Innodata Q3 2023 Earnings Call Transcript

Quartr
Provided by Quartr
November 2, 2023

There are 11 speakers on the call.

Operator

Greetings, and welcome to Innodata's Third Quarter 2023 Earnings Call. At this time, all participants are in a listen only mode. A question and answer session will be recorded. I will now turn the conference over to your host, Amy Agress, General Counsel. You may begin.

Speaker 1

Will be

Speaker 2

answered. Thank you, Paul. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Applehoff, will be CEO of Innodata and Mariz Espinelli, Interim CFO.

Speaker 2

We'll hear from Jack first, who will provide perspective about the business, and then Marisa will follow with a review of our results for the Q3. We'll then take your questions. First, let me qualify the forward looking statements that are made during the call. Will be answered. 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.

Speaker 2

Forward looking statements include, without limitation, may be recorded, forecast, indicated or implied by 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 will be recorded. We will now begin the question and answer session. Thanks, Mark. I will now turn the call over to Eric.

Speaker 2

Thank you, Mark. Thank you, Mark. Thank you, Mark. Thank you, Mark. And Hamas' attack against Israel and the ensuing conflict, investments in large language models that contracts may be terminated by customers, will be recorded.

Speaker 2

Projected or committed volumes of work may not materialize, pipeline opportunities and customer discussions, which may not materialize into work are expected volumes of work, acceptance of new capabilities, continuing Digital Data Solutions segment reliance on project based work and the primarily at will nature of such contracts and the ability of these customers to reduce, delay or cancel projects, will be recorded. 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 and the limited number of customers

Speaker 1

may be a potential inability to replace projects

Speaker 2

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 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, may be. Potential impairment of the carrying of goodwill and other acquired intangible assets of companies and businesses that we acquire, may be related to 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, will be in a listen only mode. A question and answer session will be in the unauthorized disclosure of consumer, customer, employee or company information for service interruption 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 ks, 10 Q and 8 ks and any amendments thereto.

Speaker 2

We undertake no obligation to update forward looking information or to announce revisions to any forward looking statements, expectation. Thank you. I will now turn the call over to Jack.

Speaker 3

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 3rd quarter revenue of $22,200,000 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 $1,000,000 in revenue with the year ago quarter, but dramatically cut spending after a significant and highly publicized management change.

Speaker 3

We're also very pleased to announce 3rd quarter adjusted EBITDA of $3,200,000 representing 100% sequential quarter on quarter growth. The $1,600,000 of sequential adjusted EBITDA growth view together with the $2,500,000 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 $4,400,000 of adjusted EBITDA growth and $3,700,000 of revenue growth. 3rd 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.

Speaker 3

We expect our work with this customer to continue ramping up in the 4th quarter and into the Q1, potentially reaching a $23,000,000 to $25,000,000 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 4th quarter revenue. In fact, we anticipate continuing to expand revenue with both of these new customers will be recorded through Q4 and in 2024. For the Q4, we are forecasting revenue of $24,500,000 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 point will be $5,000,000 in revenue in the Q4 of 2022.

Speaker 3

Our 4th 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 Q4, we're forecasting adjusted EBITDA of $3,700,000 or more, which would be approximately 15 or more times adjusted EBITDA from the Q4 last year. I am also very pleased to announce that in September we signed a master services agreement for AI development with yet another of the world's largest tech companies, a company whose AI programs we've been trying to break into for a year now. Based on our research, this large tech company is likely to spend several $100,000,000 on generative AI data engineering services in 2024.

Speaker 3

So this win, like the others we announced this summer, packs a lot of potential. Will be answered. 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

Speaker 1

will be

Speaker 3

available 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.

Speaker 4

Will be available. 1st, we will support large

Speaker 3

technology companies building generative AI foundation models. 2nd, 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 5 of the largest technology companies in the world under which we are providing generative AI program support. Landing these agreements was non trivial.

Speaker 3

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 company's 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. 1 of these companies specifically stated that it believes it will drive tens of 1,000,000,000 of dollars of revenue over the next several years from generative AI innovation.

Speaker 3

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 5 Tech customers and both a program expansion and a new program with an existing Big 5 Tech customer, all to help develop and train large language models. We announced the first new Big 5 customer win on July 18 And on August 29, we announced the program had been expanded.

Speaker 3

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 $23,000,000 to $25,000,000 by the end of the year with which to start next year. We're now in discussions with this will be available to the customer about

Speaker 1

potential further program expansions and potential additional programs.

