Why AI Will Transform Customer Experience: Cresta CEO Ping Wu and Sequoia’s Doug Leone

Ping Wu built Google's contact center business before becoming CEO of Cresta, where he's pioneering a unique approach to contact center transformation. Rather than full automation Ping advocates a dual approach, automating what's ready while using AI to assist humans with the rest. He makes the case for an abundance mindset—imagining new customer experiences like talking to airline apps or turning synchronous interactions asynchronous. Ping breaks down the technical challenges of deploying Contact Center AI at scale, from solving latency to orchestrating 20+ models in real-time. Sequoia’s Doug Leone shares his framework for building AI companies at speed and why he believes we're at the front end of an Industrial Revolution 2.0. Hosted by: Sonya Huang and Doug Leone, Sequoia Capital 00:00 Introduction 01:13 The Evolution of Contact Centers 02:05 Debating AI's Impact on Call Centers 04:07 Challenges and Opportunities in Contact Centers 08:14 Technological Waves in Contact Centers 11:10 AI vs Human Agents: The Future 13:35 Customer Experience and AI 16:33 The Role of Data in AI Automation 19:05 Competing in the AI Space 22:34 Building a Company in the AI Era 24:05 Instilling Speed in AI Companies 24:53 Management Experience and Growth Challenges 26:01 Identifying Leadership Potential 26:37 Cresta's Leadership Transition 28:34 Future Goals for Cresta 29:56 AI Market Cycles and Investment 35:38 Cresta's Technical Stack 45:11 AI's Impact on Business Communication

• 0:00Speaker 00

  Today, if you think about the business, they feel like multiple personality to the customer. So in the sales phase, they call you very, very aggressively. And once you sign up and become a customer, you're dealing with entire different personality, right? And you're dealing with service departments. I feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, long-going conversation throughout the entire customer journey. And LLM is a perfect tool to do that. And that will really bring the level of personalization, the level of customer experience that wasn't possible before.

• 0:55Speaker 01

  Hi and welcome to Training Data. Today, we're joined by Cresta CEO Ping Wu and Sequoia's Doug Leoni, who sits on the Cresta board. Today's episode dives into the gnarly world of the contact center, a giant legacy industry filled with slow-moving incumbents that is responsible for driving the vast majority of company customer conversations. Ping understands this world deeply, having first built Google's contact center business before becoming product leader and then CEO of Cresta. Ping joins us to talk about the different waves of technology that have hit the call center, how he sees the future of customer experience evolving with LLMs towards an abundance future, and why his playbook is to meet customers where they are, blending human agent assist with autonomous digital agents. Doug Leoni also shares his perspectives

• 1:40Speaker 01

  from several decades of investing in company building and his hot takes on whether we're in an AI bubble. He also shares where he believes the value will accrue in AI. Hint: It's in the application layer in this gnarly last mile. Enjoy the show.

• 1:55Speaker 01

  Ping, welcome to the show. And thank you for bringing along our special guest Doug Leoni as well on your board.

• 2:01Speaker 02

  My pleasure. Thank you.

• 2:03Speaker 01

  Thank you both for joining. Ping, I want to start by asking, a big part of the AI thesis is that AI is going to replace labor globally and that the TAM is in the tens of trillions of dollars. Obviously, the contact center, the call center, is a big pool of labor that is just begging to be automated. If you had to guess, how much of call center labor spend will actually be automated fully by AI?

• 2:25Speaker 00

  Yeah, so we internally, we've spent a lot of time debating about this. The reality is, I don't think anyone knows for sure. And if you ask, it depends on really what they're selling. And you ask different people and they give you different answers. And some people will say that 100% human will be gone in contact center. And some garners, research actually shows that none of the Fortune 500 over the next five years will have contact center gone entirely humanless. So it's also probably falling to somewhere in the middle. And in fact, we got asked this question two years ago when GPT-4 first came out.

• 3:05Speaker 00

  And a lot of people will say that maybe in two or three years, there will no longer be humans in the contact center. So at that time, our belief is that you'll probably, the transformation, especially for existing Fortune 500 companies, will probably take way longer than a lot of people think.

• 3:23Speaker 01

  Doug, what do you think? What's your bet?

• 3:24Speaker 02

  At the limit, it's 100%. But I'm mindful that there are still IBM mainframes and cobalt being used in America in a banking system. So to me, it's not really what percent. To me, is the speed of which this is going to happen. Is it going to happen within 10, 20, 25 years, 30 years? Because whether the answer is 30% or 60%, if it happens in 50 years, that means one thing for companies like Crest. If it happens in three years, it means something else. So the end number is not the relevant metric for me. To me, is the speed of adoption.

