Transcription
Today, if you think about the business, they feel like they have multiple personalities to the customer. So, in the sales phase, they court you very, very aggressively. And once you sign up and become a customer, you're dealing with an entirely different personality, right? And you're dealing with service departments that feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, ongoing conversation throughout the entire customer journey, and LLMs are 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.
[Music]
Hi, and welcome to Training Data. Today, we're joined by Cresta CEO Ping Woo and Sequoia's Doug Leone, 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 Leone also shares his perspectives 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 occur in AI. Hint: it's in the application layer, in this gnarly last mile. Enjoy the show.
Ping, welcome to the show, and thank you for bringing me, bringing along our special guest Doug as well on your board.
My pleasure. Thank you.
Thank you both for joining. Uh, 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, you know, 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?
Yeah. So, we internally, we have spent a lot of time, uh, debating about this. Um, the reality is, I don't think anyone knows for sure. And if you ask, uh, depends on really what they're selling, and, um, you ask different people, and they give you different answers. And some people will say that 100% human will be gone in contact centers, and some Gartner's, um, research actually shows that, um, none of the Fortune 500, over the next five years, will have contact centers, uh, gone entirely humanless. So, you know, it's, the answer probably falls into somewhere, um, in, in the middle. And, um, in fact, we got this asked this question, uh, two years ago when GPT-4 first came out, and a lot of people will say that, um, maybe in two or three years, there will no longer be humans in the contact center. Um, so at that time, uh, our belief is that, you know, probably the transformation, especially for existing, for, for Fortune 500 companies, will probably take way longer than a lot of people think.
Doug, what do you think? What's your bet?
At the limit, it's 100%. But I'm mindful that there are still IBM mainframes and COBOL being used in America in a banking system. So, to me, it's not really what percent. To me, it's 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 Cresta. If it happens in 3 years, it means something else. So the N number is not the relevant metric for me. To me, it's the speed of adoption.
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?
Yeah. Um, when first talk about contact center, a lot of people will naturally think about call centers. A lot of humans sitting there, uh, listening, answering calls, right? Uh, contact center really is a broader, uh, category that includes the omnichannel interactions, from emails to digital chats, and, you know, on websites and in apps, and also including calls, of course. And the overall market is, uh, quite big. And there are, uh, historically, um, um, there are around 17 to 20 million, uh, agents, human agents, actually work in the contact center. For the software market, it's probably, uh, in the tens of billions. And, uh, for the AI market, according to some research, it will be in the high tens of billions of dollars.
And is the use case mostly, you know, customers calling in to complain? Customer support? Is that what these, these contact centers are mostly used for?
Oh, so, yeah, so customers call in, there are all kinds of reasons they call in, right? Complaints, fixing the issue. But also, I think a lot of people may not realize that there are probably a quarter of the contact center, 25%, is actually revenue generating. They're including, um, selling stuff, or collecting money, or, uh, retaining customers, and that kind of conversations. So, it's not 100% customer support.
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 30 years, you heard names like Avaya and God knows whoever else that's barely living in and out of bankruptcy. You go back 15 years, you see Genesis of the world. What caused a bright young engineer or Ping Woo 15 years ago to be attracted to this market? And 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 opportunity. But turning the clock back 10 years ago, what attracted you to this market?
Um, first of all, 15 years ago, I didn't even realize that's a long history of slow growth of the market. Otherwise, maybe I will think differently. And second, um, at that time, I just, um, do you remember there's a period of time there's a lot of excitement, the conversation AI technology, and especially around, um, consumer-facing speakers. And, um, at that time, um, people think that that would disrupt Google, that would become the entry point for all the consumer, uh, 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, issues that traditionally people get excited about, VCs get excited about. It's a massive market, a lot of humans working there, and it's in the middle between businesses and customers, right? And it's all interaction going through. And also, no one's happy in contact centers. 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 the agents, by the way, I think a lot of people may not realize the agent, the workforce attrition in contact centers is massive. It's on average, it's 35 to 40%. In some cases, during COVID, some companies have more than 100% turnover. That means
I just get yelled at all day long.
Right. So it's very high stress, and it's, it's not a very fun job. And, and also the business, um, 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, um, and technology to bring abundance. And then abundance is the answer, in my opinion, to solve all these issues.
