Thank you for standing by and welcome to Appian Quarter 2023 Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during this session, you'll need to press star one one on your telephone. If your question has been answered and you'd like to remove yourself from the queue, simply press star one one again. As a reminder, today's program is being recorded. Now I'd like to introduce your host for today's program, Sri Anantha, Vice President, Finance and Investor Relations. Please go ahead. Thank you, Operator. Good morning, and thank you for joining us to review Appian's fourth quarter and full year 2023 financial results. With me today are Matt Calkins, Chairman and Chief Executive Officer, and Mark Matheos, Chief Financial Officer. After prepared remarks, we will open the call for questions. You can follow along with our earnings presentation by downloading it from the main page of our investor site at investors.appian.com. During this call, we may make statements related to our business that are forward-looking under federal securities laws and are made pursuant to the Safe Harbor Provisions of the Private Securities Litigation Reform Act of 1995. These include comments related to our financial results, trends and guidance for the first quarter and full year 2024, the benefits of our platform, industry and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers, and our ability to acquire new customers. The words anticipate, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views only as of today. They do not represent our views as of any subsequent date. They are subjected to a variety of risks and uncertainties that could cause actual results to differ materially from expectations. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our most recent annual report on Form 10-K, quarterly reports on Form 10-Q, and other filings with the SEC. These documents are also available on our investor section of our website. Additionally, non-GAAP financial measures will be discussed on this conference call. Refer to the tables in our earnings release and the investor section of our website for a reconciliation of these measures to their most directly comparable GAAP financial measures. With that, I would like to turn the call over to Matt. Thanks, Sri, and thanks, everyone, for joining us today. In the fourth quarter of 2023, Appian's cloud subscription revenue grew 26% to $81.3 million. Subscriptions revenue grew by 24% to $115.8 million. Total revenue grew 16% to $145.3 million. Our cloud subscriptions revenue retention rate was 119%, and our Adjusted EBITDA was a gain of $1.0 million. For the full year, Appian's cloud subscriptions revenue grew 29% to $304.5 million. Subscriptions revenue grew 21% to $412.3 million. Total revenue grew 17% to $545.4 million. Our Adjusted EBITDA was a loss of $44.8 million. I want to mention two milestones at the top of this call. Last year, for the first time, our revenue exceeded $500 million. Second, and an interesting complement to the first observation, we achieved the highest non-GAAP gross margin in our public history last quarter at 78%. In our presentation deck, we've included, one last time, the special bonus metrics we tracked quarterly in 2023. We didn't get the recession I expected, but there were some macro complications, and you can see it all starting on page 5. Last year was the year of AI talk. Now, the conversation will shift to more tangible things: shift features, successful deployments, practical value. That change will be good for Appian. We have a distinctive approach to the AI market based on years of leadership and existing technology. We are focused specifically on the application of AI to data. We're leaders in Data Fabric, which is like a virtual database uniting the customer's enterprise. And we are leaders in AI, and now we will be leaders in the combination of these two things. I think everyone understands by now that AI is only as good as the data behind it. More data, better AI. Appian has an open data strategy that allows AI to benefit from information scattered across the enterprise. Ask a question, and your response will be informed by everything known to the business, not just the contents of one silo. Same for generating new content. The more data that supports the AI, the more sources you bring to bear, the better the output. Let's explore this with an example. The purpose of this example is to show you the advantage of having AI draw from multiple data sources, that AI is better that way. We work with a large U.S. state government entity that awards hundreds of millions in dollars in contracts every year using Appian. Its procurement processes are highly regulated and must comply with federal and local laws. In Q4, this organization deployed Appian AI to optimize its awards management process. As you know, contracting usually involves many data objects of different types in different formats, typically in many different locations. Now, AI will understand thousands of regulatory and procedural policies from various sources so employees don't have to manually search for information. Appian AI is embedded in the customer's workflow doing work and making the customer's data more usable than before. Procurement officers and supporting staff can ask AI questions in real time and get great answers. This allows them to advance their procurement with speed and accuracy. Our second advantage is simplicity. Appian takes a practical view towards technology. Our goal isn't just to make extraordinary software, but to make it accessible. Programming in Appian is done with a mouse, not a keyboard. We take the same approach to AI. One of the