Tales from the Five Percent: Tangible AI Success, w/ Tuio's Juan Garcia

(upbeat music) Welcome to Raw Data with Rob Colley. Real talk about AI and data for business impact. And now CEO and founder of P3 Adaptive, your host, Rob Colley. - Hello friends. Every week here at Raw Data, we receive multiple unsolicited emails pitching us on having a new guest on the show. And most of the time, the suggested guest in the email is a flat no for us. And there are several flavors of flat no. One group is the scammy influencers. You know, the kind of people you see on LinkedIn with deliberately provocative content that proves to you to be like lacking in substance or honesty or both. Nope. Then there's another group, people who are so clearly selling a product and only selling a product. That there's no way we'd be able to have a conversation that's useful or enlightening for us or the audience. Also, nope. But every now and then, and I mean like once or twice a year tops, there's a ringer. Someone truly interesting. And those rare exceptions, like today's guest, Juan Garcia, are the reason why we keep reading those emails every week. Because the occasional diamond makes it worth sorting through the otherwise steady stream of noise. Juan is co-founder of an insurance startup in Spain called Tuyo. Now, they didn't set out to be an AI-powered insurance company. They set out instead to be a data-driven and modern insurance company. But then, after chat GPT hit the scene, they added AI into their operation. So they're not one of those, ooh, let's apply AI to industry X pop-up companies. They were already in business when AI landed on the scene. And then they chose to take advantage of it much sooner than most. That's important, because the before AI and after AI of the whole thing makes for a more relatable story for our most companies find themselves today, in that before AI state. And because Tuyo's story is much more tangible than those we're gonna apply AI to industry X companies. Those stories for those companies tend to be breathless and shallow and fall into the, we're not having them on the show category. But that's not Tuyo. They're an existing company with an existing business plan that decided to take advantage of AI along the way. A couple more things that stood out for me about this conversation. One, we're still hearing about the 95% failure rate for AI projects. That number came up again last week in the news from the Davos summit. And if we believe that 95% failure rate is still true, it's increasingly important to hear from the other 5%, the ones who are succeeding so that we can learn how to emulate their approaches. And two, we had never met Juan before and never once spoken with him before we sat down to record. And yet, it was like we were reading from each other's playbooks. Tuyo's approach to AI matches our approach to AI. And I think that's just what happens over time. A couple years from now, the AI playbooks were following at P3 and Tuyo. We'll just be considered the best practices everywhere. Then I can be called the P3 method or the Tuyo method. It's just gonna be called the method. We're not there yet in the world, but it's coming. In the meantime, all over the world, there's a small minority of companies independently cracking the riddle. And I don't think there's a lot of different ways to crack it. I think it's closer to one way. Sure, there'll be all kinds of differences in details, but a handful of specific themes are going to be found repeating themselves in every success story. Those are the themes we talked about on the show today. Those are the themes I'm capturing in the book that I'm writing. And rather than feeling threatened by Juan and his company having figured out the same sorts of things that we have, I felt all kinds of validation and kinship. We could have talked to Juan all day. And I think we're gonna have to come up with a reason to bring him back. Alrighty then, let's get into it. Welcome to the show, Juan Garcia. How are you today and how is beautiful, beautiful Spain? - Well, I have to give you a bit of a heads up. I am not from the part of Spain that's really sunny all the time. I'm actually from the north. So we have this beautiful mountain range that grabs all the clouds and it rains. I wouldn't say all the time, but it rains quite happily. So you can think about where I'm from. That's kind of like the Pacific Northwest. - The Seattle of Spain, yeah. - Yeah, yeah, yeah. Okay, so Juan, we're really excited to talk to you today. Tell the audience really quickly. What your job title is, what your company is, and what you're up to. - My name is Juan Garcia. I am Juan Francisco Fujillo. I run mostly growth product and furnaces here at Tuyo. I have two co-founders, Juan Jose Maria. He runs all finance, strategy and operations and the 30s assays, runs technology and data. We are an insurance company. We leverage technology and now, yeah, obviously it's the password of the age. We are focused on customers from 25 to 55. We realized that this is a very underserved market, at least in Spain. And it's underserved because of two reasons. One, as a customer segment, this segment is used to be like, oh, we can consume content from Netflix. We can listen to podcasts online and Spotify. And then we can purchase stuff on Amazon and we can even buy our groceries online, right? And we're used to these sub-services all first interactions with the companies that we work with. In insurance, there's nothing, or at least when we started, there wasn't anything like that at least in Spain. And looking at it from the industry side, and this is a big problem because the industry, when you look at the profitability of these customers from 25 to 55, they are non-profitable at all for the companies. And they are usually subsidized 55 from plus and 60 from plus. And this is because these customers, they tend to leave more online. They compare a lot, they browse and they very interested on what's going on with the products. And they look a lot into price comparison websites. And if you're comparing on prices, I mean, that puts pressure on your prices, so that's a top-line problem. But also, this generation is more financially literate. So as they're comparing, they learn a lot of the products that they're buying, and they are more aware of what they are buying. And since you're more aware of what you're buying in insurance, it tends to use the product model, which companies don't really like. Let's be frank about that. So you have also a bottom-line problem. Since it's a profitability is worse, your cost of claims is higher than the different generations. So you have a top-line problem, and a bottom-line problem means that basically you have a non-profitable segment. And why would you invest in a non-profitable segment if you just want the very hugely profitable 60-plus-year-old so that, I don't know, 65% of your margin in cases of some insurance companies? We saw that problem, and we thought, oh, there's something that we can do here. Because when you look at insurance, it's a general. What you see is that the company is just put into market like a very universal product. And it's like, oh, this is the product that you have. You buy every one, and if you don't, you don't. But then they don't look at segments in a way of, oh, how can we particularize our product, our value proposition to this segment? And in our case was, so if we can make these customers go for a self-service, detailed first platform, and then we deploy all these cool technologies that are coming into market, then maybe you can make it more of a product that they like more and also a profitable product. And yeah, that's how we started. We are focused on personal lines. You started with homeowners' insurance, and then we moved into term life, which is not really personal line, but yeah, it's very close to homeowners because of the market isn't everything. And then we released PetHealth, and now we're about to release Auto, which is the other large line. And then by mid-year, we will have problems as well. We really love stuff like this. The idea of going into a corner of the market that the traditional players haven't succeeded with, and making it more efficient, making it a better product for the customer base, we're vibing with that. We feel that here at P3, even from the AI and data consulting perspective. So when did you found to you? So we started at first in January 2021, finding enough. So pre-chat GPT, right? Pre-chat GPT. We were born as a native AI company. We developed a native AI company. I love it. The business need and the ambition to be a better product and to be more efficient, pre-existed AI. You're not one of these like, oh, let's go slap AI on insurance. I cannot just like this wash rinse repeat formula. It's like, you had a strategy to begin with. And then AI fell into your lap that you could take advantage of. So I think that's really, really cool. Yeah. We started thinking about how we can leverage distant technologies. Just do better for this customer segment. And then 10 AI came and turbo-chart our strategy. And even before that, we were thinking about AI as a technology. And a C is used to have this joke that he would say that what's the difference between machine learning and AI is that machine learning lives in Python and AI lives in PowerPoint. [LAUGHTER] It is what it is, right? But yeah, we started from the very beginning thinking that these distant technologies would help us. But there wasn't anything like Gen AI at that point. And