So, you, you were working on this at Google 10 years ago. I, I imagine this was the small language model wave and the BERT days. Uh, was the technology ready at that point? And maybe walk us through the different waves of technology that have hit the contact center.
Yeah, so that's a great question. Even long before that, there's technology called IVR, and that you press one, two, three for different routes, and through, uh, for different call reasons. And then since then, there are innovations around the input, right? You can, instead of pressing, you can directly speak natural language. And that's with the advance of natural language, uh, processing and, um, TTS, and text-to-speech generation, that experience is getting better and better. For, um, when we first started in contact center AI at Google, um, it's even before BERT, actually. If it's before Transformers, uh, it's mainly using AI, or at that time, using AI to do classification, intent classification, and entity extraction, using pre-Transformer models. And, and then, but the conversation experience is still manually crafted, right? So, um, so that's the last generation of technology. And then after that, of course, the Transformer came along. But initially, it's also for classification purposes. Still, the experience is manually crafted. But then the LLMs entirely changed the whole thing, not only the conversation experience on the automation side, but also just you can understand conversation in a way that never was able before.
And what does that mean practically in terms of the rollout of this technology inside contact centers? Did it mean that, you know, 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 is the evolution of technology, uh, changed the customer experience?
Yeah. Um, I think the way we would like to think about it is, uh, really, um, from the first principles, right? And, you know, the, there are a lot of the conversations, um, shouldn't even happen. In, in our view, um, you know, the fact that it happens because customers are not happy. Um, I think the solution for that is to use the AI to really understand, to bring 100% visibility into all interactions 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 that usually reflects some process broken, um, or website updates that freak out people, or, you know, firmware updates that bring down the network, and all that kind of stuff. So, you need to fix that first, right? 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 wants to have those interactions. Those are what we call low-emotion, low-value interactions that should be self-served. And then on top of that, I do think that contact center AI will enable new interactions, um, that ones that you cannot afford to do that today. Um, so all these are improving customer experience.
Do you think end customers will ever prefer talking to an AI agent over a human agent? And have we reached that point yet?
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 never met anyone that has 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, um, you know, some of the companies and 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, um, 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.
I, I'm maybe too techno-optimistic or AGI-pilled here, but I've, I feel like I've seen some recordings now where, you know, the AI can be emotionally intelligent. It has infinite patience, right? It's not trying to hit some metric on time to resolution. Uh, and so, for example, if somebody calls in and they're having a really bad day, for example, your AI can be a lot more patient and empathetic, uh, than a human agent even could. And so, I'm, I'm, I'm sort of optimistic on the side of the, the bots here.
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, 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. It is clear.
Not investment advice.
Not investment advice, but it is clear that that the agents, by definition, and a lot of which don't even reside in America. The, 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.
Bitcoin as digital gold is a really interesting analogy to, to the agent, the digital agent versus the human agent question.
Yeah. Um, from our perspective, we really want to meet customers where they are today. So, uh, unlike self-driving cars, uh, you really have to automate the entire thing 100% of the time, otherwise you do not have the economic impact. For contact centers, we, we find it's very unique. Is that the work is very divisible? So, first, the conversation is, um, you know, those are every conversation is an independent unit. And you can automate X% of conversations that are ready to be automated. And for, uh, for a lot of reasons we can get into details. And then for the remaining ones, um, 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. 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, um, you know, mutually exclusive. And as long as we feel like customers are not ready to say that, "We just need to turn on our call center today and then go full AI," we feel like there's a long, you know, depending again, come what kind of business and what kind of, um, you know, technical, um, you know, the IT infrastructure, um, so I think the journey will probably take different time frames. But our goal is really to meet the customer where they are.
Yeah. So Cresta 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. Um, 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 experimenting with that? Where is the customer today?
It depends on the customer. If you and I started an e-bike store today on Shopify, and we can automate 100%, I'm sure. Because it really depends on how complex is our product. Um, it can be an order of magnitude difference between like a simple product like an 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, 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, um, before you actually enter the contact center, you will feel like, "Oh, this should be easily, very easy to automate." But the reality is, um, a lot of those, um, things that humans do in the contact center today are is optimized for humans. So those system records, or the system action ticketing systems, these have 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 graphical user interface that's 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 opportunities that we work with our customers to develop those real-time APIs. And then so that's why we feel like those transformations depend on the nature of the business would take different time frames.
