Hello everyone, so glad to be with you today. In the next fifteen minutes or so I will share with you exciting innovation we've made available to the market recently and also give you a glimpse of what is coming in twenty twenty four. First I'd like to give you an update on our platform with all the new capabilities that we've introduced recently and where we're going. So first, as you're familiar most of you are familiar we had the very extensive connectivity layer with those are the logos as you see at the bottom where we go after content data in various data sources across the enterprise. And we make that data available through AI search and recommendations, personalization across those different point of experience at the top. So, website, commerce, service and workplace and you see those, different use cases across those point of experiences. We get access to this data through connectivity and push API with security. We have an index. We have ways to configure pipelines extension and do index enrichment. But now what we've added, because of generative answering we've invested substantially in passage extraction content vector to add to our product vectors capabilities that we already introduced last year in the context of e commerce. All of this helps feed our different behavioral Machine Learning models and Deep Learning models so we can better understand the intent, we can improve relevance for those different use cases where Coveo is being embedded and now we're using those passages and this relevance from the various, the various elements of the platform really to ground a prompt that we use to ask a large language model that is running on our platform to generate answers in addition to the search results and the recommendations so on that we're providing and we'll talk more about that in the next few minutes. We obviously have all of the administration capabilities built into the platform. It's important for us not to be a toolbox where, the orchestration is made by, by third parties. We think that we need to provide a complete platform where all of those elements work all together under the same umbrella. So, we have administrative deep administrative capabilities and then we have all sorts, all a broad array of capabilities from a new UI and API frameworks. So if you wanna use out of the box components and insert that directly in your experience as possible, but if, developers want to go directly at the API level or headless level to make it available directly in the app natively integrated it's also possible. We have, different set of plugins for Salesforce and SAP and Commerce and ServiceNow As most of you know, that is also part of the platform. And finally, we have advanced merchandising that is critical for e commerce. We'll talk more about that in the next few minutes. So, at the end, as of today, we provide advanced semantic search, AI recommendations, now generative answering and unified relevance all coming from the same platform. So let's get a little bit more into the details. First we provide relevance and relevance is really, to match content with the user intent. It's understand what the user wants and deliver what is the most relevant for that user. But at the same time we need to couple that with business outcomes and that's merchandising. So we provide the capabilities for the administrator or the merchandiser in the context of commerce to really optimize for its business outcome. And the key here, the magic of the Coveo platform is the ability to couple those two different objectives together. So, focus on relevance but then align with business outcomes from the merchandisers, that's the key to success. And then we need to do that across different methods all based on the same platform. So, of course, we need to power lexical search and lexical search is what people would describe as classic keyword search and this is not going away. This is not going away because often people don't know what they are looking for, they want to discover what's out there, they may be, I mean some would say lazy, other would say efficient. Let's see if I get what I want with one keyword instead of one long sentence, right? So speed matters, lexical search is not going away. Semantic search is the ability to understand in the intent across a longer sentence, a more a deeper a deeper question if you want. So, obviously, we're supporting that. Recommendations is about understanding where you are in the experience and surfacing content that, the AI believes is relevant for you. And then finally, this is new of last year, end of last year, question answering is to leverage our semantic search and our relevant stack and security to surface the best excerpts or passages of results and use that to ground an answer that we leverage for our LLM services or Large Language Model services in our platform. So, and I'll demo that a little bit later in the presentation. So these different methods are tailored for different audiences. We have a lot of customers that are using Coveo in an anonymous fashion. Think about large B2C commerce or retail, people tend to log in once they're ready to buy. But all of this anonymous experience before buying is super important critical hence why we have invested a lot in past years into session based personalization and the ability to know with a few clicks what is really relevant for the user or the shopper. We have the ability to provide the semantic proximity now for those, for those users depending on the questions they're asking and then of course we tailor for business outcomes. When you're authenticated you have access presumably to user profile, deeper user profile use, pass transactions and so on and in some cases, security filtering when it's required. So the Coveo platform we believe needs to support all of these different methods for these different audiences at the same time. It all needs to run a same platform. And then we believe that when authenticated there's a lot of value at synchronizing with a CDP, a customer data platform. One example I want to share here and this is coming from our partner SAP, this is a slide that they're sharing with their customers right now in the context of commerce where Coveo synchronizes with SAP CDP. So we share, Coveo shares with this SAP CDP instance at one customer all of the behavioral data and the intent that is captured into the Coveo experience of this customer so the CDP can then use this behavioral data and repurpose it in other models or other experiences, elsewhere into the organization. At the same time Coveo will also leverage user profiles, additional customer information that may be available in the CDP so we can tailor the search, the AI relevance, and recommendations in, even a more precise fashion. So, we believe that in twenty four we will have more and more of those integrations. We expect Salesforce Data Cloud to be a target of choice for customers and we expect to be natively integrating with Salesforce data cloud in the future. Now, let's talk about merchandise. This has been a huge priority for Coveo in twenty twenty three. We have basically rebuilt our