Greetings, and welcome to Innodata's third quarter 2023 earnings call. At this time, all participants are in a listen-only mode. A question-and-answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. Please note, this conference is being recorded. I will now turn the conference over to your host, Amy Agress, General Counsel. You may begin. Thank you, Paul. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata, and Marissa Espineli, Interim CFO. We'll hear from Jack first, who will provide perspective about the business, and then Marissa will follow with a review of our results for the third quarter. We'll then take your questions. First, let me qualify the forward-looking statements that are made during the call. These statements are being made pursuant to the Safe Harbor Provisions of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended. Forward-looking statements include, without limitation, any statement that may predict, forecast, indicate, or imply future results, performance, or achievements. These statements are based on management's current expectations, assumptions, and estimates and are subject to a number of risks and uncertainties, including, without limitation, impacts resulting from the continuing conflict between Russia and the Ukraine, and Hamas's attack against Israel and the ensuing conflict, investment in large language models, that contracts may be terminated by customers, projected or committed volumes of work may not materialize, pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work, acceptance of new capabilities, continuing Digital Data Solutions segment reliance on project-based work, and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay, or cancel projects. The likelihood of continued development of the markets, particularly new and emerging markets, that our services and solutions support, continuing digital data solutions segment revenue concentration, and the limited number of customers, potential inability to replace projects that are completed, canceled, or reduced, our dependency on content providers in our Agility segment, a continued downturn in or depressed market conditions, changes in external market factors, the ability and willingness of our customers and prospective customers to execute business plans that give rise to our requirement to our services and solutions. Difficulty in integrating and deriving synergies from acquisitions, joint ventures, and strategic investments. Potential undiscovered liabilities of companies and businesses that we may acquire. Potential impairment of the carrying of goodwill and other acquired intangible assets of companies and businesses that we acquire. Changes in our business or growth strategy. The emergence of new or growth in existing competitors. Our use of and reliance on information technology systems, including potential security breaches, cyberattacks, privacy breaches, or data breaches that result in the unauthorized disclosure of consumer, customer, employee, or company information, or service interruptions. Various other competitive and technological factors and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission, including our most recent reports on Forms 10-K, 10-Q, and 8-K, and any amendments thereto. We undertake no obligation to update forward-looking information or to announce revisions to any forward-looking statements, except as required by the federal securities laws, and actual results could differ materially from our current expectations. Thank you. I will now turn the call over to Jack. Good afternoon. We're very excited to be here with you today, and we have lots of good news to share. Today, we are pleased to announce third quarter revenue of $22.2 million, representing 20% year-over-year growth. It's worth noting that the year-over-year growth was 27% if we back out revenue from the large social media company, which contributed $1 million in revenue in the year-ago quarter, but dramatically cut spending after a significant and highly publicized management change. We are also very pleased to announce third quarter adjusted EBITDA of $3.2 million, representing 100% sequential quarter-over-quarter growth. The $1.6 million of sequential adjusted EBITDA growth, viewed together with the $2.5 million of sequential quarter-over-quarter revenue growth, demonstrates strong operating leverage as well as successful cost management. Looked at year-over-year, we see the same thing. We returned $4.4 million of adjusted EBITDA growth on $3.7 million of revenue growth. Third quarter growth was driven by the start of ramp-up for generative AI development work with one of the new big tech customers we announced this summer. We expect our work with this customer to continue ramping up in the fourth quarter and into the first quarter, potentially reaching a $23 million-$25 million run rate at the end of the year with which to start next year. At the very end of the quarter, we also kicked off our generative AI development program with the other new big tech customer we announced this summer, and we expect it will also contribute to fourth quarter revenue. In fact, we anticipate continuing to expand revenue with both of these new customers through Q4 and in 2024. For the fourth quarter, we are forecasting revenue of $24.5 million or more, representing 26% or higher year-over-year growth. Again, if we back out revenue from the large social media company, which contributed $0.5 million in revenue in the fourth quarter of 2022, our fourth quarter forecast would represent 30% or better year-over-year growth. Since there was no revenue from the social media customer in Q1 2023, beginning in Q1 2024, revenue from the social media customer will no longer provide a drag on year-over-year comparisons. For the