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? Uh, for you guys, because you are the call center assist, you're the agent assist, you actually get full data of the conversation, whether it's, you know, voice, whether it's, uh, whether it's conversational based digitally. And you, that can become a training base for customers to automate more and more of their, uh, conversations over to the agent over time.
Yes, 100%. And in fact, when we first, my first, um, the journey when I first started, uh, seven, eight years ago, it's really automation only. I really believed it should be automation. Fast forward, we ran into all kinds of real deployments, and then we really actually broadened my own horizon. Then I believe that in order to really do the best possible automation, it's counterintuitively, 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're seeing on the screen. That's super important, um, to actually build the best automation possible.
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 our business are 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 have 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 made sure we invested. We not only we double down on the operational system for agent assist, but we also developed the sex appeal product because that, that's what a lot of customers want to talk about day one.
Yeah. And another aspect of it is really just tied to the, uh, the point I made earlier, um, 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, you know, 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. Love that. Uh, customer support is one of those, you know, canonical examples of where people think large language models will be most transformative. And, you know, it's almost a consensus category for venture startups at this point. Uh, how do you compete? What is it like to, to compete when everyone has access to the same LLMs and is, you know, latching on to the same big picture vision?
Yes. So, um, again, um, in order to really deliver value in the contact center transformation, it's not just the models. It's just not a model. A model is a bunch of weights and the data, and, you know, itself is not going to provide 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 I would argue probably, um, you don't have much, um, opportunity to create 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 the case for contact centers. And, um, where the majority of the agents are still on-premise, and where a lot of, there are so many, look, on average, agents in the Fortune 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 systems may not even talk to each other, you know, depends on where you book the flights, or depends on where you book the hotel, they may need to log into different systems, right? So that's the reality we're talking about. Um, so that's why you, we believe is our strategy is meeting customers where they are, and, and then drive value on day one.
Vertical integration from the steak to the sizzle. That's how you win. Um, what do you think is overhyped and what's underhyped in the kind of contact center AI space right now?
Yeah. For overhyped, I think is the mindset of scarcity. Um, is the job displacement. I think in the short term, it's probably a little overhyped. And what's underhyped is the mindset of abundance. Um, you think about new experiences that, um, AI can enable. You know, for example, can you talk to a website? Can you directly talk to the app? And can you turn a synchronous interaction into an asynchronous interaction? Can you talk to the, um, airline app and say that, "I want you to do this XYZ, and then call me back when you get it done"? Right? And then can you, uh, have that super, um, um, you know, multi-language AI agent to have those conversations? Or there are so many interactions that today you just cannot happen, uh, just simply because you do not have the staff, right? And then, and then the other thing, actually, I feel is really underhyped is people really seem 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, uh, over the next few years, you will probably see a race to getting the AI assistant on the consumer aggregators, and, um, and then a lot of things that consumers probably will dedicate to the AI assistant, including making the phone calls. So I think that's maybe an interesting thing to pay attention to.
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?
Um, we're no comment on that.
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, seen the movie a few times.
Means I'm old.
That's what you just said.
I, I was trying to say it nicely. Yes.
Um, how is building a company right now? You're seeing this live with Ping. How's building a company in AI different from, from your, you know, your last few decades of building legendary companies?
Uh, it's not very different. Uh, what I mean by that is you need a terrific founder. And we'll talk about the Cresta situation a little later, hopefully. Uh, you need to plug in world-class engineers at the very start. Unless you start with A+es, 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 managers 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. 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, are very similar.
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, 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?
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 co's job is to remove those rocks. So when you give me next year's plan, I don't care that's a 150% net new ARR growth. I want to know why the plan is the plan. And I want to challenge you why it's not 3x that. And maybe the answer is funding. Well, we can get funding in this market. Maybe the answer is, uh, 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. And so in my mind, it's forcing, it's forcing the understanding that these companies are capable of doing things 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 going to happen. And 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 got to train them all the same." Baloney. Give us, please, a revenue ramp that's linear so we can mid-course corrections up and down, and let's not be stuck by these numbers. We have 10 fingers, 100% growth. That's all. How fast can we possibly grow? That's always been the mantra in all the boards that, that I've served on. AI is not different.
