C3.ai, Inc. (AI) on Q4 2021 Results - Earnings Call Transcript

Operator: Good day and thank you for standing by and welcome to the C3.ai Fourth Quarter Fiscal Year 2021 Earnings Call. At this time, all participants are in a listen-only mode. After the speaker’s remarks, there will be a question-and-answer session. I would now like to hand the conference over to your speaker today. Paul Phillips. Please go ahead. Paul Phillips: Good afternoon and welcome to C3.ai’s earnings call for the fourth quarter and full year fiscal year 2021, which ended April 30, 2021. This is Paul Phillips, VP of Investor Relations of C3.ai. With me on the call today are Tom Siebel, Chairman and Chief Executive Officer and David Barter, Chief Financial Officer. Tom Siebel: Well, thank you, Paul and good afternoon, everyone. I am very pleased to give you an update on the state of the business. Bottom line, Q4 was a great quarter and fiscal year ‘21 was a great year. I am pleased to report that C3.ai is well-positioned to substantially accelerate growth and continue to gain market share in the coming year. Let’s talk about our fourth quarter results. We exceeded our guidance for both revenue and non-GAAP operating income. Our bookings grew, believe it or not, over 500% in Q4 compared to the quarter a year earlier. Our bookings grew 179% quarter-to-quarter. Revenue in the fourth quarter was $52.3 million, an increase of 26% year-over-year. Subscription revenue for the quarter was $43.1 million, up from $36.8 million a year ago, an increase of 17% year-over-year. Gross profit for the quarter was $40.6 million, a 78% gross margin compared to $32.1 million gross profit a year ago, an increase in gross profit of 26% year-over-year. Our remaining performance obligations were $293.8 million compared to $239.7 million a year earlier, an increase of 23% year-over-year. Including cancelable orders, our non-GAAP RPO was $345.1 million compared to $246.9 million a year ago, an increase of 40% year-over-year. Our total enterprise AI customer count at the end of the year was 89, representing an 82% growth rate year-over-year. David Barter: Thank you, Tom. We exceeded our guidance for both revenue and profitability in the fourth quarter, while also building significant backlog that will help drive future growth in fiscal year 2022 and beyond. Revenue in the fourth quarter was $52.3 million, up 26% from a year ago due to increasing demand for our enterprise AI applications, with particularly strong deal volume late in the quarter as reflected in our accounts receivable, deferred revenue, calculated billings and remaining performance obligation. Our fourth quarter revenue growth is a meaningful improvement over the 19% growth in Q3 and the 11% growth in the first half of the fiscal year, which reflected the impact COVID had on our business. Subscription revenue increased to $43.1 million in the fourth quarter, while professional services revenue grew to $9.2 million reflecting strong customer implementation activity and engagement with Baker Hughes that will make our virtual data and reliability applications even more compelling for oil and gas customers. In Q4, 82% of our revenue was from subscriptions and 18% from professional services. On a full year basis, 86% of our revenue was from subscriptions and 14% was from professional services consistent with our revenue mix in fiscal year 2020. We continue to anticipate subscription revenue mix in the upper 80% range on a trended basis. However, there may be some variation in the revenue mix quarter-to-quarter. Our revenue growth was highlighted by contributions from 8 different industry verticals, including some newer verticals, such as high-tech, life sciences, financial services and telco. Over the course of fiscal year 2021, these newer verticals contributed 17% of revenue compared to 8% in fiscal year 2020. Geographically, our revenue diversification also increased as activity in EMEA and APAC continue to expand. On a full year basis, EMEA and APAC drove 34% of our revenue compared to 22% in the prior year. Our sales execution in Q4 also drove a meaningful increase in our contracted backlog. Total remaining performance obligation, or RPO at the end of the quarter was $293.8 million, an increase of 23% from a year ago and a 19% sequential increase from the third quarter. Current RPO, which we expect to recognize in the next 12 months, was $145.2 million, an increase of 11% from the prior quarter. In addition, we had $51.3 million of additional contracted backlog from contracts with the cancellation rate. When combined with our GAAP RPO, this produces a non-GAAP RPO of $345.1 million. Our non-GAAP RPO grew 40% from a year ago, and this represents a sequential increase of 17% from the third quarter. It’s important to note that our non-GAAP RPO does not include any backlog associated with Baker Hughes that does not have an existing customer contract. This commitment at the end of the fourth quarter was $219.3 million and it leads to an adjusted RPO of $564.4 million, an increase of 31% year-over-year. Operator: Thank you. We have our first question coming from the line of Daniel Ives with Wedbush. Your line is open. Daniel Ives: Yes, thanks. Can you talk just about success that you are having vertically speaking when I think about utilities in oil and gas versus financials? Are you starting to see just more and more penetration across verticals from a customer base? Tom Siebel: Well, yes, I mean, we began – we had a huge concentration in utilities here as you will recall and then a couple of years ago we entered the oil and gas business and now that’s a pretty big chunk of our business. We are seeing initial success that’s quite significant in financial services with Bank of America and Standard Chartered Bank and now with relationship with FIS and a number of discussions we have going on