fourth quarter, we're forecasting Adjusted EBITDA of $3.7 million or more, which would be approximately 15 or more times Adjusted EBITDA from the fourth quarter last year. I am also very pleased to announce that in September, we signed a master services agreement for AI development with yet another of the world's largest tech companies, a company whose AI programs we've been trying to break into for a year now. Based on our research, this large tech company is likely to spend $several hundred million on generative AI data engineering services in 2024. So this win, like the others we announced this summer, packs a lot of potential. While this relationship is at an early stage, we see huge potential in it. As we look ahead and plan for 2024, we foresee an exciting and transformative year ahead. We believe we have the strategy, business momentum, and customer relationships to deliver significant revenue growth and adjusted EBITDA growth. We currently intend to provide guidance for 2024 revenue and Adjusted EBITDA growth on our Q4 call. Our strategy for growth is twofold. First, we will support large technology companies building Generative AI foundation models. Second, we will support enterprises across a wide range of verticals that seek to integrate and fine-tune Generative AI models. Let's first double-click on the large tech market opportunity. We now have master service agreements in place with five of the largest technology companies in the world, under which we are providing Generative AI program support. Landing these agreements was nontrivial. Our success at having done so, I believe, testifies to the strength of our value proposition and our capabilities. With these agreements now in hand, we believe we are poised to deliver significant growth in 2024. Over the next several years, we believe that these technology companies will be building bigger and better generative AI models. Indeed, when you listen to the large tech companies' earnings calls this quarter, what emerges is an overwhelming sense that generative AI is their number one strategic priority, that it's their biggest investment area for 2024, and that they believe generative AI is a foundational platform shift that is just at its very beginning. One of these companies specifically stated that it believes it will drive tens of billions of dollars of revenue over the next several years from generative AI innovation. The product-centric large tech companies are talking about creating generative AI-powered experiences across their product lines, transforming the way people use their products. The infrastructure-centric large tech companies are talking about deploying new and differentiated generative AI services and bolstering their AI infrastructure to serve their customers' AI training and inferencing needs. Both product-centric and infrastructure-centric large tech companies are talking about increasing capital investment into generative AI as a result of the strong demand that they see. This, we believe, bodes very well for us. During the summer, we announced winning two new Big Five tech customers, and both a program expansion and a new program with an existing Big Five tech customer, all to help develop and train large language models. We announced the first new Big Five customer win on July 18, and on August 29, we announced the program had been expanded. Our program began ramping up in early August. We anticipate that we will continue to ramp the program through Q4 and into Q1, reaching a revenue run rate on just this one customer of potentially $23 million-$25 million by the end of the year, with which to start next year. We are now in discussions with this customer about potential further program expansions and potential additional programs. We announced our second new Big Five customer win on August 10, and on August 22, we announced that our agreement got signed. While in our announcements, we stated that ramp-up could begin early in the fourth quarter, I'm pleased to report that we were able to kick things off at the tail end of the third quarter. While we had a little bit of revenue from this customer in the third quarter, we anticipate that revenue from this customer will impact our fourth quarter results more significantly. We are now in discussions with this customer about scope of the initial program, which has the potential to be quite large, as well as other programs. The customer has authorized $2.5 million in spend to get us started, has promised that an additional $1.5 million authorization will arrive soon, and has stated that it intends to supplement these authorizations as we move forward with program expansion. On June 27th, we announced that an existing Big Five customer had selected us to perform AI data annotation and LLM fine-tuning as a white-labeled service for its cloud and platform customers. On June 14, we announced that the same customer had engaged us for its LLM build program. In the latter announcement, we stated that we anticipated potentially exceeding $8 million in revenue from this customer in 2023, up from approximately $3 million last year. We believe that we are on track to meet or exceed this target. Included in this year's forecast is approximately $330,000 of revenue from the white label program, consisting of 6 won or late-stage opportunities. We believe this white label program will contribute more significantly to 2024. For 2024, we already have several million in pipeline opportunities, including two opportunities that we value at $2 million and $1 million, respectively. It is worth noting that we believe the $2 million opportunity could potentially open an exciting new market for us. We're hoping to close both of these opportunities in Q1. Under the White Label program, we are seeing a