that definitely is making our lives easier. So when ChatGPT dropped in late 2022, how long did it take before you all were going, there might be something we could do here. It takes a while to set in. It might have been like the first day, but for most people, it isn't. How long until you started your first, even like whiteboard session of, let's see what AI can do for us operationally or as part of our product offering? How long of a lag was there? I would say we were 2023. We're a smaller company. For us, this is not a matter of streamlining operations and reducing costs. This is like how we can scale without cost exploding. Because if I can control my cost base, then obviously I can grow faster without having to get more people for operations and for center agents and all these game adjusters and all that. So we started pretty early by Spanish standards, I would say, probably half 23. It wasn't even four. It was 3.5, I think, the first that we started building with. And since I said, we pushed pretty massively for details of service. We had a lot of interactions through WhatsApp. So we felt like, oh, this is clearly the case that this thing called chatGPT is bit for. I mean, if he's speaking with me through text, why can't I speak with a customer through text? But if you remember, I'm sure you do. And most of your listeners will as well. 3.5 had a distinct problem with hallucinations. He could say whatever he wanted at any time and we've complete this regard for the truth. So we built this machine, this agent, we call it Laya. You will see a 3D program that we have away with names. We name every agent that we build and they are funny names. As you should. We still do. We could have a whole tangent about naming agents one. So that first one was Laya, which is kind of like Laya, but the Princess Laya, because text teams are always geeks, and if they're not, they just should fire them. So we call them Laya, because IA, as opposed to AI, IA in Spanish is Intelligent 355, so it's Laya. It was a chatbot, but it was built on top of chatGPT and it had a huge issue that hallucinated none stuff. So we had this thing and we didn't really build one agent. We built multiple agents, a little experts. They knew one thing and one thing only. It's one of those agents we can control what he said and what he could not say. We are not Laya figured out. So we've reengineered that a few times already. The models advanced so fast that things that you had to do that this way in two years ago, they're just like, I mean, you're going to get rid of it. So I remember it took us, I think, two months and a half to build that Laya one at all. Maybe I'm over exaggerating, but we probably had a tree of about 75 to 100 agents, because each one of them one knew about water coverage, the other one knew about the fire coverage, the other one knew similar abilities, because we have to build it very, very, in a way, stupid. So it did only mean you about one thing and that thing only because it's not we couldn't control our hallucinations at the point. Let's zoom in on that for a moment. Even though the models have gotten better, this problem hasn't gone away. It's just become, you don't hit the problem as quickly, but you can still hit the problem, right? You're saying you've had like 75 plus sub agents in Laya 1.0. For one product. It wasn't 75 different chat bots. The user didn't have to go to a different agent to ask like, oh, I need to go ask the agent that knows the answer to this question. Yeah, we had this orchestrator that was actually the one that got the input from the customer. Then did one send it to the actual agent that knew about that. And then we reviewed that question. There was another agent that reviewed that answer. And if there were many questions, there was another agent that would actually compose one single text message and then the orchestrator would go back to the customer. It was a pretty complex process. I mean, it helped us a lot to understand the technology and how important it could be. So two months and a half to build that first iteration. When we built the second iteration, like September last year, it took us three weeks. The models are so much, it's important, when you have like, Victoria databases. And you have all these new technologies that came with it. And you have memory now. And at some point, Laya 1.0 was about prompt engineering. Then Laya 2.0 was context engineering. And now we're at 3.0, and it's a completely different. We didn't even have a 3 anymore. It's just like the speed, the technology advances. It's just a mind-blowing. So would you say that Laya 3.0 is from an engineering standpoint is significantly simpler than Laya 1.0? Yeah, yeah. Even 2.0 was way simpler. And now 3.0 is just like, it does so much more stuff now with a way simpler architecture that says, yeah, it needs two years. It's not a lifetime away. As a leader in your business, when you look at some of the challenges with AI, we've seen in our last few years, who is nations being one of them? There's tremendous incentive for these problems to get solved. And we see them getting solved. Like you were saying, better models, better tools, better extensibility. But it gets better so fast. So like it's a product leader when you see the advancements, how do you think about managing your customer experience when things like this keeping up activity that's always ongoing, how have you guys navigated that, might be curious? There is this thing that technology teams, even in product teams, that you need to be aware of for the sake of building. It doesn't really improve customer experience. And I mean, you need to be very aware of why you're building what you're building. And sometimes it's just not customer experience. It's just like easier way of maintaining it. For example, 3.0 is not about customer experience in our case, which I mean, there are improvements in customer experience. It's just like, it's going to be so much easier to maintain that two-door door. And two-door door wasn't really way easier to maintain it one-door. Because one-door door for the product teams, it wasn't nightmare. Because every time you touched whatever in the coverage in your policy, then you needed to go to media and it just changed it manually in every agent that was talking about that. So you need to avoid the excess of building for, because it's possible now. And you just need to be very careful with why you're building what you're building. And in our case, the constraints are also the size of the team. I mean, if you had limited resources, then we would build whatever, probably. But since we're a startup, and we have limited resources, we need to be very thoughtful of whatever building and why everything. And since we've been releasing new products, then sometimes you just need to-- these development resources are going to new products. And sometimes it just goes to something different. So where was Leia 1.0 used? Like, where were the customer having countered Leia 1.0? I think it's really wise that you are judicious about where you expose these sorts of technologies to the user. If you're a company that said, ooh, look, chat GPT, we should go do something for, I don't know, pick an industry, throw a dart at the map. Let's go do AI for insurance. That's what a tech-thinking company would try to apply it everywhere and just absolutely face-plant. The customer needs to think it's better. They need to like it more than they liked whatever the other process would have been. They have to like it more to engage with it. I love that you've been applying that discipline. I didn't quite catch, though, in the early going where Leia would have been deployed in a successful way. At the time we deployed Leia 1.0, we realized-- and this is only because you are operating in the market-- that 70% to 80% of the questions the customer had were about coverages and do you cover these? Do you cover that? Where are the limits? This is very repetitive, very informational type of questions that they had. I mean, that's what Touch-Bt3.5 was done for. I mean, you can ask him things and they would spew whatever he had on his mind. So yeah, at the very beginning, Leia, what we figure is that if we can build these to be aware of our products, then most of these questions they just go away. Our human agents just help people with purchasing a policy or maybe you need to do this or maybe you need to do that. And it's what I always say with automation technologies. We automate the low-hungry fruit. And then people can dedicate themselves to just more value at tasks. And in that case, Leia, what the door was exactly that. We automated coverage related and process related and app related questions. And everything else would go to agents. The lens of empathy, I think, is a really good idea for people, regardless of the industry building these types of solutions. Because that's such a hard thing to control in an agentic system, whether through system instructions or a knowledge base. And especially in insurance, I've had to make claims because of an auto accident or a burglary. And you want someone that can give you the human, it's going to be OK. I'm going to pass your information along or misquote what can be done. So I've had experiences with other chatbots where it's just so clear there's no empathy occurring. I just don't want to use it. It's wild, isn't it? When you're talking to an employee of a company in a call center, you already know as a customer. But the person on the other end of the line probably