Transcript for "How Rippling's Engineering & People Leaders Run Their Teams with Data Cloud": Alright. Hello, everyone, and welcome to our webinar. Thank you so much for joining us today. And before we get started with this webinar, I just wanna set a couple of quick expectations. This webinar today, it's not a product walk through. You're actually going to hear from two people who actually run teams here at Rippling. They're gonna talk about how they're using this technology today in their own work every single week. And as you hear what they have to say, if any questions come up, which I'm sure they will, feel free to drop it over in the q and a, and we're gonna get to as many as we can towards the end of the presentation. So first things first, my name is Divya. I'm a platform storyteller here at Rippling, and I'll be hosting us today. Joining me today are Paul Barber, who is our VP of product go to market, and Varun Saini, who's our VP of engineering. So Paul is going to walk through why this moment matters specifically and then show you exactly how businesses just like yours are using Rippling AI and data cloud to answer really critical business questions in the moment and not after the fact. Then we're also gonna hear from Varun Saini who's our VP of engineering and Varun is gonna tell us a little bit about how data cloud actually works underneath the hood. And he's also gonna show what he's built specifically to monitor and optimize engineering productivity. So because we've got such a great mixed audience today, for those of us who are not familiar, Rippling is a software provider. We've been around for over ten years now and serve over 40,000 customers globally across every industry and every size that you could possibly imagine. And most of you all are probably already familiar with our HR product, but we actually have so much more across IT and spend and just beyond everything you could think of. And what really distinguishes Rippling is our employee graph which is a data layer that serves as a single source of truth connecting all of your business operations. And that's because Rippling knows that your people data is your business data and they are indistinguishable. Your revenue, your inventory, scheduling, your sales, it all comes back to your people. And when you can see the full picture, you can actually make decisions that move the needle. And this is exactly why we built data cloud, which is essentially the ability to bring all of your third party data in external tools natively into Rippling and connect that back to your people data. We know that this enriches your business context so that you can do better work and faster. Paul is going to take you on the journey from the old way to the better way. Over to you, Paul. Good morning, everyone. Thank you so much for joining us. Grateful that you've given us a few minutes of your morning. Again, my name is Paul Barber. You know, for about half the audience today, you recognize what's on screen. This is your home page when you land. You know, you log in to Rippling in the morning. For those of you that may not be familiar, this is what it looks like for an admin to log in to Rippling. Left hand side of the page, I have my various products that I'm subscribed to. And right in the middle of the page, I have this ability to to chat with Rippling. And down below, I have my tasks. And this was Rippling up until, you know, roughly a year ago, six months ago. I would come to Rippling to do things like to be shared. I would onboard a new hire. I'd order them a laptop. I would manage someone's commissions, you name it. But as we started to see the world around us evolve and our customers of all shapes and sizes have more and more data, we realized that we were missing something because every customer had one of these. And, my bet, I've I've spoken with literally hundreds of customers around the world, if not thousands, and everybody's got this thing somewhere. There is a custom application that someone's nephew built that runs on a server in the closet. There is an Excel workbook that, Nancy has maintained for twelve years that if it were ever to crash, you would spend any amount of money, anything to recover. And that's because business data continues to exist across multiple systems. And so we set out to solve that problem. For us, Rippling, of course, we are the single source of truth for our customers' businesses. We know everything about our customers' employees, who they are, where they work, the department they're in, who their manager is, the computer they're running, what their commissions were two quarters ago. You name it, and Rippling knows it. And then there's this layer of abstraction that goes one layer beyond, which is where I want to connect information about my employees, for instance, back to the CRM or as Varun's gonna walk us through today, back to GitHub. And thus, data cloud was born. Data cloud is the ability to ingest data from third party sources directly into Rippling so we can marry that data very, very tightly, to the employee record. And the impact has been, pretty fantastic. As we think about you know, these are functionalities that were traditionally available for large enterprises, through things like a data warehouse and ETL pipelines, etcetera. But my cousin is actually a Rippling customer, and she runs a small wellness clinic. Mary, my