• 4:04Speaker 01

  Great distinction. Ping, you've been working in the contact center AI space for well over a decade. Prior to becoming CEO at Cresta, you ran the equivalent function over at Google. And so maybe for those of us in the audience that don't know the contact center market, can you tell us a little bit about what it is, how big it is, and how technology has served it so far?

• 4:27Speaker 00

  Yeah, when first talk about contact center, a lot of people will naturally think about call centers. A lot of humans sitting there, listening, answering calls. But contact center really is a broader category that including the only channel interactions from emails to digital chats and on website and in apps and also including calls, of course. And the overall market is quite big and there are historically, there are around seventeen to twenty million agents, human agents, actually work in the contact center. For the software market is probably in the tens of billions. And for AI market, according to some research, it will be in the high tens of billions of dollars.

• 5:12Speaker 01

  And is the use case mostly customers calling in to complain, customer support? Is that what these contact centers are mostly used for?

• 5:20Speaker 00

  Oh, so, yeah, so customer call in, there are all kinds of reasons they call in, right? Complaints or fixing the issue. But also, I think a lot of people may not realize that there are probably a quarter of the contact center, twenty-five percent, is actually revenue generating. They're including selling stuff or collecting money or retaining customers and that kind of conversations. So it's not a hundred percent customer support.

• 5:44Speaker 02

  So I have a question for you that I never asked you. If you look at the contact center, and I'm old enough to date myself, you go back thirty years, you heard names of Vaya and God knows whoever else that's barely living in and out of bankruptcy. You go back fifteen years, you see Genesis of the world. What caused a bright young engineer called Ping Wu? Fifteen years ago, to be attracted with this market, then one could have said, it's always been a stodgy market. It's always been of low interest. It always created these slow growing companies. What is it that interested you now? Of course, now we understand it's a vibrant market with lots of opportunities.

• 6:27Speaker 02

  But turning the clock back ten years ago, what attracted you to this market?

• 6:32Speaker 00

  First of all, fifteen years ago, I didn't even realize that's a long history of slow growth of the market. Otherwise, maybe I would think differently. And second, at that time, I just do remember there's a period of time, there's a lot of excitement of conversation, AI technology, and especially around consumer facing speakers. And at that time, people think that that would disrupt Google, that would become the entry point for all the consumer interactions. And I happen to really believe that contact center will probably be the most exciting opportunity for conversation AI to transform. And it's because it has all the issues that traditionally people get excited about, VC get excited about. It's a massive market, a lot of humans working there.

• 7:16Speaker 00

  And it's in the middle between businesses and customers, right? And it's all the interaction going through. And also, no one's happy in contact center. So if you, you know, by no one, I mean, there are three different parties. There are customers that call in that most of us may not be too happy because the wait time is very long. And agents, by the way, I think a lot of people may not realize, the agent, the workforce attrition in contact center is massive. It's on average, a thirty five to forty percent. In some cases during COVID, some company have more than a hundred percent turnover. That means.

• 7:47Speaker 01

  They just get yelled at all day long.

• 7:48Speaker 00

  Right, so it's very high stress and it's not a very fun job. And and also the business also feel like there are always the opportunity to do more with less. It seems no one is happy and it's a massive market. But I think, you know, that's the great opportunity for AI and technology to bring abundance. And to and then the abundance is the answer, in my opinion, to solve all these issues.

• 8:11Speaker 01

  So you were working on this at Google 10 years ago. I imagine this was the small language model wave, the BERT days. Was the technology ready at that point? And maybe walk us through the different waves of technology that have hit the contact center.

• 8:24Speaker 00

  Yeah, so that's a great question. Even long before that, there's technology called IVR. That you press one, two, three for different routes and through for different car reasons. And then since then, there are innovation around the input, right? You can, instead of pressing, you can directly speak natural language. And that's with the advance of natural language processing and TTS and text to speech generation. That experience getting better and better. For when we first started in contact center AI Google, it's even before BERT, actually. It's before transformers. It's mainly using AI or at that time, using AI to do classification, intent classification and entity extraction using pre transformer models.

• 9:09Speaker 00

  And then, but the conversation experience is still manually crafted, right? So that's the last generation of technology. And then after that, of course, the transformer came along. Initially, it's also for classification purposes. Still, the experience is manually crafted. But then the LMS entirely changed the whole thing. Not only the conversation experience on automation side, but also just you can understand conversation in a way that never was able before.

• 9:35Speaker 01

  And what does that mean practically in terms of the rollout of this technology inside contact centers? Does it mean that customers were just extremely unhappy when it was IVR? And then they were slightly less unhappy when you started to have kind of more transformers in the flow? And now, now customers are very happy to be talking to an LLM based agent? Or how has the evolution of technology changed the customer experience?

