Innodata ( INOD ) Q4 2025 Earnings Call Transcript

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<h2>Date</h2>
<p>Feb. 26, 2026 at 5 p.m. ET</p>
<h2>Call participants</h2>
<ul>
<li>Chairman and Chief Executive Officer — Jack Abuhoff</li>
<li>Interim Chief Financial Officer — Marissa Espineli</li>
<li>General Counsel — Amy Agress</li>
<li>Senior Vice President, Finance and Corporate Development — Aneesh Pendharkar</li>
</ul>
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<h2>Takeaways</h2>
<ul>
<li>Revenue -- $72.4 million for the quarter, a 22% increase year over year.</li>
<li>Full-year revenue -- $251.7 million, representing a 48% year-over-year growth rate.</li>
<li>Adjusted gross margin -- 42% for the quarter, above the externally communicated 40% target.</li>
<li>Adjusted EBITDA -- $15.7 million, or 22% of revenue; exceeded analyst consensus by $1.2 million.</li>
<li>Cash -- $82.2 million at quarter end, up by approximately $8.4 million sequentially and $35.3 million year over year.</li>
<li>Debt utilization -- No drawdown on the $30 million Wells Fargo credit facility.</li>
<li>Innovation and investments -- Growth-driven investments in COGS and SG&amp;A, specifically in capacity, engineers, data scientists, and customer-facing leadership.</li>
<li>Customer mix -- Management expects spend from the largest customer to increase, with aggregate growth for the remaining customer base anticipated to occur at a faster rate and to include the MAG-seven, domestic AI innovation labs, sovereign AI initiatives, and leading enterprises.</li>
<li>Customer diversification -- Revenue growth is expected to become less concentrated, driven by an expanding and increasingly diverse set of large customers.</li>
<li>Revenue guidance -- Forecast of at least 35% year-over-year growth for 2026 based on visible, active programs and recently awarded wins; management notes potential significant upside due to the pace of LLM and AI-driven initiatives.</li>
<li>Workflow transition -- In the first quarter, approximately $20 million in post-training workflow revenue run-rate for the largest customer was deprecated and replaced with new post-training and scaled pre-training programs, resulting in a positive net revenue run-rate impact.</li>
<li>Adjusted gross margin guidance -- Management expects early 2026 adjusted gross margins in the 35%-40% range with normalization toward the 40% target as new programs ramp and workflow innovations scale.</li>
<li>Technological advancements -- Introduced and expanded proprietary systems for agent evaluation, agent optimization pipelines, adversarial simulation, and large-scale data engineering for physical AI, including applications to egocentric and affordance datasets.</li>
<li>Benchmark performance -- Developed an AI model for drone and small-object detection achieving a 6.45% improvement over prior state-of-the-art benchmarks, emphasizing commercial and dual-use applications.</li>
<li>Interest from hyperscalers and cybersecurity -- Managed services and adversarial training initiatives have attracted new engagements and interest among hyperscalers, cybersecurity companies, and relevant government experts.</li>
</ul>
<h2>Summary</h2>
<p>Management revealed new innovation initiatives across generative AI, agentic AI, and physical AI, highlighting data-driven methods as core to product evolution. Proprietary platforms for agent evaluation and adversarial simulation are facilitating new customer traction, especially among hyperscalers and security-focused clients. With continuous reinvestment in people and technology, leadership at Innodata (<a href="/quote/nasdaq/inod/">INOD</a> 7.88%) projects both margin improvement and recurring revenue expansion linked to hybrid software-human offerings, while underlining confidence in early-stage engagement conversion and broadening enterprise relevance.</p>
<ul>
<li>Company management stated, "we believe we are entering a golden age of innovation at Innodata Inc. as a result of investments we have made and intend to make in the future."</li>
<li>Leadership emphasized that future gross margin expansion is expected, driven by automation, synthetic systems, and evaluation platforms that structurally increase our operating leverage.</li>
<li>Management clarified growth guidance is intentionally conservative, with upside possible as LLM initiatives spin up quickly.</li>
<li>In discussion of customer diversification, management shared that new wins and accelerated demand are enabling Innodata to migrate from a vendor to a foundational layer within AI ecosystems.</li>
</ul>
<h2>Industry glossary</h2>
<ul>
<li>LLM: Large Language Model; an AI model trained on large datasets to understand and generate natural language text.</li>
<li>MAG-seven: Management’s reference to the seven largest U.S. technology companies, typically Microsoft, Apple, Google (Alphabet), Amazon, Meta, Nvidia, and Tesla.</li>
<li>Egocentric data: Data captured from the first-person perspective of a robot or sensor-equipped device, reflecting direct environmental experience.</li>
<li>Affordance data: Structured data teaching AI systems about possible actions or interactions with physical objects in context.</li>
<li>Adversarial simulation: Systematically generated, complex data used to test AI robustness against sophisticated attacks or real-world threats.</li>
</ul>
<h2>Full Conference Call Transcript</h2>
<p>Operator: Good afternoon, ladies and gentlemen, and welcome to the Innodata Inc. 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 0 for the operator. This call is being recorded on Thursday, 02/26/2026. I will now turn the conference over to Amy Agress, General Counsel. Please go ahead.</p>
<p>Amy Agress: Thank you, operator. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, Chairman and CEO of Innodata Inc., 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 will 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 will then take questions from analysts.</p>
<p>Before we get started, I would 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 Forms 10-K, Forms 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.</p>
<p>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.</p>
<p>Jack Abuhoff: Thank you, Amy, and good afternoon, everyone. Q4 was another strong quarter for Innodata Inc. We generated $72,400,000 in revenue, reflecting 22% year-over-year growth. This brought our full-year revenue to $251,700,000, 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 $15,700,000, or 22% of revenue, also exceeding analyst consensus by $1,200,000. 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 $82,200,000 in cash, up sequentially by approximately $8,400,000. We achieved these results while making meaningful growth-oriented investments in both COGS and SG&amp;A.</p>
<p>In COGS, we carried capacity ahead of revenue ramp, which consistently proved to be the right move. And in SG&amp;A, we invested in engineers, data scientists, and customer-facing account leadership, which investments also proved prudent. Building 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 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.</p>
<p>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 R&amp;D schedules, budget approvals, or shifts in research priorities could influence the pace at which revenue materializes.</p>
<p>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-seven, 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.</p>
<p>To illustrate, in the first quarter of this year for our largest customer, we deprecated a meaningful number of post-training workflows that represented in the aggregate approximately $20,000,000 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 plan for 2026 and beyond. The truly exciting news is we believe we are entering a golden age of innovation at Innodata Inc. as a result of investments we have made and intend to make in the future.</p>
<p>I am now going to share some of our recent innovation initiatives. For competitive reasons, we will be appropriately circumspect, but what we share will give you a meaningful window into how we are thinking, where we are investing, successes we are having, and how we intend to capitalize on the opportunity ahead. I will 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.</p>
<p>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 will 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 is how that works. We begin by identifying performance gaps using our evaluation frameworks. We then engineer targeted datasets and validate their impact by fine-tuning either the customer's model or a structurally similar proxy model.</p>
<p>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—an AI model's ability to observe and reason over very large amounts of information at once. This remains one of the industry's most important technical challenges.</p>
<p>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 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 discover that many AI agents that performed impressively in controlled laboratory settings degrade in real-world production. The real world is chaotic.</p>
<p>It is 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