Hello, everyone. Welcome to Coveo, and to our webinar for creating powerful site search experiences with intelligent power, AI powered search. My name is Sarah Samnani, product marketing manager here at Coveo, and I also have Paul Sheridan, our solution architect at Coveo, who will be walking you through, a really good demo on how to provide the best site search experiences using an AI powered search. So let's jump into today's agenda. We'll start with introduction. We have some live examples of sites using Coveo, and then we'll also take a quick look under the hood and then start with the q and a. Alright, Paul. Ready to get started? Absolutely. Awesome. So before we jump into the details, right, like, let's, like, really think about, our customers or put ourselves into the customer's shoes. When we think about the various areas that, a customer can interact with your site, they can come to your dot com. They can potentially go to your ecommerce page and buy products from you. They could be, logging into their customer only portals or their agent only portals and searching for relevant information. And then at the same time, they might be looking at resource pages or help centers, trying to find the information for your product, for the services you're providing, or even use a potential chatbot. What Forrester has found that forty three percent of visitors will immediately go to the search box. So whether they're landing onto your resources section, whether they're landing onto their your ecommerce page, the search bar is the very But we've also seen on the other side, if they cannot But we've also seen on the other side, if they cannot find what they're looking for in exactly three searches, they will go elsewhere. Seventy three percent. That's quite high. You do not want, your first impression or the brand experience or the search experience or the site experience that you're providing to your users, your customers, your visitors, your agents to lead to such unsatisfactory experiences. However, even after, you know, COVID has come kinda gone and we're returning to our worlds again, Ninety nine percent of the customer companies have been struggling to deliver relevant search experiences to users. You'd think it would be a little different, but this is the data that we're seeing. But this is where Coveo comes in. Right? Our AI powered search critical capabilities can provide you the access and the leverage across your sites, whether it be your public facing sites, your dot com, your chatbot, your resource centers, or even your private logged in sites for your Internet, maybe for your agents, for your dealers, even your training centers to help these stakeholders, like your people visiting to your sites, your customers, your employees. And that can help create great experiences that will help future proof your business. It can also help decrease the cost through productivity and improvement in efficiencies. And if, you know, you've decreased the cost, improve the experiences, they all will result into higher revenues where the customers are able to find what they need and then they want to convert. So, you know, let's take a peek into what Coveo can do for you. Paul, take over, please. Gladly. Thank you, Zahir. So just before we get into, some demos, some live examples of, Coveo search, implementations addressing some of the different use cases that, Seher was talking about, I thought I should take a moment and just talk a little about, Coveo at a higher level. What does Coveo really provide, you know, in in in very concrete terms? What Coveo is is a software as a service, cloud based, search and relevance platform. Where do we start, with that? Well, we start by building a unified index of content. So we read information from a wide variety of sources. If you're building website search, of course, some of those sources will be your website or websites. It might be, other channels, your YouTube channel, other video sources, might be your Twitter feed, might be knowledge articles from a specific kind of, knowledge repository, Salesforce knowledge, for example, or ServiceNow. That's, you know, very much for the, the online, the the website kind of search, of course, but there's many different kinds of websites as as Sahar was describing. Could be a product catalog we're dealing with here. Could be, discussion boards, on a on a community page. On your Internet, of course, as we move across the top here, on your Internet, you're gonna have a lot of different applications, that people need to be able to search across in a unified, secure manner. You've got SharePoint. You've got collaboration tools like Atlassian. You've got Slack. Kaveh was able to index all of this information. Again, reading the full text of the documents, the metadata, the permissions, of course, are super important both for authenticated communities and corporate intranets and, this sort of service or agent use case as well. So that's step one, building that unified index. In many ways, you know, traditional search capabilities are coming into play there. Reading reading these documents, they remain, of course, where they, where they reside. Coveo builds a searchable index of those that enables you to really quickly and relevantly, if that's a word, search across that information. But what do we do next? Well, we enable, more than just search, certainly recommendations and personalization. You can define, rules if required as an administrator of the Coveo system to promote, demote certain kinds of content, featured results for particular, queries. You can personalize those as well based on a user's context. Think about the, Internet use case, for example, or a logged in community. You know something about the people who are searching for information on on on those kinds of sites. You know their preferences. You know their location, perhaps. You know what they've purchased before in a commerce, world. So you can use that, through Coveo to personalize results further. I'll see some interesting examples of that. And what else do you need to do? Well, you need to learn about what are people looking for on your site and how are they interacting with that. That's