Welcome to Relevance three sixty. I'm Louis Tetsu, executive chairman at Coveo. Here's the thing. Most digital experiences frustrate both customers and employees and waste money. It's time for a bold change, one that leading enterprises are already mastering. It's not about just using AI, GenAI, or Agentic AI. It's about making these powerful tools truly relevant. AI relevance is your secret weapon to stand out, transforming generic persona based interactions into highly relevant one on one experiences from all your data. This is the AI experience advantage. Coveo AI powers experiences at seven of the world's top ten tech companies, including SAP, Dell, Salesforce, Cisco. These companies don't gamble on AI experiments. They invest in AI that delivers the best measured results. That's what we do best for service, websites, workplace, and commerce across all your data and all your points of experience. Over the next hour, you'll hear from leaders who actually know what it takes to make AI work at scale in digital experiences. Jeff Harling, global VP of customer experience at Zoom, on how they moved from GenAI pilot to GenAI success. Nineteen percent fewer cases, twenty percent more self-service success, real business impact. John Ragsdale, VP of Technology Ecosystems at TSIA, on the biggest mistakes enterprises make when adopting AI, and how you fix them before they cost you millions. And our very own Patrick Martin, EVP of CX and leader of our service business here at Coveo, will share how your service experiences can drive superior self-service and much lower costs. This isn't just a webinar. It's a playbook for AI success. We want you in the conversation. Use the chat. Ask questions. Our Coveo experts are here to help in real time. So let's get started. For those of you who may not be familiar with Coveo, we use data and AI to augment relevance at every point of experience within knowledge and commerce experiences for large and global enterprises. And we do that using our common unique platform, which we'll talk about today, which is called the Coveo AI relevance platform. Coveo is about eight hundred applied AI experts. We operate globally across the US and North America, as well as Europe and parts of Asia Pacific. And we've been recognized in that space, and I should say our leadership has been recognized in that space for a number of years by key analysts. AI relevance is our sport. And we'll see today how important that is because not only it's a game changer, but it's a game changer. We've been obsessed at Coveo with this idea of pursuing pinnacle relevance, harnessing the power of AI to unlock massive financial benefits for enterprises. Today's session is about applying AI relevance within knowledge experiences, whether it's within customer service or workplace or website experiences, including agent tech. This is a critical area that is available today where you can deploy AI to deliver massive benefits as we have proven. We're talking about millions of dollars of savings. We're talking about double digit improvements, massive reductions in customer service costs, etcetera. Well, think of it this way. What happens when every customer and every employee can tap into broad data and organizational knowledge stitched at their fingertips, brought to them in context. What happens when people can handle thirty percent more complexity on their own because they can gain knowledge and proficiency ninety percent faster every time. That is really the revolution that we're talking about. This is a very important revolution. And as leaders of companies, and this is an example here of a recent BCG report that talks about the fact that seventy five percent of executives really want to put AI and GenAI to work, but only twenty five percent are seeing results. We're in an era where that I called, of course it's AI. So people are a little bit tired. There's a bit of AI fatigue. People are a little bit tired of hearing about AI. They want results. Well, digital experience is clearly an Barca, as we'll see today, that generates a lot of results and a lot of benefits and very, very quickly. Digital is table stakes, but relevance is not. Every enterprise delivers millions of points of interactions all day every day across websites, commerce, customer service, knowledge types of applications, and workplace types of applications. And that together is drawn from a lot of data sources, three hundred and sixty degree sources of data, and provides three hundred and sixty degrees signals, in fact, around every person and every interaction. What we're talking about here is unified relevance, the ability to deliver in a very coherent and consistent way knowledge and relevance across all these experiences, whether it's search results or conversations or answers or recommendations in a unified way across the enterprise. Why does that matter? In very simple terms, what people are really saying to every company, every enterprise, whether they're buyers or customers or employees, is as simple as don't waste my time. Because I'm only a browser window away from getting a better experience, an experience that is relevant, that is hyper personalized, that is even prescriptive, and now given the world of generative AI advisory. That's what we call relevance, is the degree to which that experience, that advice, those results, those recommendations are aligned and anticipate my needs, my preferences, my context everywhere. And the degree to which companies do that creates a massive competitive advantage. I have five pieces of advice today. First, AI relevance is what powers the experience