INOD - NASDAQ

Greetings and welcome to Innodata's Third Quarter 2023 Earnings Call. [Operator Instructions] 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 statements 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' attack against Israel and the ensuing conflict, investments 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 our 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 market, particularly new and emerging markets, that our services and solutions support; continuing Digital Data Solutions segment revenue concentration in a 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 requirements for 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, customers, employee or company information or service interruptions; and 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 expectation. 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 some 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're also very pleased to announce third quarter adjusted EBITDA of $3.2 million, representing 100% sequential quarter-on-quarter growth. The $1.6 million of sequential adjusted EBITDA growth, viewed together with the $2.5 million of sequential quarter-on-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 to $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 search and 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 have been trying to break into for a year now. Based on our research, this large tech company is likely to spend several hundred million dollars 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 levels. 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 5 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. And 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 2 new Big Five tech customers in both the 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 July -- 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 to $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 our announcement -- 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 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 27, 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. And 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 six 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 2 opportunities that we value at $2 million and $1 million, respectively. It is worth noting that we believe the $2 million opportunity potentially opened 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 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 degenerative 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, it 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 props. 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. And 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 subscribe to our platforms rather than having to undertake complex and expensive builds themselves. In the third quarter, we closed 3 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 3 customer use cases pertaining to legal services. And the third, we will be creating data sets 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 borrowing based limitations. I'll now turn the call over to Maris 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 3 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 $3.2 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 9 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. And that concludes my recap for the third quarter results. Again, thanks, everyone. I will now turn over this to Paul. Paul, we are now ready for questions.

[Operator Instructions] The first question today is coming from Brian Kinstlinger from Alliance Global Partners.

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 to $25 million of annual revenue. Was there a meaningful contribution in the third quarter? You highlighted it for most of the customers but I didn't hear if it made a significant contribution and maybe if you can quantify it for the third quarter?

Sure. So indeed, that it did make a significant contribution. And 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. And then just 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 to $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 are different offerings, you're providing more testing. And so you're testing more times, fine-tuning more in terms of volume? I'm just trying to understand what drives scale, 3 to 8 or 0 getting to $25 million?

Yes. So I think if we take the 3 to 8, that's probably the best example to use and then maybe we'll apply it to the $25 million. In the 3 to 8 example, we started with one program, one model, one initiative that they had in place. We did very good work and then that we got 2 or 3 more opportunities that we had. We did good work there and then that enabled us to further scale to start working with other programs, other development groups, other engineering groups within the account. And we refer to that as 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 graduate. If you do good work, you expand. And that's what we're seeing. Now we believe that, that revenue growth that we saw, 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. So we intend to expand our presence. We intend to go from one program to multiple programs. And we believe that by doing good work, we enable exactly that to happen.

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, what investments you have to make?

So we're making investments across the board. We're making investments in people, in process and technologies in the engineering work that we're doing. 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.

The next question is coming from Tim Clarkson from Van Clemens.

Good to see you the other a couple of weeks ago. I just want to ask the same questions I asked you in person on the call. And the first question was, historically, 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 you're not looking to be a one-and-done project that it's going to continue to grow in 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 going to build?

Yes. So I think it's a great question, Tim. Firstly in the past, we were operating in a very relatively small market. We had in that small market, a few numbers of customers. There were 5 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, we're at the crossroads. And of 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 in generative AI and I believe that we're at the early stages of 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, they're only now getting out of the gate on that they're only now starting with. Beyond those 5 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 all the companies that are going to be looking to use these capabilities and we've got a ton of experience in 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, yes and the other key question I asked and asked publicly is, is this 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?

Yes. So far, things are going very well for us. As I mentioned to Brian, it's the work that we've done that's enabled us to 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 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 newer relationships as well. And 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, 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, consistent data sets and complex subject areas, how many companies in the world 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. And on top of all of that, we made a really good decision about 6 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 we're -- 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.

When I look at your contracts, one $5 million a quarter, another one potentially up to $10 million a quarter. I mean, it's certainly -- I know you're not giving any kind of projections for next year but it seems like you should be able to do over $30 million plus at some point next year just based on these contracts playing out.

