I wanna talk about, the relevance revolution and and and really build up on what, Michael and Ray have been, have been have been have been talking about. And and really start with with a a really foundational belief that that we have that actually may be a bit provocative. We don't think actually we're we're no longer in the digital economy. We think that's old news. That was a decade ago. We're in the experience economy, and and and Ray kinda mentioned a few examples here. Digital is stable stakes. You put content online, doesn't mean people will necessarily self serve you. Products online doesn't mean that people will buy them. This economy is powered by search, data, and AI. And and if you think about, you know, the companies that are challenging every segment, think about it for a second. They don't they don't win. They don't challenge every business. If you think about Wayfair and the furniture industry or, obviously, Spotify, Booking dot com, Amazon, etcetera, they really serve people in very individualized ways. And and and they use data and they use AI, in fact, to to truly set a new bar for, expectations. And and this is really you know, I build I'm building on what Ray mentioned. You know, this really creates this whole notion of digital Darwinism where, effectively, only the most relevant survives. And and and this is really what we're gonna be talking about today. But more importantly, you know, how do we how do we effectively, you know, compete? What really happens is what we refer to as the Moore's Law of Experience here, which is which is really that once you experience something that is more personal, that doesn't waste your time, that is really designed for you, you never go back, in fact. So people expect experiences that are centered around them, that are product person centric, not product centric, that are very cohesive and and and and really follow you. And we'll give you we'll give you a number of examples of that. So for us at Coveo, the foundational belief is that relevance wins, and it's really that simple. And this is a a really profound topic and, and a really important one because for many companies, it's it's really a matter of, of, of survival. And what we did in in twenty twenty is we we we commissioned a third party firm to actually go and survey about two thousand participants and literally ask them questions around digital experiences and relevant relevance and so on. And some of it is intuitive, but it's really good to put numbers on it. You know, at at a very simple, you know, level, close to half of the millennials and actually forty three percent of all consumers besides the millennials, which is which is the surprising part to me, you know, basically said that they would actually pay more if they found what they needed in in a few clicks. And and to us, this is not a merchandising problem. It's really a relevance problem. And in fact, these same users said that they would also pay more if they got, you know, personalized recommendations and if they could find supporting content. Because if they can't on your property, they'll just pop a new window and get elsewhere. This is this is not a commerce problem. This is a relevance If you think now about customer service, you know, essentially, you know, people you know, digital patients is an oxymoron. It's like a jumbo shrimp. It doesn't exist because people know that choice is a commodity. And in fact, most dissatisfied customers, you know, where they when they really feel that companies are wasting their time because they can't deliver relevant and contextual information, you know, very quickly, they'll abandon. Three strikes, you're out, essentially. And what we're seeing also is the consumerization of the workplace, where, essentially, now employees in the workplace expect the same level of experience that they get as consumers. And and in fact, you know, forty one percent of the time, you know, people report that information that's provided to them is not really relevant for their role. So the whole notion of of of of relevance is really the common attribute here. It's not workforce. It's not service. It's not commerce. The common thread here is that the problem is a relevance problem. And so so to illustrate that, and you'll see that in a demo in a few minutes, because we're actually gonna show that, you know, what does a relevant experience feel like? And and and, essentially, in simple terms, you know, this this looks like a normal web page, except that it's a if it's a relevant experience, this is not vanilla content. This is an experience that understands actually who you are and and and what you like and can literally make the, you know, twenty five, fifty millisecond decision about what content to recommend recommend, what might interest you. It can actually follow your journey and understand where you left off and and across multiple digital venues, properties. It can it can it can really triangulate what you Coveo even more through the data. It can recommend. So it can anticipate. People don't just expect you to respond to their ask and and and and inquiries and and and demand, but really to be able to anticipate and recommend. And then finally, you know, it can drive a relevant conversation. It can it can drive an intelligent conversation because it understands you and, ultimately, can even carry all that thread to, the people talking to you so that, in fact, when they pick up the phone with you, they don't waste your time. And so this is become this has become an expectation that, companies need to deliver on. And and if I sum it up, essentially, it's about people. It's about talking to people about them, not about product. It's about persons, not persona. So how do you deliver at scale? Essentially, a million different experiences to a million different people. And the headline is here that only AI. It's not humanly possible to do that. Only AI can do