in the world we expect to see substantial expansion of financial services as our products are used for anti-money laundering, customer churn, cash management, Volcker Rule compliance, margin lending. Manufacturing remains a big business for us as particularly as it was fantastic optimization of the supply chain production optimization. So, I mean, we are clearly diversifying across a wide range of industries. And I think we will see increasing diversification both in terms of additional industry segments and a wider range – instead of only doing very, very large deals like we did, 3 and 4 years ago, and now we have a mix of large deals, medium deals and small deals that is resulting in substantial reduction in our average contract value. So, as this plays out, just like the relational database business did and the mini computer market did and CRM did, I think that enterprise, the AI will be adopted across all sectors, precision, health, travel, transportation, aerospace, you name it and we expect to play in all those sectors. Daniel Ives: Great. And just a quick follow-up, can you just talk about from a conversation that you are having with customers, when you are talking to CIOs, CEOs, how it’s changed in terms of where C3 stands today versus even 6 months ago or a year ago? I mean, is it – has it really gone from to just more strategic and it’s almost more of a pull versus push, can you just compare and contrast, especially just given what you have seen the last 30, 40 years? Thanks. Tom Siebel: Well, I think we really hit an inflection point after – I mean, the first two quarters of this calendar year where it should have calendar year ‘20, okay, we are tough, right. And we were doing these large enterprise transactions and then COVID hit, Paris closed, Rome closed, London closed, New York closed, Chicago closed. And so that did slow us down. Now, when we get into May, June, July of last year, we saw dramatic acceleration. And this mandate towards digital transformation seems to have made it at the top of everybody’s agenda. And digital transformation is very much about the application of enterprise AI to make stuff more efficiently, plan stuff more efficiently, deliver higher quality products into the hands of more satisfied customers at lower cost, we are clearly more credible today in the market than we ever had been. I think that – I think we are doing a pretty good job of demonstrating thought leadership in AI, the work that we are doing with major research institutions like Illinois, Carnegie-Mellon, Princeton, MIT, Stanford, Berkeley, KTH, is kind of really helping us at the high end, but now we have production use cases in all over the place in utilities, all over the place in manufacturing and financial services in aerospace. And so the pipeline has never been larger, okay. And the sales cycles are shorter than they have ever been. So we are – right now we are pretty optimistic about what the next coming 2 years look like. Daniel Ives: Great, thanks. Operator: We have our next question coming from the line of Brad Sills with Bank of America. Your line is open. Brad Sills: Great. Thanks guys for taking my question. I wanted to ask one, just about the general environment for AI in the sales audience. Are you seeing a change where now your deals are sponsored more by a data scientist owner, if you will, or is it still very much a CIO sales as a line of business? As AI becomes more at the forefront of critical capabilities for these companies? Is the sales audience changing and are you seeing that more pervasively through these organizations? Tom Siebel: Well, it’s a good question, Brad. And I think it is changing. If you see like the uptake on Ex Machina, they were selling to basically individual data scientists and citizen data scientists to all these organizations like $500 at a time or something. And the uptake there is pretty substantial. But I would say, it varies from organization to organization, someplace, it’s starting in divisions at other places, like Bank of America, starting at the very top or at or near the top. So, whether we start at the bottom or we start at the middle, we seem to make it to the top sooner or later, Bank of America or Standard Chartered Bank or Koch, it’s this is a rapidly growing hot market, with a lot of people really interested. And they have been – I think they are a little bit frustrated with what they have been attempting to accomplish in the last 3, 4, 5 years, but haven’t been able to accomplish. So, we present the prospect of being able to fix that and business is good. Brad Sills: That’s great. Thank you, Tom. And then one more if I may please. As you pivoted towards these smaller deals, smaller land deals, what does that mean for the expansion opportunity? How is that different from some of these larger deals where you land bigger? Should we see a greater velocity of expand deals in some of these early wins that perhaps are smaller in footprint? Thank you so much. Tom Siebel: I think it’s a really good question. And the answer is yes. I mean, as you get into selling to small and medium businesses, selling CRM, selling Ex Machina, I mean, there you are selling $500 or $1,000 at a time as it becomes a – as it becomes a NRR game. And NRR has been really less important as a metric for us historically, because we are landing contracts that were so long in duration and so large. I mean, you can remember and fiscal year ‘18, and 19, you were here when we are doing $30 million, $40 million, $50 million deals all the time. And that’s clearly changing now. So, I think it’s a healthy mix of large deals, medium deals, and de minimis transactions. I would say, $500 a month by our standards is certainly de minimis. But it certainly looks good in the long run in terms of evening out, getting a lot business out of bookings. So we don’t have to deal with that