cousin, was never going to deploy Databricks or Snowflake for her small wellness clinic. But with Rippling, she's able to bring in store data and sit it right next to the employee record. It's been a fantastic experience for her. And so Rippling started with this concept of we are empowering our customers with a single source of truth. Data cloud unlocks the ability to bring data externally from over a 100 sources, from Salesforce, from HubSpot, from Mindbody, from Google Sheets, from Excel files, you name it, and nestle it right next to the employee. And the unlock is awesome. So let's walk through just a few examples, and I'm gonna kinda culminate here in where this really hit home for Rippling as a business. Classic example for an operator. I wake up. I might run a small store. I could run a large fitness chain. You name it. It's a very common question. Who's working today? And, of course, because Rippling is your single source of truth, and in this example, because we're using time and attendance, I'm able to just very quickly work with Rippling. Their AI is able to process and check the schedule. You can see the thinking here. What's really neat about Rippling AI is that there are no hallucinations. We treat employees and records inside of Rippling as a very specific element, where because we know who these people are, because we know who they're where they work, the location they're at, etcetera, the information is trusted. It is true, and the results are very, very finite and granular. Moving from there, I might ask a question as an operator about, tell me about my store's performance. Right? It's great that your your your HCM provider can tell you who's working, Paul. That's not particularly exciting. But then I might wanna see or ask, well, who's my most effective employee? Which employee brings in the most revenue? And that data traditionally had been in a solution like Mindbody if you were running a clinic or Square or Toast if you were a merchant. Or even if you were in sales, it might have been in HubSpot. Using data cloud, we're able to bring that data in, as I mentioned earlier, and nestle it right next to the employee record. So when I ask a question like, tell me about store performance, I can use Ripley and I just have to create a very quick dashboard for me that I can access in the web or on mobile, you name it, to see things like who is my most productive, front desk person, who books the most appointments, what rep made the most cold calls today. We would not have been able to do that natively inside Ripley if we had not brought that data in via data cloud. Now for us, Rippling, we, of course, like, you know, we, we don't run a wellness clinic. We don't have storefronts. Our problems are software problems. But like many of you, something has happened in the world, and that is AI spend. And this is a true this is a true story. I'm gonna I'm kinda, like, gonna fudge a little of the facts, if you will, to protect, our our folks' privacy. But several months ago, as was, shared in TechCrunch, we had a surprise bill from one of our AI providers. And my guess is that I'm not alone. My guess is that many of you have had a similar experience where, you know, you you get the invoice in your email and you say, like, my goodness. I did not know that we were going to spend that much money with AI provider x. And so we did what we asked our customers to do, which is to ask Ripley AI. And, of course, we had already connected our AI providers to Rippling. We were importing data from from Claude, from ChatGPT, from CodEx, from Cursor, and we were capturing all this data. And the result was a tool that we are actually releasing that anyone can use called AI Spin Console. So what started as a problem for us, we realized it was a problem for everybody. And what's on screen today is actually literally anybody right now can go to, ripley.com and sign up for this free tool. This is how we navigated what we recognized was in a very, very critical urgent issue for us to solve using Rippling, which, of course, Rippling runs on Rippling, and using Data Cloud, which was ingesting all this data externally. For us, it was a matter of point and click integrations. There was no coding required. The data simply flowed into Rippling. We were able to connect the dots between, who in sales had spent $5,000 Vibe Coding a bot that to never be used. That's simply not a good use of funds or engineers that were using the most expensive models for perhaps the least productivity, which Varun is gonna walk us through in just a second. So I share that story to really bring it home. If you have experienced an an AI build that you weren't expecting, I encourage you to check this out. And without further ado, I'd love to introduce Varun, who is going to walk you through what is likely the most expensive cost center in your business engineering that has the highest level of leverage and impact, with AI. So, Varun, go ahead and take it away. Hello, everyone. My name is Varun Seni. I'm a VP of engineering here at Rippling working on data cloud. Before Rippling, I was planning new data organizations at HubSpot and Tableau. Today, I wanna give you a peek, under the hood of data cloud and show how it does all the incredible things that, Paul, just showed, plus some of the things that I myself, do with data cloud. I need to switch to the next slide. Give me a