does this all day long and can't afford to really care that much about you. Like the human thing on the other end of the line, how well you feel taken care of and how well you feel empathized with is just due to their personality and or how well they embrace the acting component of their job, right? It's such a fine line between when is a human like required? When do we absolutely need to talk to a human being? Versus like when my own personal experience is with AI, that just I did not anticipate was this couple's coach that I built for my wife and I to help improve our relationship. And wow, Sonnet 4.5 is actually warm. It checks all the boxes. You can talk to a professional therapist, couples therapist or whatever. And like, but they're being paid to do it. They're doing this all day long. Of course, they're empathetic to an extent. But like there have been times where we're just like almost like in tears, like feeling seen, you know? And not in a way that was like sucking up to us, right? Which is the other problems with AI, right? Like it also checks me when I'm trying to get away with something like Rob, I think you're cheating a little bit there, right? You need to hold yourself responsible, you know? But you got to get that right. Yeah, and there's a few ways of looking at it. And we've looked at it from different perspectives. And obviously at the beginning, we looked at it one way and we now look at it a bit different. And so the first thing and just to close on the layer one at all chapter, one of the most surprising things that we had with layer one at all. And obviously consistency in the answer was one. Like you had absolute consistency on the answer that you were getting once we got freed of host nations. Obviously, 100% consistency is one of the main advantages of AI in general. And we'll talk about that also in with Watson, which is another of the ages that we've deployed. Second one is that I think it's just way easier to deliver an empathetic experience on text and voice. And that's how you started through WhatsApp. The NPS of the chats that were full on machine versus the NPS of the chats that were a person was 15 points higher than the NPS that we had on human agents. And obviously, there's a caveat there. I consistently hire NPS around 15 to 20 points. But now, it was true also that whenever it had to switch to an human agent, there were usually trickier problems. So that's personally explained by the trickier problem. But when you look at the first month versus the last month, you could see an improvement in NPS straight away, just because of consistency and because of the way that machine was talking. And also the space, it's sometimes just being timely. It's just everything. It doesn't really matter what the answer is. It just matters that you answered in 10 seconds, as opposed to be waiting for 10 minutes for an answer. And that was part of it as well. So consistency and loneliness was something that we learned helped us a lot in Leia, one at all. An empathy, empathy is a tough one. Like I said before, it's just way easier to deliver an empathetic experience on over text and over voice. We started text, so Leia, in 2023, as I said, half 33. We only started voice half 2035 because the technology wasn't there, it was way, way trickier. Even when we started, if I look at Sonya, Sonya is the name of the voice agent, which is a Spanish name, but also answered by AA, which is one of the main motivators of that main name. We're moving to Spain. The nameings of things are just wide open over there. You're not as camped in URLs as the English space is. We have this investor on our company that says that if the first version of whatever you released doesn't embarrass you in six months' time, then you just released too late. And that's what happened with Sonya. I look at it now and it's pretty embarrassing now. But the technology was what it was. I mean, a voice LLM wasn't that good. The LLM that actually did the reasoning, it wasn't that as fast. So I wouldn't say a bad experience, but it wasn't as good as a text experience, but just because of the trickiness of the technology and the medium. But if you look at life kit now, it works amazingly well. If you look at the new agent platform that 11 labs. 11 labs? Yeah. The agent platform of 11 labs now is pretty amazing as well. We are here and towards deploying life kit now, because it's just way more flexible. It kind of like, we think about life kit as the land graph of voice. So it's basically its open source. Is this framework that you can do all sort of cool things? You can plug the recent LLM. You can plug the older voice LLM. So for our needs, it's probably going to be better and being open source and being able to deploy in our own infrastructure always helps. But we're using right now 11 labs. And we started using them. But the technology was on there six months ago. But now if you look at the agent platform that they released, it's just amazing. We just happen to have a different use case which years are towards life kit. This creates the opportunity for product leaders to just rethink how they go about doing what they do. Just to put a period on it, like you said. Focusing on the value, the experience for the customer, the business outcome, you're trying to affect is way more important than keeping up with what we would say here in the States, keep it up with the Joneses, right? It would pressure on you though, because you have to be very aware of whatever is happening in the market and what's the new releases and how they can impact you. And if they impact you, and if you want to develop on that impact and nothing is as static as it used to be anymore in that way, circling back. So think about that question. How much more effective it is today anyway, delivering an empathetic experience for the user using AI-generated text than AI-generated voice. One of my theories about that, I hadn't thought about that until you said it, is that in the text case, you can kind of imagine the other person's voice. It's a way that we interact with real human beings that is already artificial. A text interface talking to another human being is already somewhat weird and artificial. It's not face to face, it's not voice, it's not, right? When you bring voice into it, now that you're inventing a whole living thing that we know doesn't exist, I mean, it's silly, right, because in both cases, in reality, whatever you're talking to, it's not a human being. In terms of like suspended disbelief, I'm willing as a human being to kind of buy in subconsciously to the idea that I'm having an authentic interaction over text. And the agent's got a name, and I'm like, oh, hi, hi, Griff. Nice to see you, you know, I still say please to Claude Code. I just never bothered to take that out of my approach. But yeah, when you start to hear like this AI-generated voice, it really forces you to confront. This thing's really trying to act like a human. Like now it's feeling, mmm, yuck. I don't know if we're ever going to get over that. But some of the voices that now you hear, especially in English, in Spanish, were a little bit behind, but especially in English, some of the voices are just amazing. Let's say that the face-to-face interaction is like the core of the human experience in a way, the human experience communication. Then if you call someone through the phone and you get rid of all their visual cues and everything else, but still there's something in the voice that's very human. And if you feel through that further and you go through text, you have the text, the communication, but you just remove everything else. So mimicking the human experience is what I think was what's difficult, not the communication part. So that's why I think-- I agree. I think mimicking that is the difficult part. And technology only recently reached seriously, technology, in voice, as near as the human experience as a text was, obviously, because I mean, there's further filtering in text than in voice. And one of the things that we were worried about that, when you looked at our trust pilot, interest is based in trust. And trust is in the digital world. It's based on reviews, and we worked very thoroughly on our reviews. And we just stood filtering for Leia. You see a lot of people thinking, Leia, for itself. It's daily, they haven't even realized that it wasn't a human. And that's probably the best thing that you can say about Leia. One of my wife's user complaints of our couple's coach is that when you start to talk to the coach, it just keeps asking you questions. It asks a question, you talk, and you're back and forth and forth, and it's constantly, quote, unquote, interested in what's next. So it keeps asking questions. I just want to be able to walk away. But it feels rude, like someone just asked me a question. It'd be rude to just walk away from this thing, you know, is really, really neat watching human psychology play out. She literally continues the conversation five or 10 minutes longer than she wants to. Like, she's been done, right? It's like the person who's in your office and won't leave. - Yeah, human psychology. And you could see that, Leia as well. I think nowadays, Leia is in many models. But in 3.5, I remember very distinctly that one of the ways we managed to reduce hallucinations if you keep your reply to whatever we've given you as context, then we leave you $100. And that worked magically. I don't remember the percentage point, but I remember it was like something like half. And then we further