Uh, 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?
So, well, first of all, it has to continue to develop product. It has to continue to put one foot, 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. Staying away from these quote, "very experienced people" that start feeling a bit like suits and administrators. Point one, that's the most important thing. Uh, but the other thing, uh, that Cresta has to do, it has to up its game in marketing. There's a lot of companies, I use the word, the sizzle. There's a lot of companies with a lot of sizzle and no steak. We have a whole bunch of steak. We're a modern company. We're best-in-class in one category. We're going to 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.
Wonderful. Well, glad you're on the podcast then. Um, maybe stepping back, Doug, you've seen some market cycles. Are we in an AI bubble?
The word bubble implies you invest money in and you lose money. 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 with Netscape going public in '95, two great companies being built in the late '90s, Google and Amazon, a few other 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 looked at these apps, and Jim Gats, my former partner, said, "How do you make money from a $19 app? How do you build a multi-billion dollar company?" Never thinking of Airbnb, never thinking of DoorDash. 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 has shrunk even further. I think AI is here. I think you have to invest. I think you're at 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 the front end of the market. I'm not talking about the SAS market in 2021 where where you're down to niche verticals. At the front end of the market, you start seeing the monocum more revenue momentum. You lean in and you hold your nose on price.
I love that. As you think about where value occurs 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 occurs?
Up.
It always accrues up. Just look at the gross margins as you move up markets. Look at the gross margins of chip companies. Look at the gross margins of the system companies. Look at the gross margins of the. Well, but that's and and 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. Uh, 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.
I agree. Um, how do you think the AI wave is different than internet or mobile?
Uh, I thought of everything else being tools to make us more productive, meaning we all became networked and we all became networked and mobile. I view the AI wave as the industrial 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 that was going to be much bigger than connectivity and mobility. 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.
Yeah, totally 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 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. Um, I think people can kind of foresee what's coming. But for AI, I feel there are so many surprises as the underlying model gets better. There are things that even the authors of the Transformer paper would not have imagined some of the capabilities that just came after the large language models, and that continue to surprise us. So I do think that, you know, a lot of the improvements is non-linear. It's really from zero to one, continuing happening at the bottom layer. So I think that's something that makes it even more exciting. You know, I'm going to remind you something. In, uh, 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 present the performance and everything. And I had a slide that talked about all the waves back from the chip wave to the system wave to the LAN/WAN 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, in 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.
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?
I'd like to. 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 some.
Are you live coding the the?
Yes. Yes. Yes. I'm live coding everything.
Uh, Ping, tell us about the tech stack.
Yeah. So we have a pretty broad, uh, surface area of product. And, um, I can maybe talk about the voice AI agent. We, um, we stream end-to-end, uh, audio, bidirectional. And we orchestrate multiple different models. And there are speech-to-text models, um, and then noise cancellation models to improve the audio. There are models that, uh, detect the turns and the speech activities and to handle interruptions. And then, of course, there's a foundation model, um, to handle the conversation. And, you know, the other side is the TTS, text-to-speech generation model, right? And then in parallel, we also run multiple smaller models to do guardrail checking and to make sure that nothing goes crazy. And as well as, um, those models will do company-specific, uh, 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 voice AI agent. And also, there's design time. There are, um, components like running large, uh, scale simulations to really stress test, uh, the AI agent to cover all the edge cases. There's test case management, uh, components. And similarly, um, if you think about our voice AI assistant, so it's also streaming, um, audio, but again, so there's a lot of similarity between the infrastructure, but it's not bidirectional, right? It's one direction, and in listening to the call and then understanding what's actually happening in the call with two humans, right? And then orchestrating 10 plus more models, actually. And, and, you know, in fact, similar, similar to Vertex, you know, AutoML, we have a platform that can allow customers to build their custom models to detect interesting events, uh, in the conversation. So, and then marry that with with workflows. And people use that to, and to detect fraud, for, even used to detect fraud call center fraud, to train agents on how to handle objections. There are so many use cases that now with that, um, tool, we call Opera, they can express and trigger workflows. And, um, underneath is teacher-student distillation to distill into really small models that we can run in real time, and to understand to human conversations.