that behavioral analytics section you see towards the right hand side of the screen. So Covey was able to log user behaviors related to search and recommendations. What are people looking for? Who are they if we know? Do they get results? What do they click on? What do they do next? What do they purchase? Do they create a support case? All these other kind of events can also be logged then into the Kaveo usage analytics platform. And this is really what feeds into our machine learning, models as well. In order to enhance type ahead, query suggestions, in order to enhance enhance the relevance, the ordering of results, effectively being able to recommend content that other people with similar context or or characteristics have clicked on previously, have purchased previously related to the kinds of queries that, that you're doing. So all of that, pulling together in many ways traditional indexing techniques, personalized search, and use generlytics powered machine learning enables Coveo to help you to build a really, really compelling, website no matter what kind of use case you're dealing with. And we're gonna get into some good live examples of that right about now. And, again, this is a somewhat informal session. I'm gonna touch on a few different use cases here. A few of our customers who are live with, with Coveo have been live in a variety of kinds of context, and I hope this will be interesting to you and maybe speak to some of the challenges and needs that you face in in in in your, applications as well. First one I'm looking at here, and we'll introduce some general concepts around Coveo and around search, some of which you might be familiar with, some of which you might not, is the advisory board. And this is very much an informational website. They're they're providing information, to their members and nonmembers. They are a member driven organization, and one of their goals is to increase membership, to increase people signing up, joining their, their community, if you will. Number of the the capabilities and concepts you'll see on the screen here are pretty common across well, certainly, Kavail implementations, but they may well be familiar to you from other kinds of, of applications as well. Certainly, as I've mentioned, a nice big search box right at the top here. You're encouraging users to to tell you what it is that they need. If If I start to type in, here a query along the lines of health care, perhaps, health equity, behavioral health, you can start to see type ahead query suggestions happening. You'll also notice that, as I start to become more, specific about my query, we're getting more specific suggestions. How is this happening? Well, this is an example of a Coveo machine learning model here, what we call our query suggestions model, where we're learning what are successful queries for other users like me. I'm not logged in. I'm not a member of this organization. So their suggestions are probably fairly, generic at this point. They don't know too much about me except perhaps I'm located in Canada. I'm on a not a mobile device. They know that kind of thing about me. But they can still make these good quality query suggestions, which we've learned from successful interactions with other users on this particular site. And that's just an ongoing continually learning model here. But from a user's point of view, as you can see, this is providing the kind of, experience that that we expect out of a modern website, out of Google dot com, let's say, as an example. But, of course, the suggestions are related specifically to what's available and what's happening on this particular site. Of course, you know, I can suggest what I can select what's being suggested to me. I can search for whatever I'd like, of course. Lot of, functionality that you would expect from, again, any modern kind of search technology here, whether it's, automatic spelling correction, did you mean kind of experience, the ability to search automatically for not just the exact words that the user types in, but also variance of those terms, let's say healthy as well as health or equal as well as equity as an example. Of course, a set of results that complete can be completely tailored to your particular needs, whether it's from the point of view of the layout, tagging the different kinds of of content to show what they are, Really, and and in this case, you're, giving an idea of how long it will take to read this particular, cheat sheet. This kind of information, I think, is really important just from the user experience to, encourage the person to interact with the content further or to, for that matter, encourage them to scroll on to something else that might be of interest to them. You'll also notice that some of their content is in fact locked down, and they've been very clear about that. And I think that's a really good idea as well. The idea here, again, as a membership driven organization is they if I was to go and click on this webinar, it's gonna encourage me with a bit of a call to action to to join, to to become a member or to to pay for the content. So there are some cases in which that, is a a really good, option to provide. In other cases, of course, if content is truly secure, Coveo is able to respect the permissions of the underlying repository that it comes from and just not show that, that content up. Over on the left hand side, you'll see other concepts that are probably familiar to you from your your personal life and various other websites, things like filters and facets. So an ability here to automatically, narrow the scope, if you will, of what it is that I'm looking for. I I know I'm looking for things around health equity, but with relation to clinical services. And you'll see that all these different components interact automatically. The the the result list, changes. The results that are being displayed change. The other facets change. So the only relevant only still relevant, filters and facets, occur, below here. I I think also just, you know, from a general website search capability here, you see things here like the ability to filter by date range. For some kinds of content, that's really important. I