Hello, everyone. Thank you for joining us today. My name is Bonnie, and I'm director of product marketing at Cobayo, and I'll be your host today. I'm also joined by Sarah and Molly, two members of Ultimate Kronos Group. UKG is a leading global provider of HCM payroll, HR service delivery, and workforce management solutions, and actually was part of a merger between Kronos and Ultimate Software in twenty twenty. So, you know, a lot of cool things happening there, and, excited to to dig into how you're leveraging AI. So, you know, if any of you have questions as we go through this presentation, feel free to drop into the q and a portion, at the bottom of your screen, and we can definitely get to that at the end of the session. To kick us off, I wanted to do a little bit of an overview and deep dive into, you know, what what we mean when we're talking about AI. Because at this point, you know, a lot of companies, a lot of technologies are offering AI powered solutions, but it's still a little unclear for many people what that means and what that actually does for you, to improve the experience. Just a couple of stats. You know, service teams are increasing their use of AI, and that that only will continue, especially as technology continues to develop, and it becomes easier to use. So it's definitely being leveraged a lot. And, you know, there are stats that show that if they're not using AI, it could be it can be more time consuming. So there's some benefits to AI. Right? But, again, what does this actually mean? This is something that we talk about a lot. It's thrown around a lot. So let's let's dig into this a little bit. The first thing is, you know, AI tends to be an umbrella term, for a lot of different types of machine learning. Right? So we have NLP. We have deep learning. There you know, it can be as broad as a self driving car or as small as, you know, a a a recommended article, for example. So there are different ways that that it can be used. What is important to understand is that you can use this AI to improve the way that you work. So it's really just another technology capability that can empower your teams and provide scalability. And the goal is really to simulate and enhance human intelligence so that you can use your time on higher value activities. So just like any technology, AI demand depends on having good data and is dependent on skilled talent managing that technology so that you can leverage it for your benefit. Here you can see some popular buzzwords and categories alongside, you know, the world of business, which is a complicated space filled with different types of technology, people, process, system. And, you know, the the ideal word we we all hear about is, you know, having that single source of truth or that three sixty degree view of our customer. And the reality is that we're juggling a lot of things to get there. And the center of all of this is the data and information. So, you know, when applied correctly and strategically, you know, AI can can help unlock data across the enterprise and be can be leveraged to to take that that experience to to scale. So today, we'll really be talking about how Molly and Sarah have been leveraging AI and their experience, and and to get dig into that a little bit. Just to make it a little bit more tangible, I'll show you a few examples of, you know, Coveo machine learning models here. So AI, again, it's not just one big bucket. Right? There are capabilities, machine learning models built for specific purposes. So at Coveo, we have several machine learning models. Each of them are built for a different purpose. So, you know, whether it's automatic relevance tuning, which is, you know, in real time surfacing the most relevant results at the top or, you know, user stitching service so you can see the full journey of the customer across all of the touch points. You know, these are all specific across all of the touch points. You know, these are all specific models built for a specific purpose, leveraging AI. Behind behind these models and and the way that AI can be leveraged to personalize is based on that user data. And so something like a user profile service that captures that user profile, you know, it can help capture, you know, who they are, you know, what they're clicking on, what their properties are, and can create clusters of of groups of users based on different dimensions. And so this is how when we talk about leveraging AI to personalize experiences, this is really, you know, what's behind that. We take that user profile. We take all of that data and the analytics and then combine all of that to create that personalized experience. So, you know, I'm I'm going quickly through this. But, you know, at the end of the day, if we're looking at AI, it's not one big bucket. It can be comprised of multiple things. Each each can be for a different purpose. But in order to leverage AI, you need you need the data, and the analytics. So, ultimately, the key to personalized service is having the data, the analytics, and the AI behind it, to create that personalization. Now, with that, I I wanted to to ask Molly and and Sarah to tell a little bit about themselves and and, and then we'll get into how you're using AI. So, so yeah. So, Sarah, why don't you take it away and and let us know a little bit more about your role at UKG? Great. Thanks, Bonnie. Thanks