second. Okay. Perfect. So, there are a few boxes in the diagram, so let me walk you through those. On the left side, all you all you see is about business data. Your business data could live in Rippling. It could live in an external app. It could live in, a warehouse, a spreadsheet, multiple different places. Data cloud has an ability to connect to that data no matter where that data is. On the right side are some boxes that, show various value surfaces, right, how you get value from there. And, Rippling AI is one of the ways dashboards, workflows, and custom apps. There are many, many, many, many more ways that you you might extract value from from data. The middle part of these these boxes is essentially a modern data stack implemented from the ground up by rippling with the intention of taking data from one end to value. And, there are different different, parts of it. There is the ability to connect to data. Our connectors are extremely smart, and they essentially try to preserve context of where the data is coming from. So we try to maintain if data is coming from GitHub, then we know that what the source is and what maps to an employee. And if it's coming from a different source, we we know that too. Within the modern data stack, you you will sort of see an ability to store data in different ways, analytical data, transactional data. We have a way to govern. So you have, essentially, things like, permissions and policies and lineage. So the data is tracked from the source all the way, to to dashboards. You can, you can see it throughout. We have SQL as an interface to access any of the data that's available in data cloud or data cloud has access to. We have transformations which are used to encapsulate your business logic. So, NET, it is essentially a full data stack and a data warehouse. And if you were not using Deepgram Data Cloud, you would be using, to get this, you'd be using four to five different products and a team of data engineers and analysts to configure it just the right way. This is before you ask any, any questions of it. K? Next, slide. The the key thing about data cloud, which differentiates it, is, when you're looking at an answer, you you care about three things. The first thing is, am I allowed to see this data? Now that is all about permissions, and Rippling Data Cloud is fully permission aware. It inherits permissions and policies from Rippling, platform itself. So when an employee joins the company, leaves the company, changes department, changes a manager, their access changes, you across all the interfaces, whether it is dashboards, whether it is, yeah, whether it is dashboards, whether it is apps, whether it is, you know, workflows, all the permissions are are inherited from the rippling platform. So it's fully permissioned event. Second is it tries to preserve the context of your business. So if you think about GitHub, GitHub account is not the same as an employee. But when I ask a question about how many PRs have team a or team b, done, that information is, I can I can infer that from the GitHub account and email and then see my employee's email and then, you know, join that data? That work is done automatically for you. In fact, we try to add context whenever the context is is dropped in transition of data. Outside of the building data cloud, most systems, when you move data, just lose the context. If you take any data and put it in a spreadsheet, you lose the context of what is each of the columns. We try to preserve that. The third thing is how can I how can I trust this data? And what that means is, that every time Rippling Data Cloud shows you an answer, a number, it is inspectable. You can see Rippling AI is actually instructed not to answer your question directly. It is instructed to write SQL, write code, get the data, and then summarize that information. What that means for you is that you can exactly see where the data is coming from, where the answer is coming from. You can look at the SQL. You can look at the code. You can you can do this yourself and gain more trust, and there is no hallucination. Next slide. So so now I wanna talk about, next slide, please. Now I wanna talk about what how I use this. Yeah. As Paul said, Rippling has provided our, our employees with tools to to do their job, which are all AI tools, including engineers. And one of the first questions I had about my team was, we're spending all this money on these tools. Are we are we using them? How is the adoption of these tools? And for that, I just asked the question. I just asked this exact same question, the prompt is on the screen. And in a within a few minutes, I had a dashboard in front of me. Next slide. Yeah. So within a few minutes, I had this dashboard. And the dashboard had essentially KPIs on the top. It has trends. It has breakdown by different team. And it gave me the information that I wanted, which is adoption overall is is going well, but there are still pockets of teams where the adoption is is lower. And because it presented me with the breakdown, I was able to go and directly take, action and talk with those teams. Now once I had this in front of me, I had another question. My next question was essentially around how what is the output that we are producing? As Paul said, you could be using AI AI tools for a lot of things. I want my team to use these tools to produce customer value. And for an engineer, that