context them. We managed to get through the point that we were very happy with the way we was answering. But it's just that, probably means that we, whenever SkyNas rises, we're all gonna be dead. Because we all use that trick. But yeah, that's basically our cognitive bias clogged into the model. - I'm here for my $100, all right. (laughing) - There was even a study in that era, like the 22 era of the difference between bribing chat GPT with like $20 versus like $20,000. It's actually more successful just to offer it like $20. - I remember that, I remember that. - Yeah, $20,000, now you're just bullshitting me. We're bullshitting you no matter what. (laughing) You're never gonna pay up. - One, I'm curious. It sounds like you guys have worked on a lot of things. And you mentioned earlier in the conversation, the distinction between looking at AI as a way to control cost. And then looking at AI as a way to create a better experience or more incremental value for the customer. How were you thinking about those things today? Maybe more of some of the other things you guys are doing in the business. - That was the big shift that happened in 2024. Mostly one of my co-authors somebody I was thinking around our strategy and what we were doing at a time and probably being insurance helped us with this insight because in the insurance, just cost to serve. Is this about 10% of your cost base? This is that, just 10%. And when you look at marketing and you look at the actual claim costs, it's about 85% of your cost base. So if you're working in something that even if you manage to improve by 50%, which probably you want, because insurance operations are pretty strong line as these. Even if you get that 50%, you're only gonna get five more percentage points on profit margin. But if you work on 85% of your cost base, if you only get 10%, which is easily attainable with this technology, you're getting 8.5. And you're probably looking at more like 20, 25% improvement and then that's 15% of the points or more. And to him was the one that came with this realization to us. I mean, I don't know if we're gonna get anywhere with this. To me, it makes sense that we put our minds onto this 85% rather than this 10% because it's just a lot of numbers. That's the way it is. So we switched it a little bit. And then we started looking at marketing and cost of clearances. It's the biggest needle movers that we needed to figure out with AI. And we figured that we had three levers that we would be investing in, which is growing efficiently, this is marketing. And the writing is smarter, which is the way you price. And it's not only pricing, it's also all the information that you have to discern risk of a customer. And I think we are pretty more granular than competitors. And then managing things, more effective. Focusing on those three things are what led us to what's probably saying no to all the different things that we could do with AI. And just we're just focusing on these three things because these three things are the most impactful in our business. It wasn't that we were more intelligent than anybody else. It was just like the industry that we were in was focusing on us on those three things. I would say, if I may, that we were pretty successful. And I'm here to tell you today. - None of the things you mentioned sort of really kind of bring back another key theme, which is there's a very deliberate blurring of the lines between data driven success and AI success. You were just talking about the granularity of pricing based on risk and all that kind of stuff. I could imagine that some reasonable percentage of that is executed with sort of quote unquote just good data, sort of traditional non-AI data driven stuff. Maybe there's an occasional LLM usage somewhere in there. It's not a sign of like being primitive. If you're solving certain problems without using AI, that doesn't mean that you're missing out on the modern thing, right? It doesn't mean that you're primitive. Like you're just choosing the right tool at the right time. But the line between the two blurs, right? Like you mentioned earlier, like context engineering and that becomes all about data and everything. So where is generative AI currently being used? I know behind the scenes, you can use it to help write code and all that kind of stuff. But in terms of like the runtime operation of your business, is generative AI being used in places that go beyond customer-facing chatbots? For sure. So of those three examples, both the one in marketing and the one in claims are still using JNAI as the core of the operation. In a way, we haven't pulled on AI into an existing insurance operation because we think that only gets you so far. So one of the things we're doing is, and this probably will be boxed by me and my partner, Jose Maria. - He'll correct you. - He'll correct me, yeah. But there's this one slide that I, when I saw it, and he showed it to me, it's like, that's a great slide. And it's just a framework on how we see the insurance operation and at the very bottom, the unified data layer. Then on top of that, you build your processes. And then on top of that, you build workflows and APIs and screens as apps. And then on top of that, you have your human agents and your AI agents. So to us, it's just like a completely unified framework. It doesn't really matter who's operating on that data on those processes, on those workflows, and how we access them. Can be a JNAI agent or can be a human agent. We have to deploy that technology to ensure that whoever is accessing that data and the process can do their work correctly. That's the way we switch it in. And we really sign our company. And because we were obviously at one point, we started before JNAI. And we were doing things as we were supposed to be done, right? But once we figure out that JNAI could do way more things down, we thought it could be possible at the time. We had to redefine the way we think about our business. I don't think studies that say that only 5% of companies see successful pilots from AI. And it's just like, yeah, because faulting AI should exist in processes and data and workflows. It doesn't really work. And there's a lot of human resistance to deploying AI, obviously, because we are all fear that they're going to ever get rid of us. Unless you rethink the way you do your business, you won't see the result that you should be seeing. Because what we see after deploying everything, we are running our business, and I think this is neat. We've moved away from operational KPIs improvement from these gen projects. We've re-engineered the whole thing. And now we see we run our business. And homeowners is the best one that I can give you an example of, because you've been doing it for the longer. The average in Spain is 5% profit margin. We are running the business of 15% profit margin. And we hope, by the end of the year, we can be running at 18%. So we have proved that if you re-engineer your company with AI at the core, that's how you became native AI. You're not born native AI. And that's how you have to do it. Then you can have outstanding results and weigh over the average. This is a theme I think is worthy of a bull button that a lot of leaders, when they're imagining the future of their company with AI, they imagine what is happening today with AI kind of sprinkled around and people are using AI. And the truth is, the organizational chart will look different. The systems and the processes will look different. And you may be benefited from being founded in 21. And then there's a lot of inertia, I think, against some of these transformations in places. And so maybe it's easier just to say, oh, well, let's figure out how to get Juan using a chat in his work or let's figure out these different things. Yeah, there's a lot of inertia. And there's also a lot of personal risks. If you are whatever director of a publicly traded company, and you just go to the CEO and to the market and to your board and say, look, we have to re-engineer all our process. We have to invest all these amount of money in AI. And you have no proof because of this point, there's not a lot of data points of success, implementation of all we do in the end of the market. And ask because it's our business. And we have to do it because unless we present a cool story, then we're not going to be invested in. But as I said, you have a crucial job in a publicly traded company just running the course. You have a brand. You have an agent network and you're at least insurance. And you're selling these amount of policies. Why would you take that risk? I mean, it's just contrary to it. If you just keep earning your salary, right? Those directors, the board of directors, if you think about it, they have two big buttons they can push. One of them is the one you just said. Yeah, you need to re-tool everything and blah, blah, blah, blah. And that's terrifying. And it's terrifying to the director as well. No one wants anything to do with that kryptonite button. But then the other button, which is really easy to press, is, hey, y'all at that company. You need to be doing AI. And you need to be doing it now. Like, come on, figure it out. And that's the button that's just being-- I mean, it's worn out. The label isn't even visible on that button anymore. It's been pressed so many times. That one is not helpful, either. It just creates a lot of pressure without a lot of answers. Now, as you say, eventually, the role's going to start figuring out answers. And the business role is a copycat