• 9:56Speaker 00

  Yeah, I think the way we like to think about it is really from the first principle, right? And you know, there are a lot of the conversations shouldn't even happen in our view. And you know, the fact that it happens because customers are not happy. I think the solution for that is to use the AI to really understand. To bring a hundred percent visibility into all the interaction in the contact center today and using AI to analyze it and then to do deep research and then find out the root cause. And then, and then that usually reflects some process broken or website updates that freak out people or firmware update that bring down network and all that kind of stuff. So you need to fix that first, right?

• 10:38Speaker 00

  And first, you know, avoid interaction if it's not necessary, right? And, you know, beyond that, I do feel like, you know, their AI can automate a lot of interactions that no one wants to have, like, you know, neither the business nor the customer want to have those interactions. Those are what we call low emotion value interactions that should be self-served. And then on top of that, I do think that contact center AI will enable new interactions. That's the ones that you cannot afford to do that today. So all these are improving customer experience.

• 11:10Speaker 01

  Do you think end customers will ever prefer talking to an AI agent over a human agent? And have we reached that point yet?

• 11:17Speaker 00

  So, look, I mean, that's a really interesting question. So I've been thinking about this, you know, on my way here. So I've never met anyone to have this experience of talking to a customer support agent on the phone and go, I'm really frustrated, send me your AI, please. And we never had that experience. And, and in fact, that I would encourage people to look up some of the,

• 11:41Speaker 00

  you know, some of the companies in a search for their customer service, right? The first question that people ask on Google and Google will surface, what is the most popular question? The first question is always, how do I talk to a live person for this type of, you know, customer service? So, so I think that that time probably hasn't arrived fully yet. It depends on what kind of interactions again.

• 12:04Speaker 01

  I'm maybe too techno optimistic or AGI pilled here, but I feel like I've seen some recordings now where the AI can be emotionally intelligent, it has infinite patience, right? It's not trying to hit some metric on time to resolution. And so, for example, if somebody calls in, they're having a really bad day. For example, your AI can be a lot more patient and empathetic than a human agent even could. And so I'm, I'm sort of optimistic on the side of the bots here.

• 12:32Speaker 02

  Well, I agree. There's the human component of patience or the subtleties of humanity, but there's also the training of the agent like versus the training of the AI. Three years from now, who's going to be much more equipped to answer a question? It's clear that AI is the answer. I kind of think of gold like versus Bitcoin. Somehow the analogy came to my mind as you said that. It is clear that Bitcoin is going to win. It is clear that Bitcoin is going to be worth more than gold.

• 13:02Speaker 01

  Not investment advice.

• 13:04Speaker 02

  Not investment advice. But it is clear that that the agents by definition, and a lot of which don't even reside in America. There's a language component. You know, I'm not saying anything bad about the agents, but there's a language component. There's a training component. There's the human component. And I think in all those dimensions, I think AI is going to win in the next two to three years.

• 13:27Speaker 01

  Bitcoin as digital gold is a really interesting analogy to the agent, the digital agent versus the human agent question.

• 13:35Speaker 00

  Yeah. From our perspective, we really want to meet customer where they are today. So unlike self-driving cars, you really have to automate the entire thing 100% of time. Otherwise, you do not have the economic impact. For contact center, we would find is very unique is that the work is very divisible. So first, the conversation is, you know, those are every conversation is independent unit. And you can automate X% of conversation that's ready to be automated. And for for a lot of reason, we can get into details. And then for the remaining ones, you can still use AI to assist humans. And to, you know, take away the initial, maybe 10% of the interactions, like authentication or intake or lead qualification, and then take away all the after-call work.

• 14:20Speaker 00

  And also have AI agents to help humans in the middle of the conversation, to do knowledge retrieval, to do data entries, all that stuff. So that's not mutually exclusive. And as long as we feel like customer not ready to say that we just need to turn on our call center today and then go for AI. We feel like there is a long, you know, depending again, what kind of business? And what kind of, you know, technical, you know, the IT infrastructure. So I think the journey will probably take different timeframe, but our goal is really meet the customer where they are.

• 14:50Speaker 01

  So Crux is in an interesting position because you both have the Agent Assist product that helps make existing contact center agents more productive. And then you have the actual AI agent product that is directly customer facing, you know, autonomous, autonomous agent. Where do you think most customers are today? Are they ready to go full force, just, you know, put the agent on my website, let it go crazy? Are they, are they, you know, experimenting with that? Where is the customer today?

• 15:15Speaker 00

  It depends on the customer. If you and I start an e-bike store today on Shopify, and we can automate a hundred percent, I'm sure, because it really depends on how complex is our product. It can be order or magnitude difference between like a simple product like e-bike or versus a real world touching many different countries and then millions of, tens of millions of people. So it's very different. And then that impacts the complexity of the conversation handled by the contact center. And then the other part is the IT infrastructure. A lot of people may actually realize that before you actually enter the contact center, you will feel like, oh, this should be easily, very easy to automate. The reality is a lot of those things that humans do in the contact center today

• 16:00Speaker 00

  are is optimized for humans. So those system record or the system action, ticketing system, these has been around for decades. A lot of them just simply do not have APIs, right? So the only thing that to make changes is through a graphic user interface that optimized for humans. And without a real time API, just, you know, again, these are not AI problems. And we believe that, you know, these are the opportunity that we work with our customer to develop those real time APIs. And then so that's why we feel like those transformation depends on the nature of business would take different timeframe.