for having us. I'm Sarah Mebben, and Molly and I are the business owners for knowledge and self-service success at UPG. Together, we, support our all of our support services, our knowledge authors and users. And, one of my primary roles is to also show the business value of knowledge and, and self-service at UKG. We leverage Coveo in a lot of different ways to do that. Awesome. And, Molly, how about you? Hi, everyone. I am the knowledge manager at UKG, and, really, I I work more with, the individuals who are our knowledge users, mostly our internals. And and through my work with them, you know, we help, hopefully, customers find, what they need and and to interact with knowledge. And I, you know, really look at data a lot to try and figure out what we're doing well, where do we need to work, and where or is there just a big black hole and we don't have any content and and try to, to reach out to individuals that can can, you know, fill those holes when I find them. So I definitely, need the analytics, to to help me. Otherwise, I would be spending a lot of time in Excel, pulling my hair out trying to figure out what do we need to fix and what is not broken. Yeah. Oh, that's great. Now one of the things that I wanted to dig into with you today, and I'll go ahead and stop sharing my screen so that we can really, you know, have this discussion. You know, I I thought it was very interesting that that you went through this big merger in twenty twenty, and that must have been daunting to try to bring both of those organizations together. So could you talk a little bit about, you know, some of the concerns you faced in that and and how how you leverage AI to kind of bypass some of those? Absolutely. One of the biggest challenges that we had, initially was because there are gonna be so many more repositories, to to consider. Whereas, you know, our prior, part of the organization, we had, you know, three, I think, places that were indexed and all served up under one search. When we merge, it's gonna be many, many more than that, more than twice as many. And, data coming from different places has different metadata assigned to it. And to have them all play well in the same sandbox was a huge, huge challenge. But one of the things that, we were really excited about is that when the merger happened, we had to decide what we were gonna do about a search engine. And we, found out that we were gonna be able to keep the one that we were used to, which we use Coveo. And, you know, for us, it's it's basically the roof that sits on top of all of these repositories. So the the house has all these different rooms. Some of those rooms, we only show to internals and some of the content in other rooms, we show to, you know, customers, but it all fit under the same roof. And that roof was Coveo for us. It sat on the top of all of those places and and pulled stuff out as was needed. So for us, this this challenge is that now our house is is is gonna have a lot more rooms. And but we still have, you know, the roof that's gonna stand the entire, you know, distance of of that house. And for us, it's just really mapping things and trying to understand what do we have to really do on the back end, like, actually change the value in a field, which is manual and it's very time consuming. And what are those things that through Coveo, we can just manipulate and not actually have to touch each piece of, you know, content that lives in that repository. And that's been a huge benefit because we've been able to find that there are many things that we can just have it adjusted at the CoAO level and not have to manually touch the record, because there are tens of thousands of those. So that was really one of the biggest things that, we were fit up against with the merger is just that we have a lot more content, and we have a lot more places that we're gonna be pulling together for that search engine to render content from. And as far as the content goes, I mean, I'm sure, you know, both groups had a large amount of content. So you're adding more repositories, more systems, and then the content itself, I'm sure there was some some things that you kinda had to streamline and and make similar, you know, whether it's, the product names or, you know, anything like that. Did you encounter that? Yeah. So one of the things that happened is everywhere where our company name was. I mean, that was just at the very basic level. The company name had to be adjusted in content. And we had, that was, you know, fairly simple for us to do it in in in a back end tool. It just was a find and replace. But then you had all these product names that lived in various places that were having new names. And we were concerned that, you know, some repositories didn't have as many resources in terms of people to update those those names that were now old. And then there would be other areas where the content there was updated and it was updated quickly. So we knew we were gonna have a a mix of content that had the old name and content that had the new name. And customers were gonna come in and they were gonna search with whatever they thought the name was. So some customers might be looking with the old name, other customers were looking for the new name, but we wanted the content for all of it to show up regardless of what they searched. So we were able to