universe. It's waiting to latch on to success stories. And there just aren't enough of them that people can trust yet. Very, very, very early in this conversation, when you were talking about Leia 1.0 and the 75 sub agent version of Leia 1.0 that you got working, I wrote down the 95% number on my notes here to bring back up so that you just brought back up. I even encountered that number, the 95% of Gen AI prototypes fail factoid in an article yesterday in response to the NVIDIA CEO's comments and remarks at Davos. Let's say we believe that. Let's take it at face value. Let's say that 95% number is still what's happening today. And, you know, 19 out of 20 projects are just face planting. And stack that up against your story of chat GPT 3.5 Leia 1.0, getting something working in the face of the technology, like trying really hard to not be ready. You go on LinkedIn and you're going to only going to encounter one of two messages. You're going to encounter the, I can't believe how before behind you all are, bro, you're not cool like me. Like, I've already transcended humanity. And I'm so far ahead of you. And I feel bad for you. And like, y'all should like click through to my content, click like and subscribe. So that's one message. The other message is, this is all of fat. It's a farce. It's a sham. It's a bubble. It's all of that. But the difference between being in the 95% or the 5% is a matter of making good decisions. And those decisions can be made. And they're not rocket science. I mean, we're bright people sitting here talking, right? But like, we're not Einstein. We don't need to be the most important things that we're talking about in this conversation or in very plain language. There are things that sound obvious when you hear them. You hear them and you go, well, of course. But you're like, no, it's not, of course. Like, that turns out to be the thing, right? You've got to do like this, this, and this. There is a formula. There's a recipe for this stuff. We're in this really wild and exciting era of the world doesn't have anything to copy yet. There's no simple sort of quote unquote dumb way to implement this stuff. You've got to be thoughtful. And that's why, wait, for us, it's a really exciting time. We're like, you know, we're pretty thoughtful about this stuff. And we're not the ones on LinkedIn breathlessly saying, I can't believe you're so far behind. And we're also not the ones out there saying sham. Guess what? The algorithm likes. The social algorithm loves the controversial takes. The ones that make you afraid or make you feel smug that you haven't done anything. Those are the buttons to press on social media, professional social media in particular. And neither one of them served the species. Like, they're not helping us. So it's a long way of saying congratulations, right? We're still seeing this 95% number. And y'all were puzzling your way through it in a disciplined fashion and getting somewhere two plus years ago. Hats off. Maybe a corner turn here. You've said similar things kind of explaining your story. You look at any business on the planet. It doesn't matter what they do. That business is going to have some way of marketing itself, some way of selling, some way of delivery and servicing people. You look at all these work streams. And there's absolutely places that AI can help in every single one of them. But you can't just do everything, even though you can be very ambitious with AI. And even more so, what's really important is, well, what's the likelihood that the thing we build with AI actually gets used and actually makes a difference in our bait, like you were saying, in our profitability or whatever. So you've had a great experience over the last few years. If you were to try and simplify your mindset for other leaders, how would you describe that? I think, Jenny, I can help you at anything. I think it can help you at any point in your business. One of the things I still see and get in the set way of a 95% that's still failing. I think there's two reasons why 95% is still failing. One is because the main reason why they are building pilots and building projects on GNI is because of that button that the board is pushing, like, you do anything. It's basically like central come over and do us to do something, and that's a matter. But one of the things that I think Jenny, I can help you with. I have a particular view of outsourcing and technology consulting. I think it's going to be very tough for these companies, because, to me, GNI is something very. When you look at it horizontally, yes, big LLM, they can do a lot of things. But if you want something to work for your business, you have to, particularly, it's very well for your business. So I think that Tinda does it as to your team, the business people that work with them as to be your business people. And also, if the new technology companies with you have product and technology working together to deliver an app or whatever, then in GNI, it's even more necessary for those teams to be mixed to have the technology people that are working with them. And you have the business people that actually are figuring what you can do with that technology, because if not, I think the days of you just saying, "Do this and bring me the results." I just are sorely done. And that won't work for GNI at all. - Yeah, we 100% agree with the spirit of what you're saying. There's one modification that I'd like to make. The same thing you just said about GNI, is really true of any project, of any IT type project that you would implement, no matter what it was in the past. And yet, we still had a very robust consulting and outsourcing culture. And when Power BI came along, when I originally founded this company, I originally thought we were gonna be really just a training company. I thought, "Hey, I, Rob Colley, learned how to use Power BI and got really good at it." That means everyone will. Like, it was sort of like this self-deprecating view of the world. And some point like in our history that Kellyn, our COO had to kind of like take me inside and say, "Hey, Rob, you know that more than half our revenue "is coming from consulting and implementation now, right?" And not just training, and I'm like, "No." You know, I had to be updated on it, 'cause it turned out that what people really want is the problem solved. They don't really wanna learn how to build it. Some people do. And those people are great. Those are the ones that we were educating. But what really matters is close to the business. Like really, really close to the business. And so our approach to data and BI projects has always been far, far, far closer to the business than any of our competitors can ever get to. And it's a hard business model. Like in the same way that you talked about that demographic of insurance people, of customers, that it's hard to make a profit on, it is hard to make a profit using our business model of being really close to the business and moving as fast as we can, because you can't milk the client. You can't milk the projects like consulting organizations do. And one of the ways that they milk it is by being slow, by being inefficient, by getting it wrong. It's almost like deliberately built into the business model. Like that's not a bug. That's a feature in their business model. And I have just put the words down in the book that is still like only like 20% written, where I said that I think this principle has always been true, the closer to the business, the better. But it's even more true for Gen AI projects. - Well, true, that's true. It's probably a little phrase. - It's just that we can't expect us to be super, super common. Like us sitting around having this conversation and there's a, you've got a small team that you work with that's involved in all of this, right? You can't expect that kind of like exceptional understanding to exist everywhere. If I did expect that, I'd be like exiting this business today. Like we would be like, okay, that's it. It's over. Last one out, turn off the lights. But from my past experience, and especially for something is groundbreaking and is completely unknown, and there's like no template to follow as AI, I think there's a very, very, very rich future number at least in years. I like our chances, like our company, but I do agree with you. Like we see it, right? The huge big four accounting firm projects, those were a disaster in BI, and they're even bigger disaster in AI. It's amazing how much money's being lit on fire, but at least they can go back to the board and say, I'm doing something. If I wasn't doing something that I've just completely disobeyed a direct order, I'm gonna be fired. But if I go light a couple million dollars on fire with the big four, I'm just like everybody else. - I mean, that's probably, yeah, it's probably a better way of phrasing it. Yeah, what I wanted to say is the typical accentures of the world is probably gonna change massively, because I don't think that, as you say, like being fired away from the business is just one work in Gen AI. Probably what I definitely see happening, something that are more boutique companies that don't have the same overhead. These companies, and they can stay longer doing things, not just putting the entry level employee just just customer facing. - Right, the pyramid. - I think the model of the volunteers of the world, but even in OpenAI, they're starting to do that, like get implants. We think the customers start working with them very closely, and just basically dedicated person for