anymore. Brad Sills: That’s great. Thanks, Tom. Operator: Thank you. We have our next question coming from the line of Michael Turits with KeyBanc. Your line is open. Michael Turits: Hey, Tom. Can you just give us a little bit more on the Shell deal? Is it an expansion? How does it impact, if it does, revenue going forward? Tom Siebel: Well, it is revenue and it’s more revenue and it’s more revenue. So, I think that the existing contracts that we had in place with Shell, Micro and I could be wrong on this, I think it was the second or third contract, okay. And it was about 4 years in duration. Originally, we did a couple of trot production trials with them. I forget what year and those were successful. And then I expanded to kind of a small enterprise deal, then they expanded to a larger enterprise deal, which was 3 or 4 years in duration. Now, Shell is very much reinventing itself around all aspects of this business with this initiative they call, Shell AI, which is a combination of basically C3.ai sitting on top of Azure and then a number of very, very bright people who are applying AI to basically all aspects of Shell’s business upstream, downstream, midstream and really importantly, renewables. I think by 2050, I am not really privy to all of Shell’s company, but all of Shell’s strategy, but looks to me like it might become an electricity business. Anyhow, they were deploying many, many successful applications they decided to renew their application a year before it expired. And so they entered into a new 5-year relationship with us to kind of dramatically expand the number of assets to which they can apply the C3 applications and the C3 stack. We work with them independently of that to develop this Open AI Initiative, which you can think of as a marketplace that’s being sponsored by Shell, C3 and Microsoft. And it’s a marketplace in which all the energy providers can basically put their C3 applications and they can trade them to one another. So, Shell is a – it’s a strategic deal. It’s 5 plus years. And it is irrevocable, non-refundable commitment. And it’s a very substantial and important transaction that we think will serve at something of a bell cow in the oil and gas industry, because Shell is perceived of as a technology leader in that space. And so we think that will help fuel our oil and gas business, which is already quite healthy. And I know a lot of people think, oil and gas is kind of yucky, but these guys are all reinventing themselves as renewable energy companies and we are very pleased to be able to play a role in that. Michael Turits: Thanks, Tom. And David, if you could, could you talk to us a little bit about the move down market from a couple of aspects, one in terms of Ex Machina in terms of the progress there and how much might be built into the guide? And then maybe what your TCV was like in the quarter if it’s really moving down? David Barter: Michael, I couldn’t quite hear the second part of your question, could you repeat it please? Michael Turits: So, again, I am sorry about that. So, all about the move down market and maybe you could approach it from a couple of angles is first of all, how much traction with Ex Machina, how much is built into the guide for next year from Ex Machina? And on TCV, what was it not just in the year, but in the quarter and how effectively are you moving that down? David Barter: Great questions. Michael, in terms of planning, the way we planned our business, we think about our revenue, our subscription revenue accelerating over the course of the year. And as you can see in our guide, we are looking at a midpoint of 26, going up to 34 Ex Machina certainly features in that. So we have thought about it in terms of our go-to-market teams and we planned in a detailed level as we thought about the outlook for the year on how to continue to accelerate our growth. So in terms of Ex Machina and then in terms of TCV in the quarter, we were at about $7.5 million of TCV in the quarter. Michael Turits: Okay. Alright, Tom. Thanks very much. Tom Siebel: Thank you. Operator: We have our next question coming from the line of Patrick Colville with Deutsche Bank. Your line is open. Patrick Colville: Hey, thank you so much for taking my question. The presentation is fascinating and I was really interested to kind of hear how you see the market evolving. Just help me understand though, just this quarter and I guess implicit in your guide, it doesn’t seem like this translates into dollars just yet. Subscription revenue is kind of basically flat sequentially and implicit in the guide is kind of flat again in the first quarter. So just help me understand just the kind of puts and takes between this fantastic long-term potential that you have articulated that we can see versus the kind of near-term and this translating into kind of dollars now? Tom Siebel: I think it was kind of flat last year, Q4 to Q1 wasn’t, Patrick, I think we are seriously correct. And I think that the growth projections that you and others had for us and why don’t you even correct me if I am wrong, for this year was about 9%. And I think we came in at about 17%. The – we are seeing, part of what’s going on in Q1 is – we – the growth the year is going to be a very healthy year, Q1 will be a very healthy quarter or you are raising guidance, okay, for Q1 over what the consensus was. Part of what’s going on in Q1 is an artifact of – you have to go back and look at bookings, which I am not going to disclose, okay, for like Q1 and Q2 of fiscal year ‘20 okay. And if you look at ‘19 and ‘20, we are kind of thrashing around quarter-to-quarter in bookings. And there is kind of an artifact there that has some downward pressure on Q1, everything as the revenue kind of waters from bookings, waterfalls out over say 36 months. And there was a quarter back there that wasn’t very big and the term of the