mix of requirements from our customers' enterprise customers. Requirements range from Generative AI data pipelines to 2- and 3-dimensional data annotation, chatbot fine-tuning, LLM-based search and retrieval, and training LLMs for multilingual, domain-specific summarization and conversation. Importantly, the program is enabling us to potentially scale an enterprise offering independent of our own sales and marketing, to leverage both our customers' brand and its significant customer reach, and to gain exposure to a wide variety of early adopter Generative AI use cases. We believe this exposure will set us up well for what we believe will potentially be our largest and most significant opportunity, LLMs, for the enterprise. I'll now talk a little bit about our enterprise opportunity and the progress we made on it in Q3. These are still early days in terms of enterprise adoption of generative AI, but we believe that a decade from now, virtually all successful businesses will have adopted generative AI technologies into their products and operations. To do so, they will require one or more of the capabilities that we offer. Enterprise data sciences teams will require support to train and fine-tune open source and proprietary LLMs to conduct specialized testing and evaluations to ensure that the LLMs are helpful, honest, and harmless. They will also require support to implement retrieval-augmented generation, or RAG for short, a technique for harnessing enterprise data assets within LLM prompts. Meanwhile, enterprise line of business managers will require support to build customized generative AI models and applications. Additionally, these line of business managers will require support to deliver the kind of business process and workflow transformation that will be possible with generative AI. When we identify opportunities to deliver AI-enabled transformation via a subscription-based platform, as we now have with PR workflows, underwriting workflows, and compliance workflows, we will enable them to, to subscribe to our platforms rather than having to undertake complex and expensive builds themselves. In the third quarter, we closed three important enterprise Generative AI opportunities with large companies. Their scope ranges from strategy to implementation. In one of the engagements, we will be helping a leading information company create a strategic roadmap for AI LLM integration for its products and internal operations, and we will be building LLM proofs of concept. In another, we will be helping fine-tune LLMs for three customer use cases pertaining to legal services. In the third, we will be creating datasets to train an LLM to support doctor-patient interactions. We ended Q3 with $14.8 million in cash and short-term investments, up from $13.7 million last quarter. We continue to have no appreciable debt. To support our growth and future working capital requirements, we have a revolving line of credit with Wells Fargo that provides for up to $10 million of financing, subject to a borrowing-based limitation. I'll now turn the call over to Marissa to go over the numbers, and then we'll open the line for questions. Thank you, Jack. Good afternoon, everyone. Allow me to recap our 2023 third quarter financial results. Revenue for the quarter ended September 30, 2023, was $22.2 million, up 20% year-over-year. The comparative period included $1 million in revenue from the large social media company that underwent a significant management change in the second half of last quarter, as a result of which it dramatically pulled back spending across the board. There was no revenue from this company in the three months ended September 30, 2023. Net income for the quarter ended September 30, 2023, was $0.4 million, or $0.01 per basic and diluted share, compared to a net loss of $3.3 million, or $0.12 per basic and diluted share in the same period last year. Revenue for the 9 months ended September 30, 2023, was $60.7 million, compared to $59.6 million in the same period last year. The comparative period included $7.9 million in revenue from the large social media company I mentioned earlier.... There was no revenue from this company in the 9 months ended September 30, 2023. Net loss for the 9 months ended September 30, 2023, was $2.6 million, or $0.09 per basic and diluted share, compared to a net loss of $10 million, or $0.37 per basic and diluted share in the same period last year. Our Adjusted EBITDA was $3.2 million in the third quarter of 2023, compared to Adjusted EBITDA loss of $1.2 million in the same period last year. Adjusted EBITDA was $5.6 million for the nine months ended September 30, 2023, compared to adjusted EBITDA loss of $3.5 million in the same period last year. Our cash and cash equivalents and short-term investments were $14.8 million at September 30, 2023, as compared to $10.3 million at December 31, 2022. That concludes my recap for the third quarter result. Again, thanks, everyone. I will now turn over this to Paul. Paul, we are now ready for questions. Thank you. At this time, we'll be conducting a question and answer session. If you would like to ask a question, please press star one on your telephone keypad. A confirmation tone will indicate your line is in the question queue. You may press star two if you would like to remove your question from the queue. For participants using speaker equipment, it may be necessary to pick up your handset before pressing the star keys. Once again, please press star one if you wish to ask a question. One moment while we pull for questions. Once again, it's star one if you wish to ask a question at any time. The first question today is coming from Brian Kinstlinger from Alliance Global Partners. Brian, your line is live. Thanks so much for that. Thanks for