that, and you can't compete against AI. So you better, you know, obviously embark on on the bandwagon. Here at Coveo, we've been studying that this problem. As Michael mentioned, we've been obsessed about this problem for more than a decade. And really it really boils down to three layers. Think about any digital experience. The first layer is search because search gives people what they want, essentially. The second layer, if you peel that onion, is is really recommendations. And the third layer is serving people individually. If you can use technology to deliver on this, you can truly amplify the outcome of any digital interaction. In the customer service world, it means that, obviously, if you can recommend, if you can personalize, and if you can be relevant on search results, people will self serve. They'll save time. They'll be happier, and they'll be returning customers. In the workplace, it means that people get, you know, the information they need according to their requirements for proficiency to accomplish a certain task. In the commerce world, it's probably where it's most the most obviously measured. If you fix search in the commerce world, you increase conversion. If you fix recommendations, you increase cart size. If you fix personalization, you increase loyalty because, ultimately, you're driving, you know, relevant lifetime, engagements. And so we've spent a lot of time at Coveo to understand how to deploy that. You know, we're seventeen hundred implementations strong across the world. And and and, really, you know, it all starts with regardless of the fact of of where of whether we're working in commerce, customer service, or workplace type of applications, it all comes down to, you know, essentially fixing search at the starting point. And fixing search, while, you know, I would argue that ninety five percent plus of companies don't have great search online, fixing search is a matter of hours and days, oftentimes, because all you have to do is mine the data, understand, you know, through the data who people are and what they like, and essentially leverage AI to adjust against those search results and give them what they want. Then from a maturity standpoint, we can evolve this into a more prescriptive world where we're really talking about for formulating individual recommendations and, ultimately, stitching the entire journey together, which which you will hear today from from some of our customers. So we understand this problem because, as Michael mentioned, we're obsessed with it, and we think it's a it's a very important, problem in the ex experience economy, and we've democratized the technology. And if you think about it over the next the past, decade, you know, we've led with a series of firsts. So Coveo started off, you know, fifteen years ago. We were the first in contextual relevance and, you know, we created all the plumbing to reach content at scale and deliver against, you know, very complicated, you know, enterprise search problems. Then, you know, we were the first ones to embark on the cloud journey and truly release the first, multi tenant cloud native application, and then and then started working in the area of machine learning, back eight years ago in two thousand twelve. You know, grew a very large team in in in that area, used behavioral analytics, started collecting behavioral analytics to deliver on the type of problems that Ray described earlier. And now, you know, we're involved, obviously, furthermore, into the area of AI semantics, deep learning, and, you'll hear today about personalization as you go and etcetera. So we've really built the full stack, you know, of, you know, to deliver on on relevance. What what we're really excited to announce today is that we're releasing the industry first, relevance platform and, which we which we named the Coveo Relevance Cloud. And this is the confluence of fifteen years of research and fifteen years of working with many of our clients, some of which you'll hear today, but really in the past three years to really stitch, you know, the experiences together. And so what we're doing here is to create what we've done is to create a platform that can truly inject recommendations, you know, relevance, and personalization into basically any digital experience. So think about think about the journey that people go through. You might start on on a web on a website, you know, that could be powered by Adobe Experience Manager, Sitecore, Acquia. Then jump on a commerce, you know, experience, which could could be powered by, you know, Magento or Salesforce or or legacy Oracle or Hybris, and then jump into a community for customer service, talk to an agent. Ultimately, you know, your car could actually be sending signals as well as your your Alexa and so on and feeding into this. So what we're really doing at Coveo is we're making we become the cloud that makes the instant decision about which content to inject into every experience. And then our AI is essentially at work to collect back the outcome. So we understand the conversion analytics. We understand the behavioral analytics so that, essentially, AI becomes this ever learning, high velocity decision cycle engine that learns from every interaction to serve the next. And we think we're in a very unique position with some extremely powerful technology, and you will see some examples today of global companies, that have deployed this at scale, obviously, across the world. So the idea of making the decisions into every interaction about which content should be recommending and then understanding how why that content matters through data to feed the ongoing cycle is really what this is all about. And my friends, this is not something that is humanly possible. So either, you know, businesses compete, with that new world or they just simply will not be able to compete. We never own the experience. Adobe Experience Manager does. Salesforce