Cresta Co-founder and CEO Zayd Enam

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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