Innodata Inc (INOD) 2023 Q3 法說會逐字稿

Greetings and welcome to Innodata 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 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' 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 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 a limited number of customers' potential inability to replace projects that are completed, canceled, or reduced.

Our dependence 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 impairments 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, cyber attacks, privacy breaches, or data breaches that results in the unauthorized disclosure of consumer, customer, 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 Form 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 with the year ago quarter, but dramatically cut spending after a significant and highly publicized management change.

We were also very pleased to announce third quarter adjusted EBITDA of $3.2 million, representing 100% sequential quarter-on-quarter growth. A $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 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 -- the 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 dollars on generative AI data engineering services in 2024.

So this win, like the others that we announced the 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 opportunities. 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 non-trivial. 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-centered 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 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 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 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 the 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 label 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 one-or-late-stage opportunities. We believe this white label program will contribute more significantly to 2024.

For 2024, we already have several million dollars 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 are 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 two and three dimensional data annotation; chat thought 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 and 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 LOMs 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 LOMs 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. 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 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 LOM proofs of concept.

In another, we will be helping fine-tune LOMs 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 revolving line of credit with Wells Fargo that provides for up to $10 million of financing subject to borrowing base 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 with $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 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 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.

And that concludes my recap of the third quarter result. Again, thanks, everyone. I will now turn over this to Paul. Paul, we are now ready for questions.

(Operator Instructions).

Brian Kinstlinger, Alliance Global Partners.

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 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 of -- we're beginning to ramp up that customer.

Great. I think your story is not 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/or different offerings? You're providing more testing? And so you're testing more time, fine-tuning more in terms of volume. I'm just trying to understand what drives scale, $3 million to $8 million, or zero getting to $25 million?

Yes. So I think if we take the $3 million to $8 million, that's probably the best example to use, and then maybe we'll apply it to the $25 million. In the $3 million to $8 million example, we started with one program, one model, 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 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 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 million to predicted $8 million this year -- $8 million 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.

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, what investments you have to make.

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

Okay. Thank you.

Thank you, Brian. Good to have you on the call.

Tim Clarkson, Van Clemens.

Hey Jack. Good to see you a couple of weeks ago. I just wanted to ask the same questions I asked you in person, on the call. 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 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 and scale?

I was using an 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 that 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, we're at the crossroads 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 and generative AI.

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 it. They're only now starting with it.

Beyond those 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 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 to help drive this forward and drive a tremendous amount of growth.

Sure. Yes, the other key question I asked publicly is you know, 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 will 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, 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, consistent datasets in 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 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, and to operations, and to learning how to how to train them to perform well.

So yes, 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, I know you're not giving you any kind of projections for next year, but it seems like you should be able to do $30 million or 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 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 a really an excellent quarter, strong profitability and EBITDA. Looks like you're doing just under $20 million annually there. What would be, in the private market, some kind of multiple sales, would 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 with 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 copilot -- is driving new winds. It's helping bolster retention. We've got more capabilities that are coming out second half of this year and maybe into next year.

In terms of leveraging AI further into those workflows, being much more creative about how AI can be used by PR professionals. So 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 -- yeah, nothing like what we've got. We haven't seen --

Great. Well, thanks. I'm done. Good quarter.

Thank you.

Dana Buska, Feltl.

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 kind of automate a lot of the stuff that you do -- fully automate it. And I was wondering, do you foresee a time when the 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. 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 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. Good to know. With the contract that you signed, the master service agreement you signed with the company that's expected to spend hundreds of millions of dollars, with AI services, what is your roadmap or strategy about going to get some of that business from that customer?

Well, I mean, I am not going to lay that out with 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 meet 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 with 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 the recipe.

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

Yes, certainly. 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, if it's tuned properly to align with human values and to align with the business case.

Alright. And what does that mean for your business that you're able to do that? 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 foundation model in generative AI. So there is fine-tuning required. There's reward models -- reinforcement learning -- there are a lot of different components and 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 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 going to go by doing that? Are you going to have to like add more salespeople -- more consultants? How are you thinking about tackling 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 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.

Okay. Thank you. That's it for me.

Thank you, Dana.

Brian Kinstlinger, Alliance Global Partners.

Yes, great. Thanks for taking my follow up. Clearly your offerings address large language models, data annotation, even when the enterprise 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 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 -- 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 the 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 we've developed in AI -- they're very receptive to.

We talked about how we announced three 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 or 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.

Great. Okay. Thank you so much.

Bruce Galloway, Galloway Capital.

Hey, Jack, congratulations on being a visionary in this area. Obviously, you have 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 community 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 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.

You know, 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 have released for growth in generative AI 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.

There are a lot of companies that we know are about our size, or somewhat larger, who have enormous valuations. We think we compete favorably with them, and, you know, 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 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 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 go well outside my wheelhouse here, but we're aware of those kinds of private market valuations.

And yes, 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.

Okay, good job. Thanks.

Thank you.

Tim Madey, White Pine Capital.

Hi, Jack. Congratulations on your quarter. Nice job. Two quick questions -- one is, could you talk a little bit about gross margins, and what do you expect over the near term?

Sure. Happy to. So in terms of gross margins, I think the way to think about the expansion economics of our business is to look at the two flavors of business we have. Fundamentally there's a services and solutions business, and there's a platform business, and our consolidated gross margin will be the sum or the factoring of both of those together.

Our adjusted gross margin on the service and solution side has probably been a range of 37% to 42%, and our adjusted gross margin on the platform side of the business is probably like at a high 60% -- 68%, 69% to about 75% -- 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 I'm 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'm a little confused there.

Yes. So no, we're happy to take you through that. Basically what we're adjusting for is a 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 keep increasing at the same rate as revenue will.

Okay. I understand now. Thank you.

Last question. I was on the Microsoft call the other day, and I couldn't help but notice that they're using Copilot also. You've trademarked that 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 was just kind of wondering, did they talk to you before they started using that name or are they white labeling that from you?

Well, you know, they're not. And that certainly isn't our biggest concern. I think it's a great description for the way home these technologies can be used to augment the work that people do and provide that kind of augmented real-time real-life assistance. And I think the exciting thing is those technologies, certainly -- our PR co-Pilot is just going to get better and better, and more and more personalized.

So I'm happy we picked the name that other people think is cool too. And there's a benefit for us in that, there's certainly no lawsuits that we're initiating.

I know that. (laughter)

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 time lineage or heritage altogether for us?

Yes, happy to. So what we've made a 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 any mistakes is virtually nonexistent. 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, healthcare, legal, regulatory, tax, financial, insurance -- on and on and on.

Now the thing to know about large language models and 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. And 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, 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 to the table.

Yeah, that's -- some of the criticism I've heard on large language models, is that if the datasets 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 dataset for the right model to make sure that you're getting the right output?

Yes. So there's a little bit of danger there and conflating two problems. One is that the model just doesn't work very well. That the language isn't helpful. Its 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 the kind of work that you're doing on performance evaluation, and 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.

Well, thank you, operator, and thank you, everybody, for your great questions. I'll recap a little bit.

We now have a 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.

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