translates to code and that translates to PRs. So my next prompt, was about next slide. Yeah. My next prompt was about that. It is about tying it to what is being produced by the teams. And as you can imagine, to answer this, you need a different set of data. You also need GitHub data along with the AI usage and and spend data, along with the employee data. I didn't have to specify anything other than I wanna see the output and what is getting produced. And within again, within minutes, I had another dashboard. Next slide. Yeah. Again, a a useful dashboard with KPIs, trends, breakdowns, and no analysts were harmed in, you know, making this dashboard. If you the thing that I gained from this dashboard was two two things. One is the chart on the top of the lines, which it shows that when we increase spend, the number of PRs didn't actually go up proportionally. So there is a minimum spend after which increasing spend doesn't buy you more output. That was that was a very useful observation for me. On top of that, Rippling AI automatically created a scatter plot for me and showed that if that that sweet spot of, of spend is somewhere between 600 and 800 for me. And I was able to sort of get that just by asking this prompt and looking at this article. Now as an engineering leader, one of the things I care about is quality. And so the next question obviously, at this point was we have adoption and we have PRs coming. The next point was, are we producing good quality output? Is there AI slop here? Is there things like, you know, number of bugs are going up for as the as the usage of AI goes up? And so I asked that question. I just asked, show me the, show me if there is AI slop essentially. And, next slide, you'll see the problem there. And, again, as you would guess, in a few minutes later, I had another dashboard, another very useful meaningful dashboard. So, next slide, please. Yeah. Again, useful KPIs, useful breakdown, all automatically sort of picked, different aspects by Rippling AI for me. And it it highlighted a team at the at the top. There's a there's a scatter plot that you would see where it highlighted a team in red for me to say the number of, failures there is higher than the other teams. So, basically, it gave me a team that I could go talk to where there is perhaps a need for more training or more human involvement in reviews. But there is there is something that I could pay attention to. And so within thirty minutes of asking a very basic question is, are these tools getting adopted? Two, knowing which teams I need to follow-up with for the quality of the output that they're producing. I could go from from that basic question to this deep, deep question. And the most important thing for me was it Rippling AI and data cloud combination reduced the curiosity tax for me. As soon as I look at a dashboard or a chart, I had the next question, and I didn't have to take an afternoon away from an analyst to get the answer to that question. I just asked and then got the next question. And it made my next question better, more nuanced, and more specific. This is the power of, Rippling AI with Rippling Data Cloud. Next slide. So we shipped data cloud about a little over a month ago. We shipped the iSpend console last week. We are shipping more this week and next week and weeks after that. If you go to the next slide, we the the pace at which we are delivering value is not slowing down. It's increasing rapidly. We're going to, in the next week to months, we are going to have the ability for you to write your own skills for you to connect to a lot more data sources, through Ripping Data Cloud, ability for you to build custom apps. We already have custom apps in the Rippling platform, but you will be able to use AI to create those apps. That's that's something that I'm very excited about, and and a lot lot more. So super excited about, where we are with data cloud, what value we can we can provide for our customers, and all the things that we're working on, for you guys. Thank you. I'll pass it on to Devino. Amazing. Thank you so much, Varun and Paul, for taking us into the world of Ripley AI and data cloud. As you've all seen, there are so many interesting use cases and ways that you can harness the tools that you're already using in your day to day business operations into the work that you are already spending so much time doing in rippling so that there's less context switching and more data backed decisions happening in the moment where you actually need it and not minutes or days or weeks after the fact. We always like to call Rippling AI like the senior data analyst that you always needed, but this one just doesn't need to sleep. It takes seconds to answer you, and it has all the context that you could possibly ever want. So, hopefully, these use cases were able to shed a little bit of light for you on how you might be able to use this in your own teams in your day to day operations. Again, a quick reminder, if you're having questions coming up, during the webinar, feel free to drop them in the q and a, and we're about to get right into that. So before we jump into the q and a where you can ask Paul and Varun your questions, both about how they're using this technology to run their teams and how they're able to not just answer questions, but then make better decisions using all this data. I wanted to quickly leave you with