nation's largest universities uses Appian to improve graduation rates. Recently deployed Appian AI to its student advisors. AI will recommend actions to take on student cases and propose meeting agendas to advisors before they meet with students. First, I want to emphasize how important it is that such a system draw on all the data in the enterprise. If you're trying to help a student complete a four-year degree, you need to know about all the threats to their progress. You need to know if they're failing any courses. That's in one system. You need to know if they're late on tuition payments. That's in another system. You need to know if they're missing classes. That's in the attendance logs. You need to know if they have friends who recently dropped out. That's someplace else. You get the idea. These are different data sources, and AI needs them all to identify risks and make a good recommendation. My other point is that such a system must be easy to set up and use. Counselors just want to ask a chatbot some questions about their students, not become AI technologists themselves. We made it easy, and it was easy to deploy as well, going live in under two months. All this talk about drawing on the full enterprise of data sounds great until you consider the implications: merging data sets within the enterprise, uploading massive amounts of data, and training AI algorithms you don't control. Appian requires none of that. We offer insights across the widest amount of data, but we do it while disclosing the least of it. We specialize in Private AI. We use our Data Fabric to provide just the information that's pertinent for every question rather than pre-training an algorithm on everything an organization knows. It's more cost-effective and much more respectful of our client's architecture and privacy. Another example to make this point: a top pharmaceutical company manages several core processes with our platform, including ones related to clinical trials and manufacturing logistics. In Q4, the company named Appian its standard enterprise workflow tool. It'll now deploy our platform to over 50,000 employees. The company aims to bring new products to market faster. Our AI is privy to their confidential documents, their lab results, etc. This is sensitive work, and needless to say, they take the privacy of their data very seriously. This global firm has decided to trust our technology, to make the most of what they know while keeping the highest commitment to protecting it. Appian landed some of our largest seven-figure deals this quarter. The total contract value of our top 10 net new software deals increased by 70% in Q4 2023 compared to the same period last year. Here are some notable stories. A U.S. military branch wants to unify its operational systems so it can better mobilize its forces. Appian will integrate data from 14 legacy systems into a single modern platform. In Q4, it purchased a seven-figure software deal. 100,000 analysts will use Appian to help train, manage, and equip personnel globally. In another example, a financial services company managing hundreds of billions of dollars in assets became a new customer in Q4 with a seven-figure software deal. The company is growing quickly and wants to modernize legacy systems that are too costly to maintain. Appian's Data Fabric will unify data from over a dozen core systems into a single view so it can run end-to-end workflows like customer onboarding and wire transfer. Appian will help the customer scale and process more than 4 million transactions annually. Next, a national police force recently adopted new strategic priorities to optimize operations and improve public safety. In Q4, it selected Appian as an enterprise-wide platform to improve productivity and unify the group's disparate systems, starting with an incident management app. Desk workers will triage inbound requests, open cases, and route them to field officers for investigation. This process had always been manual. Now, thousands of officers will be able to respond to incidents faster using Appian. Last example now: a top health insurance provider is running a company-wide initiative to modernize systems and save $1 billion. It selected our platform to aid this effort, starting with its enrollment process. The provider made a seven-figure software deal in Q4 and became a new customer. Appian's Data Fabric will consolidate Medicare and Medicaid requests into a single application so employees can do eligibility checks, fix discrepancies, approve applications, and initiate the billing process. The organization expects to process millions of requests annually on Appian. Now, a few final notes. Appian expanded our credit facility this quarter with participation from existing and new lenders. The aggregate principal amount of the term loan facility is now $200 million, up from $150 million, and the revolving credit facility is $100 million, up from $75 million. We welcome the additional financial strength. Appian has made progress in our intention to aim high in this market and sell more large deals. We've held to our financial discipline and done so without taking anything from growth. You'll hear more about our plans at our investor meeting on April 16th at Appian World in Washington, D.C. We'll talk strategy, results, technology, AI, and more. I hope to see you there. With that, I'll hand the call to Mark. Thanks, Matt. I'll review the financial highlights for the quarter, and then we'll provide guidance for Q1 and the full year 2024. We closed 2023 on a strong note with revenue and Adjusted EBITDA coming in above the high end of our guidance range. We saw continued healthy contribution