Speaker 3

We announced our 2nd new Big 5 customer win on August comes from the line of Ken. 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 Q4. I'm pleased to report that we were able to kick things off the tail end of Q3. While we had a little bit of revenue from this customer in the Q3, we anticipate that revenue from this customer will impact our 4th quarter results more significantly.

Speaker 3

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 $2,500,000 in spend to get us started, as promised that an additional $1,500,000 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 LLN build program. In the latter announcement, we stated that we anticipated potentially exceeding $8,000,000 in revenue from this customer in 2023, will be up from approximately $3,000,000 last year.

Speaker 3

We believe that we are on track to meet or exceed this target. Included in this year's forecast is approximately $330,000 of revenue from the white label program consisting of 6, 1 or late stage opportunities. We believe this white label program will contribute more significantly to 2024. For 2024, we already have several 1,000,000 in pipeline is including 2 opportunities that we value at $2,000,000 $1,000,000 respectively. It is worth noting that we believe the $2,000,000 opportunity could potentially open an exciting new market for us.

Speaker 3

We're hoping to close both of these opportunities in Q1. Under the white label program, we are seeing a mix of requirements from our customers' enterprise customers. Requirements range from generative AI data pipelines to 2 and 3-dimensional data annotation, chatbot fine tuning, LLM based search and retrieval and training LLMs for multilingual domain specific summarization and conversation. Importantly, the program is enabling us to potentially scale an enterprise offering independent of our own sales and marketing to leverage both our customers' brand and its significant customer reach and to gain exposure to a wide variety of early 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.

Speaker 3

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. Will require 1 or more of the capabilities that we offer. Enterprise Data Sciences teams will requires support to train in 5.2 in 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, the 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.

Speaker 3

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 Q3, we closed 3 important enterprise Generative AI opportunities with large companies. Their scope ranges from strategy to implementation. Will be 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.

Speaker 3

In another, we will be helping fine tune LLMs for 3 customer use cases pertaining to legal services. In the third, we will be creating datasets to train in LLM to support doctor patient interactions. We ended Q3 with $14,800,000 in cash and short term investments, up from $13,700,000 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 $10,000,000 of financing subject to borrowing base limitation.

Speaker 3

I'll now turn the call over to Maris to go over the numbers and then we'll open the line for questions.

Speaker 1

Will be answered.

Speaker 5

Thank you, Jack. Good afternoon, everyone. Allow me to recap our 2023 Q3 financial results. Revenue for the quarter ended September 30, 2023 was $22,200,000 up 20% year over year. The comparative period included $1,000,000 in revenue from the large social media company that underwent a significant management changed in the second half of last quarter as a result of which it dramatically pulled back spending across the board.

Speaker 5

There was no revenue from this company in the 3 months ended September 30, 2023. Net income for the quarter ended September 30, 20 2023 was $400,000 or $0.01 per basic and diluted share compared to a net loss of 3,300,000 or $0.12 per basic and diluted share in the same period last year. Revenue for the 9 months ended September 30, 20 23 was $60,700,000 compared to $59,600,000 in the same period last year. The comparative period included $7,900,000 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.

Speaker 5

Net loss for the 9 Months ended September 30, 2023 was $2,600,000 or $0.09 per basic and diluted share compared to a net loss of $10,000,000 or $0.37 per basic and diluted share in the same period last year. Our adjusted EBITDA was $3,200,000 in the Q3 of 2023 compared to adjusted EBITDA loss of $1,200,000 in the same period last year. Adjusted EBITDA was $5,600,000 for the 9 months ended will be recorded. September 30, 2023 compared to adjusted EBITDA loss of $3,500,000 in the same period last year. Our cash and cash equivalents and short term investments were $14,800,000 at September 30, 2023 as compared to $10,300,000 at December 31, 2022.

Speaker 5

And that concludes my recap for the 3rd quarter result. Again, thanks, everyone. I will now turn over this to Paul. Paul, we are now ready for questions.

Operator

If you wish to ask a question. Session. The first question today is coming from Brian Kinstlinger from Alliance Global Partners. Brian, your line

Speaker 6

is 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 $23,000,000 to $25,000,000 of annual revenue. Was there a meaningful contribution in Q3, 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 Q3.

Speaker 3

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.

Speaker 6

Great. And then just I think your story is 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 $23,000,000 to $25,000,000 or even Your second contract that you expect to generate $8,000,000 compared to $3,000,000 is it you're providing more services And there are different offerings. You're providing more testing and so you're testing more times, fine tuning more in I'm just trying to understand what drives scale 3 to 8 or 0 getting to 25,000,000?