What's the latency when I talk to, to one of your agents?
Um, so it's around below 800 milliseconds.
Wow. So it feels like talking to a human.
Yes.
Huh. Yeah.
So, so you're running all these models in, in near real time then?
Yes.
Are you running open-source models, or are you running, you know, 11 Labs and the equivalent?
So, across the platform, there are 20 different models. Some are open source and fine-tuned. There are small models that, for example, we only do chat or email, uh, for human agents, and we autocomplete their sentences, type ahead. Those are very, very small models. And for TTS, we, um, we, yes, we use 11 Labs. They're a great, they're a great partner. We also use, um, other vendors, and we constantly compare, um, the performance.
Really cool. And then the actual meat of the conversation, though, 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 both worlds?
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 a boba tea, milk tea, um, you know, those are physical workflows, but they cannot do other things for for human conversation. It's very messy. It's non-linear, right? So it's like, you know, so that's how the agentic workflow comes in. That's where LLM is really good at. And, and then on top of those, you want determinism, right? And that's how we're introduce the testing, the simulation, and then the guardrails to make sure that whenever you have a change in any part of the system, um, the behavior is still expected.
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?
Yeah. So we have a tool that can map from, um, 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 LLM to do all these analysis and extract what are 577 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 products. And then they, in fact, the tooling gets better, the forward-deployed engineers will just be a lot more efficient. And then there are also other ways we use the human side of conversations. For example, uh, we extract the model for the visitor. So that's how you build your simulation. And the simulation is a huge part of improving the AI agent. And we believe that having access to exactly how your real customer humans come in and describe ways and in different ways, sometimes very messy, you can extract a model and then to better simulate on your AI agent as well.
And then what methods do you use to make LLM really bespoke for 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?
Yeah. So we use, um, almost everything, um, to, so definitely prompting, and then, um, and 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? 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, 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 of sentences, and that kind of stuff. I feel like, um, there's a lot of room to extract from, uh, the fine-tuning open-source models.
Yeah. What goes into building a successful flashy demo versus production-ready AI systems?
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 the spectrum, you have rocket launches. The rocket launch, 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 an example, right? So, auto-summary. Auto-summary feels like a commodity capability that, you know, anyone can use to create auto-summary. But in order to deploy in some call centers that today that 20,000 people across multiple continents, call centers, and the challenges, huge list of challenges. You know, first, how do you get the real-time audio? In the demo, you can, you know, demo very easily on, on Twilio in the cloud. But remember, 50% of the conversation happens on-premise, right? And then, and then sometimes, you know, how to 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, 20,000 agent calls, there are transfers. There are a lot of transfers. And then there are third-party callers that come in, that's 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 like, you know, for different call reasons, there can be different templates. You really, really want to extract these type of information. You cannot miss that. How do you make sure you do that almost 100% of the time? And by the way, how do you handle PIIs? And then you cannot have the personal identifying information, um, you know, on rest. And then by the way, how do you handle, um, you know, data residency if you're talking to a multi-continental, multinational bank or, uh, our healthcare provider? So all these become additional requirements that make, um, something that would feel very commoditized, like, um, you know, auto-summary, become very, very much harder to do, um, in actually contact contact center.
And that's why you need a product-minded chief executive officer for one of these companies.
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.
That's right.
Yeah, I tend to agree with that.
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?
Um, I think that AI will, just like any technology before it, like electricity, it will disappear. It will disappear into workflows. And I think, you know, 20, 30 years later, no one will realize that they may actually be 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 they have most multiple personalities to the customer. So in the sales phase, uh, or the marketing phase, they really, really want to talk to you. They court you very, very aggressively. And once you sign up and become a customer, you're dealing with an entire different personality, right? And you're dealing with service departments, and they tend to use the terms like, "tier defense," "deflection," to just handle, you know, to refer to the exact person that they were courting 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 department will come in, "What's your feedback? How about you fill out this survey?" Um, you know, to our business, feel like these are really disconnected, right? And I do feel like AI agents can make this entire experience a continuous, uh, ongoing 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.
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 bring into into this field.
Thank you, Ping. Thank you, Doug, for joining us today. I love this conversation.
Thank you. Thank you for having us.
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