advantage. It is the only way. It is no longer possible to program rules around persona and create datasets around persona. People expect to be served in highly individualized ways, and you need to bring your entire enterprise to everyone. That's a particular challenge within large enterprises because the volume and variety and sources of content are everywhere. And that's compounded by the fact that the audience is typically very large and is typically very diversified, sometimes across the world. The compound effect of those two problems, especially in a world where you wanna make money, is a problem that only AI can solve. So pursuing pinnacle relevance in business is really about driving that hyper personalization at every point of interaction and deliver that contextual and prescriptive accuracy while at the same time optimizing business outcome. And the companies that do that truly are setting the competitive experience gold standard. And again, something that only AI can solve. Number two, the value of relevance and the money is in the long tail. It's not just about firing a chatbot that answers simple questions from a single source of data with a generative or an LLM experience, such as how do I change my password? The costs, the effort, the reputational risk, the churn lives in the long tail where customers have complex situations, ask complex questions where the content is complex from multiple sources, and the same holds true for employees. So that ability of dealing with intricate long tail problems is really where relevance must be measured. Number three, remarkable experiences are unified. And what we mean by that, and we talked about this earlier, is how closely aligned search, generative answering, recommendations, conversations, and product recommendations can be all aligned. So if you think about the experience of the future, it looks like this. People will wanna go online wherever they engage and have the ability to answer a question or a query and essentially get results that are grounded in real sources of truth because you don't want hallucinations. You need full traceability and lineage to that. And that experience needs to be compatible across the board, you know, with search results, with the ability to navigate and disambiguate the conversation, recommending next best questions and next products to purchase or next content to look at. And this is precisely what unified relevance is all about. Many customers we see make the mistake of treating generative experiences separately from search. We believe firmly at Coveo, and we've seen that, that it's a huge mistake, particularly because search, as we will see, drives generative AI. So the common attribute with our customers is that their content and their context data is everywhere across multiple platforms, and their experiences are also everywhere and tend to be disjointed across multiple points of experience. Think about websites on Adobe or Sitecore or commerce sites on SAP and Shopify moving to a service experience that may be a combination of ServiceNow, Genesys, Salesforce, etcetera. So the ability in fact to connect to that content in a high performance and highly agile way and deliver that generative experience combined with the search and relevance and recommendation experience into any point of experience, whether it's a human at the other end or now an agent as we'll talk about agent tick, is really, you know, what the future is all about. And I should say what the present is all about. For that to happen, there are four things that need to happen. I've already talked about it. Unifying data, tailoring data. So stitching data, tailoring it to every user. Number three is the unification of generative AI with search. And number four, the ability to add on top of that AI models that can actually optimize business outcomes at every interaction. Number fourth advice is that search and relevance is the most critical part, the foundation for GenAI success. And so we at Coveo have created a platform. Our roots are in search, obviously. And Coveo the Coveo platform, which is one single multi tenant platform that evolves all day every day with no technical legacy, no technical debt, is really a history of first. We started using machine learning in two thousand twelve, branched into deep learning, intent detection, vectorization and semantics, and now generative AI. And we're finding, in fact, with our customers that this all needs to work together. So it's really about using the power of AI, the whole stack, in fact, to, create the experience. For us, RAG, which is normally retrieval augmented generation, really stands for relevance augmented generation. And here is why. If you think about this diagram that I've shown before, let's peel the onion a little bit. So everything starts with a unified hybrid index. And it's hybrid because it contains both the indexing and the security part and it and the security providers, for example. And it contains the vectorization and semantic part at the same time. And it's refreshed at very high performance, oftentimes for our customers across structured and unstructured cloud and on prem data and across the world. That allows us to feed that into hybrid search and relevance AI models. So think of them as a combination of advocates, AI models that work together, in fact, to generate, you know, the search and relevance cocktail into every app, and that's available through an API. But that result, it also feeds the relevant passages that are ranked on based on the AI models and the semantic relevance into the generative part. Whether you use your own