use, just the machine learning that, that Coveo did, it was able to figure out very quickly that if they, you know, want this, I I should provide all of this, the old, the new name. This is the the same thing. And we didn't have to really have a nightmare about the fact that half of the content wasn't gonna show up for them because they used one or the other name. And that was really a huge huge thing. Because when you have an enterprise this large, you've got certain things where they cannot yet change a document where a name is we're trying to figure. There may be legal issues involved that we couldn't change it there. But other areas, it was like, no problem. We'll just change the interface and have the new name. And it was a a long tail to how long it was gonna take for that those name changes to permeate every one of those individual repositories. And it was just magnificent that it worked as well as it did. Yeah. And that's that's one of those big things I feel like, you know, when you when you're working with knowledge and and trying to combine systems and and do all of that, we we kind of forget that, you know, it's down to the word, right, that that we're going to have to check. We're not just checking the big broad you know, do we have the knowledge base accessible? It's like, are the words you know, do we even have the right words in the content? And so it sounds like from from what you're sharing, you know, even if you didn't get all of the name changes in, you know, people people will search one way or another, and it was still able to to provide the most relevant answers regardless of if they were using the wrong the old name, for example. Absolutely. And that is you know, that was a specific example of of a name that we knew was, needing to change. But it's the same way when it comes to internally or, you know, for for us in the industry. We call something, an off cycle payroll, for example. And customers might be looking for, you know, a a a spot bonus. Well, they're not the same word, but, essentially, you would use the same piece of functionality to do it. And so Coveo has been able, to, you know, use machine learning to say they're the same. When someone wants information about this, I'll show them stuff that might have a title that doesn't match. It's not a keyword search that is is being rendered. Instead, it just knows that's the right content. And that that happens all the time in an industry. We have words that we use that are not the customers are the words our customers use, and and that can create a lot of trouble. So that's been a great So it kind of catches it starts to learn the voice of the customer sometimes even before you do. Exactly. And we can look at the way the words that have been queried and and identify what are they really looking for. It might be something we're not aware of that maybe legislation that is, you know, in a particular state on a very minute scale that they're looking for content from us for. And we're able to, you know, start figuring out what is it that they think, you know, we would have. But we definitely use those keywords, to see what our what language is our customer talking. Mhmm. No. That's great. So what about, you know, when it comes to when it comes to bringing the other content in and combining it together, did you have to make a lot of changes with, you know, updating metadata and structuring the content taxonomy and things like that? We had both. And one of the reasons that the the metadata was a really big deal is because they're not it's not all the same kind of content. You know, like a PDF, it's all a document. Well, some of these things weren't documents. They were discussions having it happening in a group. So the kind of metadata that would be available on that is different than the kind of stuff that a writer, like, builds into the document as these are the keywords, this is, you know, the category, etcetera. So, that was really important because we could index all of these things with our, you know, search engine with Coveo. And it was able at the Coveo level and not going in and trying to hard code something at, you know, further down in the actual room or the repository. We were just able to bring things in and and have them mapped the right way to be able to be rendered in in the search the way the customer was wanting it to happen. And then when it came to taxonomy, we we weren't all on the same page. You know, content traditionally, we know that when you write a piece of content, you choose taxonomy. Well, who who creates taxonomy for a discussion, or who creates taxonomy for these other types of thing? Because it's not really a piece of content that one individual authored. And so we've we've really needed to, come together and figure out how do we create buckets that, you know, essentially, that that label that like items can can be put in. And it's been a process. So even though we don't have that all completed, Coveo, through machine learning and through the models that are applied, it's able to to know that this value from this content is equal to this value from this other place. And it it is, been a huge time saver for us because, again, for us to have to have a writer or some human being change things at the actual, you know, document or artifact level would be, it would be hugely expensive, and the timeline would