a couple of resources. If you are interested in learning more about Rippling AI, you can visit this site here, rippling.com/ai. And the great thing is that if you're a customer, you can try it for free today. You can also if you're looking for a little bit of inspiration and want some help thinking of prompts to get you started, whether it's just to kind of learn more about your business through Rippling AI or if you actually wanna build something like a dashboard, then you're welcome to try out our Rippling prompt library, which is just flush with incredible use cases, both from internal teams as well as our customers all around the globe. You can see a little bit about the context of the question, how it's helping their team, and what information they got out of it. And as Paul and Varun both mentioned, we know that AI spend is top of mind for so many of you right now as you're thinking about, just who's using these models and what the payoff is and how it's actually helping the business and how you can measure the quality of the output using these AI tools. We have the AI spend console, which is available today. You can access it, on our website. So definitely recommend checking that out. And we have a poll going up where if you are interested in getting a demo to understand more about how Rippling AI and data cloud can be used for your own business needs, then feel free to just answer that in the poll now so that we can keep track and we will have someone reach out. So now I know that there must be a lot of questions here. We'll go ahead and shift into our q and a portion. I'm gonna invite Paul and Varun back on stage to answer a couple of questions. We'll kick off with a couple of the questions that we're seeing here in the chat, and they'll take it away. Alright. So, I have one that I came in as a DM, and it's easy for a vendor to show a dashboard and say, like, oh my gosh. It's the greatest thing ever. But let's be real, Varun. When we when we built, this AI spend dashboard, did we save any money? Did we find any spend that was frivolous that we were able to turn off? What was the impact? I, of course, know, but why don't you share it with with the world? across across the the engineering spend, millions millions of dollars, through through this. It's about a 40 more than more than 40% reduction in our cost. Yeah. The important thing here, guys, is that it's not to turn off these tools. It's to make sure that people understand really what it costs. And we found that before we were looking at this data and asking these questions, that people simply didn't know that they were spending $1,000 a week, dollars 5,000 a month, sometimes more, sometimes less. You need to be able to share that information, the insight. And so Rippling is just one example of kind of these overages that are happening and, you know, the tools available for you to use. Next, there was a question in here regarding outages. I'm I'm gonna take this one. And the question was something akin to, well, hey, if Rippling goes down, do I lose access to my data? And, the the answer there is, of course, yes. Rippling is a SaaS provider, and if Amazon Web Services, as an example, were to go down, Rippling would be inaccessible. That is, you know, kind of a fact of life with operating on the Internet these days. But, you know, we publish our uptime statistics. We even publish our help desk SLAs so people can actually go and see how long it takes Rippling, to respond to tickets. But Rippling operates its services around the world to, you know, hundreds and hundreds and hundreds of thousands of users in real time. We're proud of the service we provide and are confident, and believe in the trust and the capability when people trust us with their data. So we do our best to safeguard it and make it available, whenever possible. Varun, there was one here about the tools that we integrate. How many integrations are there today? And then the follow-up question, of course, is, well, what happens if there's one I want that you. don't. have? We we have we have about, about a 100, integrations today, and we are able to add integrations in a matter of days. So if you have a tool that you, want access to, just just reach out to us. And if it's not there, we'll be able to add it add it add it quickly. Yeah. What what's quickly? I'm gonna hold you. accountable, man. What is what is. quickly. We are? we are at, a few days, three to four days, right now, to for for turning around a connector. And, we're working hard to get to, a point where we can do one connector a day. Love it. Another question that came in, and I'm gonna read this one verbatim. I just joined a company using Ripley now. I'd like to try the a AI feature. How do I do that? Two paths here. The first is that, actually inside of your Rippling account, there should be a banner that says try Rippling AI. You should be able to click that. If for some reason you dismiss the banner, you should be able to reach out to one of your admins, whoever that is within your account, and they can click the banner. Alternatively, you can reach out to your account manager if you know who that is, and they can enable it for you. But for the SPEND console that we shared today, there's actually just a publicly available URL that Divya, had on screen. I think it's rippling.com/ai, and we're gonna share that with everyone. And anyone, regardless