• 16:32Speaker 01

  Yeah. It's interesting you made the self-driving car analogy earlier because I was thinking about your business earlier this morning. And if you think about Tesla, part of the beauty of them getting to full autonomy is that they have so much data coming in from their cars even when they're on L2, right? For you guys, because you are the call center, or you're the agent assist, you actually get full data of the conversation, whether it's voice, whether it's conversational based digitally. And that can become a training base for customers to automate more and more of their conversations over to the agent over time.

• 17:06Speaker 00

  Yes, 100%. And in fact, when we first, my first, the journey when I first started, seven, eight years ago, it's really automation only. I really believe it should be automation only. Fast forward, we run into all kind of real deployments. And then we really actually broaden my own horizon. Then I believe that in order to really do the best possible automation, it's counter intuitively. You need to know what actually happened in the contact center, what are humans actually doing. So not only just the conversations, but also what they are seeing on the screen. That's super important to actually build the best automation possible.

• 17:45Speaker 02

  One of them is the sex appeal. It's the sizzle. It's what everybody wants to talk about. Which you have to have, otherwise you're a tired old company. The other is the realities of a business to run and what they need. And so if you are one of these new age companies, you're quickly going to hit a wall because you don't have the data and you don't have the systems that you really need to run a contact center. But if you're of the former, don't have the latter, then you're labeled as an online company. So here in our case, we understood this a while back and we make sure we invested. Not only we doubled down on the operational system for agent assist, but we also developed the sex appeal product because that's what all the customers want to talk about day one.

• 18:30Speaker 00

  And another aspect of it is really just tied to the point I make earlier, is that a lot of those calls shouldn't really happen. People call in, there's no way to make them happy. It's because they're not happy to begin with, right? And if your product works, if your process works, they shouldn't really happen. So if, look, if this room, we feel really, really cold, maybe the answer is not a heater. Maybe there's a broken window or there is a patio door wide open. The solution is turn on the light and see the root cause and then fix that first before you turn on the heater. Yeah.

• 19:04Speaker 01

  Love that. Customer support is one of those canonical examples of where people think large language models will be most transformative. And it's almost a consensus category for venture startups at this point. How do you compete? What is it like to compete when everyone has access to the same LLMs and is latching on to the same big picture vision?

• 19:24Speaker 00

  Yeah, so again, in order to really deliver value in the contact center transformation, it's not just the models. It's just not a model. Model is a bunch of ways and the data and itself is not going to provide a value, right? And now the question is how much you need to build on top of it to deliver that value. If that layer is very, very thin, then our argument probably is you don't have much opportunity to achieve value, right? And then also if that layer will be gone when the model gets better, there's no way you have a durable business. But that is not a case for contact centers and where majority of the agencies is still on premise and where a lot of there are so many, look, our average agents in the Fortune

• 20:09Speaker 00

  500, we look at some surveys, they interact with eight to 10 different systems. Remember, these companies also acquire other companies over years, over decades. Those backend system may not even talk to each other. It depends on where you booked the flights or depends on where you booked the hotel. They may need to log into different systems, right? So that's the reality we're talking about. So that's why we believe is our strategy is meeting customer where they are and then drive value on day one.

• 20:39Speaker 01

  Vertical integration from the stake to the sizzle. That's how you win. What do you think is overhyped and what's underhyped in the contact center AI space right now?

• 20:49Speaker 00

  Yeah, for overhyped, I think is the mindset of scarcity is the job displacement. I think in the short term is probably a little overhyped. And what's underhyped is the mindset of abundance. Think about new experience that AI can enable. For example, can you talk to a website? Can you directly talk to the app? And can you turn a synchronous interaction into a asynchronous interaction? Can you talk to the airline app and say that I want you to do this X, Y, Z? And then call me back when you get it done, right? And then can you have that super, you know, multi-language AI agent to have those conversations?

• 21:34Speaker 00

  Or there are so many interactions that today you just cannot happen just simply because you do not have the staff, right? So and then the other thing actually I feel is really underhyped is people really seems obsessed with one side of the conversation, which is the workforce. And then people ask, you know, how many of the workforce were replaced by AI? But no one ever asked the question is how many inbound calls will be replaced by AI? So my belief is that there will be, over the next few years, you will probably see a race to getting the AI assistant on the consumer aggregators. And then a lot of things that consumer probably will dedicate to the AI assistant, including making the phone calls.

