Hello, everyone. Good morning, good afternoon to those, in Europe, and welcome. And thanks for joining us, our session today, our new in ecommerce showcase. So it's the spring twenty twenty two edition. My name is Shereen Reid. I work on the product marketing team here at Coveo, and I'm joined by my colleague, Simon Lange, who heads up the ecommerce product management team, and Anthony Delage, or some of you may know him as AD, a senior product manager in ecommerce. And they're both be walking, you through some of the newest and hopefully exciting, functionality in the product today. So first, before we get started, I do have a couple of housekeeping items to cover quickly like we always do. Everyone is in listen only mode. However, this session is, of course, designed for you, and we want to get, we want you to get the most out of it. So please send any questions that you have along, using the q and a section on your screen, and we do have some time reserved at the end of the session for Simon and Anthony to answer those. And I'm gonna answer a popular question that always comes in as well. Today's webinar is being recorded, and you will receive the the presentation within about twenty four hours, of the conclusion of the event. So, with that being out of the way, and before we dive in, I wanted to give a special welcome to our Qubit customers as this will be the first product showcase they will be joining. And, also, before I hand it over to Simon and Anthony, I wanted to give just a bit of a perspective on what this combination of both Coveo and Qubit allows us to do. And I think this is a slide that I've used many times, and it really drives that message home. Because when we think about it, making ecommerce experiences relevant and engaging, we have to think of every type of shopper and also about the entire buying journey. So we have to consider that there's really a spectrum of shoppers out there. There are people that discover products in different ways. So there are those shoppers that know exactly what they want. They wanna be able to go to your site, have fast, responsive, accurate search that gets them to the right product for them so they can don't waste their time. They can add to cart and and go. Then all the way at the other end of the spectrum, there are shoppers that are browsing. So they need guidance, more personalized recommendations, maybe offers to guide them in the right direction to find what's best suited for them. And then there are those that also might wanna understand better how to use the product. So when you think of more complex items like electronic equipment, if you purchased a new router or a new small appliance, and you wanna go back to the site and figure out how you're supposed to be using, this piece of equipment that you've just purchased. And finally, there are those that might be returning customers. They might want to repurchase items that they've ordered before, and they want to be able to do it quickly and efficiently. So you need to, of course, cater for all of these different permutations and commendations at these varying stages of the journey and still make them relevant. So it's a tall order. So from discovery all the way through to evaluation, and, of course, I'm not gonna go through all of these different elements right now, but just to show that there's a lot of different functionality that Coveo offers along that purchase journey that helps, experiences more relevant. So for example, in the product discovery phase, you know, you have things like predictive query suggestion, AI powered product listing pages, which removes removes some of that shopper friction and eases product findability. And then in the aware stage, with the acquisition of Qubit, we can now power personalized welcome messages, site wide banners, special offers for new customers, tailor it more to the audience that's coming, to your site. And so it continues all the way through. So with that being said, I wanted to just quickly, give a preview of the agenda. So today, Anthony is going to show some new merchandising features for creating audience based recommendations and also ways to monitor campaign and placement health. Then Simon will, walk us through some of the AI driven personalization as you go features, specifically focusing on intent to wear product rankings, as well as additional product indexing capabilities that, we're launching or we've just launched, new headless controllers, and, improvement in SAP connectivity. So, we'll round it out at the end with a look at the road map, especially around the evolution of the merchandising hub. And with that being said, I'm going to stop sharing my screen and hand it over to Anthony to take it next. Awesome. Thank you, Shireen. So thank you, everyone. Thanks for your time today. Nice to to to everyone I haven't met before. Anthony joined, Qbit in in, June of twenty fifteen, so it's been a while. And so really happy to be sharing this stuff with you guys today. So a couple couple topics that I wanna share in terms of things that that that we've big changes we've made to the product over the last six months, that that are really kind of gonna affect people. The first is, we've rebranded our products. So this is kind of a simple change, but it's a powerful one. We really do believe that we're better together with Coveo and Qubit. And, from that perspective, we've kind of changed with the the the look and feel of the product for to to rebrand it. And we've also renamed their products, and that's, the big step in kind of expressing what these products are meant to do. And so we have the Coveo Merchandising Hub and the Coveo Experimentation Hub, which were formerly