that customer. The thing with that, as you just said, is it's a very tough, but it doesn't scale as VCs want it, which is a completely different segue of conversation, but it's definitely not a VC model. And some of that, I see some of the questioning of the current model, if I may. That's actually relevant. We think that we're going to see the massive deployment of the expertise of the world for B2B, which is what is going to really justify the valuations that we're starting to see. I mean, just the consumer prioritization of Chertipiti is not going to justify the valuation that these companies have. Only the deployment in the B2B world will justify it. And if that, what's the model to deploy it? And I think it's more companies going very close to the customer rather than the extension of the world. But what scales very well is the extension of the world, because being this model and having senior people with the customer, that's, I mean, that costs money. And sometimes money doesn't scale as well. I've watched the last, like, I don't know, five years. This tremendous wave of consolidation in the IT professional services industry, all around the idea of offshoring. I talked to a founder and CEO of a consulting firm in Indianapolis, where I used to live, with a bigger company than ours, and completely wired around the traditional model that we want nothing to do with. And he was merging with another bigger company explicitly so that he could get access to offshore resources. So like, the industry still has this tremendous, like, inertia towards getting farther from the customer. Drive-down costs, drive-down hourly costs, probably don't pass that on to the customer. Just juice margins with it, of course. But drive-down the cost, but get farther from the customer. Industries just don't turn on a dime. Like, it seems like that's still continuing at breakneck pace, even as we speak. While we're sitting around, people like the three of us going like, wait, what? Like, what? You're driving in the opposite direction. You're going towards the volcano. Like, don't do that. I agree, I agree. I've always kind of disliked that model. When I started in consulting after my years in technology, there was, like, the huge movement for outsourcing called centers and breaking down value chains. And I don't know, but I have these hypotheses that that didn't work. And then you saw the pendulum comes back. And then there's this insuring trend, as well, a few years ago. And I guess it depends on the years whenever you came to H in a way in the business world. But I always kind of disliked that services, also given services to a customer from very far away. I tend to think that it didn't work as well for the customer. The one that's paying really, so yeah. I had two random questions. One of them is that slide that you mentioned earlier about the different layers and how to approach your business. Would you consider that slide proprietary? Or is that something that you'd be willing to share either with us, just for our own edification? Or would you be willing to share it with our listeners? I don't know why we wouldn't. And the other question I had was, my ears really perked up at this. You mentioned that Gen AI has been something that you're using in marketing. Can you expand on that a little bit? Very interested in this. Marketing came later. So we learned first of all claims, and then we learned about how to deploy that in marketing. I can start by claims because it explains a little bit how we think of Gen AI and what we think Gen AI is really good at. A claim doesn't really follow a straight line. But previous automation technologies were really good. It's just building trees and just if A, then B, then C. But claims just don't follow that path. They branch depending on the different coverage as different trees, the severity of the claim. Obviously you have a fire, but it's done. It's severe, but it's not a surgeon. But if you have, if you lost your keys, it's very urgent, but it's not a severe, then obviously if you have fraud signals that we work a lot on. And then if you work with third parties or not third parties, and what's your expert? Because a given claim, the same claim, look through the lens of two different claim adjusters, probably gonna have two completely different outcomes. So what Gen AI does very well, is actually picks up a bunch of information through it since and give conclusions. With this insight, we felt like, oh, so maybe we don't need to automate the whole tree. We just need to automate the decision-making points. And that's why I said we just stop chasing the absolute cost to serve efficiency path. And then we start chasing the making better decisions path. Because for us now, we apply this insight to marketing. What we think Gen AI is really good at is building NBA machines. It's building next best action suggestions. This is what I said about rephrasing and rephrasing in your company before how a claim would work. Do we get all the information from the customer? And then a claim adjuster would look at it and then, oh, I know what I'm doing because I have these expertise. And I would do this and I would plug maybe a further expert because you need to give the appraisal of the claim because you're not sure. And then maybe you send a repairman because in Spain, at least we do a lot of repairs. We don't reimburse for the cost. We repair it. We send the plumber and we send the paint and everything. So it was a very artisan type of process. And what we built is that in our case, a claim starts digitally with video and images and a declaration from the customer and Watson, that's the NBA machine we use for claims. It enriches and brings up data, but also brings data from the customer history, the policy itself, then manuals. We built a lot of manuals and this is where context engineering comes to. Working with our adjusters, our more senior adjusters, we built a manual on how you would at every step of the claim you would treat. So the robot has that manual as well and then it also brings similar claims because you would think that every claim is different but it's not really. And then you also bring external signals like the weather patterns and everything because if you have a water damage, then obviously knowing the amount of rain that it brings helps as well. So it takes everything and then runs these triple analysis. It's severity urgency and duplication. And then with all that, well, it gets all these suggested actions and the confidence level for every action. And then our claim adjuster comes in and then revise it and then it's okay or not okay. - Human in the loop. - Human in the loop. - Exactly. - That's the way we work. - Love it. - You would never think of working that way if you just plug your AI to give superpowers to your adjusters. Now the reality is the justice and other tool of the NBA machine of what's right. So some of the actions that it can suggest like about in reserves, reserves are really something from instances that when you have a claim and there's this table, it's like, oh, this is a fire claim and it's an X amount of square meters. So this is gonna cost us, I don't know, $50,000. Forever. But it hasn't cost that yet because you need to finish the analysis. This is the reserve. So you, more or less, the prediction of what's gonna cost you, right? So to do that, which is, doesn't have impact on the customer. It's not as critical. So that we have almost fully automated. If it has a confidence on that suggestion above 70, then it's fully automated. We don't need a human to just review that because it's just a reserve in a way. Or a scaling preparation. Once it is a state that has to look into everything and has gone into the appraisal and yet we more or less know what it has to do. And you have to schedule an external repairment. Then it can do it for the adjusted. If it has more than 70% confidence in that suggestion. So there are things that helps that the human doesn't have to do anymore because it would take time for him to do all that and it just does it automatically. There are things that we don't want it to do ever. So for example, pay else or rejections. And this is where empathy comes from. If you're gonna reject someone in a moment of need that I don't know, I had my house on fire and it's completely destroyed. But for whatever reason is that really covered in your policy. I mean, that's a very vulnerable moment for the customer. So even if you have to reject it because it's not covered in the policy, I mean, you don't want a machine to do. Even if you can deliver an empathetic experience, it's something that you really want a human to do and that's something you design on your business, right? And pay else and issues with payments is also well because money is a very delicate issue. So we don't want a machine to do anything about it automatically. Even if it's better for the customer, we want to always a human to review it. So we have a human in the loop always. If the decision doesn't hit a certain threshold or there are several decisions that are designed just as human only type of decision. So it used to be that our adjusters could treat 10 claims a day. Now they can do 50. Just because all the gathering of the information is just done automatically for them. They can just review all the different decisions. Some of the decisions, some of the actions are taken by the machines. So it's just like a speedier process, but also a flexible process because you're not fixing the tree. You're just