INOD earnings call analysis

NASDAQ:INOD Q1 2026 Earnings Call Transcript Generated on 6/6/2026 Jack Applehoff | Chief Executive Officer: on our call today for the large $51 million contract. We're providing what's called pre-training, mid-training, and post-training data. Soon we anticipate providing evals as well. And you can think of those as all classifications of data that's required in order to train and fine tune large language models. In terms of one of the other customers we talked about, we're providing trust and safety services. We're evaluating models. We're testing them. We're isolating areas where they're underperforming. We're prescribing the data mixes that are required in order to mitigate that performance. Similarly, another one of the wins that we talked about, or the soon-to-be wins, scaled data generation, large-scale data to train and improve models, testing for alignment with responsible AI. We're getting into creating data sets that are required for physical AI. You can think of physical AI as embodied intelligence or robots. So it's really along the full spectrum of capabilities that are required by the foundation model builders from a data perspective in order to support their products.

Investor Relations | Head of Investor Relations

That's great.

Thank you so much. Operator | Conference Operator

Up next is Hamed Khorzan from BWS Financial.

Hamed Khorzan | Analyst, BWS Financial

Hi. First question is, was there anything of one-time nature in the first quarter results as far as the revenue is concerned, or should we expect this to be a good baseline going forward?

Jack Applehoff | Chief Executive Officer

I'd say both. There are things that we're doing that we won't be doing next quarter. There are things we're going to be doing next quarter that we're not doing this quarter, but I think that It was a strong quarter. I think next quarter is going to be a strong quarter. I think, you know, the quarters after that are going to be good. You know, we're providing, we're not providing quarter by quarter revenue guidance because the fact is that things do start and stop. You know, when we talk about the phases of training a model, those don't necessarily dovetail perfectly. But we've got more and more things going on, and that tends to even things out. And we're doing some things now increasingly that are of an ongoing nature. So no, I don't think you should think of the quarter as aberrational at all. And I think that as we move through the year, there are going to be things that we're doing increasingly that are driven by innovation, that are going to be margin accretive, margin supporting. Yeah, we're excited about the year.

Hamed Khorzan | Analyst, BWS Financial

And then my other question was, has the composition of revenue changed at all or is it still, the scope of work is still the same? And you're talking about something that might happen in the future as far as the agentic and the valuations and so forth.

Investor Relations | Head of Investor Relations

No, these are things we're doing today. So when you, I mean, the thing that doesn't change is Jack Applehoff | Chief Executive Officer: Our mission for the company and our mission is to be the data partner to foundation model builders and to be the intelligence infrastructure layer for enterprise. That's not changing. What does change is as the models and the capabilities seek to do more and perform better, the mix of what we do does change. But that's our job to stay research-led and to ensure that we're a little bit ahead of where our customers need us to be.

Hamed Khorzan | Analyst, BWS Financial

Okay.

Thank you. Operator | Conference Operator

And everyone, at this time, there are no further questions. I'd like to hand the call back to Mr. Jack Applehoff for any additional or closing remarks.

Jack Applehoff | Chief Executive Officer

Thanks, operator. So, yeah, to wrap up, Q126 was a record quarter for InnoData across all the key metrics that we're reporting. You know, revenue adjusted gross profit, adjusted EBITDA cash. We delivered 54% revenue growth. We expanded margins meaningfully. We generated significant cash without having to draw on a credit facility. And based on these results and our forward visibility, we are raising 2026 revenue growth guidance to approximately 40% or more year over year. We continue to view this outlook as I'll use the term prudent. We see potential upside as additional programs that are not included in that forecast convert and scale. A big tech customer that generated no revenue for us 12 months ago is now on track to become our second largest customer this year. Our customer concentration is improving in the very best possible way. Faster growth from the broader customer base while our largest customer continues to grow in absolute dollars. We're also continuing to innovate at an increasingly rapid pace. The strength of our research bench is showing up in customer outcomes and in external recognition, like Esther's two ICML 2026 paper acceptances and her one spotlight designation. Really exciting stuff. We launched our evaluation and observability platform in beta in the quarter, and no sooner did we launch than we closed a $1 million opportunity with one of the world's largest hyperscalers around that platform. So we're really excited about what lies ahead. We're confident that 2026 is going to be an exciting and tremendous year for the company. And yeah, I thank everybody for being on the journey with us.

Operator | Conference Operator

Once again, everyone, that does conclude today's conference. We would like to thank you all for your participation today. You may now disconnect. jsPDF 3.0.3 D:20260606090149-00'00'

NASDAQ:INOD Q4 2025 Earnings Call Transcript Generated on 6/6/2026 Background Audio | Music/Applause: Thank you. Thank you. music music ¶¶ Thank you. Thank you. Thank you. Thank you. We'll be right back. Thank you. Thank you. Thank you. Thank you. © transcript Emily Beynon Thank you. Thank you. Thank you. © transcript Emily Beynon Thank you.

Conference Operator | Operator

Good afternoon, ladies and gentlemen, and welcome to the InnoData 2 Report fourth quarter and fiscal year 2025 results conference call. At this time, all lines are in listen-only mode. Following the presentation, we will conduct a question and answer session. If at any time during this call you require immediate assistance, please press star zero for the operator. This call is being recorded on Thursday, February 26, 2026. I would now like to turn the conference over to Amy Agras, General Counsel.

Please go ahead. Amy Agras | General Counsel

Thank you, operator. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Applehoff, Chairman and CEO of InnoData, and Maryse Espinelli, Interim CFO. Also on the call today is Anish Pentakar, Senior Vice President, Finance and Corporate Development. Rahul Singhal, President and Chief Revenue Officer, is unable to be here today, but looks forward to joining us on our next call. We'll hear from Jack first, who will provide perspective about the business, and then Maryse will provide a review of our results for the fourth quarter and fiscal year 2025. We'll then take questions from analysts. Before we get started, I'd like to remind everyone that during this call, we will be making forward-looking statements, which are predictions, projections, and other statements about future events. These statements are based on current expectations, assumptions, and estimates, and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release in the risk factor section of our Form 10-K, Form 10-Q, and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward booking information. In addition, during this call, we may discuss certain non-GAAP financial measures. In our earnings release filed with the SEC today, as well as in our other SEC filings, which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliations of these measures with comparable GAAP measures. Thank you. I will now turn the call over to Jack.