QBit for merchandising and QBit for customer experience. And so just this quick change is is is something that I think that hopefully drives home where we see these products going, where we see them fitting within organizations. And and and I'll kind of really focus on merchandising today in terms of of what's been done. And so as as Sharmini alluded to, there are two big features that I wanna speak about. The first one is gonna be what we call campaign and placement health. And so, the the genesis of this was that, you know, we're we're we're a very, data heavy product. And, you know, if you're gonna execute, these kinds of revenue driving activities, content recommendations, badging that both need quality data to to to to perform and then also need accurate tracking to analyze. There's a lot of steps in the chain that need to be monitored to make sure that we're actually being kind of kinda clean end to end. And so what we saw is that there were kinda certain patterns emerging around kind of, issues that could could come up, more often than not. And when we saw those patterns, we decided to to to kinda nip them in the bud. And so what we've invested in is this this notion of campaign and placement health. And what that means is in real time, what we are looking at is the data coming in for your campaigns, the data coming for your placements, and we're making sure that that matches our expectations. And when it doesn't match our expectations, we are gonna go and and tell you why, and it will help you fix it quickly. And so it's all about really, fast detection and solving issues. Now if you look over the course of the year, if you're down for for a matter of hours, that that's a very big difference in in in revenue driving potential from being down for days or weeks because you're you're only finding out that things are broken, you know, weeks later. And so it's really about being proactive, giving you guys the tools to respond quickly, and giving users as well the the tools to to feel confident when things are green that everything is good. And so here, kind of a a more deep look at what we're actually looking at. Well, a lot of what we're doing is what we call it heuristic based, looks at the data where, you know, the the the common kind of patterns that emerge that are that are that are faulty are pretty pretty much the the same all the time. So are you are you is your campaign actually receiving impression events? Do we know who is seeing it? Is your campaign receiving click through events? And here, we add this layer of, well, what did we expect? Because some campaigns can be clicked on, some can't, some that's actually a placement thing. We look at visitor splits. So if you intentionally set a a ninety nine to one, split as in this case, Does the data coming out near the side look like that? If not, we might have a a flaw in the process. We look at JavaScript errors for for injected placements. And then what we recently added is what we call these API outcomes. So this is just kind of a descriptive look at how our API is responding to specific, or to to to the to the campaign. And what that means is that you this can be good debugging support when the campaign doesn't support as intended. So here's a case where, basically, I'm expecting click through events in the control. They're not coming through. This would be a question where I would go work with a developer who developed this placement, make sure that we're actually sending that data through. And so this is, again, quick detection, quick response. We're looking at real time data, and this, you know, in the long run, gives you more revenue driving potential as I said. So that's campaign and placement help. The the the same kind of principles apply at the placement level. So we'll see kind of a little badge come up here. Needs review. Helps you fix it quick. Very simple. Second thing I wanna talk about, as Shareen alluded to as well, is this idea of recommendations decisioning. So what is recommendations decisioning? It's it's a way of ultimately, delivering better recommendations to campaign. It's very simple. And, there's two kinds of patterns that we saw that led to this. So the way that our recommendations formally work, so let's say I go to set up recommendations on my product detail page, is that we only allowed you to target, or set up one recommendations configuration per placement on the site. And so if you were setting up your PDP recommendations, you could target one audience at a time with one strategy, one headline, and one set of rules. That actually drove a lot of revenue for users. It was actually very effective. You know, when you have really powerful models, you can you can do a lot just by letting the model do its work and by having rules that apply to everyone. But but it became very clear that people wanted to do more. They wanted to go deeper. And so when people were stuck into this configuration, they ended up deploying kind of middle of the ground or or middle of the road configurations that would work for everyone, and they weren't necessarily going to the ends of their their abilities when they came to personalization. And so what I might have an kind of intuitive, feel for or a a data backed reasoning for is knowing that, hey. My my returning users actually, I wanna engage these users rather than focusing on conversion, because that's kind of a priority for them, and and this kind of model might work best for them. Well, what I can do today now with recommendations, decisioning is quickly add more of these. So regard you know, with all the audience capabilities that we have, whether we're pulling in third party data, using the data that