Speaker 3

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. In the 3 to 8 example, we started with 1 program, 1 model, one initiative that they had in place. We did very good work and then that begot 2 or 3 more opportunities that we had.

Speaker 3

We did good work there and then that enabled us to further scale to start working with other programs, other development groups, So there are 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.

Speaker 3

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 revenue growth that we saw 3 to predicted 8 this year, 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.

Speaker 3

The fact that instead of starting with a $200,000 initial engagement, we're starting with a $25,000,000 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 One program to multiple programs. And we believe that by doing good work, we enable exactly that to happen.

Speaker 6

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?

Speaker 3

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

Speaker 6

Okay. Thank you.

Speaker 3

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

Operator

Thank you. The next question is coming from with Tim Clarkson from Bank Evans. Tim, your line is live.

Speaker 7

Hey, Jack. Good to see you the other 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, Inadata 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.

Speaker 7

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?

Speaker 8

Yes. So I think

Speaker 3

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.

Speaker 3

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 and generative AI.

Speaker 3

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 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 So I think we've got the strategy. I think we've got the tailwinds to be very is successful and we can leverage what we're uniquely good at to help drive this forward and drive a tremendous amount of growth.

Speaker 7

Sure. Well, yes, and the other key question I asked and asked publicly is, is this work You're doing it within the framework of Interdata's competency or even more specifically so far, are all the clients delighted with the kind of work you've done so far?

Speaker 3

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.

Speaker 3

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 datasets 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.

Speaker 3

So We've had a good strategy.

Speaker 1

We've

Speaker 3

had a bit of luck, I think, and now we're poised to reap the benefits of it.

Speaker 7

When I look at your contracts, dollars 1, dollars 5,000,000 a quarter, another one potentially up to will be $10,000,000 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 $30,000,000 plus at some point next year just based on these contracts playing out.

Speaker 3

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 We're in 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.

Speaker 3

And most certainly, I think $30,000,000 quarters are Not at all outside our reach in the near and medium term.

Speaker 7

Right. Now getting back to Agility, you had really an excellent quarter, strong profitability and EBITDA. It looks like you're doing just under $20,000,000 annually there. What would be in the private market some kind of multiple of sales would a company like that be worth?

Speaker 3

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 Engility. We're having strong and increasingly solid quarters in terms of booking new business. We're seeing solid retention numbers.

Speaker 3

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 2nd 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.

Speaker 7

Will be answered. Do any of your competitors have any comparable AI capability in that area like agility?

Speaker 3

Yes. Nothing like what we've got. We haven't

Speaker 7

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

Speaker 8

Thank you.

Operator

Thank you. The next question is coming from Dana Busca from Telco. Dana, your line is live.

Speaker 3

Hi, Jack. Good afternoon, Dana.

Speaker 8

Congratulations on an excellent quarter.

Speaker 3

Well, thank you

Speaker 8

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 And the literature is that there's a big attempt to kind of automate a lot of the stuff that you do is 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.

Speaker 3

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 training for classical AI.

Speaker 3

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 is an accepted practice today.

Speaker 8

Okay. Okay. Good to know. With the contract that you signed or the master service agreement you signed with the company that is expected to spend 100 of 1,000,000 of dollars with AI Services. What is your roadmap or strategy about going to get some of that business from that customer?

Speaker 3

Well, I mean, I'm 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 an order to work with you. You meet 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.

Speaker 3

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 rest of it.

Speaker 8

Okay. Okay. Excellent. One of the announcements you made, you talked about creating a golden dataset for medical Information company or like insurance company, could you tell us what a golden data set is and what it means to your business.

Speaker 3

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

Speaker 8

All right. 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?

Speaker 3

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 will be a question and launch 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.

Speaker 8

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 about doing that?

Speaker 8

Are you going to have to like add more salespeople, more consultants? How are you thinking about tackling that?

Speaker 3

Yes. So a couple of ways. We're very excited about the white label program that we've not 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.

Speaker 8

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

Operator

Thank you. The next question is a follow-up from Brian Kinstlinger from Alliance Global Partners. Brian, your line is live.

Speaker 6

Yes, great. Thanks for taking my follow-up. Clearly, your offerings that address large language models, data annotation, even with the enterprises is will be growing or if not, we'll 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.

Speaker 6

So Is that business still stable, declining or growing as we think about next year for our own sake?

Speaker 3

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 business information sector.

Speaker 3

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 an 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 that we've managed service capabilities.

Speaker 3

But now we're going back to them with a different value proposition That they're very much receptive to and embracing.