Jack Applehoff | Chairman and CEO

Thank you, Amy, and good afternoon, everyone. Q4 was another strong quarter for InnoData. We generated 72.4 million in revenue, reflecting 22% year-over-year growth. This brought our full-year revenue to 251.7 million, representing 48% year-over-year growth for 2025. Our Q4 consolidated adjusted gross margin was 42%, exceeding our externally communicated target of 40%. Our adjusted EBITDA totaled 15.7 million or 22% of revenue, also exceeding analyst consensus by 1.2 million. In fact, our results exceed the analyst consensus across the range of key metrics, including revenue, adjusted EBITDA, net income, and EPS. We ended the year with 82.2 million in cash, up sequentially by approximately 8.4 million. We achieved these results while making meaningful growth-oriented investments in both COGS and SG&A. In COGS, we carried capacity ahead of revenue ramp, which consistently proved to be the right move. And in SG&A, we invested in engineers, data scientists, and customer-facing account leadership, which investments also proved prudent, yielding innovation that has expanded our opportunities. We believe our business momentum to be at an all-time high. We are seeing robust demand across the entire generative AI lifecycle, spanning development, evaluation, and ongoing model optimization. And we believe we are gaining traction with a broad and diversified number of large customers. As a result of market demand and growing traction, we anticipate another year of potentially extraordinary growth in 2026. We currently estimate our 2026 year-over-year growth to potentially be approximately 35% or more. This estimate reflects active programs, recently awarded wins, late stage evaluations, and opportunities where we have clear line of sight. Because we are early in the year and because LLM initiatives spin up quickly, we believe there may potentially be significant upside to this range. However, we prefer to guide conservatively and adjust upward as visibility increases. At the same time, given the scale and complexity of the programs we support, timing variability and customer round schedules, budget approvals, or shifts in research priorities could influence the pace at which revenue materializes. Embedded in our outlook is the expectation that spend from our largest customer will increase somewhat in the year, and that the remaining customer base in the aggregate will grow at a faster rate. We expect this other customer growth to come from a mix of the MAG7, domestic AI innovation labs, sovereign AI initiatives, and leading enterprises. We believe this will meaningfully contribute to customer diversification. Our customers are moving fast driving shorter development cycles and responding faster to research breakthroughs in 2025 we succeeded in this environment in no small part because we followed the research anticipated customer needs and pivoted were required to illustrate. In the first quarter of this year, for our largest customer, we deprecated the meaningful number of post-training workflows, which represented in the aggregate approximately 20 million of annualized revenue run rate, but replaced them with a combination of new post-training workflows and scaled pre-training programs, an area of recent focus and investment. From a revenue run rate perspective, the net effects turned out positive. Indeed, we believe continuous innovation is critical to achieving our ambitious plans for 2026 and beyond. The truly exciting news is we believe we are entering a golden age of innovation at InnoData as a result of investments we have made and intend to make in the future. I'm now going to share some of our recent innovation initiatives. For competitive reasons, we'll be appropriately circumspect, but what we share will give you a meaningful window into how we're thinking, where we're investing, successes we're having, and how we intend to capitalize on the opportunity ahead. I'll briefly walk through our recent innovation in three areas, generative AI model training, agentic AI, and physical AI. Before I do, I want to underscore a unifying theme. Every innovation I am about to discuss is fundamentally a data innovation. Whether the goal is more capable LLMs, more reliable autonomous agents, or more intelligent physical AI systems, data quality, data composition, data validation, and data engineering at scale are at the heart of the matter. These are our core competencies. We'll start with generative AI training. Historically, customers told us the kind of training data they wanted. Increasingly, however, they're asking us to diagnose model performance, design the right training data sets, and demonstrate that those data sets will materially improve outcomes. Here's how that works. We begin by identifying performance gaps using our evaluation frameworks. We then engineer targeted data sets and validate their efficacy by fine tuning either the customer's model or a structurally similar proxy model. Only after we measure and demonstrate performance impact do we scale. This shifts the discussion from how much is the data to how effective is the data. We believe this shift is being driven by two forces, the accelerating pace of AI research and the cost and time incurred to train ever larger models. And conversations about data efficacy play directly to our strengths. We are also advancing methods for creating datasets that improve long context reasoning and AI models ability to observe and reason over very large amounts of information at once. This remains one of the industry's most important technical challenge. Solving it requires not just architectural improvements, but advances in the creation at scale of very specific types of structured training data. Creating training data that improves long context reasoning is a nontrivial problem, but we have made and are continuing to make meaningful progress on it. A secondary of innovation is around evaluating systems of autonomous agents and improving them through targeted data set creation. We believe that autonomous agents may represent the most significant business innovation opportunity since the advent of electricity. But companies quickly discover that many AI agents that performed impressively in controlled laboratory settings degrade in real world production. The real world is chaotic. It's shaped by edge cases, conflicting constraints, unpredictable user behavior, and adversarial conditions. Addressing this is fundamentally a data challenge. Agents must be continuously trained and rigorously stress tested with datasets that are realistic, diverse, and complex. For this, we have developed a set of three highly complementary hybrid solutions. The first is an agent evaluation and observability platform. Data scientists can use our platform during development to visualize and annotate agent trace data, to build LLM as a judge evaluators, to create business aligned evaluation rubrics, to generate golden data sets for regression testing, and to generate test data at scale. Then, once the agent is deployed, Our platform can be used to continuously monitor its performance, perform root cause analysis and performance issues, and obtain mitigation data sets. we're pleased to share that we anticipate soon kicking off a managed services engagement with a hyper scalar in which we will use our platform to create test data and scale. perform automated evaluations and identify critical model vulnerabilities in order to improve performance of its customer facing intelligent virtual assistant. The second innovation is a managed agent optimization pipeline designed to systematically train for and therefore neutralize the chaos of real-world deployment at scale. The pipeline generates realistic test scenarios, automates evaluation, rigorously measures constraint satisfaction, and produces reinforcement learning datasets. Using this system, we have demonstrated improvements of up to 25 points and constraint satisfaction. Importantly, agents trained using conventional techniques tend to degrade significantly as task complexity increases. By contrast, agents trained through our pipeline sustain their performance under escalating real-world difficulty. In the most demanding scenarios, the performance gap between standard approaches and our system widens to more than 31 points. We currently have multiple AI innovation labs and enterprise customers actively exploring the system. The third solution we've designed to support enterprise agentic AI is an adversarial simulation system that generates high-quality, semantically diverse and scalable adversarial attacks to stress test agents. The system generates a full spectrum of attack types, direct jailbreaks, indirect prompt injection via RAG pipelines, multi-turn social engineering stenographic payloads, and compound attacks that combine injection techniques with domain-specific knowledge. Once vulnerabilities are identified, it generates highly targeted mitigation datasets to strengthen guardrails. We believe our system generates realistic adversarial attacks of scale in a meaningful way that exceeds existing alternatives. Many tools on the market produce simplistic or templated hostile content that lacks the nuance and sophistication of real-world threat actors, fails to scale across diverse scenarios, or relies on generic tactics that models quickly learn to anticipate and overfit to. But by contrast, our framework is designed to simulate adaptive, multi-step, and strategically coherent attack patterns, including highly sophisticated model extraction, cybersecurity, cybercrime, and Soberny threat scenarios that better reflect how advanced adversaries operate and allow our partners to stay ahead of emerging threats. The result is adversarial training data that is both scalable and durable, forcing models to generalize rather than memorize and enabling more robust real-world resilience. Our work is garnering interest from CISOs, and security leaders at some of the world's premier AI and cybersecurity companies, as well as relevant experts in government, and has led to early stage engagements with several of them. At a time when the cyber industry is experiencing significant disruption, these capabilities bolster our position in the emerging field of AI trust and safety, an area where we are meaningfully deepening work with several hyperscalers. We believe Enerdata is well positioned to emerge as a leader in prompt layer security, protecting AI systems at the point of interaction rather than relying solely on traditional perimeter or endpoint defenses. Taken together, we believe these solutions position us not just as a data supplier, but as a lifecycle partner in agent reliability. We believe 2026 will also mark the acceleration of physical AI, intelligent systems that perceive and interact with the physical world. While robotics provides the mechanical framework, physical AI provides the intelligence. The primary bottleneck in this domain is data set quality and scale. Manual annotation and static QA sampling simply do not scale to billion-sample corpora and continuously evolving environments. We have developed a large-scale data engineering system that incorporates structural validation, distribution monitoring, temporal consistency checks, and model-in-the-loop instrumentation. This enables us to identify and correct defects in datasets before they propagate into performance failures. We're already using components of this system in the high visibility engagements we recently announced with Palantir. We recently secured a significant engagement to create foundational datasets for next generation robotic datasets, including egocentric data. Egocentric data captures the world from the robot's point of view, what it sees and experiences in motion. We are also working with a leading robotics lab to create affordance data at scale. Affordance data teaches the system what actions are possible in a given setting, not just identifying objects, but understanding how they can be used. Ego-centric data and affordance data taken together form the cognitive scaffolding that allows machines to act intelligently in dynamic environments. This work also positions us to support the development of so-called world models, internal simulations that allow AI systems to anticipate outcomes, reason about cause and effect, and plan several steps ahead. World models require richly structured data sets that capture interactions over time and the consequences of actions, precisely the type of data we are now engineering. We recently developed an AI model for drone and other small object detection that exceeds prior state-of-the-art benchmarks by 6.45%. In a field where progress is often measured in fractions of a percentage point, a 6.45% improvement is a material advance. The model improves detection fidelity under real-world conditions where small size feed, cluttered backgrounds, and environmental noise make reliable perception extraordinarily difficult. We believe this advancement has compelling dual-use implications that we are now actively exploring with potential customers. I'd like to underscore one of the important points I just made. For decades, InnoData has specialized in creating high-quality complex datasets. Today, these capabilities are central to unlocking the next generation of AI systems. Advanced LLM reasoning, agent reliability in chaotic environments, and robotics perception in the physical world all depend on engineered data ecosystems. And this is precisely where we operate. Our innovations in LLM training, agentic AI, and physical AI are not separate initiatives, rather, They are extensions of a single strategic advantage, our ability to engineer data that measurably improves model performance in real-world conditions. We believe our innovation pipeline will be margin-enhancing as well as revenue-enhancing. We expect early 2026 adjusted gross margins to be in the 35 to 40% range as we ramp up new programs, with normalization toward our target 40% or better adjusted gross margins as new programs ramp up and as innovation-driven workflows scale. Automation, synthetic systems, and evaluation platforms all structurally increase our operating leverage. I'll now turn the call over to Maryse, who will go through the numbers.