we collect, We can get quite sophisticated as to how we break down people, you know, returning purchasers, non purchasers, people on given URLs, people who are part of specific cohorts. All that audience, segmentation capability combines with recommendations, and now I can get much more intricate in how I set up my recommendations. And so what I can do here equally is start to, you know, go for, you know, specific rules, specific visitors so I could decide that our returning visitors shouldn't just get this engagement based model. I also wanna promote products over a hundred pounds. And what this is gonna do is increase the probability of those products showing up in the carousel. And this is just an example of what I could do where I decide that my returning visitors kind of have a, more affinity to more expensive products or that this might be a a cohort of visitors that I can upsell a bit more. We make it super easy to go in and preview this on the site. And so just in the app, we can already see that this promotion rule is really boosting up these more expensive products for these returning users, and that's exactly what we want. And so we're giving you the tools to both set up sophisticated configurations, understand what what happens on the site. And here's an example of that working in in in practice. Here's how our reporting breaks down. So, you know, as per usual, we always have that high level reporting for for any campaign where you can AB test the control against the variant, and look at a high level. But we'll also break down the results by audience. And, this is a demo site, so there are actually no returning users. But the principle remains where you can actually kind of AB test at a more intricate level and see how those configurations change. And so we're working on now now that we've kind of deployed this general availability is really upping our best practice in this area. And for a long time, we've known that these kinds of configurations are probably a good idea. Right? If there's a there's a there's a good chance that you can get more out of your recommendations when you make sensible choices about who gets which models, who gets which rules. Well, now it's about kind of developing best practice, really pushing these kinds of ideas. And as we figure out kind of what works better, we're gonna create kind of a virtuous cycle where we're able to help our users do more, get more value with it. And so that's a big part of recommendation decisioning. What, I don't have on this demo site here, but I think I can speak to is this kind of notion of, multi region recommendations. And so a multi region recommendation would would be very relevant to someone who works in, say, continental Europe. It doesn't just cover a a UK catalog and you know, British English and pounds, but also would have a French catalog, a a German catalog, a Dutch catalog. Historically, that's been a very tough kind of recommendation set up to to to configure. Either you end up with really complicated front end implementations that are very static where you have to, have, like, very bespoke API calls for each. You end up sometimes in, like, these very complicated tenant models with with recommendations providers where where where where you might have to have, like, a a separate tenant for each of these regions. What we can do now is actually just kind of target multiple catalogs just like you would here, and and have each, region kind of handled in the same in the same campaign. The nice thing about that is that, you have one placement be deployed globally. And as a merchandiser, all you have to do is go into one UI and control all your recs. And so, this is definitely something that we see really can be relevant to a lot of our very global kind of customers or even ones who who tend to operate heavily in constantly Europe where you tend to kinda cross cross, regions. So those are the kind of two big focuses of recommendations, decisioning. Again, I mentioned the reporting side of being able to take those configurations and analyze them on the back end. I'm gonna hand over to Simon who's gonna kind of, take it from here. Thank you. It's alright. Alright. Thank you. Thank you, Adi. Okay, folks. So now for, the core of the product being obviously the search engine, and our investment into AI, as some of you might know, those that have been following us for a little bit, we have been investing heavily into what we call vector based search. So some of you probably have seen this before, but I just wanted to make sure we're all on the same page. We all have the same understanding. So vector based search, is the idea of taking a product catalog and creating a vector space out of it. Meaning that let me just, zoom it here. There you go. Meaning that every single product within the catalog will have a specific space and will be clustered based on the interaction that the users have with said product. So for example, if I take a current catalog today or, you know, your current catalog or even a brick and mortar store, there's always that kind of logical placement of product, usually through categories, brand, or usage. But as we learn from the from the the shopper's behavior, we can change that type of, that type of taxonomy or even categorization, for that specific user when they are actually completing the journey. So mostly about, you know, every few hour or every day, we will reconstruct that vector space and we'll reconstruct our vision of the catalog. And then when the user actually go on the site or a shopper goes on the site or or any of your ecommerce solution, they, they walk through that that vector space. So mostly, they are being sent to