from existing customers and strong growth from key industry verticals. Let's go into the details. Cloud subscription revenue was $83.1 million, an increase of 26% year-over-year and above guidance. On a Constant Currency basis, Cloud subscription revenue grew 23% year-over-year. Total subscriptions revenue was $115.8 million, an increase of 24% year-over-year. On a Constant Currency basis, total subscriptions revenue grew 21% year-over-year. Professional services revenue was $29.5 million, down 9% year-over-year. As previously noted, services revenue can be volatile from quarter to quarter, and a few large projects can influence performance. Our professional services will continue to be a strategic offering focused on enabling partners and driving customer success. Long-term, we expect Professional Services Revenue to continue to decline as a percentage of total revenue. Subscriptions Revenue was 80% of total revenue compared to 74% in the year-ago period and 76% in the prior quarter. Total revenue was $145.3 million, an increase of 16% year-over-year and above our guidance range. On a Constant Currency basis, total revenue grew 13% year-over-year. Cloud Subscription Revenue Retention Rate was 119% as of December 31, 2023, up from 115% a year ago and 117% in the prior quarter. As a reminder, we continue to target a Cloud Subscription Revenue Retention Rate of 110%-120% on a quarterly basis. Our international operations contributed 36% of total revenue compared to 34% in a year-ago period. Our cloud software net new ACV bookings were approximately 80% of the total net new software bookings in 2023, consistent with last year's mix. Now, I'll turn to our profitability metrics. Non-GAAP gross margin was 78% compared to 73% in the year-ago period and 75% in the prior quarter. Subscriptions non-GAAP gross profit margin was 91% compared to 90% in the year-ago period and 89% in the prior quarter. Professional services non-GAAP gross margin was 26% compared to 27% in the year-ago period and 30% in the prior quarter. As noted on prior earnings calls, we continue to invest in customer success management. These advisors help our customers achieve the most from our technology and increase adoption of our platform. As a result, professional services non-GAAP gross margin should decline to the low 20% range in 2024 and beyond. Total non-GAAP operating expenses were $114.1 million, down 4% from $119.1 million in the year-ago period. Adjusted EBITDA was a gain of $1 million versus our guidance of a loss between $16.1 million and $12.1 million compared to an adjusted EBITDA loss of $24.8 million in the year-ago period. In the fourth quarter, we had approximately $11.1 million of foreign exchange gains compared to $8.5 million of foreign exchange gains in the same period a year ago. We don't forecast movements in FX rates. Therefore, they aren't considered in our guidance. Non-GAAP net income was $4.9 million or $0.06 per diluted share compared to non-GAAP net loss of $20.6 million or $0.28 per diluted share for the fourth quarter of 2022. This is based on 75.3 million diluted shares outstanding for the fourth quarter of 2023 and 72.7 million diluted shares outstanding for the fourth quarter of 2022. As noted above, fourth quarter 2022 Non-GAAP net loss was aided by $11.1 million in foreign exchange gains or a gain of $0.15 per share, which was not included in our original guidance. Now, before I turn to the balance sheet, I wanted to briefly update on the recent amendment to our credit agreement. On February 12, 2024, we increased the aggregate principal amount of the term loan facility by $50 million and the limit of the revolving credit facility by $25 million. The total aggregate term loan facility is now $200 million, and the revolving credit facility is $100 million. Additional details on the terms of the financing will be in the 10-K filing. Turning to our balance sheet, as of December 31, 2023, cash and cash equivalents in investments were $159 million compared with $196 million as of December 31, 2022. For the fourth quarter, cash used by operations was $8.2 million versus $12.6 million for the same period last year. Total deferred revenue was $240.7 million as of December 31, 2023, an increase of 20% from the year-ago period. As we have stated on past calls, the majority of our customers are invoiced on an annual upfront basis, but we also have large customers that are billed quarterly or monthly. Due to the variability of our billing terms, changes in our deferred revenue are generally not indicative of the momentum in our business. I'll now recap our full year 2023 results. Cloud subscription revenue was $304.5 million, representing 29% growth year-over-year. On a constant currency basis, cloud subscription revenue grew 26% year-over-year. Total subscriptions revenue for the year was $412.3 million, an increase of 21% compared to 2022. On a constant currency basis, total subscriptions revenue grew 19% year-over-year. Professional services revenue was $133 million, an increase of 4% compared to 2022. Total revenue was $545.4 million, up 17% compared to 2022. On a constant currency basis, total revenue grew 15% year-over-year. Adjusted EBITDA loss was $44.8 million compared to $76 million loss in 2022. Non-GAAP net loss was $59.2 million in 2023 or a loss of $0.81 per diluted share compared to non-GAAP net loss of $89.2 million or a loss of $1.23 per diluted share for 2022. This is based on 73.1 million and 72.5 million diluted shares outstanding for 2023 and 2022, respectively. For the year ended December 31, 2023, cash used in operations was $110.4 million versus $106.6 million for the same period