• 22:19Speaker 00

  So I think that's maybe an interesting thing to pay attention to.

• 22:22Speaker 01

  That's really cool. Okay, so you could talk to the United Airlines app and have it, you know, asynchronously go figure something out for you and call you back. Is that something that you're working on?

• 22:31Speaker 00

  We're no comment on that.

• 22:33Speaker 01

  Okay, very cool. Okay, I want to transition to talk a little bit about company building. Doug, you've been around the block for a while, seen the movie a few times.

• 22:41Speaker 02

  Means I'm old. That's what you just said.

• 22:46Speaker 01

  I was trying to say it nicely.

• 22:47Speaker 02

  Yes.

• 22:48Speaker 01

  How is building a company right now, you're seeing this live with Ping? How is building a company in AI different from your, you know, your last few decades of building legendary companies?

• 22:59Speaker 02

  It's not very different. What I mean by that is you need a terrific founder, and we'll talk about the Cresta situation a little later, hopefully.

• 23:12Speaker 02

  You need to plug in world-class engineers at the very start. Unless you start with A pluses, you'll never move up, you'll only be moving down. You have to plug in salespeople that are not administrators, that are fresh. Maybe they were regional sales manager early on because one, you can't get the world-class people. And two, if you get them, they're too big for the company. You have to figure out what the ramp is that you're willing to fund. You have to figure out what the role of marketing is. You have to solve this thing that I call the merchandising cycle that's been getting some play online, which is from product marketing to BDRs to revenue.

• 23:54Speaker 02

  Wherever that's broken, it looks like a bad sales guy, a bad VP of sales, but you have to get that right. And so I think the business fundamentals are very similar.

• 24:05Speaker 01

  I do think one of the characteristics of the companies that are doing the best in AI right now is they just move with extreme speed. And maybe that's always been the case, but I think it's even more intense right now. How do you think about instilling the need for speed in the companies you work with? And even at Sequoia?

• 24:19Speaker 02

  So I thought of answering that as part of my answer. And the reason I left it out, all the boards I'm on move with extreme speed. And that's because I paint a picture for the founders of a river, a river with rocks. And the founders and the CEO's job is to remove those rocks. So when you give me next year's plan, I don't care that's 150% net new AR growth. I want to know why the plan is the plan. And I want to challenge you why it's not 3X then. And maybe the answer is funding, where we can get funding in this market. Maybe the answer is management experience. Well, that's often a good answer. Some people will say market. Well, no way that's market. We're a little company that is.

• 25:04Speaker 02

  And so in my mind, it's forcing, it's forcing the understanding that these companies are capable of doing things.

• 25:13Speaker 02

  Which they don't believe they are capable of doing yet. And to remove those rocks. And I push and I push and I push. And I said, why can't we go faster? And do it in a linear fashion? Because God forbid, something isn't gonna happen. If you hire 250 salespeople in Q1, and then you realize in Q3, something's wrong. In Q3, something's wrong with the product. Then you're stuck with a burn. So I'm a believer. And I hear, no, we gotta train them all the same. Baloney. Give us, please, a revenue ramp that's linear. So we can make mid-course corrections up and down. And let's not be stuck by these numbers. We have 10 fingers, 100% growth. That's all bullshit. How fast can we possibly grow?

• 25:54Speaker 02

  That's always been the mantra and all the boards that I've served on. AI is not different.

• 26:01Speaker 01

  What does Cresta need to do next? What does Cresta need to do over the next five-plus years in order to become a great company, a legendary company?

• 26:08Speaker 02

  So, well, first of all, it has to continue to develop a product. It has to continue to put one foot in front of the other. It has to always see whenever some people reach a Peter Principle of their role, it has to be relatively aggressive in making sure it hires people that are capable of taking it from that point on and forward. Stay away from these, quote, "very experienced" people that start feeling a bit like suits and administrators. Point one, that's the most important thing. But the other thing that Cresta has to do, it is to up its game in marketing. There's a lot of companies, I use the word "the sizzle." There's a lot of company with a lot of sizzle and no steak.

• 26:53Speaker 02

  We have a whole bunch of steak. We're a modern company. We're best in class in one category. We're gonna be best in class in the other category. We have beautiful growing run rate in both the agent assist and in the AI part of the product, in the automated part of the product. I just think we need to attach a marketing overlay so we become a household name out of the market.

• 27:16Speaker 01

  Wonderful. Well, glad you're on the podcast, then. Maybe stepping back. Doug, you've seen some market cycles. Are we in an AI bubble?

• 27:25Speaker 02

  The word "bubble" implies you invest money in and you lose money.