taking my questions. Jack, I'm curious, as it relates to the first big five customer that you expect may be able to reach an exit run rate of $23 million-$25 million of annual revenue, was there a meaningful contribution in the third quarter? You highlighted it for most of the customers, but I, I didn't hear if it made a significant contribution and maybe if you can quantify it for the third quarter. Sure. So you know, indeed, that it did make a significant contribution. And, you know, most of the revenue growth, the vast majority of the revenue growth that you're seeing sequentially was as a result of ramping up that, or beginning to ramp up that customer. Great. Then just, you know, I think your story isn't as well known right now, and it may become, but I want to understand how these programs are scaling. Is it that, for example, the one going to $23 million-$25 million, or even your second contract that you expect to generate $8 million compared to $3 million, is it you're providing more services and there are different offerings, you're providing more testing, and so you're, you know, testing more times, fine-tuning more in terms of volume? I'm just trying to understand what drives scale $3 million-$8 million or, you know, 0 getting to $25 million. Yeah. So I think if we take the 3-8, that's probably the best example to use, and then maybe we'll apply it to the 25. In the, you know, 3-8 example, we started with one program, one model, you know, one initiative that they had in place. We did very good work, and then that begot two or three more opportunities that we had. We did good work there, and then that enabled us to, you know, further scale, to start working with other programs, other development groups, other engineering groups within the account. And, you know, we refer to that as, you know, our land and expand strategy, if you will. The tough thing is to get into one of these programs. It's a little bit like getting into Harvard. That's the tough part. Now, once you're in, if you do good work, you know, you graduate. If, if you do good work, you expand. And that's what we're seeing now. You know, we believe that that revenue growth that we saw, you know, 3 to predicted 8 this year, 8 quite conceivably doubling again next year. We believe that that same set of characteristics will apply to others of these large companies that we're now just getting started with. The fact that instead of starting with a $200,000 initial engagement, we're starting with a $25 million initial engagement, I think bodes very well. But that expansion opportunity exists all the same. We intend to expand our presence. We intend to go from one program to multiple programs, and we believe that by doing good work, you know, we enable exactly that to happen. Great. And then as you're scaling these programs, what are the investments you need to make? Is it people? Do you need more infrastructure? Just trying to understand, as revenue grows, you know, what investments you have to make? So we're making investments across the board. You know, we're making investments in people, in process, in technologies, in the engineering work that we're doing. You know, the investments are in all of those areas. I think the important thing is that we don't foresee having to invest way ahead of the opportunity. We're able to, at this point, having invested a lot in the business over the last several years and having the capabilities we now have, there's a tremendous amount of leveraging of those capabilities. So as we scale the programs, we incrementally invest in a way that doesn't require significant capitalized expenses, and doesn't require that we're investing in OpEx very far ahead of revenue recognition. Okay, thank you. Thank you, Brian. Good, good to have you on the call. Thank you. The next question is coming from Tim Clarkson from Van Clemens. Tim, your line is live. Hey, Jack. Good to see you the other couple weeks ago. I just wanna ask the same questions, you know, I asked you in person on the call. You know, the first question was, you know, historically, you know, Innodata has done great work and gotten projects, and then the projects have ended, and the stock has gone way up and then gone way down. What's different about the kind of work you're doing now that it- you're not looking to it to be a one-and-done project, that it's gonna continue to grow and scale? I was using the analogy of a skyscraper, and you guys are putting in the initial foundation. How would you describe how this is gonna build? Yes, so I think it's a great question, Tim. Firstly, in the past, you know, we were operating in a very relatively, you know, small market. We had a, in that small market, a few, you know, numbers of customers. There were five large companies, and on occasion, when they would build a substantial new product, they would come to us to do that work, but that had a beginning, middle, and an end. And it was kind of a one-off thing. I couldn't possibly contrast more sharply, what's going on today. Today, you know, we're at the crossroads and of, you know, the biggest technology revolution, I believe, of our lifetimes. We're relevant to it. The work, the kind of work that we've done in the past is directly applicable to Large Language Models and Generative AI. I believe that we're at the early stages of, you know, where this is going. I think we've got the signed agreements with the major players that will enable us to cement that relevance and to drive that growth, not just for one project, as it would have been in the past, but across multiple projects that they're only now getting out of the gate on, that they're only now starting with. Beyond those, you know, five companies that we're now working with, there are other tech companies