revenue was not very long in the quarter. And that’s a little bit downward pressure on Q1. The Q1 is going to be, it will be a fine quarter and the year is going to be a great year. Patrick Colville: Great. That’s very helpful. And I guess as we kind of think beyond fiscal first quarter into kind of fiscal second, third and fourth of next year. I mean, it’s sticking the numbers here, again, the guide suggests quarter a material reacceleration. I mean, I remember the time of the IPO, when we are talking about a number of factors, including the exit of coronavirus. We are talking about collapsing of the Baker Hughes contract reset. Are they still the kind of key reasons for this reacceleration in subscription revenue in the second half of fiscal ‘22 or are there other factors that we should be aware of? Thank you. Tom Siebel: Well, I think what we saw was a reacceleration of bookings in the second half of fiscal year ‘21, really, okay. And as we – I mean, when we came into fiscal year ‘21 blowing and gone, I think wasn’t the growth rate in fiscal year ‘20, like 91% growth or something? David Barter: 71%. Tom Siebel: 71% okay, sorry. David Barter: It was big numbers. Tom Siebel: It was big number. It was significantly non-zero. Okay, it was big, and then we got kicked in the teeth with COVID. Okay, and the – and I think what you’re seeing is just what we saw in the second half of the year is just a reacceleration of business. And COVID is clearly over. And digital transformation is the interest in that is more acute than it ever has been. Okay, the interest in enterprise AI is more significant than it ever has been. Okay, and we’re perceived as a our more substantial, more reliable provider than we ever have been. So I think what we’re just seeing as the acceleration of business, it’s a good thing. Patrick Colville: Great. Thank you so much. Really appreciate for taking the time. Operator: We have our next question coming from the line of Jack Andrews with Needham. Your line is open. Jack Andrews: Good afternoon, thanks for taking my question. Hey, Tom, I was just wonder if you could speak to just the hiring environment, it’s historically been difficult for applicants to secure opportunities at C3. And so could you speak to are you able to scale the organization the way you want to in terms of finding the right types of people to build out the organization these days? Tom Siebel: I reviewed those data to date, Jack. It’s great question. Last quarter, I think we had 12,000 applicants, okay, job applicants from all over the world to C3.ai, count them 12,000, okay. And this annualizes to roughly 50,000. And what we’re in the heart of Silicon Valley, this is supposed to be a very, very challenging hiring environment. Of the – of those 12,000 I think we had interviews and one former number there was almost 3,000, and we hired a net of like, 56. So, we have really the brightest and most trained, highly trained and experienced data scientists in the world, and an application engineers and sales people and sales leadership, okay, and marketing leadership, who want to join us. And so we’re very, very fortunate in that respect, we trying to need to figure what’s going on. And get the bottle this, as we go forward, but the rate of interest in people coming to work with at C3 has not slowed down. Okay, and I rate of interest in hiring people has definitely not slowed down. And if you go and I encourage, anybody who’s interested to either you get a pretty good feel for what the culture is like, and what the morale is, like, if you go look it up by Glassdoor.com. But it was, I think, 12,050 people, or 12,500 people who apply for jobs here in the last quarter, it’s really rewarding. Jack Andrews: That’s great. Thanks for the color around that. Just as a follow-up question. I think, in your prepared remarks, you referenced strong deal volume late in the quarter, I was wonder could you provide some more context around that. Was that something that just happened organically within your customer base? Or is that the result of maybe some of your partnerships really coalescing any further clarity would be appreciated? Tom Siebel: Jack, I don’t think, I said anything like that. David Barter: I think Jackie may have heard it in one of my paragraphs. Tom Siebel: But I missed that I was out of the room. David Barter: And so I think all we were highlighting is the correlation, Jack between our bookings and how that manifested in terms of accounts receivable deferred revenue in the just strengthening of our performance measures, like RPO. And so what you just saw… Jack Andrews: Okay, thanks. David Barter: You just saw bookings begin to percolate through the financial results. Jack Andrews: Thanks. Operator: We have our next question coming from the line of Mark Murphy with JPMorgan. Your line is open. Mark Murphy: Yes, thank you, Tom. How is the signaling from some of your end markets that benefit from higher commodity prices like oil and gas, or higher inflation and interest rates, such as financial services? I’m just wondering, because that’s a pretty good portion of your customer base. Is it safe to assume that’s on much firmer footing versus a year ago where you would see that driving strong pipeline growth for later in this year? Tom Siebel: Well, oil and gas, I didn’t know what gas prices were a year ago, but I remember about a year and a half ago, you couldn’t give oil away, right. It was like navigating $7 a barrel or something, where is it today, you know better than I have a roughly $67 or something like that. So you get to know – it’s easy to do business with oil at $67 a barrel than it is at negative $37, I can assure you of that, and the banks all seem to be printing money around the world. So those segments do look healthier than they have in some time for us. And we expect to see outsized growth in the segments. We’re