Maryse Espinelli | Interim CFO

Thank you, Jack, and good afternoon, everyone. Revenue for Q4 2025 reached 72.4 million, up 22% year over year. Sequentially, revenue increased 15.7% from Q3, 62.6 million. Adjusted gross profit for Q4 2025 was 30.1 million, an increase of 6% year-over-year and 9% sequentially, with an adjusted gross margin of 42%. Adjusted EBITDA was 15.7 million, or 22% of revenue, and net income for the quarter was 8.8 million. To reiterate, this is net of significantly expanded data science and engineering efforts that are yielding the types of innovation Jack just spoke about. We ended the quarter with $82.2 million in cash, up from $73.9 million at the end of prior quarter, and $46.9 million at the year-end 2024. And we did not throw down on our $30 million Wells Fargo credit facility. As Jack mentioned, based on our current momentum, we presently forecast 35% or more year-over-year revenue growth in 2026. Thank you, everyone, for joining us today. Operator, please open the line for questions.

Conference Operator | Operator

Thank you. Ladies and gentlemen, we will now begin the question and answer session. Should you have a question, please press the star key by the number one on your touch-tone phone. You will hear a prompt that your hand has been raised. If you wish to decline from the polling process, please press the star key followed by the number two. If you are using a speakerphone, please lift the handset before pressing any keys. One moment, please, while we assemble the queue. Your first question comes from Trevor Sutton of Craig Hallam.

