamental principle all the way back to my Stanford
thesis, which is the focus on intelligence augmentation and really the concept
of the bicycle of the mind. And I think to some extent, there’s a way to look at
artificial intelligence and I sometimes characterize it as lazy artificial
intelligence, which is that you take an existing process and you say that I’m
going to automate this process end to end, and I can use some kind of automation
or some kind of AI to go do it. But that kind of overlooks, what’s possible when
you approach it more creatively. And in that sense, you kind of look at AI as a
building block — how does this capability combine with humans unlock things that
just weren’t possible before? And that’s a fundamental approach that we took.
It’s been our goal and our direction in terms of that, this is what we believe
is the right approach to this and ultimately results in larger upside and larger
potential. And especially in our application, there’s, yes, we’re not at
human-level AI for conversations, but even then, there’s opportunities for
humans to have a continuous and big impact in terms of the companies that they
work with.

Ishani: If you play it forward 10 years, how much more or less are humans
involved in the contact center process?

Zayd: Our vision is that you get to this point that you have this concept of
experts on day one. So, folks come into an environment and within the first day,
they gain this expertise of all business information and all the knowledge and
subtleties of their particular environment and are able to use a whole set of
support and decisioning systems to get to the right decisions and the right
effectiveness in their role. And what they’re bringing to the table is
creativity in terms of understanding how to approach something that isn’t
encoded in the patterns of the data yet. They’re bringing that creativity to the
table, and they have a whole support decisioning system that’s helping them be
that expert on the first day. And as soon as they’re able to encode and
establish a best practice through their creativity, that becomes a part of the
system again. And then the human is just focused on the next thing and the next
thing. And so, our approach with these kinds of augmentation systems is that
we’re constantly figuring out what’s the best practice, what’s really working,
what’s effective, building support systems and information systems that can help
deliver that scale to many people, and then figure out how could the humans
continuously act as in some ways like a mutation process, that’s identifying
what’s a new thing and what’s the creative approach to this problem. And then
keep doing that and you just build a system that just gets smarter and smarter.

Ishani: A bit of a separate question. If I look at the investors around the
table of Cresta, you’ve got folks like Greylock and Tiger Global that more
traditional institutional venture investors. All makes good sense. You also have
a coalition of investors from kind of legacy industry players. How has that
experience played out? You know, having strategic involved can always be a
little bit of a double edge sword in terms of having folks around the table.
Tell us about your experience.

Zayd: We’re really fortunate to work with great partners from an investor
perspective. So, in this last round, Genesys, Five9, and Zoom invested in
Cresta. We have great partnerships with those folks in terms of leveraging
Cresta on top of the platform to really see a big impact for their businesses.
We’re seeing go-to-market acceleration through those partnerships, and what that
really has done is mark our leadership in this space in terms of this real-time
intelligence for the contact center.

Ishani: Super exciting. Yeah, I think investors can represent impactful
connections, powerful networks that enable you to, again, just build more of a
flywheel. Right? So, I love that concept of solidifying you as a winner in this
space.

We typically end these podcasts with a lightning round of three questions. So,
I’m going to shift into that. First, aside from your own, what startup or
company are you most excited about in the intelligent application space and why?

Zayd: Oh, that’s a good question. I think Tesla is doing some very interesting
things overall in terms of how they’re approaching autopilot systems. I’m not
sure if it counts, but I think that vision is something that will take that
company far, I believe, in terms of the way they have this loop for model
predictions and data collection. I haven’t quite seen other startups operate at
that level of data flywheel. And I think that’s the right approach.

Ishani: Love it. Question two, outside of enabling and applying AI to solve
real-world challenges, what do you believe will be the greatest source of
technological disruption over the next five years?

Zayd: Some of these contact centers are still working on these green screens
with these old ’80s-style computers. And it feels surreal, but the cloud hasn’t
actually fully happened yet. And a lot of companies are working in all kinds of
different environments. I think that just better systems, better cloud systems,
better UX, better integrations — that stuff has a big impact. We see with our
customers as well that the AI has a lot of value, but they also get a lot of
value from just better data integration and better UX to do their day-to-day
workflow.

Ishani: Yep. The concept of customer obsession going and being on-site and
visiting your customers and being integrated in them, even at the intern level,
really gives you a real-world perspective for how true that is and how much the
install base of technology takes a while. And a couple of cycles to come up to
where we think about it, whether it’s in an academic lab or as a startup CEO, or
as an investor,

Zayd: Right. Agreed.

Ishani: Final question. What is the most important lesson — you know, maybe
something you wish you did better — that you’ve learned over your startup
journey so far?

Zayd: So, I asked Scott Cook this question and I think the biggest one is
probably self-awareness. And I think if you unlock self-awareness, then a lot of
other things can happen in terms of development as a leader and development just
as a company. I think understanding your own weaknesses, understanding what you
need to get better at, and then approaching those things with the growth
mindset, that becomes really fundamental. It sounds a little fluffy or
psychobabbley, but I think it’s true.

Ishani: Awesome. Zayd, thank you for talking about Costco hot dogs, interning at
your company, growth mindset, and the next generation of machine learning ops.
Super appreciate having you on the podcast and your time today.

Zayd: Awesome. Thanks so much, Ishani.

Coral: Thank you for joining us for this IA40 spotlight episode of Founded and
Funded. If you’d like to learn more about Cresta, they can be found at
Cresta.com — that is C-R-E-S-T-A.com. To learn more about the IA40, please visit
IA40.com. Thanks again for joining us, and tune in in a couple of weeks for our
next episode of Founded and Funded. We’ll be spotlighting another IA40 winner
next month.

 


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