be would we wouldn't be finished yet. Yeah. Yeah. Now so okay. So when thinking about how you've how you've leveraged AI, it's definitely assisted with, you know, streamlining, you know, you unifying all of that content, simplifying that the the restructuring and things like that. What else what else were you able to to leverage machine learning for? I mean, did did you have to tweak it a lot? Do you have to manage it a lot? What does that look like? Well, initially, when we first, you know, kinda used the search engine, instead, you know, for for our company, we were nervous because we thought, you know, we're gonna need to go in there and fine tune it. But, you know, we know our customers and it's a complex, you know, group of customers. Our our our products are complex. We really thought that we would have to go in and set up a lot of synonyms, and we would have to be ahead of it. So in in times where, initially, we we thought that, you know, we were gonna need to really be ahead of the search engine because we were gonna learn and teach it faster than it would learn from customer interactions, we did some AB testing. And in that, we said, okay. For these customers coming in, we're going to have the the model be applied that is the one we've set up, the one that we've kind of decided how it should go. And then for the other group of customers, when they come in, we're just gonna let Coveo do what it thinks it should do. We're not gonna, you know, do anything. And then let's look at click rank. Let's look at, you know, what did the customer do after they searched and see which group seems to be more happy with their search results, seems to be able to find the content they're looking for more quickly. And hands down, the machine learning did much better than we did as we tried to intervene. So, it was great because eventually, you know, when we had changes roll out and we knew we had you know, it was gonna affect search, it was gonna affect our customer self-service experience, we just learned to, you know, go on autopilot. We're not gonna we don't have to do anything. Let's just let it learn. It will, and it'll learn faster than if we try to go over here and manipulate things. So we really don't feel like we have to go in ahead of some change and change something in the way the search algorithm is gonna work because we know it will figure it out, and it'll figure it out very quickly. So, that's been really great. Yeah. That's that's great to hear. And and I remember when I was I was working with knowledge and, you know, one of the one of the big things was we knew our customers more than anyone else, and we know what they need and what they're going to look for. And we spend a lot of time trying to predict journeys and defining those journeys and, you know, creating that specific guided experience. But it's hard to duplicate that for every individual without having AI there to kind of support you and and help do those things for you. I think a lot of people end up not trusting that AI is going to do it just because it has been so you know, it's such a black box. Right? And we can't really see it working or know what's happening behind there. But you're but you're able to actually see how machine learning is is working, within the product and platform and make adjustments as needed without kind of having to touch it every day. Exactly. Yeah. It's just like four zero one k that you put the money in and then just don't touch it all the time. So we just install and then just step back. It it's gonna be okay. Yeah. And it's been great. It it learn it learns incredibly, quickly, and we we have a program manager dedicated to that tool. It's not that we don't have someone to do it, but that she's just she spends her time on helping us, you know, create dashboards and, you know, actionable data, instead of worrying about literally configuring anything with our search. So we just she just really focuses on what's the data telling us and helping to get that word out to the people that are the decision makers. So I'm really grateful she can spend her time doing the latter. Yeah. The first. And so for for your customers then, with Ultimate Kronos Group now being that that the the one company, you know, as you're going through these changes with content behind the scenes, what did that look like for the customers? I don't think that they were aware of it, really. They, they they would receive communications that, you know, that things were changing, that were one place. We have, you know, been in the process of saying, okay. Because we had two instances of, a community. There was the community that their old customers thankfully, the tools are the same. We've used the same platforms, so it would be familiar in that way. But we haven't, had to really teach them how to search. You know? That seems really odd that you would, but people all the time say, but what's the clue? How do I use your search so that it get I'm thinking, do, you know, do you tell somebody how to Google or you there's the search bar. So, thankfully, we we just say, here's your new you know, your URL may have changed and where you come to interact with us, but, you know, because our search engine is the one that's come with us, it's the same one, we know that it will do a great job and we just just say, this new new URL. That's all you need to