Please go ahead. Trevor Sutton | Analyst, Craig Hallam

Thank you, Jack. I feel like I just sat through an advanced AI data science class, so thanks for that. wanted to uh step back a little bit because i think people have the assumption that some of what's working for you is somewhat temporary and i think you've you've done an interesting job of kind of walking us through in past quarters from post training as a start then pre-training and now there are dramatic other use cases including things like robotics and autonomous agents Can you just talk about the breadth of the things you're seeing and sort of where you see us in this continuum of data science opportunity for you?

Jack Applehoff | Chairman and CEO

Sure. Thank you, George. Thank you for the question. So as we look out near term, 2026, we see ourselves as being incredibly well set up by the innovations that we invested in in 2025. And we see that innovation output as a flywheel, we're getting better, we're getting stronger, we're creating solutions that are solving problems that are the actual impediments that enterprises have when they're looking to integrate AI into their operations. So when you look across the spectrum of current capabilities in AI and future capabilities in things like agentic systems, you know, physical AI, robotics. All of this boils down to challenges in terms of data engineering. Of course, there are going to be continuous improvements in architectures. It'll be, you know, bigger models. There'll be narrower models for, you know, domain-specific, you know, challenges. But at the heart of it, in terms of making systems reliable, making them safe at an enterprise level, it's going to be about innovations such as the ones we're announcing today in data sets that are used for evaluation, data sets that are used, you know, for training and improving safety and reliability of models. So we think that we're at the very beginning and that our relevance is by no means diminishing, but only increasing. It's increasing not just at the level of foundation model builders, but it's clearly extending through the enterprise. We're super excited. about where we are right now and about the uptake that the innovations that we're creating are having and are going to be having over the next several years.

Trevor Sutton | Analyst, Craig Hallam

That's great. And just one other question. Having lived through the last couple of years where you started the years with an expectation and you then ended up meaningfully exceeding those initial expectations, is Is anything set up differently going into 2026 relative to what you see in your sites relative to what you're committing to today?

Jack Applehoff | Chairman and CEO

No, not at all. We're following exactly that same methodology. You know, we're really limiting our or we're taking a conservative approach to forecasting growth based on opportunities where we have a very clear line of sight. but where we can't predict a close rate, where we can't feel pretty confident in something happening, we're just not baking that into our guidance. Our aspiration is to surprise and to beat expectations. When I look at this year, I think it will likely be another year of doing exactly that. We're We're seeing enormous opportunity with a much larger set of customers. We think that that's going to result in growth. I think it's likely that we'll be increasing guidance as we move through the year. And I think it's going to be a year where we accomplish very meaningful customer diversification. On top of that, as we already discussed, I think it's going to be a year where, you know, we're starting to see increasingly hybrid human slash technologically driven solutions. That spells or presents the promise, I believe, for increased recurring revenue. I think it promises greater margins over time, greater stickiness, a whole lot of things that will over time be, I believe, consistently improving revenue quality as well on top of everything else. In terms of the work we do with foundation model builders, you know, we're seeing tons of traction, not just in our largest customer, but in others as well. We're very much aligned with what they're looking to accomplish in things like long-context reasoning improvements. We have innovations that are contributing to that. So we're tremendously excited about where we are right now.

Trevor Sutton | Analyst, Craig Hallam

All right, good stuff. Thanks, Jack.

Thank you. Conference Operator | Operator

Your next question comes from Hamid Khorband of BWS Financial.

Please go ahead. Hamid Khorband | Analyst, BWS Financial

Hi. Just a first question. You were talking earlier about scaling your operations as revenue ramps. Do you have enough employees now? Do you see the need to add more employees? What's your timeline as far as expecting gross margin to move up from here?

Thank you. Jack Applehoff | Chairman and CEO

Sure, thanks. So I think it really depends on what we're seeing. I think if we begin to project internally growth rates that are very significant, we're going to be making investments in order to ensure that we capture those growth rates. I do think that as a result of digesting some of those you know uh people investments that we're making in cogs um as a result of the innovations that we're discussing um you know different things like that I do think that we're going to uh you know I do think that we're going to be seeing movement you know back toward our target gross margins over time Hamid Khorband | Analyst, BWS Financial: Okay. And then is there a timing as far as this pipeline of deals that you're talking about with other customers other than your largest customer?

Jack Applehoff | Chairman and CEO

So there are pipelines, but the deals that I'm referring to are largely deals that we're closing or have closed. So we're not depending on We're not speculating about what will be happening. These are things that are actively underway.

Hamid Khorband | Analyst, BWS Financial

Okay.

Thank you. Conference Operator | Operator

Your next question comes from Alan Clee of Maxim Group.

Please go ahead. Alan Clee | Analyst, Maxim Group

Yes. Hi. For 2025, I think your adjusted EBITDA margin was around 23%. And I know it's important for you to reinvest back into the business for the health of the company. My question is, is there any reason to think that you would target a higher or lower adjusted EBITDA margin than what you did in 2025?

Jack Applehoff | Chairman and CEO

We're very much focused on seizing opportunity right now. We believe that we can do that and stay profitable, but we also believe that it's more important to seize opportunity and to do some of the things that we are describing and prove out those innovations than it is to track adjusted gross margin percentages and try to maintain a certain percentage. So, you know, we're going to be actively reinvesting in the business. The more opportunities we see, to some extent, the more we'll be reinvesting. We do believe, though, that maintaining profitability is something that we can do while we drive very aggressive growth and while we become more progressively more critical to a larger and widening set of customers.

Alan Clee | Analyst, Maxim Group

Okay. One of the bullet points you had on the innovation was the structural foundation for margin expansion through automation, synthetic data generation, and evaluation platforms. Can you explain a little what you mean of which margin extension are you referring to?

Thank you. Jack Applehoff | Chairman and CEO

Yeah, so we're referring to overtime gross margin expansion. So a lot of the innovations that we're working on now and that we're bringing into the market are hybridizations of software and human teams. And I think that over time, we're going to be seeing the gross margins associated with those capabilities to be perhaps well in excess of the gross margins that we target today.