not now we’re not snapping in the air. I mean, you’re going to expect us to be investing this year in a big way, in defense and intelligence. And that’s been manifested in some of the hiring that you’ve seen Telco, where we’ll put it, we’ve put together a large organization around Telco, you’d expect to see a large investment in precision health. So yes, we will be further penetrating oil and gas businesses and the banking business. And those industries today look very healthy. Mark Murphy: Okay. As a follow-up, I guess, regarding the show, partnership extension, are you able to comment on the dollar amount? And I wasn’t clear to me whether that is reflected in the RPO balance that we’re seeing for Q4 or is that something would that manifest in the Q1 metrics? Tom Siebel: That’s in RPO. We closed it in Q4, and it’s in RPO. And but in terms of the size of it I could tell you, if you take all of our deals, the size of the some of the deals and divide by the number of deals, it’s what I say sounds like $2 million, and that the size of shy, it’s a good one. It’s bigger than a bread box, it let’s say didn’t contribute to bringing our average total contract value down. Mark Murphy: Yes. Okay. And then just one final one, David, on the CRPO, I think it grew well, sequentially. I think it grew 9% or 10% year-over-year, is there any perspective on maybe how to drive that current piece of RPO a bit faster there? I think prior analyst was commenting that we see it in the kind of longer term portions. But can – do you look for instance, do you think that the CRPO number could be growing a little faster? A couple quarters down the road? David Barter: Yes. This is the short answer. Mark Murphy: Thank you. Operator: We have our next question coming from the line of David Hynes with Canaccord. Your line is open. David Hynes: Hey, thanks very much. Tom, you highlighted CRM is kind of the opportunity that excites you the most. I’m just curious, like, where do you see the most low hanging fruit in CRM and what’s the home run vision for that market? Is you going to reimagine it with AI? Tom Siebel: Well, I’m not sure it’s exceedingly excited me the most but I’m really excited about it. Okay. It’s not going to be our biggest market. Our biggest market is going to be enterprise AI writ large thing precision health, think banking, think oil and gas, travel, transportation, what have you. That being said guys, CRM today’s an $80 billion addressable market, okay. The next generation of CRM is all about AI enabled CRM, okay, an AI enabled CRM is basically when you take the data that are in the CRM system, and combine it with all sorts of exogenous data, let’s take a hypothetical of a manufacturing company, maybe Boeing, okay, so Boeing has all these they used to sell $60 billion worth of commercial aircraft, I have no idea what they sell today. Okay, but they have a CRM system, probably sales force or Siebel or something, they’re all kind of the same. Okay, and they where they have all the sales forecasts that the guys put in and the sales the systems are just like the systems that you guys have it JPMorgan Chase and everyplace else, the sales guys just put whatever they needed to get it and put it in there to get the sales manager off the back. Okay. And so they put in all of these records already have 35,000 salespeople at Merck, each of which are putting 100 lines of garbage and so you get, 350,000 lines of kind of garbage in your forecasting system. And you think some of that is supposed to be your sales forecast. Well, it really doesn’t work that way. Now, however, the data really aren’t useless. And if we think about this a hypothetical, because we’re not talking with them about this, but let’s think about Dave Calhoun is Boeing. And he’s got, he’s got the information that’s in their CRM system about contacts about opportunities about deals that are supposed to close at Lufthansa, at Bank of America at, at Southwest Airlines, and it’s just the information in the sales people put in now imagine combining of that data with almost 9x more that are order of magnitude more exogenous data about the market think, commodity prices, jet fuel prices, the equity prices of Boeing’s customers, Southwest Airlines, Lufthansa, American Airlines, NLP on social media, okay, NLP on analyst reports, equity prices of all these companies, GDP growth rates, travel, passenger travel amount, is the country at war, is the country at peace. Okay, and NLP media, if Southwest Airlines is a announcing a 15% layoff for whatever reason like airlines do from time to time, and their stock just goes down 30% what’s the probability that this order for like 100 737 Max, this is going to close this quarter? That would be zero, okay. So you can see how we can take all of those data 10s of 1000s signals from the market. Analysts reports, news reports, stock prices, commodity prices, jet fuel prices, GDP growth rates, is the country at war, is the country at pace and build very precise machine learning models that tell Dave Calhoun, what deals are actually going to close? Okay, not only is that going to set Calhoun aside because they let’s talk about something like the Procter and Gamble, okay, so Procter and Gamble not only needs the revenue – forecast revenue, they need to forecast products, because they need to make the right amount of stuff at the right time to meet the demand function in order to realize their revenue, right. So we can build a – we have now demonstrated that we can build these AI models, okay, that are literally in order of magnitude more precise. As it relates to revenue forecasting, customer forecasting, an excellent product, an excellent offer customer churn that what’s going on in CRM today. And you’re going to see us releasing these products this year for banking, okay, for aerospace, okay, for manufacturing, for health