does. Hybris does. Your Commerce Engine does. It doesn't matter to us. We power the content and experiences that make every single experience, and I shall say every single interaction relevant, and And we're very excited about that. And you know what? Instead of you know, rather than talking about it, we'd like to show that to you. Thank you, Louie. I'm gonna tell you a story about a company called Speedbit and the journey of one of their customers. Let's call her Susan. Speedbit manufactures smartwatches and dedicated GPS devices. They cater for runners, cyclists, and hikers. They sell direct to consumer, have a self-service community and assisted support for their devices, and a companion mobile app. They use the Coveo Relevance Cloud to provide more tailored content and recommendations across all of these digital properties. But they initially started by simply fixing their search. And here, KaVEo AI powered query suggestions appear as soon as someone starts typing. Searching for GPS delivers results like GPS smartwatch, cycling computer, and so so on. But back to Susan. Susan is an aspiring runner, she is reading up on heart rate tracking and how it could help her take her running to the next level. She finds the speedbit blog via a Google search. She lands on the blog on the speedbit website and sees articles relevant to her search: continuous heart rate tracking to get you closer to goals, which she clicks on. She starts to also see other popular articles, which have been personalized and recommended to her by Coveo in real time, based on her click stream activity. She clicks on the tips article. Now, we see this blog post has relevant personalized product recommendations well based on her click traffic and intent. So, she clicks on the product link. This takes her over to the speedbit ecommerce site and she starts looking at the Alta fitness tracker. And this product page is generated by Coveo. She sees recommendations for more popular, more full featured trackers included with the GPS. So looking at these cross sells. She decides to search on the store and looks for GPS. However, now notice that the Blaze GPS Smartwatch in running is recommended to her, because it's a personalized query in real time based on what she's doing. She navigates to the Blaze GPS watch. And here on the automatic product page, she decides to buy the Blaze and add it to cart. In the checkout, Susan sees complimentary accessories recommended to her based on her product selection, things like bands, and so on. These cross sells are all automatically recommended by Coveo using another machine learning model. Susan checks out, quickly receives the watch, and off she goes. However, sometime later, Susan has an issue, tracking her heart rate. So she visits the speedbit community for the first time to look for help, and Coveo aids her with her self discovery. At the moment, she's anonymous, but immediately sees relevant content that Coveo is recommending based on what others have found interesting. However, she has an account, so she logs in, and now sees that that content is tailored to her and personalized based on her purchase and her profile. And we know that she loves running because of the content that she looked at way back on the blog. Susan goes to search and finds information about heart rate tracking. Notice, Coveo automatically corrects for her typo using another machine learning model there as well. And delivers the results sorted by relevance, unified from all sorts of content sources and types, dynamically changing the facets and navigation based on what would be most useful to her. She sees a video she looks at and goes into it, watches the video, that's cool, comes back and decides to actually look at some of the other knowledge. And Coveo has recommended an article here very much tailored to her intent. She reads the article, she sees there are additional supported related material that have come up from there, she could also trigger the chatbot, which Coveo powers as well. However, she decides to submit a case. And now, Coveo intelligent case assist uses deep learning to understand what she types into the form, and intent detection really enables us to dynamically categorise and help classify the issue. You'll see that the first one suggested is tracker setup and heart rate tracking. She could engage with Coveo question and answer system at this point, to narrow her down her choices, but she chooses to contact support. And now, even in the phase of contacting support, Coveo is still automatically recommending related content to her, dynamically driven, to help her avoid submitting a case. Nonetheless, she persists. Meanwhile, at speedbit, a support agent now receives that case and picks it up. Here we see the agent in the CRM, in this case Salesforce. And the Coveo agent panel on the right is already recommending content to the agent right in their window and in their flow of work. The agent can see insights related to this case. He can see what Susan has already looked at in the session summary, and can look at the user activity to see other events that, Coveo has tracked in her Caveo user profile, including that Blaze purchase. The agent can also see that Susan has already viewed one of the docs, so no need to send that to her. That would be irrelevant. He scrolls down to see what else and decides to send her the whole user manual straight from the console and becomes more proficient dealing with every support call using the Coveo agent panel on the way. A few months later, Susan is experiencing some challenges. Up till now, she's been happily running with her Blaze and is a frequent user of their accompanying App. And the speedbit App developers use Coveo relevance API to call content directly into their App. However, anytime she wants to get help within the App, Susan can literally click on the help file, which is powered by the Coveo in app