helping them make decisions at every stage of the process. So this was something that's very particular to insurance the claims process, right? But once you get that, generally, if AI is really good as an MBA machine, then I mean, any process that's based on decisions and actions, it can be automated the same way. So that's where we took it to marketing. Because in the end, marketing, what is digital marketing really? Just look at your campaigns in the different platforms. You look at how they are performing and then you take decisions. You can increase your expenditure or remove several companies that are performing. Remove keywords, informational keywords and they have less good flow ratio or they don't sell policies on that keyword, right? So it's just the same exact process applied to marketing as opposed to applied to claims. So once you get the insight of, oh, generally, if AI is really good at taking information, processing it and making decisions with different levels of confidence, obviously, we used to be a tough cookie to automate with previous technologies. Now, it becomes very clear that you can do it. - How close to marketing are you specifically? - I'm pretty close to marketing. - Yeah, I thought so. I thought you'd talk about it. I was like, this one, yeah, I see the scars, Justin and I share them. If you look at it, it used to take you a lot of time. And one of the things that Google made, for example, and I'm sure you're listening to that out of marketing client and you guys, you have the exact match for the keyword. You have broad match. And before, unless you were pretty sure that something and you usually used it to negate keywords, you only used exact match to negate keywords. Because I mean, it is a very tedious, very time consuming job, so you use broad match. But now, with this machine, you can use only exact match. And that's one of the things that we figure that we can have this absurd campaign tree. And it works because Atom, which is our growth agent, that's for us. - I think the thought process here on human and the loop is really important for people to think about because just the idea you've thought about, okay, at what level of confidence, honestly relative to risk of a mistake, are we comfortable with? And then also thinking about like, when do we just not want the AI to do anything? A trap I've seen some people in when they've started trying to build some of these workflows is when the AI arrives at a decision point or a recommendation, it's always human in the loop. It's, I could do A or B, want to tell me which one. That happens 100% of the time. And the problem is what you're doing there is you've now created another inbox of work that you already didn't have the capacity to deal with. We've done some examples here of some of the agents, we've built it's like, okay, what percentage of the time are we comfortable? It kind of not doing it exactly how we would want it to do it. Or where do we need to invest more? And even the year lay at 1.0 example, a lot of people just gave up at the hallucination. It was like, this thing can't do anything. But you pressed and you said, well, let's figure out how to solve this problem. - Probably working on how these nations gave us the insight for the confidence level. - Oh, yeah. I think that level of training of you having to deal with 3.5 at that point in your business has benefited you tremendously. It was like, you can imagine the old, grumpy old man attitude here like, no, no, you come to work here. No one gets to use Sonnet 4.5 out of the gate. Y'all gotta go back to chat GPT 3.5. It's kind of like the Wall Street firms when you join as like an analyst or a trader, they sit you now with a computer and they don't give you a mouse. They explicitly don't give you a mouse because you have to get good at using the keyboard to operate Excel. Touching the mouse, lose all kinds of time, too slow. They burn that discipline in. - That's why I never made it as a Wall Street trader. - Me either. And they pry keys off of their keyboards too, right? Like certain keys that you hit introduces like a seven-second delay like the help system loading or whatever. Like, oh my God, I want nothing to do with a sweatshop application of Excel like that. You mentioned the original Leia 1.0 and the GPT 3.5. I did have one more thing to circle back there as well. So GPT 3.5, you weren't using it out of the box. GPT 3.5 didn't know anything about your offerings, your anything like that. It couldn't answer any of those questions. That wasn't in GPT's pre-trained information. You were still having to with all these subagents that made up Leia, you were having to teach. Like every time GPT 3.5 got called, you were having to give it information about your business. And you mentioned that that first version was about prompt engineering and then subsequent versions were about context engineering. And I'd like, again, I understand I think what you mean there, but I want to make sure that we explain to our listeners what we mean. Can you explain the difference between prompt engineering and context engineering between Leia 1.0 and Leia 2.0? - So for example, since the amount of leeway that we would give to any of these agents in Leia 1.0, so every one of them had everything to know in the prompt. - In the system prompt? So it had, let's say it's an expert on water damage. You're an expert of prey alone, the coverage of water damage. This is water damage and you would have the piece of, they could be coverage in there in the prompt and you cannot do these things and that. You cannot think these things and that. So everything of the behavior of the subagents was there in the prompt. You didn't need to concern yourself about where is this subagent going to find all the information so you didn't have to deal with RIG databases or vectorial databases or anything like that because the context window was so limited, you couldn't give it very many information before it started to hallucinate or to forget what it was saying. So it was like every subagent had everything that it needed to know in the prompt. So you just need to work the prompt to make sure it didn't hallucinate, to make sure it had all the information about the coverage or whatever it needed to do, to know how to behave. For example, how it would give the answer and that's something that we actually started with. But then, as I said before, it had one singular subagent that would actually write down what to say so we just removed that part from the system prompt. It didn't need to know the way we wanted it to talk because it wasn't going to talk with the customer, it just needed to know and give the correct answer. Just made sure that didn't hallucinate, it had the information and give it the context like, oh, do you know of these, whatever. So yeah, that's what I expected. That's what I thought the answer was. And it'd be even more clear for our listeners. When you're saying prompt, this wasn't a prompt that the user, the chatbot ever saw, no one's typing this prompt into a text box. That prompt is living like in your code behind the scenes and every time your regular normal CPU, regular software code called chatGPT's API, it would hand this same prompt every single time so that it knew what it was doing. And then when you transition into context engineering, you get, first of all, there's an opportunity to give it more than that, but also the opportunity to let it kind of like go grab what it needs, which was not an option. You couldn't give the 3.5 release some sort of like access to a database so that it could do self-service and find the information that it thought was relevant, which you can do now. And so this gives sort of like a lot more flexibility. It's still the same problem, right, in a way, which is like making sure that this LLM knows what it's talking about and has all the information it needs, but you just have a lot more degrees of freedom in it than you used to. - That's exactly right. So Leia, one of those for your listeners, each one of these sub agents would look like a custom GPT from chatGPT that you can still do today, where you give everything that it needs to know and behave and the custom whatever system prompt. So each one of those were kind of like that. So imagine to maintain that on 675. So whenever you need to change something, you just need to find out what exactly one and then change it. And then if there's something about something new that we've realized didn't work well and it wasn't from the coverage itself or something that we had mistakenly done in the prompt, then you have to change it in every one of them. So this is completely different. And whenever you just have separate databases and your context window is bigger and then you can mix and match whatever it goes into look and then you give it tools as well then it's just way more maintainable. It's a different problem. Before you only need to know a prompting, now you need to know our relational databases and vector databases. And you have to edit, for example, our policies. Then now you have to chunk it and then you have to mark it down. And then you have to do all these different things and most people think, oh, it's just technical journal but it's not. And it's a real thing that you as a business person also have to learn because most of the time, the manuals and the policies, they have to be treated by the business people which are the ones to know how you want your customer to interact with your machine. - And if you don't do that, you're