Innodata Inc Q4 FY2025 Earnings Call

Good afternoon, ladies and gentlemen, and welcome to the Innodata to Report Fourth Quarter and Fiscal Year 2025 Results Conference Call. This call is being recorded on Thursday, February 26, 2026. I would now like to turn the conference over to Amy Agress, General Counsel. Please go ahead.

Thank you, operator. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, Chairman and CEO of Innodata; and Marissa Espineli, Interim CFO. Also on the call today is Aneesh Pendharkar, 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 Marissa 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 Factors 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 reconciliations of these measures with comparable GAAP measures. Thank you. I will now turn the call over to Jack.

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 exceeded 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 life cycle, 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 in customer ramp 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 Mag 7, 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 where required. To illustrate, in the first quarter of this year for our largest customer, we deprecated a 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 pretraining programs, an area of recent focus and investment. From a revenue run rate perspective, the net effect 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 are asking us to diagnose model performance, design the right training datasets, and demonstrate that those datasets will materially improve outcomes. Here's how that works. We begin by identifying performance gaps using our evaluation frameworks. We then engineer targeted datasets 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 an AI model's ability to absorb and reason over very large amounts of information at once. This remains one of the industry's most important technical challenges. 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. The second area of innovation is around evaluating systems of autonomous agents and improving them through targeted dataset creation. We believe that autonomous agents may represent the most significant business innovation opportunity since the advent of electricity. But companies quickly discovered 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 LLMs as evaluators, to create business-aligned evaluation rubrics, to generate golden datasets for aggression 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 of performance issues, and obtain mitigation datasets. We're pleased to share that we anticipate soon kicking off a managed services engagement with a hyperscaler in which we will use our platform to create test data at scale, perform automated evaluations, and identify critical model vulnerabilities in order to improve the 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 data sets. Using this system, we have demonstrated improvements of up to 25 points in 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, steganographic 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 at 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 multistep and strategically coherent attack patterns, including highly sophisticated model extraction, cybersecurity, cyber-crime, and sovereign 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 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 Innodata 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 life cycle 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 dataset 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 data sets, 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. Egocentric 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 datasets that capture interactions over time and the consequences of actions, precisely the type of data we are now engineering. Finally, 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, speed, 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 workflow scale. Automation, synthetic systems, and evaluation platforms all structurally increase our operating leverage.

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's $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 innovations Jack just spoke about. We ended the quarter with $82.2 million in cash, up from $73.9 million at the end of the prior quarter and $46.9 million at the year-end 2024, and we did not draw 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.

Your first question comes from George Sutton of Craig-Hallum.

Jack, I feel like I just sat through an advanced AI data science class. So thanks for that. I wanted to 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 done an interesting job of kind of walking us through in past quarters from post-training as a start to then pretraining. 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?

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, 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. There'll be bigger models. There'll be narrower models for domain-specific 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 datasets that are used for evaluation, datasets that are used 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.

That's great. And then 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 anything set up differently going into 2026 relative to what you see in your sights relative to what you're committing to today?

No, not at all. We're following the same methodology. We're taking a conservative approach to forecasting growth based on opportunities where we have clear visibility. If we can't predict a close rate or feel confident in something happening, we won't include that in our guidance. Our goal is to surprise and exceed expectations. I believe this year will likely be another opportunity to do just that. We see significant potential with a much larger customer base, which we think will lead to growth. I anticipate we'll be adjusting our guidance upward as the year progresses. This year should also bring meaningful customer diversification. Additionally, as we've mentioned, we expect a shift towards increasingly hybrid human and technology-driven solutions, which I believe will enhance our recurring revenue. It promises better margins over time and greater customer loyalty. Overall, this will lead to consistently improved revenue quality. In terms of our work with foundation model builders, we're experiencing considerable traction, not only with our largest customer but with others as well. We align closely with their goals, including improvements in long context reasoning. We have innovations contributing to this, so we're very excited about our current position.

Your next question comes from Hamed Khorsand of BWS Financial.

So just the first question, you were talking earlier about scaling your operations as the 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?

Sure. Thanks, Hamed. 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 people investments that we're making in COGS as a result of the innovations that we're discussing, different things like that, I do think that we're going to be seeing movement back toward our target gross margins over time.

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?

There are pipelines, but the deals I'm referring to are mostly deals that we're closing or have already closed. We're not relying on speculation about future events; these are things that are actively underway.

Your next question comes from Allen Klee of Maxim Group.

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?

So 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 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 progressively more critical to a larger and widening set of customers.

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

Yes. So we're referring to, over time, 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.

Got it. That makes a lot of sense. The last question I had was about the first quarter of '26. Is there anything you would like to highlight that might stand out in terms of revenues or expense spending?

I won't say it's necessarily next quarter, but I believe we'll soon see quarters where our revenue exceeds the total revenue from three years ago. That's encouraging news. As the year progresses, you'll notice more evidence and engagement with some interesting companies around the innovations we're discussing. I think we'll begin to show that we're transitioning from being a vendor to a fundamental player within AI ecosystems, helping enterprises unlock the potential of AI through complex agents that plan, use tools, execute workflows, and generate significant value. I expect to see evidence of this in the first quarter and throughout the year.

Maybe one last quick one. When you were talking about your largest customer, I don't know if I fully understand, you mentioned something about $20 million that maybe is going to be replaced with more than that? Or could you just explain a bit?

Yes, the key point we were emphasizing is the significance of innovation for our company today and its growing importance. We complete certain projects while initiating new ones. By embracing innovation, similar to Wayne Gretzky's idea of anticipating the future, we can phase out less relevant offerings while remaining responsive to emerging needs. We're finding that new requirements are becoming more compelling from a business, revenue quality, and differentiation standpoint than previous ones. Our investments are paying off, allowing us to expand our engagements and secure new customers, some of which could be quite significant this year. This will help address our diversification goals. Looking ahead to 2026, we anticipate substantial growth and believe we will likely revise our guidance upward. The solutions we provide and our integration into workflows are becoming increasingly relevant and are expected to enhance our margins and revenue. It's shaping up to be an excellent year in all these areas.

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

Thank you, operator. So yes, 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 guided 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 in 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.

Ladies and gentlemen, that concludes today's conference call. Thank you for your participation. You may now disconnect.