Alan Clee | Analyst, Maxim Group

Got it. That makes a lot of sense. And the last question I had was just for first quarter 26, is there anything you'd want to point out in terms of that might stand out just in terms of I don't know, revenues or expense spend?

Jack Applehoff | Chairman and CEO

Well, you know, I'm not going to say it's next quarter necessarily, but I think, you know, very soon we're going to be seeing quarters that, you know, from a revenue perspective are beating what our revenue was for an entire year three years ago. So that's pretty good news right there. As we move through the year, I think you're going to be seeing more proof points and more evidence and more engagement that we have with some very interesting companies around the innovations that we're describing. I think that we'll start to demonstrate that we're somewhat migrating from a vendor to a foundational layer within AI ecosystems, becoming someone that is able to unlock the promise of AI within enterprise engagements, a company that's able to help enterprises embrace complex agents that plan, call tools, execute complex workflows, and create a lot of value. You know, I think we'll be seeing that. I think we'll see evidence of that in first quarter. I think we'll continue to see evidence of that through the year.

Alan Clee | Analyst, Maxim Group

Maybe one last quick one. When you were talking about your largest customer, I don't know if I fully understand, but you mentioned something about $20 million that maybe is going to be replaced with more than that, or could you just explain what Jack Applehoff | Chairman and CEO: Yeah, I think the point that we were making there is how important innovation is to our company today and how it's becoming increasingly important. You know, there are things that we complete and we're starting new things, and by following the path, of innovation by, you know, what did Wayne Gretzky used to say, by skating to where the puck is going. We're able to deprecate things that the companies no longer require, but be there for them for the things that are the emerging requirements. Again, you know, we're seeing the emerging requirements to be more interesting from a business perspective. and a revenue quality perspective and a differentiation perspective than the things that came before. So the investments are proving out. They're enabling us to scale and increase the breadth of engagements. They're enabling us to win new engagements and new customers that some of which we think are going to be very substantial. They're going to really flower this year. That's going to address the diversification issue. So, you know, when we look at 2026, you know, we see a huge growth here. We believe that we're going to be increasing likely our guidance from what we're starting the year at. We think that the solutions and how we're embedded in workflows is going to be progressively more interesting and margin and revenue enhancing. It promises to be a tremendous year on all of those fronts.

Alan Clee | Analyst, Maxim Group

That's great. Congratulations.

Thank you. Thank you. Conference Operator | Operator

There are no further questions at this time. I will now turn the call back over to Jack Abuhoff.

Please continue. Jack Applehoff | Chairman and CEO

Thank you, operator. So, yeah, to wrap up, 2025 was a great year, and 2026 holds the promise of being even better. In 2025, we delivered strong top line growth. We exceeded expectations across major financial metrics. We expanded margins. We strengthened our balance sheet. We invested successfully ahead of demand. And those investments proved wildly successful and set us up well for 2026. I believe that 2026, is likely to be an incredible year. We've got it to approximately 35% growth based on visibility today, but I believe there may be very considerable upside to that. We'll update you through the course of the year, much like we have done the last couple of years. I also want to underscore our belief that this year we will potentially diversify our revenue stream significantly. And we believe expertly engineered data ecosystems are going to be every bit as important as bigger models and new architectures will be in terms of advancing language models, media models, autonomous agents, robots, world models, and other kinds of AI that hasn't even been conceived of yet. So we're very excited about what lies ahead. We're very confident in our positioning. We're very committed to building one of the most important and we think most capable AI enablement companies in the industry. It's going to be an exciting year. So thank you all for being on the journey with us. Look forward to next time.

Conference Operator | Operator

Ladies and gentlemen, that concludes today's conference call. Thank you for your participation. You may now disconnect. jsPDF 3.0.3 D:20260606090150-00'00'

NASDAQ:INOD Q3 2025 Earnings Call Transcript Generated on 6/6/2026 Michael | Conference Operator: Good afternoon, ladies and gentlemen, and welcome to the InnoData Report third quarter 2025 results conference call. At this time, all lines are in listen-only mode. Following the presentation, we will conduct a question and answer session. If at any time during this call you require immediate assistance, please press zero for the operator. This call is being recorded on November 6th, 2025. I would now like to turn the conference over to Amy Agress.

Please go ahead. Amy Agress | Investor Relations

Thank you, Michael. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abelhoff, CEO of InnoData, Rahul Singhal, President and Chief Revenue Officer, and Maryse Espinelli, Interim CFO. Also on the call today is Anish Pazekar, Senior Vice President, Finance and Corporate Development. We'll hear from Jack first, who will provide perspective about business followed by remarks from Rahul, and then Maryse will provide a review of our results for the third quarter. We'll then take questions from analysts. Before we get started, I'd like to remind everyone that during this call, we will be making forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, assumptions, and estimates, and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release in the risk factor section of our Form 10-K, Form 10-Q, and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward-looking information. In addition, during this call, we may discuss certain non-GAAP financial measures. In our earnings release filed with the SEC today, as well as in our other SEC filings, which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliation of these measures with comparable GAAP measures. Thank you. I will now turn the call over to Jeff.