going to get a vector database that is incredibly efficient program by some technical whizz bang but it just gets everything wrong all the time. That's what you're going to get. - I mean, technically solid. - Totally solid, really efficient, token use. All the ratios look great. - So good. It's one of those things that I'd never thought I started telecommunications in January but then I moved away to the dark side to Australia. But then one of the years ago, would have never guessed that he would be speaking at a podcast about chunky. Just in particular. I mean, and now it's like, oh, chunky, yeah. And then you have to mark them and you have the AML and you have Jason's and it's all fun and dandy but then one of those things, one of those crazy predictions that maybe sometimes make. I think we will all be to a certain degree, obviously, technical in the next few years. We will turn technical again. - I never left the technical realm that turns out, you know, I didn't go strategy. Maybe I should have. Maybe I should have gone strategy for a little while. - But you were talking about analysts in Wall Street and how they couldn't get them. Most is I was like, yeah, it was one of the ones with PowerPoint. - I don't talk to me about PowerPoint. I got a strong PowerPoint game one. - It's true. - I can't actually make it look good but I can animate the heck out of it, man. - This has been awesome, by the way. It's freaking awesome. I love this. - No one, is there anything you wanted to share with the audience that we didn't cover? - We can talk about underwriting, if you want, because to me, underwriting, the engine here is called Lisbeth. So one of the things with underwriting is that, as you said before, a lot of the technology that existed before, it's useful. You don't have to use GNI to do a pricing engine or to do a prediction engine. One of the things that we've done, and that was definitely a cease from the very beginning. We didn't know, at 2021, we didn't know what GNI was. Nobody knew. But we knew that if we're building all these tech first platform for insurance, one of the things that we will be able to gather is data. So from 2021, one of the first things that we built is this thing that we call the customer DNA. So we capture everything from the very first moment that you're starting navigating, even in Google, and which app you click. So we get all these data points across every interaction, we gather it at onboarding, we gather you navigate on our blog, or even if you're an existing customer, how you behave during your claim, and we reach that with third party data, like social media, social login, I'm sure it gives you a lot of information. But yeah, if you compare that with the 20 data points that normal homeowners insurance gather, by their brokers and the actress, you can provide the risk way better, right? And it's not only because of the price, because some of the things that we've seen with this is just not price based, for example, but you can use it throughout the whole value chain of the insurance. For example, I can give you two examples. Unfortunately, if you're like me an Apple product user, you're gonna have a more expensive homeowners insurance with us, because one of the things that we realize that, if you're navigating in purchasing your insurance with us, from an Apple product, more often than not, your claims are gonna be more expensive than claims with an Android user or Windows user, and why is that? Because your devices are more expensive. So it's one of these things that now it makes complete sense, and it's really like, oh, yeah, of course. But then you need to have these digital first infrastructure that you actually are gathering, and then proving, which is the way it works, that if you're purchasing from an Apple product, and then you have a claim, you have another, it's more expensive claims than other users, right? So me and some of you guys and some of your listeners, that if you ever come to Spain and you buy a homeowners insurance policy with us, then you're gonna have more. - Bring a laptop, bring a PC. - Yeah, bring a PC just for getting your insurance, but they'll learn about it later. - This is really price-based, but we have another insight that we usually share, because I mean, they're not proprietary, and when you think about it, they make sense. And the other one is that, if you have someone that's purchasing the policy, and they don't read any coverage, but they just stop on one coverage, and they read it very thoroughly. More often than not, they're gonna have a claim in the very first week to two weeks at the start of the policy. Then when you look at it, it's like, oh, maybe we don't do anything about pricing, because you're not sure that this is gonna happen, but you flag it. And whenever that person has that first week or second week of the of stunned policy claim, then you just realize, oh, this was one of these. Then maybe I ask for more pictures, or maybe I ask him what happened with the claim, and you start to try to figure out if this is a real claim, more often than not, what happens is that they already had the claim. They just bought the insurance, so they could get covered. And the funny thing about this is that you actually prove it very easily. Sometimes the pictures that they send you, even in the name of the picture on the metadata are from before they purchase the policy, or the video was taken before they purchase the policy. I mean, it's just not very sophisticated, but it's just like, you just need to look for it. And you only look for it by treating the data, and that's where ML comes into place. It's not J&A, it's pretty neat as well. Now I know how to navigate the whole system. You got to really take your fraud game to the next level with these guys. Whatever pictures you take, you need to subsequently screenshot them and re-save them on the right date. One of the things that we saw, and we just saw it today, that's really funny. We had our problem of the first one, but the first one that was AI generated, but so bad that we actually didn't want to. And it was a really awful picture. I mean, it's like, "Look, guy, you couldn't even go to Nanobanana. "There's not free. It's free, and it would be better." I mean, you could definitely see there was AI, and then you get it through the filters, and obviously it was a guy. Was it like a picture of a car that, what was it? No, it was a glass, it's broken, but it was so awfully built. It's like, it's like, the table where you put the glass is not the same table of the very next picture. So it wasn't very coherent as a story. We had four pictures that the tables were different, and we didn't match with the living room pictures that you sent us, and then the actual giveaway was. It did seem that the glass was floating over the table. You know, with this sensation, with the pictures like, "Yeah, this is definitely AI generated." And that was, it was funny. It wouldn't even occur to me at some of it, either. Of course, we've seen a bit of everything. We've seen some people that they would send us a picture from Google images. But it wouldn't even be on the 10th page. It would be like the very first answer from Google images. We've seen pictures of what they're marked as well. It's a getty image. It's a stock photo, yeah. Having to look for the metadata is kind of sophisticated for what we've seen. Yeah, digital forensics, yeah. That's awesome. I mean, look, at least there isn't a tremendous reservoir of highly sophisticated adversaries out there, right? Probably they're very sophisticated. You don't even realize it is broad. Yeah, I just leveled up. If someone gets a nano banana picture so good, then they actually, you can actually pass it through us for a real claim. I mean, more power to them. They just, they just deserve it. I just, I just, I just, I just, I think I just love them. That's it. This is like a cool red team business idea, Rob. You could just be like the guy that tries to break one's AI defenses with nano banana. I don't think that's the money. I don't think the money's in that. I think the money is in, not a good laugh, though. No, no, I think the real money is in sophisticated services for end users who look to defraud, right? Insurance companies. I think, you know, like we just, we take a cut. Tell us about your problem. Okay, we got you. Does this have an in Spain where like contractors will go around neighborhoods trying to convince people they have roofing damage? And they'll like offer to like take over the whole insurance process, you know, like that's what you're saying. Like you want to be the guy that convinces everybody that they got hail damage. Yeah, like what ails you an insurance company. I'll get them to pay even though it happened six months ago. There you go, one. You came on the show and now Rob's your arch nemesis. Got a new SaaS platform. I'll have cloud code working on it. And it'll be spun up probably somewhere. What's a good country to base this operation in that would be hard to sue? This is where I should have gone to strategy. Right. I would have been an after all. Maybe not. We will not be pursuing this business idea. We have an opposite ethos here. It turns out the previous segment was for comedic purposes only. Well, Juan, listen, I really, really, really enjoyed this. I appreciate you reaching out. And I mean, I'm serious about coming up within a reason slash excuse to have you back. It's been a real pleasure and a heck of an enlightening conversation. So thank you so much. Sure.