different clusters, and we will adapt these clusters based on their current intent. So several, several features have come out of this, and they mostly rely on, the same pattern, which is we calculate the vector space distance to understand your intent. So mostly as you as you move from one product to the other, we understand if you're still within the same intent. So you're still, for example, browsing for a specific type of product, or we will understand that we don't know what is your current intent, which, you know, knowing that we're wrong, is an important part of actually bringing the right product. So we will know that, for example, your intent is currently changing, and you might be looking at completely different type of products. So, obviously, this is not just based on category. It's based on everything we understand from from the the product inside with catalog and outside of the catalog using user behavioral data. So for example, in this case here, you know, I'm I'm looking more at a, you know, specific color, a specific category, which is more sports shoes. And then, you know, I'm kinda changing to, for example, a more dress formal shoe, a pair of boots, and then finally back to the sport shoe. So in between these different products, we calculate that there is a vector distance. So mostly you're changing completely intent, and we'll try to figure out, you know, what's your new intent and reorganize the product accordingly. So we've done this at the first phase for query suggestion. So mostly being able to, understand the terms. So this was using mostly semantics and NLP. So being able to understand, for example, when you start typing something, what is the context of the rest of the the sentence? So, you know, the a a good simple example of, vector based semantics will be if I'm looking for if I'm if I start to type bank, the word bank could mean the financial institution and could also mean, for example, the side of something like the bank of a river. So, obviously, the context will tell us if the bank you're looking for is actually the financial institution or the side of the river. It's the same thing here in a catalog. So if I start tapping a jacket, if I was looking previously more into, for example, the hiking, situation, I was looking for things for men, then, you know, the query suggestion will start looking at, you know, hiking as a category or jacket for men as a query. While if I was looking more into, you know, running or I was looking more at at women, type clothing and apparels, I might be more into the jacket for women in sport running, type of scenarios. Now we've expanded that, recently to, what we call intent aware product ranking, which will do the exact same thing, but reordering completely the product list. So on this, let me switch here to a demo. So here, I'm an anonymous user. Let me just zoom here, realizing a bit far. And I start, for example, looking for let's say I start, for example, for shorts. And the first thing you'll see here is, obviously, since I'm completely anonymous, it's the first time I've ever logged in here. I have not done any kind of browsing at the moment. I have not even looked at any articles or anything like that. This is really my first query. It's completely cold. I'll start seeing, you know, the most popular products as shorts, so mostly a variety of, you know, gender category brands, that are being shown to me. Now if I search, for example, for, let's say, you know, golf shoes, for example, here, the the the first thing that you see is obviously, you know, I've been auto categorized. So this is using the dynamic navigation experience, model that is already offered by Cuvel. And you can see the different golfing shoes that are available to me. So I'll I'll choose, for example, this first one here, and I can even choose a second one. So here, these, products are recommended, using Quvale recommendations. So I'll add to cart here, and then I'll start looking, for example, for gloves. So already you can see query suggestion. It's starting to look, for example, for men. It's starting to categorize me into the golf into the golf category. So I search for gloves, and I can even remove the auto filter here in this case. And you can see that already, the gloves for golfing have been pushed to the top of the page as opposed to, for example, the gloves for skiing. And now if I search, for example, for shorts, I get pretty much the same thing. So I no longer see, you know, all types all different types of shorts. I'm pretty much being, you know, categorized into that sport, sorry, into that golf category, that I've seen before as well as mostly looking at men's wear, because I was pretty much, you know, adding to the the cart, men's shoes before. So this is currently being rolled out, in some in some client's environment. For example, Laurent, during the if you've seen the kickoff, was presenting, some of the some of the model being rolled out at Bass Pro Shop. So this is one of the thing being tested. If you're interested today, if you're a current client, you're interested into that feature, let us know. Reach out to your CSM, and we'll take a look at your current setup as well as your data structure, see if, we can put that in place quickly or what needs to be changed, in order to for you to be able to leverage that feature. So this was, the intent aware product ranking, part of the personalization as you go suite of product. It includes, as I said, the personalized the predictive query suggestion includes the dynamic navigation experience, which is the auto facet filter that I've shown, and it also includes, the session aware recommendations. And we'll keep keep investing