Greetings and welcome to Innodata's Third Quarter 2023 Earnings Call. 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 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 expectations. Thank you. I will now turn the call over to Jack.

Good afternoon. We're thrilled to be here with you today and have some positive news to share. We are excited to announce that our third-quarter revenue reached $22.2 million, which is a 20% increase compared to last year. If we exclude the revenue drop from the large social media company that spent $1 million in the same quarter last year but reduced spending significantly after a major management change, the year-over-year growth would have been 27%. We are also pleased to report an adjusted EBITDA of $3.2 million for the third quarter, showing 100% growth from the previous quarter. The increase in sequential adjusted EBITDA of $1.6 million, along with a $2.5 million rise in revenue from the previous quarter, reflects strong operational efficiency and effective cost management. When we compare year-over-year performance, we see similar outcomes, with adjusted EBITDA increasing by $4.4 million alongside a revenue increase of $3.7 million. The growth in the third quarter was fueled by the initiation of our generative AI development work with one of the significant new tech customers announced this summer, and we expect this collaboration to continue expanding into the fourth quarter and the beginning of next year, potentially reaching a run rate of $23 million to $25 million by the year's end. Towards the end of the quarter, we also began our generative AI development efforts with another new major tech customer, which we anticipate will also add to our fourth-quarter revenue. In fact, we expect to further grow our revenues with both of these clients throughout Q4 and into 2024. For the fourth quarter, we project revenue of at least $24.5 million, resulting in a minimum of 26% year-over-year growth. If we exclude the revenue from the large social media company, which contributed $0.5 million in Q4 of 2022, we would be looking at a forecast of 30% growth or better year-over-year. Since the search and media customer did not generate revenue in Q1 of 2023, we expect that starting in Q1 2024, the social media customer's revenue will no longer negatively influence our year-over-year comparisons. For the fourth quarter, we anticipate an adjusted EBITDA of at least $3.7 million, representing a growth rate of approximately 15 or more times compared to the fourth quarter of last year. Additionally, I'm excited to share that in September, we signed a master services agreement for AI development with another leading tech company, one we’ve targeted for a year. Our research indicates that this company is likely to invest several hundred million dollars on generative AI data engineering services in 2024, highlighting significant potential. Although this relationship is still in the initial stages, we see great promise. Looking ahead to 2024, we expect an exciting and transformative year, confident that we have the strategy, momentum, and customer relationships necessary for substantial revenue and adjusted EBITDA growth. We plan to provide guidance regarding our 2024 revenue and adjusted EBITDA growth during our Q4 call. Our growth strategy concentrates on two main fronts: supporting large technology firms in building generative AI foundational levels, and assisting enterprises across various sectors in integrating and refining generative AI models. We have successfully secured master service agreements with five of the world's largest technology companies to provide support for their generative AI programs. Our success in these negotiations underscores the strength of our value proposition and capabilities. With these agreements, we believe we are well-positioned for significant growth in 2024. Over the coming years, we expect these technology firms to develop increasingly sophisticated generative AI models. Currently, there is a strong sentiment among these large companies that generative AI is their top strategic priority and their greatest investment area for 2024. One company has even projected that generative AI innovations will generate tens of billions in revenue over the next few years. Product-centric technology companies are focusing on creating generative AI-enhanced experiences across their product lines, reshaping how consumers interact with their products. Conversely, infrastructure-centric companies are planning to implement innovative and differentiated generative AI services while enhancing their AI infrastructure to meet client needs for AI training and inferencing. The strong demand for generative AI is prompting both categories of companies to increase capital investments, which is promising for us. This summer, we announced partnerships with two new Big Five tech clients to expand existing programs and initiate new ones aimed at developing and training large language models. Our first new client engagement was announced on July 18, with an expansion revealed on August 29. Our work commenced in early August, and we expect it to continue ramping through Q4 and into Q1, potentially achieving a revenue run rate of $23 million to $25 million by year-end. We secured our second Big Five client on August 10, and by August 22, we confirmed that our agreement was finalized. Although our announcement indicated that ramp-up could start in early Q4, I'm pleased to inform you that we commenced operations at the end of Q3. While we generated some revenue from this client in Q3, we expect a more significant revenue impact in Q4. Discussions are underway regarding the project's initial scope, which is expected to be substantial, along with other potential programs. The customer has approved an initial budget of $2.5 million to commence the work, with an additional authorization of $1.5 million expected soon, and has committed to increasing this budget as we expand the programs. We also communicated that an existing Big Five client has engaged us for AI data annotation and large language model fine-tuning as a white-labeled service for its cloud and platform offerings. Previously, we indicated we could exceed $8 million in revenue from this customer in 2023, up from approximately $3 million last year, and we are confident we’ll meet or surpass this target. This year's forecast includes about $330,000 in revenue from the white-label initiative, comprising six opportunities that are either won or in late-stage discussions. We expect this white-label program to contribute more significantly in 2024, with several million in potential opportunities already identified, including two valued at $2 million and $1 million, respectively. We hope to finalize both in Q1. Under the white-label initiative, we’re observing varying requirements from our clients’ enterprise customers, spanning generative AI data pipelines, two- and three-dimensional data annotation, chatbot fine-tuning, large language model-based search and retrieval, and training large language models for multilingual, domain-specific summarization and interaction. Crucially, this initiative allows us the chance to scale an enterprise offering independent of our own sales and marketing by leveraging our partners’ strong brands and customer outreach while gaining exposure to diverse early adopter generative AI use cases, positioning us for what we believe could be our most significant opportunity—large language models for enterprises. Now, let me discuss our enterprise opportunity and the progress made in Q3. While we are still in the early stages of enterprise adoption for generative AI, we believe that a decade from now, nearly all successful businesses will have integrated generative AI technologies into their operations and products. Such integration will necessitate one or more of the capabilities we offer. Enterprise data science teams will require assistance in training and fine-tuning both open-source and proprietary large language models, conducting specialized tests, and ensuring the models are beneficial and safe. They will also need support to implement retrieval-augmented generation, a method to effectively use enterprise data with large language models. Additionally, line-of-business managers will be looking for help in developing custom generative AI models and applications, as well as in facilitating the transformation of business processes and workflows through generative AI. When we identify chances to deliver AI-driven transformations through subscription platforms, as we currently offer for PR, underwriting, and compliance workflows, we enable clients to subscribe instead of undertaking complex and costly builds independently. In the third quarter, we successfully closed three significant enterprise generative AI projects with major companies, each varying in scope from formulated strategy to implementation. One engagement focuses on assisting a prominent information firm in creating a strategic plan for integrating large language models into its products and internal processes while also developing proofs of concept. Another project involves refining large language models for three specific use cases in legal services. The third project aims to produce datasets for training a large language model to aid in doctor-patient communications. We concluded Q3 with $14.8 million in cash and short-term investments, an increase from $13.7 million in the previous quarter, and we continue to maintain minimal debt. To facilitate our growth and future working capital needs, we have a $10 million revolving credit line with Wells Fargo, subject to borrowing-based limits.