• 27:32Speaker 02

  Because either due to lack of supply of companies or abundance of capital, there's certainly an abundance of capital. But I've noticed over the last two cycles, the internet cycle when Netscape going public in ninety five, two great companies being built in the late nineties in Google and Amazon, a few others, names that came to me. Then a bit of a pause, even the words I heard, the internet is a fraud, it's not going to do anything. And then three years later, the world went crazy. That latency was a lot less in mobile. I remember when we first look at these apps and Jim Getz and I, a former partner, said, how do you make money from a nineteen dollar app? How do you build a multi-billion dollar company? Never thinking of Airbnb, never thinking of DoorDash.

• 28:16Speaker 02

  A year or two later, we saw Airbnb and DoorDash. Again, that from initial birth to real market shrunk from the internet. I think this is shrunk even further. I think AI is here. I think you have to invest. I think you're the front end of a cycle, which doesn't mean you have to invest in everything. But one of the mistakes that we made at Sequoia is whenever we see a bit of revenue momentum, we have some geniuses around the partners meeting that say, oh, it can stop, it can be substituted. Keep it very easy. You see a small company with very momentum in a front end of the market. I'm not talking about the SaaS market in twenty twenty one where you're down to niche verticals.

• 28:58Speaker 02

  At the front end of the market, you start seeing the model of revenue momentum you lead in and you hold your nose on price.

• 29:06Speaker 01

  Love that. As you think about where value accrues in the market, there's, you know, there's compute, there's other infrastructure, there's the foundation models, there's the application layer. Where do you think value accrues?

• 29:17Speaker 02

  Up.

• 29:18Speaker 01

  Up?

• 29:18Speaker 02

  It always accrues up. Just look at the gross margins as you move up markets. Look at the gross margins in the chip companies. Look at the gross margins of the system companies. Look at the gross margin of this. Well, but that's Nvidia, of which we were the first investor, is a great company. Jensen was able to see the future many years ahead and he pulled one of the great, probably the greatest coup in Silicon Valley, what he did. It's just spectacular. But if we're looking over time, I think value is going to accrue to quote the application layer, what that ever looks like. You know, it's going to accrue up near the customer, near the money, near the business user.

• 30:01Speaker 01

  I agree. How do you think the AI wave is different than internet or mobile?

• 30:07Speaker 02

  Uh, I thought of everything else being tools to make us more productive. Meaning, we all became networked and we all became network and mobile. I view the AI wave as the industrial, the revolution 2.0. I think this is much, much larger. I remember thinking, boy, we have just seen the biggest market caps five years ago. Why is it? Because it was connectivity that created this revenue growth. Never imagined that there was this thing, it was going to be much bigger than connectivity and the mobility.

• 30:48Speaker 02

  It was a complete redoing of humanity, of how humanity exists, works, lives, enjoys. And I think AI is both going to be a wonderful thing for us and maybe even a kiss of death for us over the next 10, 20 years.

• 31:06Speaker 00

  Yeah, Tori agree with what Doug said. And I think one thing AI is very unique is that there are so many surprises. There are surprises of underlying capabilities that you've never seen before in internet or mobile age. If you take, you know, if you take the world view in 2015 and take a time machine to give that to someone in 2007, when Steve Jobs first introduced iPhone, I think someone can resonate with that. And then same for internet. I think people can kind of foresee what's coming. But for AI, I feel there's so many surprises as the underlying model gets better.

• 31:47Speaker 00

  There are things that even the authors for transformer paper will not have imagined some of the capability that just came after the large language models and that can change and surprise us. So I do think that, you know, a lot of the improvements is nonlinear. It's really from zero to one continued happening at the bottom layer. So I think that's something that make it even more exciting.

• 32:10Speaker 02

  You know, I'm going to remind you of something. In March of 2022, which now sounds like an eternity, it was my last annual meeting where we meet with all the investors. And it was a goodbye kind of thing, you know, where I presented performance and everything. And I had a slide that talked about all the waves back from the chip wave to the seven to the systems wave to the land slash one wave to internet to mobile. And the next box, a short three and a half years ago, was a question mark. We did not know as a partnership and we are as advanced as anybody. We are, we are the bleeding edge investor, right?

• 32:54Speaker 02

  And seed, we did not see the wave coming. And this wave has been a tsunami and I don't think there's, there's any end in sight.

• 33:03Speaker 01

  Thank you. Thank you for sharing those insights. Do you want to talk about Cresta's technical stack or should we, should we bug Ping on that?

• 33:10Speaker 02

  A lot. Well, in fact, I'm going to have to go in a few minutes because I'm in a process of recoding some of the.

• 33:17Speaker 01

  Are you vibes coding the?

• 33:18Speaker 02

  Yes, yes, yes, yes. I'm vibes coding everything.

• 33:21Speaker 01

  Ping, tell us about the tech stack.