Jack Abelhoff | CEO

Thank you, Amy, and good afternoon, everyone. Our third quarter was another record quarter for InnoData. We delivered record revenue of $62.6 million, representing a 20% year-over-year organic growth and a 7% sequential quarterly growth. Adjusted EBITDA was $16.2 million, or 26% of revenue, up 23% sequentially, showing margin expansion. even after factoring in growth investments I'll be talking about later in this call. Cash rose to $73.9 million, up by $27 million since year end, and $14.1 million since last quarter. Our results exceeded analysts' expectation across key metrics. As a result of strong business momentum, we reiterate prior guidance of 45% or more year-over-year growth in 2025, and we anticipate potentially transformative growth in 2026. This afternoon, I'll share the basis of our confidence, including the significant growth we are anticipating from existing strategic vectors and the strong early returns from new investment areas. I'll then share how we are preparing the organization to reach the next level. I'll start with our existing strategic vectors. Since we last reported, We have continued to make substantial progress, deepening relationships of trust with high dollar value big tech customers. Our deal momentum continues to accelerate with meaningful expansion across a diverse set of foundation model builders, both existing and new customers. Of the eight big tech customers we talked about recently on these calls, we are currently forecasting six of them to grow next year, several, quite substantially. For example, we just received verbal confirmation for additional expansions with our largest customer and verbal confirmations of a deal we expect to potentially result in $6.5 million of revenue with another big tech. Beyond that, our expectations are grounded in the assessment of these customers' 2026 training data and evaluations budgets and the accelerating trust we believe we're earning with them through proofs of concept, pilot, and scale deployments. Now, in addition to these eight customers, we landed in Q3 or expect to finalize shortly five additional big techs. We believe all five of these new big techs are poised to contribute meaningfully to our 2026 growth. Three of these new five, we believe, are positioned to allocate up to hundreds of millions of dollars annually to generative AI data and evaluation. and we believe we're well positioned to capture a share of that spend. It is worth noting that two of these are global leaders in commerce, cloud, and AI. Now let's turn to our new 2025 initiatives, six in total, several of which I'm sharing with you for the first time today, all of which are already bearing significant fruit, and all of which we believe will contribute significantly to 2026 growth. The first initiative has been creating pre-training data at scale. Now, pre-training data teaches the model language, skills, and knowledge. Up until now, our business has been primarily focused on post-training data, which teaches models how to reason, follow instructions, and perform tasks. But earlier this year, we observed researchers drawing increasingly strong correlations between LLM benchmark performance and the quality of pre-training data. Models that trained on higher quality pre-training Kepora consistently did a better job understanding nuance, context, and intent across languages and domains. And when we saw this research, we concluded that our customers would increasingly be seeking sources for higher quality pre-training data. So we invested about $1.3 million to build new capabilities to create high quality pre-training corpora. This has proven to be a great investment. We've since signed contracts we believe could result in approximately $42 million of revenue. And we expect to soon sign contracts which we believe could result in approximately $26 million of additional revenue on top of that. So that's $68 million of potential revenue from these programs that are either signed or likely to be signed soon. These programs span five customers. There are only a few months in motion and are just ramping up. We believe the majority of the anticipated revenue would flow through 2026, but we've already fully recaptured our investment. As pre-training data gains recognition as a strategic differentiator for next generation LLMs, we believe we are well positioned to capitalize on this early trend. Today we announce the launch of InnoData Federal, a dedicated government-focused business unit designed to deliver mission-critical AI solutions to U.S. defense, intelligence, and civilian agencies. We expect this business unit to be a material revenue generator for us in 2026 and beyond. Today we're also announcing that the business unit has won an initial project with a new high-profile customer. We anticipate this initial project to result in approximately $25 million of revenue, mostly in 2026. We have additional projects under discussion with this customer, and we expect them to be large. This new relationship is strategically significant not only for its potential size, but also for the visibility and market leadership we believe it will convey. We expect to issue a joint press release about the relationship prior to year end. We view it as a potential game changer for our next phase of growth. Additional early market validation includes the company's first direct government award from a major defense agency. potential engagements with other prominent defense technology companies, and submitted proposals spanning the DOD, intelligence community, and civilian agencies. What sets InnoData Federal apart is our ability to deliver the complete AI lifecycle, not just data annotation or point solutions, but true end-to-end capabilities from data collection through model deployment and operational support. Our platforms and expertise already serve the world's leading technology companies and Fortune 1000 enterprises. We are now bringing that same proven excellence to federal missions with the security, compliance, and speed the government operations demand. We believe the timing could not be better. Federal agencies are moving decisively to adopt AI. In July, the administration released America's AI Action Plan, and signed three executive orders to streamline procurement and accelerate deployment. The General Services Administration, or GSA, is now revamping its acquisition processes to make AI services easier for agencies to procure. Historically, federal procurement has been slow and complex, but that's changing rapidly, and we intend to meet that demand and that opportunity head on. As we announced today, General Retired Richard D. Clark, a retired four-star Army general and former commander of U.S. Special Operations Command, has joined the InnoData Board. We're excited about his expertise and relationships in helping guide the trajectory of InnoData Federal. Another key focus this year has been on advancing our participation in the emerging sovereign AI market. Initiatives by governments around the world aimed at independently developing, deploying, and governing AI systems as a matter of national interest. These efforts seek to ensure national control across the entire AI technology stack, from the semiconductors on which models are trained to the data that gives them intelligence. We believe this is one of the most significant structural shifts in the global technology landscape. The drive for sovereign capability has already triggered large-scale state-directed investment programs, effectively creating government-backed demand guarantees for the entire AI ecosystem, from chip makers and cloud platforms to data engineering providers like us. As we have toured several countries in the far Middle East, we've been struck by the level of interest in our services. These countries in most cases do not have a homegrown enterprise like InnoData with a proven track record of helping enable generative AI and LLM initiatives. We were rapidly engaging in advanced discussions with sovereign AI entities across several regions, and we expect to announce one or more strategic partnerships over the next few months. Their economic capabilities and desire to move quickly is truly impressive, and we could not be more excited about this newer area of growth for the company. Meanwhile, our enterprise AI practice is also gaining traction and holds promise for 2026. It provides full stack support to help enterprises integrate generative AI into products and operations. For example, the practice is helping a major social media platform automate its content monitoring and monetization workflows using generative AI and assisting a hyperscaler to integrate generative AI into their data center operations for real-time analytics. We expect these projects to typically start in the $1 to $2 million range and offer strong expansion potential and repeatability. We are also in discussions about strategic relationships that could help propel our enterprise AI practice forward in 2026. The next initiative I'll talk about is agentic AI. As I've said before on these calls, we believe agentic AI will unlock the usefulness of generative AI in the enterprise, and their autonomous agents will soon be as ubiquitous as human employees performing many of their tasks. It's still very early days for agentic AI. We're working with big tech model builders to evaluate and refine autonomous agents across many real-world use cases, creating evaluation models and human-in-the-loop systems designed to measure, interpret, and guide agent behavior. We start by judging tasks' success. Did the agent achieve the goal? And then we analyze why the agent behaved the way it did and profile how it generally behaves to inform further fine-tuning. These capabilities, diagnostic judge, task success judge, and profiling judge, are increasingly used in RLHF and RLHA frameworks for agentic systems where agents act autonomously across multi-step real-world workflows. We've also been building agents within our agility platform as a way of enhancing the product and consulting with a number of enterprise customers about incorporating agents within their environments. This brings me to our sixth area of 2025 investment, model safety. As agents gain autonomy, companies must learn how to monitor and continuously improve them. Our goal is to become a trusted partner to software companies and other enterprises, helping them benchmark for safety, reliability, and ethical behavior. Here's one example of the work we are now doing. Recently, we began engaging with a leading chip company to stress test its multimodal AI products. simulating real-world risks like data exfiltration, privilege escalation, instruction manipulation, and multimodal injection attacks. And once we identify vulnerabilities, we generate targeted mitigation data, fine-tune the model, and prove the results with repeatable benchmarks. Our objective is to increase model safety with no degradation in model capabilities from the retraining. We believe the area of model safety holds enormous potential, so much so that we've engaged one of the world's top consultancies to help us refine our product and go-to-market strategy around model safety. That's a quick recap of the six investment areas that we've driven in 2025, several of which we're announcing publicly for the first time today. In every case, our investments have been modest, but our returns have been outsized, and product market fit has come quickly. We believe that there are startups that have raised tens of millions at ambitious valuations to chase some of these same opportunities. Yet we're getting more done, faster, and with far less capital investment at risk. This year, we anticipate incurring approximately $9.5 million of capability-building investments in these and other similar initiatives. This includes $8.2 million of SG&A and direct operating costs and 1.3 million of CapEx. We were also absorbing costs for substantial excess capacity within the organization in anticipation of likely soon to be captured business. While we could have elected not to incur these costs and instead present higher adjusted EBITDA, we believe these investments represent compelling short-cycle investments that position us for accelerated growth in markets. We believe we're prepared to serve and we believe will yield considerable benefits in 2026 and beyond. We've also strengthened our leadership bench and operational foundation for the scale we're anticipating. I'm pleased to announce the appointment of Rahul Singhal as President and Chief Revenue Officer. Rahul joined InnoData in 2019 and has been instrumental in helping shape our strategy and building deep relationships with our largest customers. We're also welcoming two outstanding new board members, Don Callahan, who brings deep digital transformation expertise from Citigroup and Time, and close relationships with Silicon Valley and enterprise CEOs through Bridge Growth Partners, and General Retired Rich Clark, a retired four-star army general and former commander of US Special Operations Command, who brings outstanding defense insight and strong federal relationships Their expertise aligns with our focus on big tech, defense, and enterprise markets. And I'm confident they'll help guide us through our next stage of transformative growth. Finally, I want to thank Nick for five years of board service. Nick has been tremendously helpful to me and to the company. He is stepping away to devote his time to a new opportunity outside of our markets, and we wish him very well. With that, I'll turn the call over to Rahul.