think of news content, for example, or anything that's very timely, being able also to sort my results, not just by relevance, but by date. For other kinds of content, that's less important. If you think of some product information, for example, the date may or may not be as as important or useful. So selecting the filters that are actually gonna be useful to your users, the sort options that are actually useful to your users, this is an important thing to do, I think, and sometimes overlooked when people, are are deploying website search. They simply take, you know, the the defaults that are provided by whatever search provider they have and splash those onto the page. But I think it's it's really useful to think think carefully about what, what you want to, use as filters, what you wanna display in the result template, and what kind of sort options are available. I should mention here that, you know, in addition to Coveo being able to report on what are people searching for, what are they clicking on, what are they purchasing, and so on, we do also report or log information about what filters people use, what sort options they use. And so that kind of information through our reporting tools can also be useful to your site designers. What are they what are people doing when they interact with your site? Are they actually selecting that sort option at all? If not, is it needed? These kind of things are are are useful for ongoing improvement of your website search. I'm gonna show a couple different, kinds of use cases here for relevance for that some of our customers have deployed and just touch on a few of the slight differences here. One interesting one that's, only just gone live recently, is Nespresso, obviously, a very well known global brand. They face a a bit of a different kind of challenge because they have different kinds of content on their site. They are trying to sell things to you, but they're also providing information. I think if I had one of these, fancy coffee machines at home, or was looking to purchase one, I might have different kinds of needs here, whether I'm looking to shop for the the newest brand or or those little pods to, to make my coffee, or if I'm having a problem and I'm looking for, support documents. Well, Nespresso provides a single search interface for this. Now you can argue that maybe it would make sense to have a separate separate support portal as well and a separate, catalog search. That's certainly a design choice that you can make. If I go in here and I'm slick searching for, let's say, for example, gingerbread, you can see a bit of a richer query suggestion capability here with a bit more of a visual interface. This is often something that we deploy on many of our, commerce related, applications. So if I'm on, even before I search for gingerbread here, I can start to see some visual, cues as to what, is going to be returned when I do that query. Let's just go ahead and and search for that. It's gonna be that time of year where, gingerbread seems to be an attractive kind of, idea or product. And, indeed, here, what you can see, in their search result, page is several different links or tabs here as well, a different way of grouping results. An interesting thing that, that you might consider in in your site, and we'll see some other examples that are similar too. Now their number one focus, unsurprisingly is is selling me stuff, products, these little capsules of gingerbread flavored coffee. And you can see, of course, that different kinds of filters are appropriate for this sort of result, whether it's, well, certainly the price range, what kind of product it is. If my query is more general, I'd probably get more, product types beyond just capsules. And if I go over and I say, well, actually, no. I'm looking for, articles about, you know, how to make gingerbread cafe perhaps or or some recipes. And, indeed, you know, if I was searching for coffee machines, I'd be finding my support documents around, around these particular machines here as well. So this again, keeping in mind different, use cases and layouts that would make sense, depending on the use case you're trying to provide. It's a very visual site as you can see here. Well, the the first result is anyway. Certainly, the products are. But providing some some really clear visualizations, to the user, as you're designing your site search, I think, is is pretty key. And I should actually add, of course, Coveo provides the components and the tools to build, the search experience. I should say, you know, what Coveo provides, in addition to this relevant platform, this relevance platform, we can build your index, you can build your search rules, and so on. From a front end or user interface point of view, what we're providing is a set of components. Now those could be JavaScript components, headless APIs, and and more that you can embed into the content management system or the website or the application that you're embedding Kaveo into, to provide that relevance where the user is doing their work. So, while we provide, toolkits, I guess, I would say, or out of the box components that can be heavily styled to suit the beneath of your particular site. And we'll see as we go through some more examples how different, the the search experiences can be. Another interesting example here, is a company called CyberSource. And, I had not heard of them previously, but they are a part of the the Visa organization, but they're really aimed at developers. So we've gone from that consumer oriented site like Nespresso to something that's more like a support website for for developers who are working with, working in the financial services industry, working with APIs provided by, by Visa. So this is kind of like that that resource center that, that Sahar was mentioning earlier. And, again, lots of commonalities here. This ability to have a nice big prominent search box encouraging your user to enter results, but see something a little different here, and that's recommendations. So I haven't done anything yet. I haven't started a search. I'm not logged in here, but Kaveo can also provide recommendations here. Now this could be, the way that