Yes. I think there's a lot that we're figuring out about these relationships. There's a lot of work that's going on with our customers to figure out 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 giving guidance. I'm happy that we're giving some guidance about Q4. I think we'll be in a position, as I mentioned a few minutes ago, to give -- shed some light on how 2024 is shaping up when we next have our call. And most certainly, I think $30 million quarters are not at all outside our reach in the near and medium term.

Right. Now getting back to agility, it had really an excellent quarter, strong profitability and EBITDA. It looks like you're doing just under $20 million annually there. What would be in the private market, some kind of multiple of sales with a company like that be worth?

I really don't know the answer to that. In terms of the value that someone would place on that specifically. I know there are a couple of comps out there recently in private markets for companies that do what Agility does and the valuations were based on my understanding, we're pretty rich, pretty healthy. We're thrilled with the progress that we've made in Agility. We're having strong and increasingly solid quarters in terms of booking new business. We're seeing 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, the PR co-pilot is driving new wins. It's helping bolster retention. We've got more capabilities that are coming out in the second half of this year and maybe into next year. In terms of leveraging AI further into those workflows, being even more creative about how AI can be used by PR professionals. So it's fun to watch. That business is really now hitting its stride.

Do any of your competitors have any comparable AI capability in that area, like utility?

Yes, nothing like what we've got. We haven't seen it.

The next question is coming from Dana Buska from Feltl.

Congratulations on an excellent quarter.

Well, thank you so much for that.

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 kind of automate a lot of the stuff that you do fully automated. And I was wondering, do you foresee a time when there's going to be no need for humans in the loop for the services you provide?

Yes. So 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 trading for classical AI. There's very limited opportunity to remove humans from the process of training large language models and they're complex 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.

With your -- with the contract that you signed or the master service agreement you signed with the company is expected to spend hundreds of millions of dollars with AI services. What is your road map strategy about going to get some of that business from that customer?

Dane, I mean I'm not going to lay that out the specificity for competitive reasons. But if you kind of dial it way back and think of it, it won't be any different than any of the other relationships that we forged. You get a foot in the door, you put in place 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 need as many people as you possibly can. You do an engagement or 2 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 is going. And you work hard. That's basically the recipe.

One of the announcements you made, you talked about creating a golden data set for medical information company or like an insurance company. Could you tell us what a golden data set is and what it means to your business?

Yes. So it means different things in different contexts. One of the reasons that you might use a golden data set is to benchmark a large language model. So you would create a golden data set of how you would want to see the model responding if it's tuned properly to align with human values and to align with the business case.

And what does that mean for your business that you've been able -- that you're able to do that? Or you're working with this customer to do that?

Well, I think it's one of very many opportunities that we've got to be relevant for engineering teams who are building large language models. It's one of many things that's required to successfully train and launch a foundational or a foundation model in generative AI. So there's fine-tuning required, there's reward modeling, there's reinforcement learning. There are a lot of different components of things that are required. There's work that you would do for evaluating the capabilities of the model, you'd be evaluating it from a trust and safety perspective within the context of that, the golden data sets can be important.

And then, one last question. When you start tackling the -- your enterprise marketplace. How are you anticipating that you're going to go about doing that? Are you going to have to like add more salespeople, more consultants? How are you thinking about tackling that?

Yes, couple of ways. 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 and gain -- to scale our business and gain exposure to enterprise use cases, independent of sales and marketing. That's a huge opportunity that gives us a lot of competitive advantage, I believe. Beyond that, I think the enterprise opportunity will be driven by direct sales for the most part, although we also do see another couple of channel opportunities that we're exploring as well.

[Operator Instructions] The next question is a follow-up from Brian Kinstlinger from Alliance Global Partners.

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 for our own sake?

So the -- from a sales execution perspective, the work that we're hunting right now primarily is 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 the enterprises run across 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 business information sector. We've worked with enterprises in the financial services sector. We've worked with enterprises in life insurance. And all of these are companies that are trying to figure out actively, 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, the capabilities that have -- we've developed in AI, they're very receptive to. We talked about how we announced 3 enterprise deals that we closed this quarter or in Q3. And a couple of those were customers that we've done things with years ago, having nothing to do with AI or very little to do with AI, they managed service capabilities. But now we're going back to them with a different value proposition that they're very much receptive to and embracing.

The next question is coming from Bruce Galloway from Galloway Capital.