of whether they are a Rippling customer or not, can actually use that dashboard. Varun, do you wanna take this next one? Why don't you read it aloud for everybody? Hold on a second. I'm just I am not able to see the chat. Give me a why why don't you read the question? That's. okay. Yeah. I'll read it. How do you get leaders and teams who aren't using Ripping on a daily basis to utilize it as a key AI tool when the integrations are set up? You know, for me, it is I I spend a lot of time with our customers trying to work through that exact question in terms of adoption. And I have found that if we just run a single session and folks I share a prompt, a question for people to ask the moment they like, that light bulb goes off, they go, That was really powerful. That's really all it takes. And so I would challenge you to think about what are the questions you and your your staff and your executives and your operators, what do you what are the things you ask every day? Who's working today? Who's on call tonight? What's our inventory look like? You just start to ask these questions, those light bulb moments, and the magic is there for everyone. to see. Well, one of the things to to add to that, that I've I've seen happen at Rippling is we'll be in a meeting and a question would come up, and somebody would just open up Rippling AI and ask that question right there. Yeah. And that is that is just magical when you can, like, not not have another follow-up meeting at the end of the follow-up discussion and just get the answer right away. And that just, get gets. people hooked. The classic example for that is when a team is behind on hiring and you're able just to ask, it's like, well, hey. How are interviews going? And, of course, the hiring manager says, great. I have all these candidates, the ability to just write into Rippling AI. How many interviews did Paul do this week live? I don't need to schedule another meeting for a few days from now to pull, you know, to pull a report from Greenhouse or Ashby, you name it. Rippling can get that data for you in in real time. Here's a question for you. I am an HR VP, HR business partner. I'm curious what kind of dashboards would bring closer connectivity between us and IT. The number one thing that I hear from customers here is clear communication around new hires and terminations. It's just as really simple. HR and IT have have tended to kind of, like, live apart in different silos, if you will. With Rippling, the data is, you know, signals versus truths, and it the best thing you can do is just be crystal clear on upcoming hires and terms. And it tends to be that HR guards that information, but IT really needs that information as well. There's a tab on the left hand side called my IT, so make sure your IT administrators have access to that so they can see this information. And, alternatively, they can make their own custom dashboards. You can make the dashboard and share it with them. They can use AI to prompt. But the key is just starting that conversation, making sure that IT is included in this process and that they know the data is accessible to them. Yeah. Some of the some of the prompts that you can, try or I've seen, people use are around, who are the employees who have left the company and still have access to some subsystems. Right? Things like that. And people who are are all the new hires onboarded correctly, and they have their laptop available, or they have, access to all systems they need access. And and Rippling AI is able to answer that that very, very quickly for you. Varun, this next question is for you for sure. As an engineering leader, what indicators should I look for in the dashboards that help me evaluate the skills of my team? What other connectors might be useful? You mentioned AI Slop. Yeah. I'm just curious. AI slot, is you know, if you click a little. deeper. What what I've what I've found is so, you can you can just ask crippling about about AI's law. It will it will make the best judgment based on the connectors you have about what or how to sort of get to that answer. But the other thing, you should look at, at least I look at is is the, as engineers get to higher levels, they are expected to help more with reviews, up leveling the team, and perhaps their own PR count, doesn't go up as much, but their review count should go up. So one of the things I look at is, hey. Are my senior and my staff and senior staff engineers as they're getting promoted, are they doing more reviews, or are they doing less reviews? And that is, that is an indication for me as, you know, if they are if they're reviewing the PRs, if they are up leveling the team, and that is what reduces AI's law. Essentially, it's it's senior people looking at the code and mentoring and guiding individuals. So that is that that is one of the questions you can ask in Rippling AI, and it'll sort of tell you. I I have a dashboard that tells me, how many people are doing, reviews and how is that trending over time. Have you, I know that we do this, and we just published an article on this, Mckinnes did on Twitter. How about model efficiency? Again, if I'm an engineering leader, there are multiple models, and there's clearly a trade off here between, you know, Fable and Kimmy in terms of productivity or crock. Yeah. How are you thinking about that, and how are you reviewing that with your directors? So we we