they're labeling it here, content that's popular with users like you. In that particular case, if I were logged in, if I were a developer, if I had a bit of a profile perhaps about my preferences of the projects I'm working on, you can imagine how these recommendations, which again are are being learned here by behavior. What have other people with similar preferences, with similar backgrounds clicked on previously? In order to make these kind of, you know, recommendations above and beyond simple search. Of course, we can go in here and do a search for, for example, let's say, checkout. Typed a little too quickly there. Pardon me. Let's say, yeah, hosted checkout as an example. And, again, we've got the the similar sort of flavor to to our layout here. We've got different kinds of content. We've got different filters that are going to be appropriate, to this sort of user base. Am I looking for a video about how to do this sort of hosted checkout? Am I looking for knowledge articles? And knowledge articles are tagged in a variety of ways. So we're pulling that metadata out of the content that we're indexing, the Kubernetes indexing, in order for you to be able to easily, display this information and allow the user to really, you know, maybe I'm, I'm looking for a glossary definition of this particular term. It's kinda new to me. So let's select that. Or am I looking for product documentation or how to videos? You'll notice here that, these how to videos are actually hosted on YouTube. Coveo has a connector to be able to index content from from YouTube, but many other video platforms as well. And, effectively, what we're doing is we're indexing, making searchable the text information around these videos here so that a user can easily see that video, can pop open a little preview of it here as well. So that's kind of a, you know, a resource center use case. Got some similarities with the other, the other use cases we're looking at, but also some differences as well. Another interesting example, of a slightly different use case, again, in kind of the technology space, is integration into an application. And this is something that we're we're starting to see more and more of our high-tech customers, I guess, I would say, being interested in. So this example here is a company named Xero, x e r o, or a prominent customer of Coveo. They have been for a good five or six years now and use this in all kinds of interesting places. The one I'm gonna highlight here is actually within their application. I've set up a little demo, or trial, account with them. And one of the challenges they have is it's a fairly complicated, application. Lots of different capabilities here. And people are looking for help, but they don't necessarily wanna go to the help website even though it's, you know, search is powered by Coveo and provides lots of excellent recommendations. They've also embedded, Coveo into their in product help. So if I go and I click over here, you'll notice I'm on the reporting, tab or section of their application. And we're already serving up relevant, information here, report layouts, account transactions reports. So we're taking I've mentioned the word context before. We're taking my context. What kind of an app what kind of a license do I have for Xero? What part of the application am I in? And we're sending that along with any query that I care to type in here, to the Kaveo index to return that most relevant information, the information that's relevant to my context. So an interesting idea about a different use case for relevance embedded within an application. Another quick example I'll show you here, and this is to highlight two different things, that that Coveo provides and that you might consider for a a website implementation. This is another customer of ours called RingCentral, and they provide, basically, a telephony, telephony software. They've done done some really interesting things leveraging Coveo. One is integration into their chatbot, which I'll show you in just a second. But the other is taking advantage of a relatively new feature in Kaveo that we call smart snippets, although many times people call this question answering. You can see an example of the output effectively here. But how did I get to this page? Well, I started to go in and type a query around, well, how do I monitor calls? I'm interested in using the the RingCentral software to monitor phone calls, presumably within my own organization and not somebody else's phone calls. Not only are we returning, as you'd expect, a bunch of knowledge articles and results down here that helped answer my question, but we're also extracting really a section of a document that does, answer my question. So what's going on behind the scenes here is there's a a deep learning machine learning model that analyzes the actual content, the the the articles, the FAQs, and so on that that could have indexes or perhaps a selection of those and extracts what we call snippets or you might think of as answers, questions and answers effectively from those articles. And then at search time, certainly, we go up and we search for how do I monitor calls, but we also do some kinda cool stuff about taking that query, translating it into a vector and doing a vector to vector match between the headings or the questions effectively, in the in the search results and the lengthier, you know, more natural language question that I've actually asked. You see that the heading of this article is not how do I monitor calls. It's monitoring calls. But you can see here how, we're applying again this sort of deep learning vector search capability in addition to our, let's say, traditional search capabilities to provide, from a user's point of view, something kinda like Google Answers, but, of course, related to the content that they're providing on their website. So I think this is a really interesting thing. It's something that not a lot of our customers have actually deployed yet, but I think it's really powerful and and an excellent user experience, especially, I think, in the world of sort of more support oriented applications. There's a couple of things I could touch on as well. Different