Rahul Singhal | President and Chief Revenue Officer

Thank you, Jack. I'm honored to step into this expanded role. Many of you may have seen Time Magazine recently ranked InnoData number 24 on the inaugural list of America's top 500 growth leaders for 2026, recognizing companies that, quote, capture trends and stay ahead of time. That mindset, seeing what's next and acting fast is core to who we are now. You're seeing the result of that today. We are deepening relationships with both existing and new Silicon Valley customers while delivering quick successes across the six investment areas Jack just outlined. An increasing number of world's largest technology companies and enterprises are seeing the value we bring today. Looking past 2026, over the medium and long term, we believe the work we do with frontier model builders will expand and will become more complex. The next generation of models won't just need more data. They'll need more smarter data. Data from simulation labs, large-scale synthetic generation, and RL gems that capture human judgment, context, and values. On top of this, the AI enterprise services market, which we are now successfully aligning to, will likely grow to be 10 or more times larger than the model builder market. We believe InnoData is purpose-built for this broad enterprise transition. Our work alongside frontier model builders give unique insights into how large models are trained, tuned, scaled, and evaluated. And we are succeeding at packaging these insights into solutions that bring value to enterprises. For example, We have just recently begun providing model safety and remediation solutions that leverage the workings we have done hand in glove over the past year or so with engineering teams from leading AI hyperscalers. Today, we are bringing those capabilities to one of the world's leading fast software companies and one of world's leading generative AI chip designers. In short, I believe we are at the very beginning of the generational technology shift that InnoData is at the center of and poised to capitalize on. When I look at the competitive landscape, they're not even a handful of companies that have the capability to service 50 million, 100 million or larger order sizes in our space. And that's the need for hyperscalers today and sovereign entities. Plus, they don't have the proven ability to scale the organization. provide flawless data accuracy, and be highly nimble to addressing the changing client needs in a very dynamic environment. What an amazing time to be alive when the world is going through a seismic change driven by AI, and to be in such a privileged position to help lead a company that is a critical part of catalyzing the change. I'll now turn the call over to Maryse, and after her remarks, we'll be available to take your questions.

Maryse Espinelli | Interim CFO

Thank you, Rahul and Jack, and good afternoon everyone. Revenue for Q3 2025 reached 62.6 million, up 20% year over year. Sequentially, revenue increased 7% from Q2's 58.4 million. Profit for Q3 2025 was 27.7 million, an increase of 4.8 million, or 21% year over year. with an adjusted gross margin of 44%. Adjusted EBITDA was $16.2 million, or 26% of revenue, up 23% quarter over quarter, deflecting the strong operating leverage in our business. Net income for Q3 2025 was $8.3 million compared to $17.4 million a year ago. The decrease was mainly due to the tax benefit arising from the utilization of net operating loss carry forward in Q3 2024. We ended the quarter with $73.9 million in cash, up from $60 million at the end of the prior quarter and $46.9 million at year-end 2024, and did not draw down on our $30 million Wells Fargo credit facility. As Jack mentioned, based on our current momentum, we reiterate our prior guidance of 45% or more year-over-year growth in 2025, and we anticipate potentially transformative growth in 2026. Thank you, everyone, for joining us today. Operator or Michael, please open the line for questions.