merchandising platform that came from an acquisition that we've made in twenty twenty one. We have replatformed that on our core Coveo platform. So, merchandising is now native to Coveo. It's a built in experience. There's no need to multiple logins and so on. So, it works hand in hand with the other capabilities of the Coveo platform. It works for product listings and products of commerce and search. And I wanna give you an example here of, what we can do with this merchandising platform. So, here we do queries and analytics for those queries and stats and performance for those queries in a given context. So, if I click here on Kayak Pedal, I can see that at the top level I can see number of views of these query's revenues, conversion rates, how many products are attached to this query or being surfaced by this query. I can see it's going decently well in terms of revenues, conversion is remaining the same, so presumably we're selling higher products in this specific case, right? KAYAK Paddle. So, if I double click on KAYAK Paddle here I can see a more detailed view on those metrics and those stats and I can understand what is working well, what is not going that well. So, not only I can see the high level of revenue conversion rate, revenue per visit, clicks on products and so on, but I can also scroll and get those details by products specifically And I can search for a specific product and see the performance of one product in the given context and given query. We have built that based on the feedback of our customers, of our merchandisers that wanted this kind of granularity and we're quite excited about rolling that out in twenty four. All of our new commerce customers have this have this in their current platform, this is being rolled out as we speak to all these new customers and we have, and we have conversations where our existing customers about how to enable this new merchandising platform in their current organization. This is an example on how you can manually boost and bury withdrawals specific, items. And you can also pin specific results, on a given position for a given query, depending on the strategies. So again, all of this is available for customers as we speak and we expect to build a lot during twenty four, a lot new capabilities in Twave. Now, if we apply generative answering to commerce, the value proposition here is very interesting. We say that it's really moving from a shopping cart operation to a destination. In other words, our commerce customers not only are they going to sell the ingredients to a recipe but maybe it would be great if they could provide also few but that is being currently implemented, with a few of our large commerce customers. This is the example where in a home improvement environment you're typing tips to build outside kitchen with a barbecue. So instead of asking Google for what to do there, get the elements and get the recipe on how to build an outside kitchen with a barbecue and then shop at each retailer for each individual items, let's enable that at the retailer based on the buying guides and how to content. So in this example here we're generating an answer based on the catalog, based on the buying guides, based on the how to content but then we make sure that it's integrated with the shopping experience. So you're asking this question, there's the quote unquote recipe at the top but then you've got category or listing pages and then we've got results here. So it's an integrated shopping experience and allows user to either look at the whole or get more into the details. We are rolling that out with our customers and quite excited about, the potential. Now at this point, at this final stage, I'd like to switch to a demonstration of what's coming and, hopefully you'll appreciate it. So this demo is built on top of an index of COIL technical documentation, created knowledge, forums, and YouTube videos, among other things. So I will go to search box and I will first type what are the different ways to build a search interface. So while the answer is rendering please note the rich formatting here so we have clear headers, nice spacing between paragraphs, and we now have tables providing an elegant way to render information to users. If we scroll down, we have citations to the documents for which answer is generated. We can see an excerpt before clicking to open the original content. We now have also the possibility to enter into a conversation with a search engine. So we have an ask follow-up search box here and we also have suggested queries here that are automatically generated. Of course at the bottom we have great search results ranked by relevance. So I will ask a follow-up to the Coveo the original question. Remember the original question was, what are the different ways to build search interface? So let's say that I will type using Atomic which is one method to build search interface with Coveo. So if I type using atomic here it will generate a new answer with the in the context of the conversation and while it's rendering here it's rendering a nice a nice list of features and benefits for Atomic here, but it's really a follow-up to the original question here and I can click here to reopen the original question and get the answer here. Right? And I can collapse this and I will get back to the using atomic here. So now let's see what what I had in terms of suggested questions here. So how does Atomic Library compare to JavaScript search framework in terms of performance and programming skills required. Let's click on this and that's very interesting right from a it will, it will compare from a performance and programming skills perspective how Atomic and JavaScript Search Framework compares with each other. I will type in new search here. I want to show you one last thing. This is pretty cool. How to change the look of Atomic. So while it's rendering the answer, of course, you will see it's rendering quite quick rather quickly. So you will see a nice table here listing all the custom properties in the description that can be used to customize, the styling of Atomic here. But please note the different, styling here in the answer, and we're using a different styling here for code and CSS, CSS here. So in addition to nice paragraphs, headers, tables, and so on, now we can also render code in the answer, which is, quite important, especially in technical documentation, the context of technical documentation. So all of this, is rolled out as we speak to existing customers, all of these great capabilities. They are customizable and can be adapted to any site layout. So we're quite excited about the outlook of this and the impact of this on user experience, and we look forward testing that with you in the future. So with that I thank you so much for your time and interest today. Hope you enjoy the presentation. Twenty twenty four will be a year of innovation. We're going to make relevance and merchandising work hand in hand like never before and generative answering will have we think a huge impact on all of our customers. So we look forward to hearing from you and working together in twenty twenty four.