last year. Adjusting for the one-time payment of $57.3 million in the third quarter of 2023 for the Judgment Preservation Insurance policy, 2023 cash usage showed a substantial improvement versus 2022. As a reminder, we continue to believe cloud subscription revenue is a better indicator of our business momentum than billings or remaining performance obligations or RPO. The latter metrics can fluctuate based on the timing of invoicing, seasonality of on-prem license revenue, and the duration of customer contracts. The true scale of the business is represented by subscriptions revenue, which includes support and all software subscription revenue, regardless of whether the customer deploys to the Appian Cloud, their private cloud, or on-prem. Now, I'll turn to guidance. For the first quarter of 2024, cloud subscription revenue is expected to be between $84 million and $86 million, representing year-over-year growth of 21% and 23%. Total revenue is expected to be between $148 million and $150 million, representing year-over-year growth of 9%-11%. Adjusted EBITDA loss for the first quarter of 2024 is expected to be between $9 million and $5 million. Non-GAAP net loss per share is expected to be between $0.21 and $0.16. This assumes 73.5 million diluted weighted average common shares outstanding. For the full year 2024, cloud subscription revenue is expected to be between $364 million and $366 million, representing year-over-year growth of 20%. Total revenue is expected to be between $615 million and $617 million, representing year-over-year growth of 13%. Adjusted EBITDA loss is expected to be between $25 million and $20 million. Non-GAAP net loss per share is expected to be between $0.73 and $0.66. This assumes 73.8 million diluted weighted average common shares outstanding. Our guidance assumes the following. First, Q1 professional services revenue will decline by a low double-digit rate year over year. For the year, we expect professional services revenue will be flat or will decline by a low single-digit rate compared to a year ago. Second, on-prem license revenue will grow year over year by a mid-single-digit rate and will track to seasonality that is consistent with prior periods. Third, our Q2 Adjusted EBITDA loss will be bigger than Q1's Adjusted EBITDA loss. This is due to the combination of on-prem license seasonality and the cost of running our global user conference, Appian World. Fourth, total other income and interest expense will be approximately $3 million in Q1 and $15 million for the full year 2024. Fifth, capital expenditures will be between $2 million and $3 million in Q1 and between $10 million and $12 million for the full year 2024. Finally, our guidance assumes FX rates as of February 13, 2024. In conclusion, we are pleased with the performance this quarter. We are investing in growth opportunities that drive long-term value and optimizing costs to drive profitability. We continue to balance our cost profile to prioritize investments in R&D, innovation, CSM coverage, and strategic go-to-market areas such as global partnerships, demand generation activities, and targeted sales capacity. And with that, we will open up the line for questions. Operator? Certainly. And as a reminder, ladies and gentlemen, if you do have a question at this time, please press star 11. One moment for our first question. And our first question comes from the line. Sanjit Singh from Morgan Stanley. Your question, please. Thank you for taking the questions, and congrats on a solid end to the year. Matt, as you think about 2024 and coming out of 2023, what are you seeing in your demand environment, in your pipeline, as it relates to this momentum around automation and getting practical value of AI? What are some of the use cases that customers are starting to pursue with Appian versus maybe some of the other initiatives out in the space? Right now, AI is a fantastic door opener, but it's best done with a very simple proposition. So we're equipping our team to be able to approach the customer with an easy-to-understand, easy-to-implement way to get in on AI and show rapid benefits. I believe that keeping it simple and having a short period of investment before you get the payoff is essential to catalyzing their large interest in the topic today. So that's helpful. I also feel good about where the pipeline stands and particularly the large end of the pipeline. As you know, we're focused a little bit more on those larger opportunities now. Yeah, that makes total sense. And then Matt, for you, I mean, the positive Adjusted EBITDA in Q4, that was really nice to see. When we think about the balance between expense or managing margins versus growth, what's the potential for that to continue from what we saw in Q4? Or said in another way, did Q4 sort of get the benefit from any sort of shifting in expenses to get to that positive Adjusted EBITDA in Q4? You want to take that? Sure. Yeah. No, we really didn't do anything out of the ordinary to get to a positive Adjusted EBITDA in Q4. That was an artifact of our strong revenue performance. We had a really good level of linearity on the top line. We're just on our plan here on the expense side that we've discussed in the past, and we're steady as she goes on that in terms of operational discipline. But the name of the game is still growth for us. We're just doing so with a lot of scrutiny on our expenses to make sure we're getting the ROI we need and running a tight ship. But there was nothing out of the ordinary for Q4 in that regard. Okay, that's helpful. Thank you, Matt. Thank you. One moment for our next question. Our next question comes from the line of Steve