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 $3.2 million or $0.12 per basic and diluted share in the same period last year. Revenue for the nine 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 nine months ended September 30, 2023. Net loss for the nine 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 an 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 an 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 this over to Paul. Paul, we are now ready for questions.

The first question today is from Brian Kinstlinger at 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 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?

Certainly, I think the example of going from 3 to 8 is the best way to illustrate this, particularly in relation to the $25 million. Initially, we began with one program and one model. After successfully delivering on that, we were able to secure two or three additional opportunities. This success allowed us to expand our collaborations with other programs and engineering teams within the account. We refer to this as our land and expand strategy. The challenge lies in entering these programs, which is somewhat akin to gaining admission to Harvard. Once we are in, delivering good results means we can grow and expand our involvement. We anticipate that the revenue growth we've experienced, moving from three to eight this year, could potentially double again next year. We believe the same principles will apply to other large companies we are beginning to engage with. Starting with a $25 million initial engagement, as opposed to $200,000, is a positive signal, and the opportunity for expansion remains intact. Our plan is to grow our presence, moving from one program to several, and by maintaining strong performance, we can facilitate that growth.

As you scale these programs, what investments are necessary? Is it additional personnel? Do you require more infrastructure? I'm trying to understand what investments you need to make as revenue increases.

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'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, that's a great question, Tim. In the past, we operated in a relatively small market with just a few customers, specifically five large companies. They would occasionally approach us for substantial new products, but those projects had a definite start and end, and they were mostly one-off engagements. Today, however, the situation is drastically different. We are at the forefront of what I believe is the biggest technology revolution of our lifetimes, and we are positioned to play a significant role in it. The type of work we've done previously is directly relevant to large language models and generative AI, and I believe we are just at the beginning of this journey. We have signed agreements with major players that will help us solidify our relevance and drive growth, not just through single projects as in the past, but across multiple initiatives that are just starting to emerge. In addition to the five companies we are currently collaborating with, we are actively pursuing other tech firms, and I am confident we will secure these partnerships. Furthermore, many businesses will seek to leverage these AI capabilities, and we have extensive experience in incorporating AI into operations and applications. I believe we have a strong strategy and the favorable conditions to achieve significant success, leveraging our unique strengths to drive this endeavor and realize substantial 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?

