Innodata Inc. 2026 Q2 Earnings Call

Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, Chairman and CEO of Innodata, Rahul Singhal, President and Chief Revenue Officer, and Jayant Chauhan, Chief Financial Officer. Also on the call today is Marissa Espineli, Chief Accounting Officer, and Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. We'll hear from Jack and Rahul first, who will provide perspective about the business, and then Jayant will provide a review of our results for the second quarter. We'll 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 Factors section of our Form 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. 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. Good afternoon, everyone. Q2 was another record quarter for Innodata. Revenue, adjusted gross profit, adjusted EBITDA, and cash all reached new highs, and we exceeded analyst consensus on all key metrics. Revenue was $92.1 million, up 58% year-over-year, exceeding analyst consensus by approximately $5.8 million or 7%, and making Q2 our 12th consecutive quarter of year-over-year growth. To put that in perspective, in Q2, as in Q1, our quarterly revenue exceeded our annual revenue of just three years ago. Our adjusted gross margin, meanwhile, was 49%, up two points sequentially and nine points above our 40% publicly stated target. Adjusted EBITDA was $25.4 million, up 92% year-over-year, exceeding analyst consensus by approximately $8.5 million or 50%. Fully diluted earnings per share were $0.41 per share, nearly double analyst consensus of $0.21 per share.

Again, this quarter, we delivered growth, margin expansion, and cash generation together while investing in innovation that converts to revenue within quarters, not years. That is the business model working as designed. Last quarter, we told you to expect our largest customer to represent a smaller percentage of total revenue. In Q2, our largest customer represented 37% of revenue, down from 56% of revenue in Q1, while the big tech customer we announced last quarter scaled from 17% of revenue to 34% of revenue, becoming our second-largest customer. While our largest customer contributed less revenue in Q2 than in Q1 as a result of a change in the quarter to program structure and service mix, we continue to expect it to grow year-over-year for the full year. We also landed an important new customer in the quarter, one of the fastest-scaling frontier labs.

The upshot is our base continues to broaden in both customers and customer programs. Before turning to guidance, I want to share an important announcement about Innodata's leadership. Effective September 30, Rahul Singhal will become President and Chief Executive Officer of Innodata and will join our board, and I will transition into the role of Executive Chairman. This is a planned transition made from a position of strength, and for me, it is also a personal one. Many of you know Rahul from these calls, from investor conferences, and from the work he has led over the past several years as a principal architect of Innodata's transformation into a strategic partner to the world's leading AI builders. He knows our customers, he knows our technology, and he knows our people. Rahul has been central to every element of the strategy behind the results you've seen quarter after quarter.

The board and I didn't have to look far for the right leader. Rahul earned this role, taking on expanding responsibility year after year and delivering every time. This is how we build this company. We grow our capabilities, and we promote our own people. As Executive Chairman, I will remain deeply engaged, focused on partnering with Rahul to build capabilities enabled by our research team. Bringing these capabilities to the federal government and to the enterprise, I believe, is where I can best contribute to creating significant shareholder value. As one of the company's largest shareholders, that is exactly what I want to be doing. Our work with the Mag-7 and leading AI labs is on a firm path to greater heights and greater diversification.

Our enterprise AI and federal strategies, built on the differentiated technology we developed for the frontier labs, represent opportunities for potentially driving high-quality recurring revenue that results in significant value creation. We are building Innodata to be a generational company. With that same aspiration in mind, we were pleased to have announced recently that Jayant Chauhan joined Innodata as Chief Financial Officer. Jayant's abilities round out an already strong finance team, with Marissa Espineli stepping into the role of Chief Accounting Officer. Beyond the traditional CFO mandate, Jayant will work strategically on capital allocation and capital markets, customer partnerships, M&A that can accelerate our strategy, and investor communications while scaling the financial infrastructure of the company we are becoming. Before I turn the call over to Rahul, let me address guidance. We are reiterating our guidance of 40% or more year-over-year revenue growth.

We have some large new potential engagements in our pipeline with both existing and new customers that we believe are likely wins. We have not yet factored them at all into our forecast at this point. As a matter of prudence, we will only factor them into our forecast when we know they're 100% won, and we can forecast the timing of revenue recognition. I will now turn the call over to Rahul to discuss the market, our strategy, and the execution milestones that we believe prove the strategy is winning.

Thank you, Jack. Good afternoon, everyone. Before I begin, a personal note. I'm truly honored by the confidence both Jack and the board have placed in me, and I intend to repay it with results. Innodata has extraordinary momentum, an extraordinary team, and an extraordinary opportunity in front of it. I intend to build on all three. One of the most significant developments of the past 18 months is the increasingly pivotal role that research and innovation are playing at Innodata. It is not overstating the case to say that research has become a growth engine and the means by which we increasingly differentiate, expand existing partnerships, and forge new customer relationships. Our growth is increasingly driven by research and innovation across the full model training life cycle, from pre-training and post-training to model evaluation and benchmarking. Our innovation is producing intellectual property and differentiation that is generating demand.

Several quarters ago, we talked about how we were benchmarking frontier model performance, isolating weaknesses, building remediation datasets to address those weaknesses, and proving the efficacy of those datasets by training small models that were architecturally similar to the big ones. Today, we are doing much more than that. I'd like to share a few examples of what we are doing now, because the work is fascinating in its own right and because it gives you a sense of where we intend to take Innodata over the next several years. Through our research efforts, we established an early position in agentic reinforcement learning, one of the most important frontiers in AI development. With a large lab, we run a significant new program covering personalization of long-horizon agents, which is now scaling.

We have also been involved with a second program covering reinforcement learning environments for desktop computer usage and tasks. In the enterprise, we see companies quick to develop AI agents but struggling to deploy them in production with confidence. We believe combining a trusted observability platform and our innovatively architected reinforcement learning gyms enable us to position ourselves as the AI deployment assurance layer. We see this as opening a huge opportunity, and this is what Jack alluded to a few minutes ago. In the quarter, we deepened delivery of these capabilities with one big tech customer and began delivery with another. This innovation has also opened active insurance and banking conversations that we expect to convert to pilots. Frontier model builders have also become intensely focused on dynamic, long-horizon agentic evaluation.

In the quarter, we released two public benchmarks, including one that tests how well models perform on multi-turn, long context, and multi-modal interactions. A benchmark is an assembly of expert author prompts, rubric constraints, and LLM judges configured to test frontier models. Both are designed to surface failure modes that standard leaderboards miss. Things like grounding drift and instruction forgetting. Precisely the failure modes frontier labs are working to improve. Each benchmark engagement results in a data strategy recommendation and sets us up to deliver scaled data generation to improve the model. In the quarter, we also expanded our capabilities in generating training data that extends the reasoning capabilities of the state-of-the-art models, delivering across five frontier labs and five domains. As AI moves from digital tasks to embodied intelligence, we are building the required data and measurement layer.

This quarter, we signed two research agreements with a leading university and committed to a motion capture lab that we expect to come online in the next few months, capable of collecting sub-millimeter precision data for training robots and physical AI foundation models. That data collection practice shifted from individual pilots to scoping enterprise-scale multimodal programs, including a multilingual speech program spanning seven languages and a roughly 2 million hour egocentric program that we hope to be awarded based on successful pilot results. Data, data engineering, and data science are central to improving AI and to making it safe and trustworthy. That centrality is what enables our research to deliver capabilities across many different spheres. Data engineering innovations can solve big AI challenges, including in domains where you might not expect to find us.