use cases we'd, we've mentioned sort of workplace applications. Of course, we don't have access to our customers' implementations of work workplace applications. But if you think of the need to, to provide a a good Internet search, as you can imagine, there's some different challenges that come up there. And some of those, of course, include security, the ability to integrate with a lot of different kinds of applications to index content from, let's say, in this case, we've got, SharePoint online. We've got, various Atlassian applications. We've got Slack channels. Your workplace is probably as complicated as mine, maybe even more so, but there's always a new application coming into play. So Coveo provides a really broad set of connectors and APIs that enable us to index content and metadata and permissions from these kinds of applications so that in a workplace kind of application like this one, we can provide not only, obviously, excellent search results, but search results that are tailored to the person, perhaps based on their location, perhaps based on their role within the organization. So various preferences can be expressed in here, very easily to to allow the user to really find what's relevant to them, but also, of course, respecting the security and the the structure, of the more complex applications that are often being used in a corporate workplace. And those are some amazing examples. Right, Paul? Like, our customers have done so much and have seen such improvements in not just their experience overall, but also, in their efficiency that has been gained through the automations that Coveo can, provide. So we'd love to just maybe in the next few minutes look in under the hood. Exactly. Then we can jump into some of the questions that, the the attendees have. Brilliant. Thanks, Sahar. So, now I'm taking you just real quickly. So here's this under the hood or behind the curtain or up to the cloud, where the Coveo administration console, is located. So, what we're providing, to our customers is a cloud based relevance as a service search platform, if you will. And this is where you as an administrator of the system would go to do a variety of things, index content, create, rules if necessary, query pipelines, to tailor, search results if you if required. But it's also where really the machine learning magic well, I shouldn't say magic. The machine learning process happens in order to automatically start to enhance search results as well. And this is also where you would go to look at reports. And I'm actually gonna start off there. So, we talked a little of oops. I'm not actually in the environment I wanted to be in. I do have access to a couple of different demo environments, and I'm just gonna move over into the one that I was intending to use. In in this example here, we've got a whole set of, report templates that we provide, but that our customers, often with the help of our customer success team, will modify to suit their particular needs. What what's of interest to you? What do you need to report on? Well, in my case here, I wanna report on what was happening on this particular website in the month of October. And you can start to see some things that you would expect, I think, around, search reporting. How busy is my site over a particular day? What are the top queries that people are doing, the top documents they're clicking on? If I was to click on that top query around credit cards, I could start to see, well, what are people what pages are people looking at when they do that query? Oh, that's that's pretty interesting. It's useful. This is this is good for the, the envelope the business analyst to to understand. You might also have noticed over here on the right hand side some idea of context in this particular example around the users who are hitting my website. Where are they coming from? Are they on mobile versus non mobile, devices? And, of course, these sort of, components are all very interactive as well. I could dig in and see what what are people from China searching for on my particular website as an example. And you can log additional context. So, again, you think of that use case for corporate Internet. If you are logging what department a given user is in, you're probably not logging their personal information, but some idea here of how you wanna personalize that content and perhaps, understand how well, for example, users from engineering versus users from HR are are using the system. What's adoption like? That kind of information is really useful as well. We also dig into here, ideas like content gaps. What are people searching for and not finding any results at all? Could be that you just don't have any content around that particular question. So perhaps, perhaps you need to create some new content to address common questions from other, other users. Or it could be that perhaps their terminology is a little different from yours, and maybe you need to, start to create, some synonyms in the cavea thesaurus to address those sort of slight mismatches. I would say also in a little more detail, you can also start to look into what we're calling here queries with low relevance. What are people searching for? Getting results perhaps, but not getting results that they click on, or they're not driving towards a purchase or a self-service event. And so those these kind of queries can be very useful as well. You don't need to check check out these reports every day or anything like that, but on a semi regular basis. Again, potentially with the help of our customer success team, you can start to look at and analyze, what's going on here and and do how well is search working on your site. Typically, the kinds of places that you might go to, take action based on what you learn from these reports might be into what we call our query pipelines, which are really sets of rules and machine learning models, about search. You can see here that you can have multiple different query pipelines. We have many customers who use Coveo in multiple different use cases. Again, we've talked through a few of those. They might have different websites, different, different user interfaces, and there might be a separate