that we will continue to be pursuing and I hope landing. I'm confident landing. And beyond that, there's, you know, all the companies that are gonna be looking to use these capabilities, and we've got a ton of experience in, you know, integrating AI into operations and into applications. So I think we've got the strategy. I think we've got the tailwinds to be very successful, and we can leverage what we're uniquely good at to help drive this forward and drive a tremendous amount of growth. Sure. Well, yeah, and the other key question I asked, well, asked publicly is, you know, is this, you know, work you're doing, is it within the framework of Innodata's competency? Or even more specifically, so far, are all the clients delighted with the kind of work you've done so far? Yeah. So far, you know, things are going very well for us. As I mentioned to Brian, you know, it's the work that we've done that's enabled us to, you know, scale dramatically and succeed as well as we have in the companies that we've been working with a bit longer than some of these new ones. But I believe, you know, we'll be rinsing and repeating. I think that same set of capabilities that we're bringing to the table will enable us to drive significant growth from, you know, newer relationships as well. And you know, the thing that's so interesting about all of this is that the capabilities that we've had historically that were unique to us that were of value to a small market, you know, the information services market, are exactly the capabilities that are relevant to now this much larger market. You need scalable domain expertise, you need global reach, you need to have the technology and the processes and the DNA to create high quality, you know, consistent data sets and complex subject areas. How many companies in the world, you know, do that at scale and have the years of experience that we've got invested in exactly doing that? So it's the perfect pivot for us. You know, on top of all of that, we made a really good decision about six years ago to invest heavily in AI and to get good at implementing models into operations and to learning how to train them to perform well. So, you know, we've had a good strategy, we've had a bit of luck, I think, and now we're poised to reap the benefits of it. You know, when I, when I look at your contracts, you know, one $5 million a quarter, another one potentially up to $10 million a quarter, I mean, it's, it's certainly- I know you're not giving, you know, any kind of projections for next year, but it seems like you should be able to do over $30 million or plus at some point next year, just based on these contracts playing out. ... Yeah, I think, you know, there, there's a lot that we're figuring out about these relationships. There's a lot of work that's going on, you know, with our customers to figure out, you know, where they need us to go and what we'll be doing. I think we're going to be in a very good position, or an increasingly better position to be, you know, giving guidance. I'm happy that we're giving some guidance about, you know, Q4. I think we'll be in a position, as I mentioned a few minutes ago, to shed some light on how 2024 is shaping up when we next have our call. Most certainly, you know, I think, you know, $30 million quarters are not at all outside our reach in the near and medium term. Right. Now, you know, getting back to Agility, you know, it had a really excellent quarter, strong profitability in EBITDA. It looks like you're doing just under $20 million annually there. What would be, you know, in the private market, some kind of multiple of sales, would a company like that be worth? You know, I really don't know the answer to that. In terms of, you know, the value that someone would place on that specifically, I know there are a couple of comps out there recently in private markets for, you know, for companies that do what Agility does, and the valuations were, you know, as based on my understanding, were pretty rich, pretty healthy. You know, we're thrilled with the progress that we've made in Agility. You know, we're having strong and increasingly, you know, solid quarters in terms of booking new business. We're seeing, you know, solid retention numbers. We're seeing improvements in terms of the average selling price, what we call the ASP. The AI work that we've done within the Agility platform, you know, the PR CoPilot, is driving new wins. It's helping bolster retention. We've got more capabilities that are coming out, second half, you know, of this year and maybe into next year, in terms of leveraging AI further into those workflows, you know, being even more creative about how AI can be used by PR professionals. So it's, it's fun to watch. You know, that business is really now hitting its stride. Do any of your competitors have any comparable AI capability in that area, like Agility? Not like... Yeah, nothing like what we've got. We haven't seen it. Great. Great. Well, thanks. I'm done. A good quarter. Thank you. Thank you. The next question is coming from Dana Buska from Feltl. Dana, your line is live. Hi, Jack. Good afternoon, Dana. Congratulations on an excellent quarter. Well, thank you so much for that. You're very welcome. I have a couple of questions. First of all, one of the things that I've been reading in the literature is that there's a big attempt to kinda automate a lot of the stuff that you do, fully automate it. I was wondering, do you foresee a time when there's gonna be no need for humans in the loop for the services you provide? Yeah. That's a complex question. The quick answer is no. I mean, we don't foresee that. There's a lot of opportunity to automate aspects of training for classical AI. There's very limited opportunity to remove humans from