So far, things are going very well for us. The work we have done has allowed us to scale dramatically and achieve success with companies we've partnered with longer than some of our newer clients. I believe we will continue to replicate this success. The same capabilities we bring to the table will help us drive significant growth from newer relationships as well. What's interesting is that the unique capabilities we've historically had, which were valuable in the small information services market, are now relevant to a much larger market. To compete at scale, you need scalable domain expertise, global reach, and the technology and processes to create high-quality, consistent data sets in complex subject areas. There are very few companies that can do this at scale with the years of experience we have. This is a perfect opportunity for us. Additionally, we made a strategic decision about six years ago to invest heavily in AI and excel at integrating models into operations, learning how to train them for optimal performance. We have a solid strategy and a bit of luck, and now we are ready to take advantage of the benefits that come from it.

When I review your contracts, one is worth $5 million a quarter and another could reach up to $10 million a quarter. While I understand you are not providing any projections for next year, it appears you should be able to exceed $30 million at some point next year based on how these contracts develop.

Yes, I believe we are learning a lot about our relationships. We are actively engaging with our customers to understand their needs and our future direction. I am confident that we will be in a strong position to provide guidance. I am pleased that we are able to offer some guidance for Q4. As I mentioned earlier, we will be able to provide insights into how 2024 is developing during our next call. I firmly believe that achieving quarterly revenues of $30 million is definitely within our reach in the near to 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, were 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 a very limited opportunity to remove humans from the process of training large language models, and there are 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 the contract you signed with the company that is expected to spend hundreds of millions of dollars on AI services, what is your strategy for securing some of that business from that customer?

Dane, I mean I'm not going to lay that out in 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, audits, 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 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 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 addressing your enterprise marketplace, how do you plan to approach that? Will you need to hire more salespeople or consultants? How are you considering handling that?

Yes, a 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 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.

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?

From a sales execution perspective, our current focus is on working with large tech companies and pursuing AI enablement for enterprises. This effort spans multiple verticals. We are leveraging our established relationships with various enterprises across sectors, including business information, financial services, and life insurance. These companies are actively seeking to understand how these technologies can integrate into their operations and products. You're right that we have connections with companies that are considering this, and they are receptive to the AI capabilities we are offering. We recently announced three enterprise deals closed in Q3, some with customers we've partnered with in the past, primarily around managed services, rather than AI. We are now approaching them with a different value proposition that they are open 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 compliments 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 complete knowledge of that. We know of a company that reported approximately a $250 million revenue and had a valuation of around $7 billion a couple of years ago. I'm not an investment banker, so I don't want to go beyond my expertise, but we’re aware of these kinds of private market valuations. Our focus remains on execution, and we are committed to continuing our current approach. We have a strategy that promotes growth in various ways, and by maintaining our focus, we can deliver strong results for our shareholders.

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. I'm glad to provide insight on gross margins. To understand the expansion economics of our business, it's important to consider the two segments we operate. We have a services and solutions division alongside a platform business. Our overall gross margin will reflect the combination of both segments. Currently, our adjusted gross margin for the services and solutions segment is in the range of 37% to 42%. For the platform segment, it's typically in the high 60s, around 68% to 69%, potentially reaching up to 75% from a modeling standpoint. Additionally, we are effectively managing our cost structure, which has positively impacted our incremental adjusted EBITDA as we scale up.

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 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.

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 picked 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?

Certainly. Over the years, we have focused on creating large-scale, high-quality data for companies that cannot afford errors. The margin for mistakes in AIM is almost nonexistent. With that in mind, we have developed technology, processes, and a foundational approach to address this need. We have applied this in various fields such as medical, healthcare, legal, regulatory, tax, financial, and insurance, among others. It’s essential to understand that when it comes to large language models in AI, while compute power is crucial for training and processing, high-quality data is the next essential element. The better the data, the more effective the AI will be. We leverage our core competencies in this area to create high-quality AI. I like to liken our extensive preparations over the years to training for the Olympics; now that we are in the competition, we have a wealth of relevant experience to contribute.

Yes. One of the criticisms I've heard regarding large language models is that if the data set is incorrect, the answer may appear logical but could actually be false. How do you ensure, or could you elaborate on the skills needed to compile the appropriate data set for the right model to guarantee accurate output?

Yes. So there's a little bit of danger there in conflating two 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 master services agreements with five of the ten 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.

Thank you. This does conclude today's conference. You may disconnect your lines at this time. Thank you for your participation.