query pipeline for for each of those depending on, needs. But here is where you can do things like potentially add synonyms or acronyms if required, where you could define and there are times where you want to define featured results or top results for a particular query or more generalized boosting rules where you might, want to promote or demote content based on on rules that that you decide. This is also where you would associate these machine learning models, which, again, we we aim to make very simple to to set up, and to continue learning from user behavior so that we can, again, start to promote, let's say, popular content for particular queries where we can define those type where you can start to learn those type ahead query suggestions and where you can define those models that we looked at for the question answering as well. Obviously, today, we don't have, so much time to get into all the details, but we definitely encourage you to certainly ask questions today and also engage with us deeper. We would be happy to talk to you about any of this. And Last thing the last thing the last the last thing I need to, wrap up with here is a little bit about how you index content. So I mentioned that Coveo has a a lot of different kinds of connectors to different kinds of applications, including, of course, websites and including various kinds of APIs. So whether you're using a site map, to index a website, whether you're crawling the website with a web connector, or for more complex, perhaps, say, headless, content management systems, you might wanna use our our REST API to call the, the API of that content management system and fetch information, pull it into the Coveo unified index. My apologies. Please, I know I sort of stepped on That's okay. This is all great information. So, I'm really excited about some of the questions that we have in chat and Great. Over. So if everyone can just stay back for a couple minutes, we'll answer those questions. So jumping into the first question, how long does it take for Coveo to learn the type ahead suggestions? That's a great question. You can adjust the sort of the sensitivity, if you will, of the query suggestion machine learning model. But, typically, we'll start to make a suggestion after a particular query, a set of words that users typed in, has been asked, at least five times and has led and each of those times has led to somebody clicking on something. I should also say that it is possible to preload that machine learning model as well. So if you do have, let's say, a hundred or five hundred queries that you know are going to be successful, you can preload them into the Caveo, query suggestion machine learning model, and then we'll start to learn from there on, how do users take advantage or how do users search, what do they, you know, what are they successful in searching, and so on. In a general sense, we find that for a relatively busy website, we're gonna get a pretty good machine learning model out of a day or two's worth of queries. And even when it's completely cold as well, you already see Coveo working really well with the type ahead as well, isn't it? Yeah. Yeah. It's, it learns real quick. Yep. And then the other question is, does Coveo support on prem and all cloud data site residencies? For Coveo itself, if that's the question, the the Coveo plat Yeah. Yeah. The Calabrio platform itself, is not on premises. It is, it is a cloud hosted, relevant search platform. We, we we are hosted on Amazon Web Services. And in that sense, our customers do not need to worry about the hosting, be it on premises or in the cloud. That's taken care of as part of the service. We're a software as a service platform. And then the last question, is is Coveo able to improve ML and AI industry specific terms? I'm not very clear about what Laura is asking, but maybe Paul. To be able to improve AI and ML. I'm sorry. The last Industry specific terms. Oh, industry specific terms. I would say that what we will learn is connections between, the terms that the user searches for and the results they click on. So that that's essentially where that comes into play. It's typically, it's not industry specific language, but we will learn from the language that the user uses and the language that is in the content that is searched, clicked on, made use of. So we're not really tied to industry specific language. You can certainly and this isn't magic by any, any regard. Of course, you can define industry specific terms and synonyms in in the thesaurus. That's not really, AI or ML. But you will what you will see is that, over time as users search for your industry specific terms, as users click on documents as they purchase products, if that's what you're doing. But those links are being made between, industry specific terms and your and your content. I hope that's at least, you know, a high level beginning answer to a question. It sounds like it could be quite a lengthy discussion as well, but, we encourage you to reach out to us about that. And because we've we've come up to time and we've gone over, I'm going to stop the questions for today, but I do see a lot more questions that we haven't been able to answer. What I would recommend is booking a quick demo with us, and asking us those questions and reaching out to our sales engineer team. We've just put the demo piece in it. We can also give you a very free site assessment, which essentially will walk you through during the demo and let you know, you know, what type of improvements Coveo can do for your site. Again, thank you, everyone. We apologize for going a little over and some of the technical difficulties, but do reach out to us. And if you have any questions, we're here to answer. Thank you again. Again, Paul. Hope. Actually, I just, that, Laura clarified your question about type, about, industry specific terms, the type ahead feature. Yes, Laura. You can preload, any terms you want, into the, type ahead machine learning model. So yeah. And, again, absolutely. Be happy to have a longer conversation with you another time, but, much appreciated. Thanks, Sahara. Alright. Thank you so much. Appreciate it. Bye. Have a great day. Cheers. Bye.