know. And they've been able to, you know, go through this with us and without really stressing out. We we've, of course, had a lot to do behind the scenes, but I don't think that our customers have felt that they had to relearn how to ride a bike because it was so different or because we were gonna have to start from scratch, and it would take us a long time to get caught up on it being able to predict or recommend. We've we've already seen as we've brought some content from their repositories over to our instance for a short term until we could all be combined, and it it has worked really well. Oh, that's awesome. And, you know, from a from a success perspective, I guess, you know, it seems like the customers didn't really realize that this change was happening. Business carried on as usual. Can you share a little bit about what success looks like? I'll go ahead and and bring up a slide here so that we can, we can actually take a look at some of your results. Here in just a second. And, Sarah Mhmm. Do you wanna do you wanna chat a little bit about this? Absolutely. So it's really important that we're able to demonstrate, value to the to the business in addition to our customers having a successful search experience. And so these are some of the core metrics that we use, to demonstrate that. And so you can see on the left the the metric and and the I'll give a brief definition. Our January and in parentheses are month over month results and then our our goal that we're shooting for. And this really is just a snapshot. We report to our senior leaders and executives on a monthly and a quarterly basis, and these are typically what we focus on unless there's something else outstanding that is particularly exciting or perhaps something that we have changed and we wanna highlight, an increase or a decrease in in a specific metric. But these are the ones that we focus on. The first two are kind of a combination of Caveo metrics and then our Salesforce case data. So our what we're looking for is a trend in the upward direction where our percent of customer visits and searches are increasing and no case is created. So this is kind of a spin on deflection or, call deflection or call avoidance. And we've been pretty consistent over the last six to eight months or so in this, forty to fifty percent range where, our percent of customers, coming in to visit are, not creating cases in the in that forty three percent range where they're coming into search and not creating cases. So we're just looking for a trend up. We don't necessarily have a line in the sand as far as the goal. Click rank is one of those, standard measures where that lets us know, okay. Is the customer finding what they need? And we're shooting for that number three, it being the third, click, or the third document or article, in the in the ranking of the search results. We're pretty pretty consistently in that four to four point five range, which we believe to be pretty good. Not not not, you know, gold star, but we're pretty darn close to having a gold star. And we do believe that even, mostly because the rest of these metrics support that as well. So even though our our click rank is not exactly the third, fourth or fifth, it's it's it's very consistent, and we know our customers are, are leveraging the content quickly. So the second two the next two, visit click through and search click through. So, users will come to our community and knowledge, and we want them to visit and then click through to an article or a document. So in January, they're doing that, eighty six percent of the time, so it's an increase of about two percent. We're very consistently in that eighty to ninety percent range, and our goal is sixty five. So that's a benchmark, and a goal that we're consistently up, over and above, and we're looking for a trend up, in that metric. Our search click through at sixty percent, also a trend upwards of two percent, where the benchmark for us is fifty, and we're looking for a trend upward. And our line is pretty straight there too. We're consistently in a sixty to sixty five percent of searches where we have a click through to a document. We really spend the bulk of our time focusing with our executives on these, these top five metrics as a group of data that help us know that we are trending in the right direction. Customers are coming. They are not creating cases. They are finding, content useful. And then explicit case deflection is certainly a metric that we, report out onto our senior leaders and our executives that just lets us know where are we, in that case deflection. We don't have a specific goal. It's truly a moving target when you think about cases and customers changing on a very regular basis. And, also, in our business, in the HCM world is, extremely cyclical. So what you see happening in January is gonna look completely different from what happens in May, just as an example. Mhmm. Mhmm. So I didn't even put month over month, although they were very comparable. So we had, a little over a thousand, explicit case deflections and, a little over one thirty five k in estimated savings. So let me talk a little bit about how we calculate that specifically because it can seem a little like, well, how do you get that number? Where does that come from? So the explicit, is really just that in a case being deflected. So when a customer user