into these vectors, in the future, so there will be more and more, good stuff coming out of that vector based, investigation. Now one element as well, that we've created around the idea of owning the catalog, owning the catalog data is to be able also to update that catalog data anytime. So we have this new feature called partial, product partial update or partial document update, which allows you now to push a single field instead of pushing an entire document to the Cavelo stream API. So namely, call the catalog source. So today, for example, if you have a part of a product into a certain repo like the commerce engine, and you have other data in in other commerce engine, which are, for example, synced not necessarily at the same time, you can simply update the field or the product attributes that are into, you know, these different repos in the time that you want to to to change them. So you don't necessarily need to repush the entire, document for it to be reindexed. Another, kind of, you know, nice to have is quality of life improvement, I should recall, is the product listing API and test controller. So when you are creating a product listing, in this case here, for example, I'm in the men's t shirt and polo shirt section. So Apasia is not necessarily powered by a search query, but that is used for product listing, and that is currently powered by Coveo. You can use our new product listing API or, for headless controller out of our Coveo headless framework. This is a much more simplified set of tools, a the API endpoint, and both the controllers are, much more simple to use than, let's say, the search controller or the search endpoint. They have less the payload, the smaller, and, and more focused around product listings. So it's a it's a kind of easier way to get started. Plus, it will send all of the data into a specific bucket so you'll be able, from the consumption dashboard, to see exactly without necessarily using search hubs, to be able to see what is the consumption of your listings as opposed to the consumption of your search page or your recommendation panel. So it's also useful for consumption tracking and to monitor your instance. And now on the indexing front, we've done a quite an important investment into SAP commerce. So commerce including hybrid on premise and commerce cloud, SAP commerce cloud. If you are not aware, we are on the SAP, partner store listing, and we have a source now using, the old data two connection. So if you want to be able to index the old data API from Kaveil, the source will soon be available in, your, your administration console. If you need it today, reach out to your CSM, and we'll enable it for you. It it pretty much use the generic rest API connection that will connect to the API with a recipe on how to get the content out. Obviously, there is some configuration around the SAP or data to get API endpoints to be done prior to indexing, but it does simplify the indexing process if you want a more, full approach to your indexing process in SAP. Now so that was, everything new for both merchandising and, more core search and listings. I will pass it back to Adi for the future of, merchandising and mostly the future connection between the two products. Adi, back to you. Let me stop sharing here. There you go. Perfect. Thank you, Simon. So what I'm gonna do now is just kind of look, as Simon alluded to, at kind of a high level view of our road map for the merchandising hub. This definitely is not the granular look that you might get, and would rather look at kind of our high level view on this and where we're really investing more broadly. So the first thing I wanna touch on is, product catalogs and what we've been doing there. And so this has been a big area of investment for us over the last couple of months, as well as going forward. And, specifically, what we've been doing to kind of hybridize our our, approach to the catalog is feeding the kind of core Coveo index and catalog with Google product feeds. Historically, we've used Google product feeds as a means of kinda getting product data for recommendations on the Kyubit side. And what we're gonna be able to do now is have one catalog, whether you're sending a Google product feed or using these more, developer oriented, but, powerful sources available on which way it's stored on the Kubeo side. And we're gonna have this kind of singular, feed infrastructions. You know, the the reason we invest in this is because the the themes of being better together and being able to personalize and and and affect the entire customer journey, those things happen when we have shared primitive shared infrastructure. So this is a huge piece for us and enables a few things. From perspective of Qubit customers, historically, we've had we always had the challenges, going all the way down to the variant level. We've been very effective at dealing at the product master level, and that's driven a lot of value. But there's kind of very valid uses of Variant data that that we want to enable. And so one is just in terms of the user experiences you can create with recommendations, and soon to the other features, just enhancing that presentation using variant data. So showing color swatches, showing size of availabilities, etcetera. The second thing is not just using variant data to show it to your end users, but actually decisioning based off of variant data. So there, what you can do is create rules that are based on availability of variance. So, you know, for an experience that I think that everyone has has probably gotten at some point is you wanna buy shoes online and, you see a recommendation, it's a great shoe, you'd love to have it. Turns out it's