Michael | Conference Operator

Thank you very much. It is now time for our Q&A. Our first question comes from Alan Klee with Maxim Group.

You may now begin. Alan Klee | Analyst, Maxim Group

Great job on the quarter. I was adding up, you mentioned a bunch of potential contract wins and what they could represent. And the ones that you put dollars amount on added up to close to 100 million. But what I wasn't sure about is, some of these could be contracts over multiple years. Is there a sense of what amount of that could potentially be in 2026? Hi, Alan.

Jack Abelhoff | CEO

So, great question. I think the contracts that we, you know, when we talk about, you know, annualized recurring revenue. Those are generally the contracts that we think will kind of roll at the number that we state is a year's value from that. Other contracts that we talk about, you know, we're going to try to do some ramping up of some of them in this quarter, but then that revenue would primarily be falling into next quarter, excuse me, next year.

Alan Klee | Analyst, Maxim Group

OK, thank you. And then In terms of, you mentioned that you're going to spend an extra, I think you said 8.2 million in incremental SG&A. Could you just explain what, that's over what time period? And the way to think of that is over what type of base?

Jack Abelhoff | CEO

So that would be year over year. And that would be incremental in 2025 versus 2024.

Alan Klee | Analyst, Maxim Group

Got it. And then with your largest customer, I think you've mentioned now more than once of the potential to expand the relationship, which could be very large. But any commentary on just the existing business of them? Is that, should that be considered kind of stable?

Jack Abelhoff | CEO

So the relationship is strong and the business is stable. I think as you'll see, you know, the business went up sequentially in the quarters. And as we discussed just a few minutes ago, we got a verbal on what's potentially a very large new program that would would would come into, you know, with that customer. We haven't really baked that into anything yet because we're not sure what the ramp up would be, but it's certainly very significant relative to next year.

Alan Klee | Analyst, Maxim Group

Okay, great.

Thank you so much. Michael | Conference Operator

Thank you very much. Our next question comes from George Sutton with Craig Hallam.

You may now begin. George Sutton | Analyst, Craig Hallam

Thank you quite an update and congrats both jack and role for your expanded roles relative to the verbal comment jack. For with your largest customer I assume that would just run through a an existing statement of work, so you could take that business on relatively quickly.

Jack Abelhoff | CEO

That's correct. I mean, mechanically, it would run through the existing master services agreement and probably be a new statement of work. But your point is correct that it will be very easy and seamless in order to onboard that new requirement.

George Sutton | Analyst, Craig Hallam

So I was thrilled to hear about your federal market win and it begs the question, and I think you addressed it with your GSA comment, but typically you need to be part of a FedRAMP program to take on material business like this. Can you just walk through how you're doing this under this GSA process or what's different than a normal FedRAMP process?

Jack Abelhoff | CEO

Yeah, so I think that the point that we were making is that the timing for us starting this practice is ideal. The federal government, you know, has clearly communicated the strategic emphasis that they're putting on AI and AI enablement, you know, both in the DOD, you know, the IC, and even, you know, civilian agencies. So you have that. On top of that, They're recognizing that the you know the procurement and acquisition programs and processes are cumbersome and they. Will impede the AI progress that they're intending to make. And therefore they've issued executive orders. I think there may even be some new pronouncements expected to come out tomorrow on that subject. So when you take these two things in combination, the prioritization that the government is placing on AI, again, spanning the entirety of, you know, federal on the one end, and then on the, you know, liberalizations that they're making in terms of acquisition and procurement, it really couldn't be a better time for us to be in that market.

George Sutton | Analyst, Craig Hallam

Gotcha. And then finally, Rahul, you made a very interesting comment that the services market could be 10 times the model builder market. I wondered if you could just put a little bit more meat on that. How much of that do you think you've started to see thus far?

Rahul Singhal | President and Chief Revenue Officer

Yeah, George. So if you think about the enterprise market today and the frontier models, these models are now getting integrated into workflows that are transforming either for cost reduction, predominantly today for cost reduction, and soon we're going to see transformative workflows that will drive new business models and revenue generating. As we talked about, we are seeing for one large social media company, we were able to dramatically save them over $24 million worth of cost. So it's early stages. We are starting to get into the stage where we are starting to deploy GenAI solutions into our customer base, and we hope to expand this service in the future.

George Sutton | Analyst, Craig Hallam

Super. Great job, guys.

Thank you. Alan Klee | Analyst, Maxim Group

Thank you.

Michael | Conference Operator

Thank you very much. That appears to be our last question. I will now turn the conference over to Jack Abouhaf for any additional remarks.

Jack Abelhoff | CEO

Thank you. Yeah, I guess InnoData is executing really from a position of strength. We had another record-breaking quarter. Revenue is at an all-time high. We see profitability growing, and the results exceeded our analysts' expectations. Looking out ahead to 2026, we see the potential for continued transformative growth, powered by deepening relationships among the MAG7 and other Silicon Valley leaders. And we see that growth coming from two sources. First, the continued expansion we're driving with existing new customers. And then secondly, the strong returns we're beginning to see from our recent investments. Today, I talked about six specific investment areas. And across each of them, across the board, we're showing what happens when we do exactly what Time Magazine recognizes for, seeing what's next and acting fast. So to recap quickly some of these early wins. First, $68 million in new pre-training data wins, $42 million that's signed, $26 million that we believe gets signed very soon. A $25 million win with a new strategic federal customer that we expect to name soon, and we believe this is potentially the first of many projects with them. An additional expansion with our largest customer based on verbal confirmation. 6.5 million verbal confirmation of a deal win with another big tech customer, and new partnerships emerging with key AI and sovereign AI players, which we expect to be announcing in 2026. So thank you all for joining us today. We couldn't be more excited about what lies ahead.

Thank you. Michael | Conference Operator

Ladies and gentlemen, this concludes today's conference call. Thank you for your participation. You may now disconnect. jsPDF 3.0.3 D:20260606090152-00'00'