care, what have you. And most products will be well perceived that we have done extensive market research on the levels of customer satisfaction in the CRM industry today. And I’m telling you, the levels of customer, these people are hate their vendors, okay. And so the levels of customer dissatisfaction are uniform, it’s an $80 billion market. And as you what’s the last segment that’s related to AI, the intersection of AI and CRM, I believe we’re going to establish a leadership position in that market. We’ve hired some very key people in CRM here. You’ll be seeing some announcements soon or some other very key people are joining us. And that is going to be exciting market, as you people know, it’s a market that I’m not entirely unfamiliar with. And so we’re going to have some fun there. I think we’re going to put existing CRM companies out of business, no way how they’re great companies, they’re going to continue to survive. Will we establish a significant toehold in that segment? That is where people are interested in using AI for these things? Don’t bet against this. David Hynes: Yes, yes. Okay, it’s helpful. And then maybe you could speak to the mix of kind of direct versus partner led business and maybe how that differs today versus what it looked like a year ago? Tom Siebel: How do you shift partner we will add? Okay, so… David Barter: Partner assisted I guess. Tom Siebel: Yes. Well, partner assisted, I think is right. Okay. I’m not really certain we really have any. It’s a great question, David. And we’re going to work and we go to market at massive scale. I would say with Microsoft, okay, with Baker Hughes and now we’re just kind of kicking into gear. Okay with FIS. Okay, and Infor and I think we’re ready to go to market with Singtel in Asia. Partner assisted. I mean, it probably right. I mean, I think our pipeline today is larger than it has ever been. I think that this is off the record. I never say it again. Okay, it’s probably off by 10%. But I think our pipeline for this year, it’s like $1.6 billion or something. Okay, and so give me some slack on that one, guys. I don’t have any numbers in front of me, but I think it’s pretty dry. Actually, it’s about right. I would say, it would be my – I would speculate that I’m 50% of those transactions, we are engaged with a partner, either Microsoft or a Baker Hughes or something like that, to bring the deal home. David Hynes: Yes, okay. That’s the helpful data point. Thank you. Tom Siebel: This partner ecosystem is an important part of the equation. Can we really rely on the partner to close the deal for us? I am not so sure. We are going to have to close it ourselves. Okay, but the partner assistance, if your partner happens to be Satya or Judson Althoff at Microsoft, there’s going to, they’re pretty good sales guys. I’m telling you. Operator: Thank you. We have our next question coming from the line of Sanjay Singh with Morgan Stanley. Your line is open. Sanjay Singh: Thank you for taking the questions. I wanted to follow-up on the previous question relating to the customer count, which picked up pretty nicely, I think they’re up to like 89 customers from around 64 at the time of IPO. And just wanted to get a sense of what’s driving out of this to better sort of spending environment or from a sort of go-to-market sales execution, sales hiring perspective, you are starting to see that sales productivity really start to come through the door to help accelerate that, that customer count velocity ? Tom Siebel: Well, Sanjay, I think, there are two things that we’re seeing that are very influencing that. Number one, you recall that pre IPO, we’re only elephants having. Okay, we’re only at a major accounts group. Okay. And since then, we’ve been building a, kind of ever judicial enterprise sales organization, a middle market sales organization, and a mass market sales organization. One of our – we did a pretty large transaction, this quarter, I believe, to place on the Microsoft, on the Azure Marketplace, they’ve done thing in Asia, they did, okay. Okay. And it is all you guys, I encourage you to go to C3.ai.com. – C3.ai now, okay, put in your name, address. And credit card number, and for 30 days use, use the X mark and up for free. I mean, hundreds and hundreds of people are doing that. Okay, so and so please do it and please also forget to dial in 30 days and cancel your subscription, okay. But I think there are two things. Sanjay, number one is, remember, you remember we said a couple of years ago we’re putting to do we’re going to expand the major accounts group, we’re going to expand the enterprise group, we’re going to put a middle market group in place, we’ll put a mass market group in place, we’re doing all that including telesales marketplaces in the internet sales, and then this is combined with the partner ecosystem, being at Microsoft, AWS, Baker Hughes, or what have you. So it’s a resulting in just a much larger diversity of different size deals. So the strategy that we said we were going to execute starting in well before the IPO, I mean, I communicated this as early as the early 2018. We’re executing it and it work. Sanjay Singh: It makes total sense, though, the follow-up question is, it’s a topic that we’ve talked about for a couple quarters now, Tom which is around like the competitive environment and sort of look at the broader landscape, including the C3 and then some of the other vendors that either own parts of the space or kind of few multiple on pro multiple parts of workflows and data science, machine learning, it seems like, everybody’s doing like a weed. And I know your view is that a lot of customers are stuck in proof of concept health. Going back to that question of customers sort of do the dance with that sort of fits together approach where they come to sort of a end to end platform like the C3, where are we in that