the process of training large language models, and there are complex, you know, data science reasons for that. Now, that said, you can make the work that's being done by humans much more efficient than it might otherwise be. A lot of the technology and the workflows that we've got are directly applicable to applying human cognition and human capability effectively on large language models, but you can't use large language models to train other large language models. That's not an accepted practice today. Okay. Okay, that's good to know. With the contract that you signed or the master service agreement you signed with the company, it's expected to spend $ hundreds of millions with AI services. What is your roadmap or strategy about going to get some of that business from that customer? Well, I mean, I'm not gonna lay that out, the specificity for, you know, competitive reasons. But, you know, if you kind of dial it way back and you know, think of it, it won't be any different than any of the other relationships that we forged. You know, you get a foot in the door. You put in place you know, the paperwork that's required so that the business can easily do business with you. That there are no impediments, that there isn't a great deal of work or permission-getting or data security auditing or anything that one of their business units would need to undertake in order to work with you. You meet as many people as you possibly can. You do, you know, an engagement or two, and you do it very, very well, and word starts to get out about the results that were obtained by working with you. And you build relationships of trust based on that. You understand where they're going. You start to build into your product pipeline and your innovation work that would then accommodate where they're likely to go. You try to skate to where the puck's going, and you work hard. That's basically, you know, the recipe. Okay. Okay, excellent. But, one of the announcements you made, you talked about creating a golden dataset for a medical information company or like insurance company. Could you tell us what a golden dataset is and what it means to your business? Yeah, so it can mean different things in different contexts. One of the reasons that you might use a golden dataset is to benchmark a large language model. So you would create a golden dataset of how you would want to see the model responding, you know, if it's tuned properly to align with human values and to align with the business case. All right. And what does that mean to be... for your business, that you've been able, you know, that you're able to do that, or you're working with this customer to do that? Well, I think it's one of, you know, very many opportunities that we've got to, you know, be relevant for, you know, engineering teams who are building large language models. It's one of many things that's required to, you know, successfully, you know, train and launch, you know, a foundational or foundation model in Generative AI. So, you know, there's fine-tuning required, there's reward modeling, there's reinforcement learning. You know, there are a lot of different components of things that are required. There's, you know, work that you would do for evaluating the capabilities of the model. You'd be evaluating it from a trust and safety perspective. You know, within the context of that, the golden datasets can be important. Okay. Okay, excellent. And then one last question. When you start tackling your enterprise marketplace, how are you anticipating that you're gonna go about doing that? Are you gonna have to, like, add more salespeople, more consultants? Well, how are you thinking about tackling that? Yes, a couple of ways. You know, we're very excited about the White Label program that we've now referred to several times, because it gives us the ability to scale our business and gain exposure to enterprise use cases, independent of sales and marketing. That's, you know, a huge opportunity that gives us a lot of competitive advantage, I believe. You know, beyond that, I think the enterprise opportunity will be driven by, you know, direct sales for the most part, although we also do see another couple of channel opportunities that we're exploring as well. Okay. Thank you. That's it for me. Thank you, Dana. Thank you. Once again, it's star one if you wish to ask a question. The next question is a follow-up from Brian Kinstlinger from Alliance Global Partners. Brian, your line is live. Yeah, great. Thanks for taking my follow-up. Clearly, your offerings that address large language models, data annotation, even with the enterprises, is growing, or if not, will be growing very fast. But if I'm not mistaken, there's significant revenue base that predates this, that you were talking about before that was, a little bit more lumpy. Correct me if I'm wrong, if that doesn't still exist. So is that business still stable, declining, or growing as we think about next year or for our own sake? From a sales execution perspective, the work that we're hunting right now primarily is, you know, the work that we're doing with large tech companies and the AI enablement work that we're looking to do for enterprises. We're very focused on that. Now, that runs across, you know, enterprises run across, you know, multiple verticals. And one of the capabilities that we're able to leverage is the relationships that we've got with enterprises. So we've worked over the years with very many enterprises in, you know, business information sector. We've worked with enterprises in the financial services sector. We've worked with enterprises in life insurance. All of these are, you know, companies that are trying to figure out actively, you know, how do these technologies apply to their businesses and