only available in size sixteen here. This is nothing against people with a size sixteen feet, but that's not me. And so, that that that experience can be quite common. And so what you would really like to be able to do is say, well, only show shoes that are available in the most common sizes and your recommendations, and, you know, people will be able to find those kind of, bigger size shoes through a more, like, PLP type context. And that way, we can kind of get people onto products that they're actually gonna be able to convert on. But those are kind of two, kind of quicker wins. The bigger kind of theme here, because of all this investment we've made on the catalog, is being able to actually start creating joint product features. And so we we really kind of have a have an ambitious vision for what we can do with, the kind of user focus of the merchandising hub and the powerful powerful technology that comes from the Preveo side. And that's what kinda leads to my next point, which is the product listings manager. So this is an area where, we are we are very ambitious in the sense that we don't really see great tools in the market today for managing product listings. You you kind of get two extremes. You get very manual approaches where people are managing their their listings through their ecommerce platforms. Everything is static. It's a lot of work to change your PLPs, and then it's just work that doesn't scale. So you end up, going with kind of very, quick and dirty kind of ways of just getting products on the listing pages. Or there's the other side where it's a bit less common when people are using really powerful AI tools, but they have no way of operating it. And so if you're if you're looking for any kind of control over over how these listing pages affect your users, you don't really have any. It's just kind of the algorithm. It's a black box, and you kinda hope it works out for you. We think there's a space in between here where we can be really effective and and and where we can deliver something that's that's really gonna be quite unique and and provide a lot of competitive advantage to our customers. And so this is something where we're we're we're kind of at the beginning stages of. We, like I said, are kind of relying on a lot of these joint investments to be able to push this through. And so what we're gonna be opening up is early access programs, or or an early access program for the second half of the year. So if you are keen to get involved in this, we'd love to, we we'd love to kinda work with with users quite closely in building this and making sure that it it really fits what people want. And, we're quite excited to to to see what this is gonna be because I think, you know, say in here or a a year or two from now, we're gonna have something that that that really kinda makes, Coveo extremely different from anything else you can do with commerce. Yeah. So, those are kinda two things I wanna touch on the road map. I'm gonna open up for q and a here. Again, I think we can kinda either ask questions in the chat, or the q and a section. Great. Thanks, Anthony, and thanks, Simon. I know that was a very brief overview. For those who want a more deeper dive, definitely reach out to your CSM, and they can go over some of these new functionalities with you. I invite you, if you have not or if you have questions, please, you know, submit them through the q and a portion of the chat, and we'll get to them. A a couple did come in. There was one, and I guess, you know, we have some, of course, some Coveo long time Coveo customers on the on the line today, and they saw the new merchandising hub or the what was the Qubit merchandising hub. So the question is around whether they are going to continue to use, the admin console, which is where typically Coveo, users log in in the future, or will they be logging in to this new merchandising I have that you showed today with the recommendations, decisioning, and such? So if you can give a bit of color, maybe, both of you on that. Yeah. Absolutely. So I can start. So the the goal here is to open the administration to new audience, in this case, really merchant. So right now, the merchant inside of the Kubernetes administration console had, you know, powers and tools that they've done they didn't really need, as well as tools that were not there that they would have needed. So really bringing this for a new audience, which is really merchant. And more power users that love to do, for example, routing, inside of the query pipeline, have some more advanced feature, do scripting, will still have access to the Kubernetes administration console. So it's not really one or the other. It's really a question of audience. So we expect, the merchants, mostly those that are doing merchandising, product boosting, brewing, re ranking, and all those, to spend most of their time, if not all of their time inside of that new Coveo merchandising hub. And, the ones that are managing more, you know, search, advanced performance, monitoring and such, to be inside of the Coveo administration console, I would say half of the time, whereas I think there would still be a lot in the merchant hub even for the power users. So that's the current, topology we want to bring forward, and, and we'll follow that path at least for the next year. Okay. And a similar question to that came in around, how can they see more functionality that is in this merchandising hub? Because, obviously, you showed part of it, AD, but you didn't show, like, the breadth and depth of of what's in the merchandising hub. Yep. AD, you wanna take that one? Yeah. I was gonna say, I think the the the best way to kinda get a demo is to reach