have, I mean, basically, people have access to different models, and engineers are allowed to pick the model and they are recommended, like, you know, encouraged to pick the model based on the task. But at the same time, you can look at in Dippling AI, you can say, hey. Tell me the distribution of PR complexity with the model that was used. And that is the kind of stuff that tells you that are people using, you know, expensive models for simple stuff, or are they using simple model for complex stuff? And that is another opportunity to to mentor and guide the teams around how to pick the best model for the for complex stuff. I'll take this next one. How are we using AI to monitor ramp time for new employees? There are a few interesting queries here. So, Rippling is a Salesforce customer, and we use this across our business from sales to implementation to support. And there are some really simple and fantastic queries that you can ask about a new hire. Just very simply, you know, how is Varun doing this week, compare him to his peers. Very simple. The AI will go through and query the data. We're importing all of that lovely Salesforce data into into data cloud. And so it's fairly quick and, frankly, inexpensive to do so. And then we can build dashboards from there to look at specific teams longitudinally over time, where we might have a dashboard just for implementation, looking at CSAT scores as an example for new hires, looking at, you know, number of completed implementations, by implementation manager. And for me, like, good management is not about asking the question once. It's about, like, having a rich set of data and looking at it regularly and coming back and back and meeting with my leaders, and asking tough questions. So we've gotta move from that world of, like, tell me about how Varun's doing to, like, let's look at the team that Varun is on and comparing him and perhaps the stack rank, etcetera, with the data we have. Alright. There's a question here. I'd like to partner more closely with our finance team to optimize quarterly spend, but not sure what metrics I would include in the dashboards they would care about. What's worked for you? You know what's really cool? All you have to do is ask Rippling AI to create you a dashboard about spend, and it will create you a dashboard about spend. I recently did this. I wanted to know how a product launch was going, and I didn't know any of the metrics that we had available to us. And so I simply asked Ripley AI to tell me about the, automated compliance launch in this example. And it came back with a dashboard with, you know, something like 11 different reports for me to look at. And from there, I was able to refine it. So, I am not an accountant. I am not a CPA. I am not on the finance team. I actually couldn't tell you exactly what the right dashboards are for you, but I would simply encourage you to ask. Just simply ask Rippling AI to build you a dashboard for maybe end of month close or to tell you about your credit card spend. And I promise you, you'll be impressed with what it comes back with, and then you can massage it from there. Yeah. And if you if you find any other metric that you learn about when you're meeting with your finance counterpart, you can just ask your opinion to add it. You can update and edit these dashboards just by asking. Alright. Well, Varun, thank you very, very much for your time today. I'm grateful. I know I learned something from you as well. And, Divya, back to you to close it, and maybe we can flash one more time the URL, for the spend console so people can go and try that on their own. Thank you both so much for joining. Really appreciate having you both here today to guide everyone through your use cases as leaders here at Rippling and also kinda share a bit of insight about how others can apply some of these learnings in their day to day work right now if they're already using Rippling, or even just checking out some of the resources like what we have up on screen now. If you're just kind of learning how some of this stuff works and you wanna understand what Rippling can do for you beyond what you already know about it and kind of coming along the journey with us is given how much the product has evolved over time. As you know that we are constantly learning from our customers, looking at their feedback, looking at how our internal processes work at rippling and trying to apply as many of those learnings as we can and sharing it with all of our customers, hence, products like the AI spend console. You have the links here. We're also gonna send you follow-up emails with, links to all of things all of the things that you see here, including how to get started with Rippling AI, how to learn more about data cloud, where you can find inspiration with the prompt library if you're looking for a couple of seeded or starter questions to ask. And, again, if you wanna just look at what your AI spend looks like right now at your business, that is available to you today. You're welcome to reach out to book a demo that's available on your screen. If you click that button, it'll automatically let our team know that you're interested and we'll reach out. If you have any questions, feel free to always contact us. All these resources will be included in your follow-up email. Thank you so much for joining us, and we appreciate you having you here. See you.