how do they apply to their products? So you're absolutely right, Brian, that we've got hooks into the companies who are actively thinking about this and the capabilities that we're bringing back to those customers. They're the capabilities that have, you know, we've developed in AI; they're very receptive to. You know, we talked about how, you know, we announced three enterprise deals that we closed this quarter or, you know, in Q3. A couple of those were, you know, were customers that we've done things with years ago, having nothing to do with AI or very little to do with AI; they were managed service capabilities. But now we're going back to them with a different value proposition... that they're, you know, very much receptive to and embracing. Mm-hmm. Great. Okay. Thank you so much. Thank you. The next question is coming from Bruce Galloway from Galloway Capital. Bruce, your line is live. Hey, Jack, congratulations, you know, on being a visionary in this area. Obviously, you were the first mover advantage, and since ChatGPT and Microsoft, you know, there's kind of like a tsunami in this area, and I'm sure there's been a major shift of capital into this area through the venture community and also the private equity communities, along with all the existing technology companies that are gonna be chasing IT services for generative AI. Can you talk a little bit about the competition and where you are with regard to the competition, and maybe talk about some of the valuations in that segment of the marketplace to give us an idea of what your company could be worth? Sure. So, well, you know, first, Bruce, you know, thank you for your kind words. I don't know that I deserve those compliments or certainly all of them, but thank you for that. You know, we're competing against you know, several companies and we'll probably be competing with more companies as we move forward in this area. There's a lot of activity here. You know, the predictions that analysts have released for you know, growth in Generative AI-related services are huge, you know, over 100% CAGR for the next 10 years. So, you know, naturally, that will, as you're saying, attract a lot of interest and a lot of money. There are companies that we know, you know, are about our size or somewhat larger, who have enormous, you know, valuations. You know, we think we compete favorably with them. You know, our focus is to, you know, keep doing what we're doing, to do it well. You know, as you've seen from the results, we're driving aggressive growth. We're lining up more and more relationships of trust. We're demonstrating that you can grow aggressively and be profitable at the same time, and close these major deals, which, you know, I think is kind of a hat trick that I'm very proud of. Yeah, there are some big valuations out there. I think our valuation will take care of itself as long as we keep executing. What are some of the valuations that are being done out there, you know, like, on a price-to-revenue basis? You know, we don't have perfect knowledge of that. You know, we're aware of some, you know, a company, for example, that has about a... Yeah, we're told a $250 million, you know, top line with, you know, a valuation of about $7 billion a couple of years ago. Again, you know, I'm not an investment banker. I don't wanna go well outside my wheelhouse here, you know, but we're, you know, aware of those kinds of private market valuations. And, you know, I think we just stay very focused on execution and keep doing what we're doing. And, you know, I think we've got a strategy now that enables growth in lots of interesting ways. You know, we can do a really good job by shareholders by staying focused. Okay, good job. Thanks. Thank you. Thank you. The next question is coming from Tim Madey from White Pine Capital. Tim, your line is live. Hi, Jack. Congratulations on your quarter. Nice job. Hi, thank you. Two quick questions. One is, could you talk a little bit about gross margins and what you expect over kind of, you know, the near term? Sure, happy to. So, you know, in terms of gross margins, I think the way to, you know, think about, you know, kind of the expansion economics of our business is to, you know, look at the two flavors of business we have. Fundamentally, there's a services and solutions business, and then there's a platform business. And, you know, our consolidated gross margin will be, you know, the sum or the factoring of both of those together. You know, our adjusted gross margin on the services and solutions side is probably, you know, within a range of 37%-42%. And our adjusted gross margin on the platform side of the business is probably like, you know, high 60s, 68, 69% to about 75% from, you know, a modeling perspective. And then, you know, I think you've seen that in combination with the work that we've done on carefully managing cost structure. You know, we're doing very well when you look at the, you know, incremental adjusted EBITDA that we're throwing off as we scale. Yes, I guess I was looking at direct operating costs over revenues, and coming to a lower number, but I figured it's somewhere in the adjustment. Certainly, the revenue growth and the Adjusted EBITDA looks fantastic, but... And maybe I can take it offline just to understand how to think about adjusting gross margins, or looking at direct operating costs over revenue growth. I'm a little confused there. Yeah. So no, we're happy to take you through that. You know, basically, what we're adjusting for is, you know, stock-based compensation and D&A. ... Okay. So there's an add back there. Okay. So that would be the add- back, and you'll get leverage on that add back, because that won't necessarily, you know, keep increasing at the same rate as revenue will. Okay, I