LLM, Coveo is entirely agnostic to LLM, or whether you use our built in LLM, the ability to generate, in fact, answers, conversations, recommendations, search results, and recommendations of products and and and the semantic encoding of all this is gets unified. And that's truly what we call unified relevance. In return, using analytics, we continuously log the outcomes and the actions of users in order to close the loop and reinforce the learning of the platform. That same API, which is available, you know, headless into any experience or any app, is available to agent tech. We believe agents will proliferate and are already proliferating across every app. And every app has its own orchestration models, in fact, for agent ticks. So the Coveo platform is basically the intelligent underneath that can feed a multi stage, multi step generation. And as we'll talk about, we'll have a session on that very, very soon. That platform is what allows us to create experiences that look like this, where an experience like this can be drawn from five or ten or twenty different sources, whether it's structured or unstructured information, answer questions, recommend products, understand customer history, and and and really combine, in fact, the best set of advice and recommendations all in one. And of course, if you think about that user on the left going through that experience, you want that user to obtain the exact same content and the exact same relevance from one single source of truth, whether they interact with a chat, whether they interact with an agent in a contact center, or get on another website, or another experience. And we have multiple of those examples in production. Dell, for instance, is a good example where in this case, you know, three different interventions lead to three different experiences. Alienware laptop leads to a commerce experience. Alienware laptop overheating leads to content related to that type of problem. And how do I turn on BitLocker on my laptop or on my Alienware laptop leads to a generative experience. These experiences, we have it in production. Dell is a good example today where, you know, we get customers to self serve and we truly augment, you know, customers online and their ability to gain knowledge on how to solve issues themselves. And as you can imagine, this has huge ramifications on, you know, the experience itself, but on the reduction of cost and etcetera. And we can even mix products in into that because we understand, and this is done across the world as you can see here with French or Japan and so on. We can understand the semantic encoding of products. We can link that to content because this all resides in the same index with the common relevance layer. Think about a consumer example. This is United Airlines. Can I fly with my pet? Well, we'll provide an answer as you can see here. But the next logical question that AI and LLMs can figure out is where do you wanna fly? And then I can recommend based on that, some, you know, people also ask or some additional questions that are not FAQs but truly LLM generated in context. And of course, you know, search results for users and etcetera. And even help a user, you know, visiting historic landmarks or museums, you know, at a certain location given all their context whether they're traveling with kids or or etcetera. This is the kind of experience that a Coveo can generate. And in commerce, that ability to bring everything together allows us to combine the semantic encoding of a catalog with content, with all the other AI models worrying about inventory and catalog coverage and margins and revenue and all of that and truly create an advisory experience such as this one here. How do I get started with surfing or where can I go fishing given I'm x y z context or or etcetera? So this is the kind of experiences that a platform like Coveo and, you know, more importantly, AI relevance in general, as I've I've described, can create. And it's a matter of days and weeks, not months and years to create that. We can literally fire up an index across all your data within a matter of days at Coveo and experience it on your data. We believe that this is a massive digital transformation that delivering relevance across all points of experience will be a binary competitive advantage. That companies that compete against companies using that technology will lose because the quantum leap in performance, as we described earlier, is in in the range of double digit cost reduction, double digit revenue growth, and etcetera. And why is it so challenging? Well, we've talked about a lot of that, but in the end, it's really challenging because relevance is a real science. And that's where Coveo excel. Number fifth piece of advice, and it might sound very bold, but I think it's to everyone's advantage. Never believe AI and Gen AI claims. There are a lot of those right now. Test and measure on your own data. This is what Coveo customers do because it lowers the risk. Prior testing on your data will show you the benefits, and the benefits are really, really important. We're talking about being able to AB test and understand double digits improvements and cost reduction and customer experiences and cart sizes and metrics of that nature. It's about really the flexibility, the ability, in fact, to create that spinal ability to adapt to all your content and all your experiences with great simplicity, speed. And of course, the cost is low to no cost because the benefits far outweigh the cost. Now I get to welcome Jeff Harling and Alex Dassa to Relevance three sixty. Jeff is the head of digital support for Zoom.