• 33:22Speaker 00

  Yeah. So we have a pretty broad surface or product and I can maybe talk about the voice AI agent. We, we streaming end to end audio, bidirectional. And we orchestrate multiple different models and there are speech to text model. And then noise cancellation model to improve the audio. There are models that detect the terms and the speech activities and to handle interruptions. And then of course, there's a foundation model and to handle the conversation. And you know, the other side is the TTS text generation model, right? And then in parallel, we also run multiple smaller models to do guardrail checking and to make sure that nothing has gone crazy.

• 34:06Speaker 00

  And as well as those models will do company's specific kind of checks. For example, never give out tax advice or never give out financial promises, things like that, right? And then that's the runtime of voice AI agent. Design time, there are components like running large scale simulation to really stress test the AI agent to cover all the edge cases. There's task case management components. And similarly, if you think about our voice AI assistant. So it's also streaming audio, but again, so there's a lot of similarity between the infrastructure. But it's not bi-directional, right? It's one direction.

• 34:49Speaker 00

  And in listening to the call and then understand what's actually happening in the call with two humans, right? And then orchestrating ten plus more models, actually. And, and you know, in fact, we, similar, similar to Vertex, you know, AutoML, we have a platform that can allow customers to build their custom models and to detect interesting events in the conversation. So, and then marry that with workflows. And people use that to, and, and to detect fraud for, even use to detect fraud, call center fraud, and to train agents to how to handle objections. There are so many use cases that now with that tool we call Opera, they can express and trigger workflows.

• 35:29Speaker 00

  And underneath is teacher-student distillation to distill into very small models that we can run in real time and to understand two human conversations.

• 35:37Speaker 01

  What's the latency when I talk to, to one of your agents?

• 35:41Speaker 00

  So it's around below eight hundred milliseconds.

• 35:45Speaker 01

  Wow, so it feels like talking to a human?

• 35:46Speaker 00

  Yes.

• 35:47Speaker 01

  Huh.

• 35:47Speaker 00

  Yes.

• 35:48Speaker 01

  So you're running all these models in, in your real time then?

• 35:50Speaker 00

  Yes.

• 35:51Speaker 01

  Are you running open source models or are you running, you know, Eleven Labs and the equivalent?

• 35:55Speaker 00

  So across the platform, there are twenty different models, some are open source and fine, fine cubes. There are small models that, for example, we only do chat or email for human agents. And we auto-complete their sentences and type ahead. Those are very, very small models. And for TTS, we, we, yes, we use Eleven Lab. There's a great, they're a great partner. We also use other vendors and we constantly compare the performance.

• 36:20Speaker 01

  Really cool. And then the actual meat of the conversation, though, the, the, the dialogue or the conversational flow. How do you how do you control that in a way that's not so rigid? That it's like the IVR systems of yesterday, but not so free form that, you know, customers can go crazy and get their refunds on airline tickets and, you know, have the bots say crazy things and embarrass the customers. Like, how do you how do you control the flow and get the both of the best of both worlds?

• 36:45Speaker 00

  Yeah, so it's really just how you train humans. You give them the specification about what's the goal and these are the tools. And then have the that's the beauty of large language models to handle those messy kind of workflows. So there's a lot of discussion about what's workflow, what's agentic. Workflow is anything you can write it down in code that's step by step. That's workflow and car wash. Car wash is actually workflow. If you think about Oboba tea, milk tea, you know, those are physical workflows, but they cannot do other things. For for human conversation, it's very messy. It's nonlinear, right? So it's like, you know, so that's how the agentic workflow come in. That's where L M is really good at. And and then on top of those, so you want to determine NISM, right?

• 37:29Speaker 00

  And that's how we're introduced the testing, the simulation, and then the guardrails to make sure that whenever you have a change in any part of a system, the behavior is still expected.

• 37:40Speaker 01

  Do you tune your customers' models to? Because because you also have this agent assist product. So you're in the flow of all these customer conversations. Do you tune the agent to that training data? Or is it completely net new forward deployed engineers on site mapping out conversations?

• 37:57Speaker 00

  Yeah, so we have a tool that can map from, you know, what's actually in the human conversation to extract the blueprint of the conversation. Right? So, you know, I think the beauty of that, again, is to discover a lot of unknown unknowns. So there are a lot of topics and there's a lot of things that reason people call in, you may not even know, that may actually contain the call volume, a very large call volume. And then, once you have that, you can now look deeper and you can use L M to do all these analysis and extract what are 57 different ways that people express the same intent? And what are the different ways that the call flow will go, right? And then we can summarize and extract that, right? So all these are building the product. Products and then they in fact the tooling gets better, the forward deployed engineers will just be a lot more efficient.

• 38:42Speaker 00

  And then there are also other ways we use the human side of conversations. For example, uh we extract the model for the visitors. So that's how you build your simulation and the simulation is a huge part of improving the AI agents. And we believe that having access to exactly how your real customer humans come in and describe ways and in different ways, sometimes it's very messy. You can extract the model and then to better simulation on your AI agent as well.