Enders from Citi. Your question, please. Okay, great. Thanks for taking the questions here. I guess maybe just to start, just maybe thinking more broadly about kind of the bigger demand environment and kind of what you're assuming kind of for 2024. I know that for '23, there were some extra conservatism kind of baked in for government shutdown and some other things. But I guess what are you seeing today in kind of the deal environment and what's kind of being assumed in the outlook here for 2024? Okay, so broadly, the deal environment, I still think that there's some macro disruption, but it never rose to the level of a recession. I think that there's genuine interest across the board in what we can do for them with AI. I think that there's recognition that we're creating real value, and that sparks expansion opportunities, and it propels demand for our industry, not just for our organization. I think this is a workable demand environment. I think this is a demand environment that we can succeed in. Okay, that's helpful. And then maybe just on the, I guess, kind of the net new versus customer expansion, I guess for one, just really good to see the net retention number tick up here. I guess maybe what drove the strength of the expansion here in the quarter, and how do you view the kind of sustainability of that moving forward and what's being embedded into the guidance for 2024? All right. Now, we don't make any guide on NRR. I am also pleased to see it tick up. However, it hasn't ticked up that substantially. It's a couple of points, and I don't want to I don't want to dwell on that. That's a blip for now. Maybe we can make it a trend, but it's a blip for now. I do think that it's something we want to excel in. We want to see more expansion, and we are focusing more on the techniques that lead to expansion, deepening the relationship that we have with our clients, having more touchpoints, having more exposure, emphasizing our humanity in contrast with the big tech substitutes or what they might conceive to be a substitute for Appian technology. I think we want to shine in the ways that we are naturally advantaged against our larger competition, and we do that by having this sort of that pervasive human connection. And that's the sort of thing that will lead to more expansion if it works. So this is an important number to me, but I don't want to make any implications about where it's going. Great to hear, and thanks again for taking the questions here. Thank you. One moment for our next question. Our next question comes from the line of Jake Roberge from William Blair. Your question, please. Hey, thanks for taking the questions, and congrats on the solid results. Matt, I know it's early, but can you talk about how you see monetization shaping up for some of the new-gen AI solutions and data fabric? Could those initiatives start to drive any growth heading into 2024, or is it still too early for that? We have a monetization strategy for both of those features. We have a stratified pricing system whereby you pay more for data fabric if it's accessing multiple data sources and more for AI, or specifically for private AI. So we are absolutely expecting that these features will drive a revenue differentiation, not just volume, not just attention, not just a competitive advantage, but also we're tagging them with revenue. Okay, helpful. And then you've made some changes to your go-to-market organization over the past year or so between leadership changes, a small restructuring, and then also just the deeper focus on the partner organization. How do you feel like the go-to-market motion is positioned as you head into this year? I feel like we're a lot stronger than we were a year ago. That's how I'd read it. I think that we've been careful with the changes that we made last year, but they've been changes for the better. Great. Thanks for taking my questions, and congrats on the great results. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Derek Wood from TD Cowen. Your question, please. Hey, guys. Thanks. This is Cole on for Derek. You flagged good strengths in the TCV for top 10 net new customers. Can you just unpack that a little bit and talk about what drove that strength? Thanks. Yeah. All right. Well, first of all, I think part of it is driven by our strategic focus. We believe we belong in the big organizations doing mission-critical things at relatively higher price points. And that strategy, I think, has something to do with the fact that we're seeing higher TCVs on our top 10 deals, and for that matter, higher on our median deal, right? We're just trying to raise the target sizes a little bit, and we're seeing that that's happening. So yeah, I'll just say it's strategically aligned, right? It's not unintended, and I don't want to make any promises about where it's going, just to say that it was gratifying to see it come in where it did because that's what we intended. Helpful. Thanks. Thank you. One moment for our next question. And our next question comes from the line of Kevin Kumar from Goldman Sachs. Your question, please. Hi. Thanks for taking the question. I wanted to ask about the international public sector and the traction you're seeing there. Maybe talk a little bit about the go-to-market investments you're making there. And higher level, I guess, how early are these public sector organizations in terms of thinking about AI and kind of implementing more intelligence into their workflows? As you know, we're a Washington company, and I'm looking at the Beltway out my window right now as I take this call. We've done a lot of business here in Washington, D.C., with the U.S. federal