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

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 dealing with entirely different personality. Right. And you're dealing with service departments 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.

Hi and welcome to Training Data. Today we're joined by Cresta CEO Peng Wu 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. Peng 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 accrue 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 Uglioni as well on your board.

My pleasure. Thank you.

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 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 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 gardeners 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 fall into somewhere in the middle. And in fact we got this, asked this question two years ago when GPT4 first came out. And a lot of people will say that maybe in two or three years there will no longer be humans. In the context of. 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.

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 Cobalt being used in America in a banking system. So to me it's not really what percent to me is the speed at 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 three years, it means something else. So the end number is not the relevant metric for me, to me is the speed of adoption.

Hmm, 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, when first talk about contact center, a lot of people will naturally think about call center. There's a lot of humans sitting there listening, answering calls. But contact center really is a broader category that including the omnichannel 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 17 to 20 million agents. Human agents actually work in the contact center. For the software market it's 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.

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

Oh, so yeah. So customer call in, there are all kind of reasons they call in, right? Complaints or fixing the issue. But also I think a lot of people may not realize there are probably a quarter of the contact center. 25% is actually revenue generating, including selling stuff or collecting money or 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 of Aya 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 called Ping Wu 15 years ago to be attracted with 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?

First of all, 15 years ago I didn't even realize there'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 the 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 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 center. So if you 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 center is massive. It's on average 35 to 40% in some cases. During COVID some company have more than 100% turnover.

They just get yelled at all day long.

Right.

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

So you were working on this at Google Ten years ago, I would 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.

Yeah, so that's a great question. Even long before that there's technology called IVR that you press 1, 2, 3 for different routes and for different call 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 start in contact center AI at Google, it's even before BERT actually it's before transformers mainly using AI or at that time using AI to do classification, intent classification and entity extraction using pre transformer models. 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 LLMs entirely changed the whole thing. Not only the conversation experience on automation side, but also just you can understand conversation in the way that never was able before.

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 customers are very happy to be talking to an LLM based agent? Or how has the evolution of technology changed the customer experience?

Yeah, I think the way we like to think about it is really from the first principle, right. And there are a lot of the conversations shouldn't even happen in our view. And the fact that it happens because the customer is not happy. I think the solution for that is to use the AI to really understand, to bring 100% visibility into all 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 that usually reflects some process broken or website updates that freak out people or firmware updates that bring down network and all that kind of stuff. So you need to fix that first and first avoid interaction if it's not necessary. And beyond that, I do feel like their AI can automate a lot of interactions that no one wants to have. 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 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.

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 on my way here. So I 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 in fact, I would encourage people to look up some of the companies in a search for their customer service. 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 customer service? So I think that that time probably hasn't arrived fully yet. It depends on what kind of interactions.

Again, 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 and 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 sort of optimistic on the side of 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 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. Not investment advice, not investment advice, but it is clear that the agents, by definition, and a lot of which don't even reside in America, there's a language component. 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. 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 the agent, the digital agent versus the human agent question.

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 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 percent of conversation that's ready to be automated and 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 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 mutually exclusive. And as long as we feel like customer not ready to say that we just need to turn on our cloud center today and then go full AI we feel like there's a long depending again what kind of business and what kind of technical the IT infrastructure. So I think the journey will probably take different time frame. But our goal is really meet the customer where they are.

So Crest 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 autonomous agents. Where do you think most customers are today? Are they ready to go full force? Just put the agent on my website, let it go crazy. Are they experimenting with that? Where is the customer today?

It depends on the customer. If you and I start an E bike store today on Shopify and we can automate 100% I'm sure because it really depends on how complex is your product. It can be ordered 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 human do in the contact center today is optimized for humans. So those system record or the system action ticketing system, these have been around for decades. A lot of them just simply do not have APIs. So the only thing that can make changes is through a graphic User interface that optimize for humans and without a real time API just again these are not AI problems. We believe that these are the opportunities that we work with our customer to develop those real time APIs. And so that's why we feel like those transformation depends on the nature of the business. Would take different time frame.

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, you're the agent assist, you actually get full data of the conversation whether it's, you know, voice, whether it's, whether it's conversational based digitally and you that can become a training base for customers to automate more and more of their conversations over to the agent over time.

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 broadened 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're seeing on the screen. That's super important 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 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 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 make sure we invested. We not only we double down on the operational system for agent assist, but we also develop the sex appeal product because that's what a lot of customers want to talk about day one.

Yeah. 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, 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 onto the same big picture vision?

Yeah. So again, in order to really deliver value in the context and the transformation, it's not just the models, it's just not a model. Model is a bunch of weights and a 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 you don't have much opportunity to accumulate value. 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 where majority of the agencies 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 8 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. It 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. So that's why we believe is our strategy is meeting customer where they are and then drive value on day one.

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 kind of contact center AI space right now?

Yeah, for overhyped I think it's 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 XYZ and then call me back when you get it done? And then can you have that super multi language AI agent to have those conversations or There are so many interactions that today you just cannot happen just simply because you do not have the staff. 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 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, the consumer aggregators, and then a lot of things that consumer 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?

We're not coming 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 the movie a few times.

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

I was trying to say it nicely.

Yes.