experience, and that immediately brings up content from the community site and other sources inside the app itself. Coveo knows her context, what she's using, what she owns, how long she's had it, and now recommends content related to battery life, which is her current issue. Coveo is the same single intelligent layer behind all of these touch points that gives Susan a relevant, coherent experience every step of her journey with Speedbit. Back to you, Louis. Well, thank thank you, Mark. And and and I think I think what we've seen, you you very eloquently, showed a number of of innovations here. I would mention, you know, a clear example of three sixty degree relevance. And and what we've seen today, although a very, very packed and and and brief demo is is is really live. It's really something that, you know, you can you can hear about and you will hear about throughout the sessions and and from our customers. So we've seen the whole notion of zero click personalized results. So we're talking about, you know you know, basically being able to anticipate the notion of predictive engines, you know, the headless, you know, ability, in fact, to integrate within any user interface across a very a journey made of very prod probably very disparate, you know, user experiences and the whole notion of the universal, profile service. So all in all, that truly creates, you know, this, relevance API that, we were talking about. I wanna talk about, you know, our design principles for the Relevance Cloud, which we've been working on, you know, over the past three years. And and it's really, you know, with a goal in mind to essentially, be the layer of intelligence behind, the common the common layer, across the entire the entire journey. So, you know, these these principles, we believe, have have led us to be in the position where we are, where leading global brands use Coveo. And and and the reason is that, flexibility that, you know, we've we've had in mind all along. So the first thing is is in order to be a leading, you know, AI platform in in relevance, essentially, you know, first of all, AI is an imperative. There is no way you can do this and and what what Mark just described, without adopting machine learning and now and now deep learning. And I would add on top of that that AI is fed by data and that the notion of analytics, understanding conversion, understanding content, and understanding behavior is, is obviously critical. And and and the reason, you know, why, you know, this must happen in the cloud is because of the innovation rate. You know, if if if, obviously, a a platform like Coveo is a multi tenant, cloud native infrastructure, we actually released, our software platform to all of our customers simultaneously more than sixteen thousand times in two thousand twenty. So, fundamentally, these are some very important considerations because what's at stake here is very, very important. There's an example of Tableau here talking about the idea that relevance and service, in fact, is saving eighteen million dollars a year. So that's a first set of elements. Number two is you need to make this simple. You need to be able to, as we said, fix search and then jump into go go towards move towards recommendations and personalization, you know, very, very quickly. Fixing search using AI should be done within, as I said earlier, within a matter of hours and days because there's a lot of automation here. And, obviously, if you can if you can, you know, launch these applications as we've as we've we've allowed, our customers to do into the most popular applications such as ServiceNow, such as hybrid, such as Salesforce and Adobe and and etcetera, that gives you, a head start. And and fixing search shouldn't be understated here, although, you know, people Coveo to companies need to move beyond search because the search page is often, as Milwaukee tool tool says here, the number one page, on on on on any site, in this case, you know, that that drives revenue. The third element, is is basically the ability to scale. You'll hear today from implementations that are truly global And, and and the ability, in fact, to, scale automatically, which, again, you know, thanks to the cloud and and thanks to AI is, is is really possible. And, and and you might hear today about about some of, the deployments that we've made at Dell, to support their commerce globally as well as their customer service globally, where we can truly, in fact, at scale, bridge this gap between expectation and and experiences as as the quote here from Will Hudson, who heads, you know, IT marketing at, at Dell Technologies. So finally, our goal is in fact, and and with relevant relevance platforms, we think will become, more and more popular because they truly become the unique relevance API, where, you know, think about it as search as a service, but think about it as recommendations as a service, personalization as a service, but all stitched together, which is really, at the end of the day, you know, relevance as a service. And that needs to be we believe that needs to be easy to integrate, that needs to be developers friendly, and so on. So those are some of the driving principles that, you know, we adopt at Coveo in, in in building those. Now customers are deploying that platform. And who best than, you know, Salesforce as an example? Because I think with Salesforce, we truly share a common view on engaging customers intelligently, around, the entire three sixty life cycle. And Salesforce has truly worked with us to deploy Coveo at scale across web properties here, across the app exchange to create that Netflix like experience, and we're gonna hear from this, about this in a few minutes, across customer service and all the way into into service cloud and their contact centers and Trailhead, which actually personalizes, learning. And, of course, you know, with their agents and, and and various communities.