Jack, congratulations on being a visionary in this area. Obviously, you were the first mover advantage. And since ChatGPT and Microsoft, 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 going to 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, first, Bruce, thank you for your kind words. I don't know that I deserve those complements or certainly all of them but thank you for that. We're competing against 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. The predictions that analysts released for growth in generate related services are huge, over 100% CAGR for the next 10 years. So naturally, that will, as you're saying, attract a lot of interest and a lot of money. There are companies that we know are about our size or somewhat larger who have enormous valuations. We think we compete favorably with them. And our focus is to keep doing what we're doing to do it well. 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 I think is kind of a hat trick that I'm very proud of. Yes, 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 on like a price-to-revenue basis?

We don't have perfect knowledge of that. We're aware of some -- a company, for example, that has about a -- we're told a $250 million top line with a valuation of about $7 billion a couple of years ago. Again, I'm not an investment banker. I don't want to get -- I don't want to go well outside my wheelhouse here but we're aware of those kinds of private market valuations. And I think we just stay very focused on execution and keep doing what we're doing. And I think we've got a strategy now that enables growth in lots of interesting ways. And we can do a really good job by shareholders by staying focused.

The next question is coming from Tim Madey from White Pine Capital.

Jack, congratulations on your quarter. Nice job. Two quick questions. One is, could you talk a little bit about gross margins and what you expect over kind of the near term?

Sure. Happy to. So in terms of gross margins, I think the way to think about kind of the expansion economics of our business is to look at the 2 flavors of the business we have. Fundamentally, there's a services and solutions business and then there's a platform business. And our consolidated gross margin will be the sum or the factoring in both of those together. Our adjusted gross margin on the Services Solutions side is probably -- we've been a range of 37% to 42%. And our adjusted gross margin on the platform side of the business is probably like high 60%, 68%, 69% to about 75% from a modeling perspective. And then I think you've seen that in combination with the work that we've done on carefully managing cost structure. We're doing very well when you look at the 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 am a little confused there.

So no, we're happy to take you through that. Basically, what we're adjusting for is the stock-based compensation and D&A, so.

So there's an aspect there. Okay.

So that would be the add back and you'll get leverage on that add back because that won't necessarily keep increasing at the same rate as revenue will.

Okay. I understand now. And last question was on the Microsoft call the other day and I couldn't help but notice that they're using co-pilot also. You trademark that with PR co-pilot. How does that work where they're using co-pilot around large language models also?

Well, I think it's a really good name.

It's a great name, I just kind of wondering, did they talk to you before they started using that name? Or are they labeling that from you or?

They're not. And that certainly isn't our biggest concern. I think it's a great description for the way these technologies can be used to augment the work that people do and provide that kind of augmented real-time, real live assistance. And I think the exciting thing is those technologies, certainly, our PR co-pilot is just going to get better and better and better and more and more personalized. So I'm happy we pick the name that other people think is cool too. And maybe a good 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 the history and what you learned on some of these projects and how it relates to your current business kind of tying that lineage or heritage altogether for us?

Yes, happy to. So what we've made the business over the years is creating large-scale, high-quality data for companies where errors are not welcomed, where errors are not tolerated. The tolerance for AIM mistakes is virtually non-existent. So we've developed 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 subject areas, medical, health care, legal, regulatory, tax, financial, insurance, on and on and on. Now the thing to know about large language models in AI fundamentally is the key ingredient beyond compute for 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 I like to think that all of the work that we've done over now decades has been kind of training camp for -- it's like training for the Olympics. Now we're in the Olympics and we're bringing a lot of very relevant trading to the table.

Yes. That's some of the criticism I've heard on large language models is that the -- if the data set is 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?

Yes. So there's a little bit of danger there in conflating 2 problems. One is that the model just doesn't work very well. The language isn't helpful. It's kind of cognitive ability isn't there and things like that. The other related issue is hallucination and you don't necessarily solve hallucination through the quality of data, you solve hallucination in some respects through the kind of work that you're doing on performance evaluation and 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.

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. I'll recap a little bit. We now have 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 of dollars 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. 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 incremental adjusted EBITDA gains we're delivering, we're demonstrating that we have what it takes to grow aggressively but to grow aggressively and profitably as we harness the opportunity that's in front of us and the tailwinds that we're benefited by. 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 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.