• 39:09Speaker 01

  And then what methods do you use to make L L M's really bespoke for a customer environments like is it? Rag, is it prompt engineering, is it fine tuning, is it all of the above reinforcement learning? Like, what are you most optimistic on in terms of techniques?

• 39:22Speaker 00

  Yeah, so we use um almost everything and to um so definitely prompting and then um um rag. And for for those simpler agents, but we're still exploring um, you know, by looking at the human behavior and then the outcomes. How do you use RL to improve this end to end performance? And but you know, for AI agent by itself, I think the foundation model itself is already pretty, pretty good. You just need to get the best out of it, um at least for a digital channel for chat. But for uh other use cases, uh there's a lot of opportunity to fine tune the models and to make them um. You know, for tasks like summarization, for tasks like um auto completion sentences and that kind of stuff.

• 40:05Speaker 00

  I feel like um there's a lot of room to extract from uh the fine tuning open source models.

• 40:10Speaker 01

  Yeah. What goes into building a successful flashy demo versus production ready AI systems?

• 40:17Speaker 00

  Yeah, so that's a really interesting question because I think one thing unique about AI. Is that there's a huge gap between the demo and production. And on one end of spectrum, you have rocket launches, the rocket launches, the demo is the production. And the production is the demo, you cannot fake it, right? But for AI, it's it's a little different. And um, you know, I can just give you example, right? So auto summary. Auto summaries feels like a commodity capability that, you know, anyone can use chat G P T to create auto summary. But in order to deploy in some call centers that today that twenty thousand people across multiple continents um call centers. And the challenge, huge list of challenges, you know, first, how do you get the real time audio?

• 41:00Speaker 00

  In the demo, you can demo very easy on Twitter or in the cloud. But remember, fifty percent of the conversation happened on premise. Right? And then, and then sometime, you know, the, how they access that will cost you a lot of money as well. And then how do you go around that? And then in the real call, twenty thousand agent calls, there are transfers. There are a lot of transfers. And then there are third parties, third callers that come in as healthcare specialists. All that need to be transcribed and summarized. And sometimes the conversation goes so long, how do you handle like three hour, four hour calls that go beyond the contact window? Right? And, and then things like, you know, is there background noise? And then things, things like, you know, for different core reasons, there can be different templates.

• 41:44Speaker 00

  You really, really want to extract these type of information. You cannot miss that. How do you make sure you do that almost a hundred percent of the time? And by the way, how do you handle PIIs? And then you cannot have the personalized information, you know, unrest. And then by the way, how do you handle, you know, data residency? If you're talking to a multi-continental multinational bank or our healthcare provider. So all these have become additional requirements that make something that would feel very commoditized, like, you know, out of summary, become very, very much harder to do in actually contact, contact center.

• 42:23Speaker 02

  And that's why you need a product-minded chief executive officer for one of these companies.

• 42:28Speaker 01

  Absolutely. And this, this is also why all the pain and all the value is in the last mile. This is why the value is in the application layer.

• 42:35Speaker 02

  That's right.

• 42:35Speaker 00

  Yeah, I tend to agree with that.

• 42:37Speaker 01

  Yeah. Talk to us about the future. What happens if everything goes right? What does that mean for Cresta and what does that mean for the world?

• 42:43Speaker 00

  I think that AI will just like any technology before it. Like electricity, it would disappear. It would disappear into workflows. And I think, you know, twenty, thirty years later, no one will realize that they may actually talking to AI or is a human assisted by AI. There's one thing I'm really excited about is that today, if you think about the business, right? And they feel like multiple personality to the customer. So in the sales phase. Or the marketing phase, they really, really want to talk to you. They call you very, very aggressively. And once you sign up and become a customer, you're dealing with entire different personality, right? And you're dealing with service departments and they use,

• 43:26Speaker 00

  tend to use the terms like tier defense, deflection, and to just handle, you know, to refer to the exact person that they were calling just a few days ago. And then even if you have a long conversation on the customer support line and share a lot of feedback, two weeks later, another departments will come in, what's your feedback? How about you feel of this survey? You know, to, to our business. Feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, long-going conversation throughout the entire customer journey. And LLM is a perfect tool to do that. And that will really bring the level of personalization, the level of customer experience that wasn't possible before.

• 44:07Speaker 01

  Yeah. The point that really stuck with me that you said earlier was about kind of the scarcity versus the abundance mindset. And, you know, how much can business to customer communications really evolve? And, you know, app experiences really evolve? If you take the abundance mindset to bringing LLMs into, into this field. Thank you, Ping. Thank you, Doug, for joining us today. I love this conversation.

• 44:27Speaker 02

  Thank you. Thank you for having us.