visits our case creation page and they start to enter a case subject into, the subject line, Coveo kicks in and is searching. And as they're searching, their case subject kinda becomes, prompted for search results. If a user clicks on one of those search results from the case creation page and does not create a case within twenty four hours, we call that a deflected case. So this is only capturing that percentage of customers that are actually visiting and searching and clicking on the case creation page, which is really just a portion of all of our community visits. So the one thousand or so and the one thirty five k in savings, the one thirty five k comes from our, average case cost, which today is just over a hundred dollars per case. So that's where the metric is coming from. To give an overall perspective, last year for all of twenty twenty one, we, had an estimated savings and deflection of over two million dollars, which when you think about just explicit case deflection coming up around two call it one million. That's significant. That more than pays for our investment in in Coveo and in the management of Coveo. Also, I think it's a really great time to highlight, Bonnie, too that these metrics come right from the dashboard console that's inside Coveo. So when you turn it on to use Coveo for the first time, you have these metrics. They're right there out of the box. Your CSM can help you use those and build insightful, data pieces to share, with your teams to make, changes in your, user behavior, and then also to help help our executives and our leaders understand, our business value. And we work really closely with our CSM to do that, which is why I wanted to highlight that. But when we think about the importance of using Coveo and sharing the success, but also more almost just as importantly, from a metrics perspective and a business value perspective, being able to understand what our users are doing and really delivering a delightful customer experience and helping our successful from a self-service perspective. It's critical to have this information at a moment's notice. We can see all throughout the day what is happening from a search perspective. It helps us understand where we might need to increase content, change content if we're if we have something misnamed or something that's missing. We're able to react very quickly because of the data we can see at a glance in Coveo that's truly out of the box. And, a comment that I was making to Molly in preparation for this webinar, Bonnie, is we couldn't do business the way we do business today without Coveo. We would have to have other full time employees getting the data out for us and being able to share it in a table like this, using Excel and spending, spending hours on hours truly of their day gathering this information where where we have a much more, agile setup inside of the the Coveo dashboard and the Coveo console. So it's extremely beneficial to how our self-service team operates, and I I would we would not wanna do business without it. Well, that's that's great to hear. I mean, I definitely I've I've been there with with digging through data, trying to pull the right spreadsheets, importing and exporting and combining, and then trying to you know, you gotta create the graphs and all of that. So it's good to hear that you haven't had to to, you know, be that manual with it. So, so that that's that's great. And, I mean, it seems like you've you've been able to move move smoothly through a merger, continue providing that good experience for your customers. What what do you have in mind next? What do you what are you gonna do next? Oh, that's such a great question. So, since the merger, we've just been learning about our customers and what they need from a content perspective. And we're really looking forward this year to, applying a deeper level of personalization and getting more understanding of what they need from a content perspective, further, closing our content gaps, but also being able to, proactively share to our customers' recommendations based on things like the industry that they're in or the type of, role that they have within their company. So there's a big difference between someone who's an end user that just needs information on their paycheck, versus someone who's an HR admin that maybe is having trouble, running a payroll cycle, for example. So we wanna be able to know that in advance and share, recommended information based on, profile that specific level of profile data and then, industry related data too. So we look forward to sharing some of that with you over the coming months. Yeah. It's amazing. So anything else anything else before we wrap that that either of you wanna add? Well, I could further compliment what Molly had shared about not having to fine tune and letting machine learning do do the work. And the AB testing really kinda blew us all away. We did some of that specifically with the product name updates and our, company name updates. We were braced for some serious work ahead of us and some troubleshooting and some potential customer customer service fallout, and there wasn't any. There just wasn't. And it's just so exciting to be able to experience that firsthand and to be able to share that out of the box. We just have this on, and it works. So, the value realization is true, and we're so happy