Speaker 6

Great. Okay. Thank you so much.

Operator

Thank you. The next question is coming from Bruce Galloway from Galloway Capital. Bruce, your line is live.

Speaker 9

Hey, Jack, congratulations on being a visionary in this area. Obviously, you are the 1st mover advantage. And since will be at the end of the call. Chat GPT 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?

Speaker 9

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.

Speaker 3

Sure. So, 1st, 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 generative AI related services are huge, over 100% CAGR for the next So naturally that will, as you're saying, attract a lot of interest and a lot

Speaker 8

of money.

Speaker 3

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.

Speaker 3

Yes. There are some big valuations out there. I think our valuation will take care of itself as long as we keep executing.

Speaker 9

What are some of the valuations that are being done out there on like a price to revenue basis?

Speaker 3

We don't have perfect knowledge of that. We're aware of some company, for example, that has about We're told the $250,000,000 top line with a valuation of about $7,000,000,000 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.

Speaker 3

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.

Speaker 9

Okay, good job. Thanks.

Speaker 3

Thank you.

Operator

Thank you. The next question is coming from Tim Mahy from White Pine Capital. Tim, your line is live.

Speaker 10

Hi, Jack. Congratulations on your quarter. Nice job. Hi, thank you. Two quick questions.

Speaker 10

One is, could you talk a little bit about gross margins and what you expect over kind of the near term?

Speaker 3

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 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 of both of those together. Our adjusted gross margin on the services solutions side is probably within a range of 37% to 42%.

Speaker 3

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.

Speaker 10

Yes. I guess, I was looking at direct operating costs over revenues and coming to a lower number, but I figured 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.

Speaker 3

So now we're happy to take you through that. Basically what we're adjusting for is stock based compensation and D and A.

Speaker 10

So there's an add back there.

Speaker 3

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.

Speaker 10

Okay. I understand now. Thank you. And last question, I was on the Microsoft call the other day and couldn't help but notice that they're using Copilot also. You trademarked that with will be PR Copilot.

Speaker 10

How does that work where they're using Copilot around large language models also?

Speaker 3

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

Speaker 10

I think it's a great name. I Just kind of wondering, did they talk to you before they started using that name or are they white labeling that from you

Speaker 3

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 a name that other people think is cool too.

Speaker 3

Maybe there's benefit for us in that. There's certainly no lawsuits that we're initiating.

Speaker 10

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 with some of these projects and how it relates to your current business, kind of tying that lineage or heritage altogether for us.

Speaker 3

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 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, healthcare, legal, Regulatory, tax, financial, insurance, on and on and on.

Speaker 3

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. That's why I like to think 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 training to the table.

Speaker 10

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?

Speaker 3

Yes. So there's a little bit of danger there and conflating to 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.

Speaker 3

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 It's just not a data quality problem.

Speaker 10

Got it. Great. Well, thanks. I'll jump back in the queue.

Operator

Will turn remarks.

Speaker 3

Great. Well, thank you, operator, and thank you everybody for your great questions. Mode. 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.

Speaker 3

We're super excited about that. We're expecting these companies to spend 1,000,000,000 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 We anticipate further growth in Q4 and continuing into 2024. As we said, we're guiding to 24,500,000 or more of revenue in Q4.

Speaker 3

Today, we also announced having signed an agreement with yet another of the world's largest Just tech companies adding to our already rich roster of opportunities. And with a 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.

Speaker 3

And again, thank you all. We'll be very much looking forward to our next call with you.

Operator

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

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Key Takeaways

  • Q3 revenue of $22.2 million, up 20% year-over-year (27% ex-social media client), and adjusted EBITDA of $3.2 million with 100% sequential growth, demonstrating strong operating leverage and cost management.
  • Secured master services agreements with 5 of the world’s largest technology companies for generative AI development and in September signed another AI data engineering MSA expected to tap several $100 million in 2024 spending.
  • Guided Q4 revenue of $24.5 million or more (26%+ yoy growth; 30%+ ex-social media) and adjusted EBITDA of $3.7 million or more, driven by continued ramp-up of new Big Tech programs.
  • Outlined a two-pronged growth strategy: supporting AI foundation-model development for large tech firms and delivering generative AI integration and fine-tuning services to enterprises via both direct and white-label channels.
  • Ended Q3 with $14.8 million in cash and short-term investments, no material debt, and a $10 million revolving credit line, providing strong financial flexibility for future growth.
AI Generated. May Contain Errors.
Earnings Conference Call
Innodata Q3 2023
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