journey do you think? Tom Siebel: Well, I think everybody’s going to try to build with themselves. And that’s what IT people do. And they try to build relation database systems themselves. They tried to build the ERP systems themselves. They tried to build their own CRM systems themselves. How that worked for Morgan Stanley? Okay. I mean, they tried, okay. They tried to build all of those things themselves. Okay, I was there, okay, how would it work for JPMorgan Chase? I mean, they tried to build all of those systems themselves and today JPMorgan Chase is trying to build their own AI platform, after that comes out of crash just they tried to build their own ERP system, they tried to build their own CRM system. All that came crashing down around them. So they will spend, I don’t know how many hundreds of millions to billions of dollars a year trying to build their own AI platform and then Jamie will be gone and they will begin in some new CEO in, fire everybody and I will buy it from a commercial vendor. That’s the way this works. So, virtually every one of our customers, Shell, ENGIE, Koch Industries, United States Air Force, Army Futures Command, have tried to build this themselves, a bigger use, that would be GE and it could work out so well. And so this is just the phase we have seen this over and over and over in the industry and it’s just a phase that everybody has got to go through. They have to try it themselves and crash and burn a couple times for them. They buy it from a reliable vendor. Sanjay Singh: I appreciate the background. Thank you. Operator: Thank you. We have our next question coming from the line of Arvind Ramnani with Piper Sandler. Your line is open. Arvind Ramnani: Hi, Tom. Most of my questions have been asked, but I did have a couple of questions. I had a question about your overall product. Can you talk about applicability of using the same code base across different industries or different applications? Tom Siebel: Yes, Arvind, I mean, it’s a really good question. And you and I have talked about this before, but I really do appreciate you asking it. So, we deliver I think you have about 21 different AI products today across five different industries. And we have a family of products for manufacturing, for gas, for financial services, for aerospace. And what’s counterintuitive is that whether we are doing object identification for the Space Command, clearance adjudication for the Defense Intelligence Agency, sarcastic optimization of the supply chain at Koch Industries, or AI-based predictive maintenance for Shell for offshore oil rigs, all of which we do. Now, these are separate products with separate documentation, separate user interfaces, separate APIs to aggregate data, but 90% of the codes that are running across all of those applications, whether it’s cash management, Bank of America, or predictive maintenance for offshore oil rigs at Shell, it’s the same code base. And so that’s counterintuitive. And this is the beauty of this model driven architecture and we have really broken the code on that. Okay, so we are able to – everything we do is reusable. And so I mean, that’s what people, we have broken the code, we all know the intellectual property the patents have been awarded to us, it is our invention, okay, this idea of using a model-driven architecture for enterprise AI and IoT applications. So, it’s a 90% of the cost, but what changes from customer to customer are the data sources, the APIs that we use for the data source trivial problem, okay. The user interface expression, it differs from say anti-money laundering to predictive maintenance for low pressure compressors on offshore oil rigs, but we can all agree I hope that the user interface is trivial. And then the part that differs from the most from installation are the machine learning models. Then the machine learning models we quite hopefully going to agree these are non-trivial, but they constitute maybe 3% of the code. Arvind Ramnani: Terrific. And I know you have answered a couple of questions on guidance, but I just maybe wanted to frame it a little bit differently. At the midpoint of the guide, you are really adding $62 million in revenue in fiscal ‘22. And then, when I look at like fiscal ‘20, which was a good year before the pandemic hit, you added $65 million. So, it seems like the guidance has fair bit of conservatism, because you have $62 million adding, but you also have some delays and some pent-up demand from the delays that you experienced last year that should kind of boost revenue add more than like $62 million. So, I just wanted to get a sense of how conservative your guide maybe? Tom Siebel: Well, Arvind, you know me a little bit and I hope that at the end of the day that people will believe that I was credible and I am credible. So we are focusing on being credible. And we are – what we want to do is meet and exceed, beat and exceed, beat and exceed. So, we are – I think that I don’t know how many enterprise application software companies are growing, what’s the expected growth rate in the middle of 33% or something? David Barter: 34%. Tom Siebel: 34%. I mean, I don’t know how many enterprise software companies are growing at 34% compound annual growth rates, but I can assure that would be in the top deck, I suspect to not really check this stuff out, but I suspect it’s in the top decile. And right now we improve – we intend to move in the top decile this year. And hopefully, it would come back the next year and move up a little higher. Arvind Ramnani: Terrific. Thank you. Operator: We have our next question coming from the line of Pat Walravens with JMP Group. Your line is open. Pat Walravens: Great. Thank you and congratulations on the quarter. So, Tom, you have got oil and gas, financial services, CRM, Ex Machina, can you just tell us for this year, for this coming here, what