How is building a company right now? You're seeing this live with ping. How is building a company in AI different from your last few decades of building legendary companies?

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 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, wherever that's broken. It looks like a bad sales guy, a bad BP of sales, but you have to get that right. And so I think the business fundamentals 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 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 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 that. And maybe the answer is funding. Well, 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. And so in my mind is 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. 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 time. Baloney. Give us please a revenue ramp that's linear so we can m 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? That's always been the mantra. And all the boards that I've served on. AI is not different.

What does CRESA 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 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. 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. 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. 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 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 looked at these apps and Jim Getzen, our former partner, said, how do you make money from a $19 app? I had to build a multi billion dollar company. Never thinking of Airbnb, never thinking of Doordash. A year or two later, we saw AB 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 and 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 2021 where you're down to niche verticals. At the front end of the market, you start seeing the model revenue momentum, you lean in and you hold your nose on price.

I love that. As you think about where value accrues in the market, there's compute, there's other infrastructure, there's the foundation models as the application layer. Where do you think value accrues? Up, 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 margin of this. Well, but that's. 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. 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. How do you think the AI wave is different than Internet or mobile?

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 I 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 that was going to be much bigger than connectivity and the 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, 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. 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 continue to surprise us. So I do think that a lot of the improvements is not lean year. 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.

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 present the performance and everything. And I had a slide that talked about all the waves back from the chip wave to the systems 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 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 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 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.

Are you vibes coding the Cresta?

Yes, yes, yes. I'm vice coding everything Ping.

Tell us about the tech stack.

Yeah, so we have a pretty broad surface or product and I can maybe talk about the voice AI agent. We streaming end to end audio bidirectional and we orchestrate multiple different models. 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 the other side is the TTS text generation model. And then in parallel we also run multiple smaller models to do guardrail checking and to make sure that nothing is going crazy. And as well as those models will do company specific kind of checks, for example never give out text advice or never give up financial promises, things like that. Right. And then that's the runtime of voice AI agent and also there's 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 test 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 now bidirectional. It's one direction and in listening to the call and then understand what's actually happening in the call with two humans and then orchestrating 10 plus more models. Actually. And in fact, similar to Vertex Automl, we have a platform that can allow customers to build their custom models to detect interesting events in the conversation and then marry that with workflows. And people use that and to detect fraud for even used to detect fraud, call center fraud, to train agents to how to handle objections. There are so many use cases that now with that tool we call Operite, they can express and trigger workflows. And underneath is teacher student distillation to distill into really small models that we can run in real time. And to understand two human conversations.

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

So it's around below 800 milliseconds.

Wow. So it feels like talking to a human.

Yes. Huh. Yeah.

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

Yes.

Are you running open source models or are you running, you know, 11 labs in the equivalent?

So across the platform there are 20 different models. Some are open source, some fine tunes that are small models that for example, we only do chat or email for human agents and we autocomplete their sentences and type ahead. Those are very, very small models. And for TTS, yes, we use 11 lab. They're a great partner. We also use other vendors and we constantly compare the performance.

Really cool. And then the actual meat of the conversation though, the dialogue or the conversational flow, 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 freeform that customers can go crazy and get their refunds on airline tickets and have the bots say crazy things and embarrass the customers. How do you control the flow and get the both of them best 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 carwash. Carwash is actually workflow. If you think about boba tea, milk tea, those are physical workflows, but they cannot do other things for human conversation. It's very messy, it's nonlinear. Right. So it's like that's how the agentic workflow come in. That's where LLM is really good at. And then on top of those, you want to determine nism. Right. And that's how we introduce the testing, the simulation and then the guardrails to make sure that whenever you have a change in any part of the system, the behavior is still expected.

Do you tune your customers models to 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 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 ALM 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. And then we can summarize and extract that. So all these are building the products. And then in fact the tooling gets better. The full 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, 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 agent. And we believe that having access to exactly how your real customer humans come in and describe ways in different ways. Sometimes it's very messy. You can extract the model and then to better simulation on your AI agent as well.

And then what methods do you use to make LLMs 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? What are you most optimistic on in terms of techniques?

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

Open source models what goes into building a successful fleshy 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 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 little different. And I can just give you example, right? So auto summary. Auto summary feels like a commodity capability that Anyone can use ChatGPT to create auto summary. But in order to deploy in some call centers today that 20,000 people across multiple continents call centers. And the challenge, huge list of challenges. First, how do you get the real time audio in the demo? You can demo very easily on Twilio in the cloud. But remember 50% of the conversation happened on premise, right? And then sometimes how do you access that will cost you a lot of money as well. And 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 third callers that come in that healthcare specialist, all that needs to be transcribed and summarized. And sometimes the conversation goes so long. How do you handle three hour, four hour calls that go beyond the contact window? And then things like is there background noise and things like for different core 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 identifier information unrest. And then by the way, how do you handle data residency if you're talking to multi continental multinational banks or our healthcare provider. So all these have become additional requirements that make something that would feel very commoditized like auto summary become very very much harder to do in actual contact center.

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

Absolutely. And 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.

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?

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 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 dealing with entirely different personality. Right. And you're dealing with service departments and they tend to use the terms like, 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 fill out this survey, 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 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.

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

Thank you, thank you for having us.