out to your CSM. We can definitely kinda show that to you. And, yeah, as as the product team, just kind of for for those who haven't, worked with us before, like, we we we strive to kind of, drive the product based on deep customer contact. We wanna be talking to you often, as the product team as to what your pains are, what the product can help you, do, and then help you kind of achieve your job. So, with that in mind, you know, those demos. And if you wanna engage with the merchandising hub more deeply, we'd love to not just work with you through through customer success, but also as a product team. Yeah. I was gonna say for those that don't have CSM, we use acronyms sometimes, but customer success manager. It's your point of contact to Coveo. There was a question that came in, but I think you answered it, but I'll ask it anyway. Just repetition, is helpful sometimes. Can can you combine the partial product updates coming in from different sources? So, like, let's say you have some attributes that are in your PIM system versus, I don't know, even pricing, which is coming from an ERP system that you might wanna update. Yep. Absolutely. And I think, you know, the I didn't mention it before, but I was mostly saying, you know, one commerce engine to the other. But, you you know, there's a good point there in the question. You know, a PIN, a CMS, an ERP, wherever it resides, as long as you can push it to prevail, we'll, you know, we'll take it. So even if the product, for example, was indexed kind of in in its whole, in the first time, but you need to update, for example, just the price, and the price comes from a different system. You simply have to push the product ID and the price change, and it will update the entire object without having to reconnect to your commerce engine or just to your CMS, have all of these systems talk to each other, reconstruct the product in its all, and then repushing it to convey. So, really, the the the goal here is to simplify the the entire process, for for those that are managing the indexing process. Yeah. It helps scale, right, when you're having when you're talking about these millions of people. That's the main goal. Yeah. Okay. And I know at the beginning, I gave kind of a vision of the the the buyer journey and all of the different aspects of where Coveo can now, help the experiences. But a question around, why do you think Qubit and Coveo products fit well together? So I don't know who wants to take that one. Yeah. I can I can start with it? Obviously, you know, when we when we did the acquisition, we had this, this question in the back of our mind. So definitely, there are several elements, that Kubrick brings to Coveo. First of all, the expertise and the current even UI and the current system around merchants, and our focus really wanted to be around merchant. Also, a a lot of maturity around data processing. So, obviously, you know, Coveo has a lot of maturity when it comes to AI. However, being able to properly handle data, stream data, have a have a product call that has been trialed and tested, into ecommerce retail environment, is something that Qubit had, and was at even a higher maturity than what Coveo had so far. So I would say, you know, these two main elements brought together means that now we can have a platform that extends on the entire ecommerce experience, and also that is more friendly to merchants. So, really, for us, from from a Coveo side of things, we want to make sure that AI the core AI investments stay there. It's the core of our business, but Coveo brings that kind of, I would say that access to that that this core AI feature that we're not necessarily available before, especially for merchant. Great. Do you have anything to add to that, Anthony? Yeah. Yeah. I'll add it from the QA perspective. You know, what we kind of before the acquisition, we're also kind of saw kind of complimentary technology in in a very similar way. We were already investing, you know, because some might be aware of in search, as of last summer. And so we definitely saw that the the right kind of product for for the market was one that would kind of blend search recommendations personalization. This is kind of where where things are going and and where you can ultimately add the most value. And so the the fact that that we've been able to kind of do the this fusion of our products and and and take the best elements of each. We think that, you know, it takes time to put these things together, but when they are put together, it's gonna be something that no one else has. Amazing. Thank you. Thank you both. And, I'm gonna wrap up now because I know we're at the end of our time. Thank you to those who joined us today for this new product showcase. Hopefully, it was informative, for you, and we're gonna have another one probably in a in a few months from now and, showcase what's new and and exciting in the platform. If you are a current customer, wanna understand more about these features, how to apply them into your instance, and reach out to your customer success manager as we man as we mentioned. Also, you know, able to, take advantage of new features and maybe test new features even before they come out or input into them, then raise your hand to be part of the early access program as well. And if you're not a Coveo customer and found your way to this webinar today and wanna find out more, then just head to our website, and you can fill out a demo request form. And with that, we thank you for joining us today. Have a good rest of your day. And for our UK based customers, I envy you. Have a great long holiday weekend. That's all we have today. Bye, everyone. Thank you, everyone. Thank you.