are your top three sort of strategic imperatives? Tom Siebel: It’s really a good question. I think there is strategic imperatives Pat and you will see a number of announcements coming in this area and there have been some announcements is making sure that we have the leadership in place to scale this business globally. And you have seen some of this with Ed Cardon, you’ve seen some of this from General Cardon, who is the Chairman of C3 Federal, the new General Manager of C3 Federal and you can expect that we will be adding a number of – you saw this with Jim Snabe, the Co-CEO of SAP joining our board. And we have been really, really focused on bringing senior leadership into the company in the last 9 months. And we are – and you are going to see a number of announcements there that I think you will agree are significant. And I think that is the – I mean, we have the technology. The market is much bigger than we can address in rapidly growing. The technology foundation we have is very rich and it works. We have us – we are leaving in our wake a string of highly satisfied customers. I think we are doing a pretty good job at building brand equity. The competitive dynamics of this market are not very significant. I mean, basically, we are selling vehicles and everybody else is selling ball bearings and wheels and carburetors, okay. And we are selling vehicles, okay. And so there is not much going on in terms of the competitive dynamics. And so we just need to make sure that we have the seasoned leadership in place to scale this business in federal systems, in Asia-Pacific, in Japan, in Europe, in manufacturing, healthcare, telecommunications, aerospace, etcetera. I think its human capital. That is the game and if you go look on Glassdoor and if you are interested, I think that this focus on human capital has been consistent for many years and it will continue. Pat Walravens: Great. Thank you. Operator: Thank you. There are no further questions at this time. I will now turn the call over back to Tom Siebel for closing remarks. Tom Siebel: Okay. Ladies and gentlemen, we thank you for taking time out of your busy day, okay to check in on us. We appreciate your interest. And you know that we are – it is now June of 2021, I am very pleased to report that there has been no day in the history of this company, when this company has been better positioned, when the market has been – whether there has been more market opportunity or whether this company has been better positioned to seize the market opportunity. So, as we approach the next 2, 3, 4 years, I can tell you, we approach it with great enthusiasm. And we will see how this turns out when it’s over, but I think there is some probability that we might build a pretty substantial company here. So, thank you for your interest. Thank you for your support and thank you for your questions. And we wish you all a great day. Operator: This concludes today’s conference call. Thank you for participating. You may now disconnect.
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C3.ai Jumps 23% on Q3 Beat & Strong Guidance

C3.ai (NYSE:AI) experienced a notable surge in its stock price of over 23% intra-day Thursday after reporting fiscal third-quarter results that exceeded expectations and provided an optimistic outlook. The company reported a fiscal third-quarter loss of $0.13 per share, significantly outperforming the anticipated loss of $0.28 per share. Revenue for the quarter reached $78.4 million, exceeding the consensus forecast of $76.14 million.

Subscription revenue was a major contributor, accounting for 90% of total revenue at $70.4 million, up 23% from $57.0 million in the comparable period the previous year.

For the upcoming fourth quarter of fiscal year 2024, C3.ai projects revenue to range from $82 million to $86 million, against Wall Street expectations of $83.91 million. The company's full fiscal year 2024 revenue is expected to be between $306 million and $310 million, surpassing Wall Street's projection of $305.5 million.

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Following the report, C3.ai experienced a more than 11% drop in its stock price intra-day today.

The company posted an adjusted loss of $0.13 per diluted share on revenue of $73.2 million. This is in contrast to the anticipated adjusted loss of $0.18 on revenue of $73.2 million.

Looking forward, C3.ai forecasts an adjusted loss from operations between $40 million to $46 million for the third quarter, on projected revenue of $74 million to $78 million. This projection contrasts with Wall Street's estimates, which anticipated revenue of $77.69 million.

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The AI software company disclosed an adjusted loss of $0.09 per share with revenue amounting to $72.4 million, surpassing expectations for a loss of $0.17 per share and revenue of $71.56 million.

Looking ahead to fiscal 2024, the company now anticipates an adjusted loss ranging from $70 million to $100 million on revenue ranging from $295 million to $320.0 million. This contrasts with previous estimates of a loss between $50 million and $75 million on revenue ranging from $295 million to $320 million.

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C3.ai (NYSE:AI) shares surged more than 33% on Friday after the company reported a Q3 beat and a better-than-expected outlook.

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Unsurprisingly, management continues to cite macro uncertainty impacting its business, which only compounds the lack of visibility to its subscription transition. There is no doubt C3 offers valuable predictive analytics applications to its customers, but the market for its model-driven architecture is difficult to ascertain amidst the tenuous macro backdrop and business model transition.

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