to to have some tangible, business cases to share, with you all. That's amazing. I mean, thank you so much for for sharing all of those details and in the challenges and successes that you've had. I do wanna give an opportunity for the audience to ask some questions. Questions. Before we do that, I did wanna make a call out. You know, the the session is is about how we're leveraging AI. I didn't get too deep into the weeds of AI, from a technical perspective, but I did want to to share some resources that we have. We do have a large AI team at Coveo. And so, you know, we're publishing research regularly. Feel free to go to our blog, and you can actually jump in to our AI labs and see what kind of research our team is actually doing and and publishing out there. So with that, we'll go ahead and move to some questions. The the question is, what tool are you using for knowledge? Are you using Salesforce knowledge or something else? Yes. Salesforce is what we use. Okay. Great. And and just to kind of add to that question, what other content sources are you indexing within Coveo? Ingenious is where our professional writing team, creates their content. So that is one repository. And then we have, a couple of new areas in the, coming in. So for example, discussions, which are is part of Salesforce. That's an area that is being indexed. We don't index everything for our customers, but we do have a couple of components within Salesforce that are indexed as well as, a learning, platform where we have our learning content and then InGenius. So that's, those are the primary ones. But within those, there are various buckets. So those are the three big places. Did I forget anything, Sarah? Yeah. Do we say Jira? I know that'll be one that we'll be indexing in from an internal perspective. Yeah. Yep. So Jira and internal. Mhmm. Great. We don't we don't do that today. So Okay. Next question. How does Coveo recognize what industry the end user is in and then marry that up to content? Is that metadata from customer user? And then do you have knowledge tagged by industry? I'm curious about industry personalization and that how that is matched. So I'll I'll share a little bit about that. And then and then, Sarah, if you wanna jump in on on what your intentions are there, we can do that as well. So I think, you know, where I'll start is with that user profile service that I I quickly shared early in the presentation. So it's really you know, it's that service that acknowledges, you know, what what products you have, what what what systems you use, which who you are, and it takes that user profile and combines that with the behavior. So what content are you looking at? You know, what do you have permission to access? So if you if you, you know, let's say you had content specifically for financial services and only people with you know, only a certain group of people had permission to that content, then they would they would see that content. Right? And it would only be, be shown to them. Now depending on how they interact with the content, if there's one person in that same industry and they they're looking at, you know, a few pieces of of content, and it seems to be a pattern. Machine learning recognizes that pattern and then can make those recommendations to the next person who's kind of going through that experience. So, Sarah, you wanna share a little bit more on that topic? Sure. So we're planning, to leverage the account information and contact information in our Salesforce account records, and we'll work with Thomas too to use the user profile, feature. I was just actually jotting down all of those different things that you were mentioning to make sure that we, that we make note of that. And we're probably gonna be piloting it with a few customers kind of behind the scenes to see how it works. In addition to the industry, level of personalization, the role is also really important in, in our business where we want to be able to understand the user that is searching what their role is within their company. Are they an admin for our product? Are they a payroll administrator for our product? That sort of thing. And and so that will be, really helpful and, not just the area of the product or the feature of the product as far as, sharing content or recommending content based only on that, but to also understand, okay. You haven't you have questions about this area of our product, and this is why this could be why because of your role within the company. And so to be able to share other admins within the company researching this topic, found these articles or these documents useful will be really powerful. Great. Alright. Any other questions before we wrap up here? Molly, there's some interest in what's on the board. It says if you're on the right path, it will always be uphill. Oh, I like it. Oh, I like it too. Alright. Well, that's that's all for the questions. Now if if any of you have questions after this, we will be sending out the recording afterward. So if you have questions, feel free to reach out, and we'll respond offline. Thank you again, Sarah and Molly Sarah and Molly for sharing your experience and and, you know, the challenges and outcomes of your outcomes of your journey with AI through this merger. So thank you so much, and we will wrap it up now. Thank you. Have a good rest of your day. You too. Bye bye. Bye.