understand now. Thank you. And last question, I was on the Microsoft call the other day and couldn't help but notice that they're using Copilot also. You trademarked that with PR CoPilot. How does, how does that work where they're using Copilot around large language models also? Well, I think it's a really good name. I think it's a great name. I just kind of wondering, did they talk to you before they started using that name, or are they- No. White labeling that from you, or? You know, I... They're not, and I, that certainly isn't our biggest concern. I think it's a great description for the way, you know, these technologies can be used to, you know, augment the work that people do. Okay. And to provide, you know, that kind of augmented, real-time, real-life assistance. And, you know, I think the exciting thing is those technologies certainly are, you know, our PR CoPilot is just gonna get better and better and better and more and more personalized. So, Mm-hmm. You know, I'm happy we picked a name that other people think is, is cool too. Right. Maybe there's benefit for us in that. There's certainly no lawsuits that we're initiating. I know that. Just last quick question. I was thinking about the question earlier. We've been tracking you for years, and you had some great projects over the years, and I was wondering if you could talk a little bit about, you know, the history and what you learned on some of these projects, and how it relates to your current business, kinda tying that lineage or heritage all together for us. Yeah, happy to. So, you know, what we've made a business of over the years is creating large-scale, high-quality data for companies where, you know, errors are not welcomed, where errors are not tolerated. The tolerance for, you know, any mistakes is virtually, you know, nonexistent. So we've developed, you know, technology around that and processes around that and DNA around that. And we've done this in lots of different domains, by which I mean, you know, subject areas, medical, healthcare, legal, regulatory, tax, financial, insurance, you know, on and on and on. Now, you know, the thing to know about large language models and AI fundamentally is the key ingredient beyond compute for, you know, training and inferencing. The next key ingredient is data, and the higher the quality of data, the better performing the AI will be. So we're able to take that fundamental core competency that we have and pivot off of that very directly for creating high-quality AI. That's why, you know, I like to think that, you know, all of the work that we've done over now decades has been kind of training camp for, you know... It's like training for the Olympics. Now we're in the Olympics, and we're bringing a lot of very relevant training, you know, to the table. Yeah, that's some of the criticism I've heard on large language models, is that if the dataset's not right, the answer might sound logical, but it could be false. How do you ensure, or could you talk a little bit more about the skill set of putting together the right data set for the right model to make sure that you're getting the right output? Yeah. So there's a little bit of danger there in conflating two problems. One is that the model just doesn't work very well. It, the language isn't helpful. The, you know, it's kind of cognitive ability isn't there- And things like that. The other, you know, related issue is hallucination, and you don't necessarily solve hallucination through the quality of data. You solve hallucination in some respects through, you know, the kind of work that you're doing on performance evaluation and, you know, the trust and safety work and the kinds of data that you're feeding into it, but it's just not a data quality problem. Got it. Great. Well, thanks. I'll jump back in the queue. Thank you. We have reached the end of our question and answer session, and I will now turn the call over to Jack Abuhoff for closing remarks. Great. Well, thank you, operator, and thank you everybody for your great questions. You know, I'll recap a little bit. You know, we now have, you know, hard-fought-for master services agreements with 5 of the 10 largest technology companies in the world for generative AI development. We're super excited about that. We're expecting these companies to spend $ billions over the next several years for training and fine-tuning Generative AI models. We're now or soon expecting to be ramping up engagements with all of these companies. I guess in Q3, we got a taste of the growth that we believe is in store, and we anticipate further growth in Q4 and continuing into 2024. You know, as we said, we're guiding to $24.5 million or more of revenue in Q4. Today, we also announced having signed an agreement with yet another of the world's largest tech companies, adding to our already rich roster of opportunities. And with the significant, you know, incremental, adjusted EBITDA gains we're delivering, we're demonstrating that we have what it takes to, you know, grow aggressively, but to grow aggressively and profitably as we harness the, the opportunity that's in front of us and the tailwinds that we're benefited by. You know, my team and I are energized by what we've accomplished, by the number of new major accounts we now have to deliver growth and, you know, the magnitude of the market opportunity that's in front of us. We believe we're now just at the early stages of exploiting these market opportunities, and we believe that these market opportunities are themselves at their early stages. So, very exciting, and again, thank you all. We'll be very much looking forward to our next call with you. Thank you. This does conclude today's conference. You may disconnect your lines at this time. Thank you for your participation.