government. The international public sector has always represented a big opportunity for us. For that matter, so has state government in the U.S. It is a largely untapped opportunity. I did mention in the prepared remarks one substantial organization in the state government level that works with us and does hundreds of millions of dollars of procurement every year on the Appian Platform. That's great, but that's the beginning. This is tip of the iceberg stuff. Even though we have notable wins in the other international or non-federal public sector opportunities, I still feel like the penetration is so minimal. We've done just enough to prove we can do it and not enough to show what we can do, how much we can do. So that's an opportunity we look forward to moving into. We're making an effort to move into it, and it's largely unsaturated right now. Great. Thanks for taking the question. Thank you. And just as a reminder, ladies and gentlemen, if you do have a question at this time, please press star 11. One moment for our next question. Our next question comes from the line of Frederick Havemeyer from Macquarie Capital. Your question, please. Hey, thank you, and good morning. I wanted to ask about data fabric in a little bit more depth here. Generally speaking, it seems like being in the enterprise data space and data integration space right now is a fantastic bit of positioning considering what enterprises are trying to do with their data and trying to make it useful. And of course, everyone's trying to have a gen AI strategy. So I'm curious, with Data Fabric, when you're helping customers implement this, what have been the most significant challenges that you're helping them to address? And also around that, what are the most significant challenges that you or your partners faced when implementing and onboarding customers to Data Fabric? Yeah. All right. First of all, you have to open up their imaginations. The typical organization does not imagine that it would be possible to merge data silos and to have synthesis or combined benefit from them. We're so used to an enterprise software landscape that is dominated by the walls, right, that is cut into silos. You have to first just tell them that it's possible. And then secondly, the integration, sometimes it's easy. Sometimes it works with APIs, and it's very intuitive. And in some cases, the entities could have been custom-built or very out of date, and then integration is a bit more of a challenge. But it's not so difficult to overcome. Once you convey the benefit, we can easily stitch these data silos together. It's simpler than one might imagine, and it's very fully featured. You can read and write. You can filter by individual permission access. It's actually a really powerful layer. And by the way, the strength of the data fabric is such that I expect that this year, more organizations will start saying these words, data fabric. They'll claim that they've got something like it. And I suspect that what they have is not going to be fully featured the way what we've built and have had for years is. It's an artifact of our divergent data strategy. Many of our competitors have a data strategy whereby they seek to claim, to unify, and to own the data in an enterprise. They are big enough, in some cases, to pull that off, to use their size, their leverage against their customer, and to force a kind of an aggregation under their own flag. We do not attempt that. Instead, we have always had a, you call it pro-customer if you like, an open data strategy that respects and empowers and enables the customer's existing data architecture. That's why we got into this Data Fabric concept in the first place, is because we wanted to be the vendor that would enable the customer to have the data the way they wanted to have it instead of trying to force it all into our database. So we have taken this we've built this technology because we first took this decision to be the sort of company that would enable the dispersed data strategy. And because our rivals have largely not taken that decision, they have also not developed that technology. I think that because this is the result of different beliefs about how the market works, it might be a more persistent technology division than it might initially appear. Thank you for that, Matt. I wanted to ask also on both renewal rates and net retention rates, understanding also, like you said earlier, that a couple of data points is not yet a trend to make. But I wanted to ask, it looks like your total gross renewal rate ticked down slightly in 2023 by quarters, while your cloud subscription revenue retention rate ticked up. So I wanted to ask, is there anything happening between the total company business and cloud that would be worth calling out at this point that could be attributable to that? Yeah. First of all, I want to address that downtick. Our Gross Revenue Retention Rate did indeed downtick from 99 to 98, bottoming at 97, and it's now risen back to 98. And I just want to clarify that though that may have been a downtick, it is still best in class. It is still remarkable numbers. And then secondly, I want to say there has been, I would say, just a little bit of migration, just a very small amount from on-premises to cloud at a point when I thought there wouldn't be any more, but there was just a little bit. And so that may be impacting the numbers a small amount. Super fair. Thank you very much for that. Thank you. One moment for our next question. Our next question comes from the line of Thomas Blakey from KeyBanc Capital Markets. Your question, please. Hi, guys. Thanks for taking my question. I have a couple here. Maybe first on the heels of Fred's great question on the Data Fabric. I think he also asked about the actual use cases, if you could maybe double-click on that, Matt. And then after answering that, if the company, as we're hearing an uptick from our calls on gen AI, especially in the enterprise, if these customers don't use your Data Fabric, what are these organizations going to do architecturally in terms of breaking down silos/bringing all their data together? Is it something akin to Appian solutions or compared to a cloud-based data warehouse like Snowflake? Or I just want to understand the pros. If they don't use you, what are they going to have to use in terms of launching these gen AI enterprise applications? And if you can give any examples, that'd be great. No, that's a great question. What are they going to do without Data Fabric? Well, Snowflake is one obvious example. Snowflake is asking, "Give us all your data." It's like a modern data warehouse. Just pile everything you can into this one data source. And when you do, we've already got a partnership lined up for gen AI on top of it. That's fine if you can move all your data there, if you can move all of it. But I talked to a lot of CIOs, and I can't remember any of them saying that they could move all of their data or even all of their pertinent data into a central repository, Snowflake or anyone else's. So typically today, AI either runs on one giant silo like Snowflake or all you can train, which I'll address in a moment, or a data fabric. If it's all you can train, then essentially you're saying that AI isn't going to run on a source. It's just everything you can upload, right? So you can upload one source after another if you want, but that you've got data loading costs. You've got data freshness issues. You've got variable levels of personal security access to that data issues. There's a lot of flaws with that strategy. And I think also just the idea of training at great length an algorithm that the CIO does not own is problematic for a lot of tech decision-makers. So I think that even though there is the data lake with Snowflake strategy and there is the train an external algorithm on everything pertinent strategy, these are not plausible strategies. And what I see happening in the absence of Data Fabric most of the time is AI is too limited on the data it knows. AI runs on one silo, and just one. And I think that is, unfortunately, the typical fallback in the absence of Data Fabric. That's interesting. Any use cases that you've seen maybe sprout out earlier in the evolution or planning to in 2024? Well, you mean use cases for Data Fabric? Yeah. Most of our customers actually use Data Fabric. We've got a terrific usage rate, somewhere 80%-90%, which is good for a participation in a feature because it's so beneficial. It makes it easy to connect to data sources. Even if you're using just one, it makes it intuitive and simple. But if you're using multiple, it's a huge step forward over what was possible in the past. And it also makes it far easier for a user to develop new applications because we objectize all of the data that's been touched by the Data Fabric so that a creator of a new report or process can just grab and drag and drop that object. All of these objects of data across the enterprise are now sort of draggable objects within the development environment. It just makes creation of new artifacts really intuitive. And as for use cases, really, the challenge is more thinking of cases where you don't need more than one data source. I mean, I mentioned one in my prepared remarks about the hypothetical student in need of rescue, right, and how it would be great to be able to know whether they'd attended their classes or missed a tuition payment or had friends who have dropped out or had bad grades or any of that. All of those things are going to exist in different systems. So even a simple application like, "How can we help this student to do well," is something that's a natural use case for data fabric. Excellent. Thanks for that, Colin. Just to follow up to that, my final question would be, at last year's Appian World, you expanded your partner programs and reached there pretty significantly, from what my understanding was. Where is Appian's infrastructure in your mind today in terms of reaching out to enterprises along these lines in terms of a sales motion? Do you have the right point of go-to-market infrastructure to have these touchpoints at large enterprises to sell this kind of gen AI solution in terms of the Data Fabric? That'd be my last question. Yeah. Well, we definitely did a strategic pivot on partners last year. We had 700 registered partners coming into the year. And we still do have a ton of partners. But we decided that we wanted to focus, really focus down, and make big investments in partners that were willing to make big investments in us. And that beneficial reciprocity is the pattern that we have set going into 2024. I think it'll be more motivational, and it will allow for a level of commitment in our partner that leads to greater expansion because it'll be greater implementation quality as well. Thank you, Matt. Thank you. This does conclude the question-and-answer session of today's program. I'd like to hand the program back to Sri Anantha for any further remarks. Great. Thank you, Jonathan. Thank you all for joining us today. We look forward to seeing many of you at upcoming investor events and on our next earnings call. Thank you, and talk to you soon. Thank you, ladies and gentlemen, for your participation in today's conference. This does conclude the program. You may now disconnect. Good day.