Innodata Inc. Aktie

Well, good day, everyone, and welcome to the Innodata First Quarter 2026 Results Conference Call. Just a reminder that this call is being recorded. At this time, I will hand things over to Ms. Amy Agress. 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; Rahul Singhal, President and Chief Revenue Officer; and Marissa Espineli, Interim CFO. Also on the call today is 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 Marissa will provide a review of our results for the first 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 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. Q1 was a record quarter for Innodata, and it was record-setting by a wide margin. Revenue, adjusted gross profit, adjusted EBITDA and cash all reached new highs. Revenue was $90.1 million, up 54% year-over-year, exceeding analyst consensus by approximately $13.6 million or 18%. Adjusted gross margin was 47%, a 6-point sequential improvement and 7 points above our 40% public target. Adjusted EBITDA was $25 million or 28% of revenue, exceeding consensus by 139%. We ended the quarter with $117.4 million in cash, up $35.1 million sequentially with no debt drawn against our recently expanded $50 million Wells Fargo credit facility. These are not incremental improvements. They are step change results.

Today, we have printed a quarter that has beaten our annual revenue of just 3 years ago. Just as importantly, our results demonstrate that the strategic position we have been building is now translating into scale, margin expansion and cash generation. With 1 quarter behind us and progressively increasing visibility, we are raising our full-year 2026 revenue growth guidance to approximately 40% or more. That is up from the 35% or more we guided to on our last call just 10 weeks ago.

We continue to view this guidance as prudent. There are several potentially large programs we have not included in our forecast. As timing and scope get finalized, we'll adjust our forecast accordingly. The fact is that the year is developing faster and across more customers and programs than our original plan contemplated.

Today, we are also announcing a new set of engagements with one of the world's leading big tech companies. We believe these engagements could potentially generate approximately $51 million of revenue this year. 12 months ago, in the first quarter of 2025, our revenue from this customer was 0, but this year, we expect it to become our second largest customer. Moreover, we believe this relationship will continue to expand over time. We see considerable headroom both within the current program and from additional programs that we're actively discussing with this customer.

For several quarters, we have told you that 2026 growth would come from a broader and more diversified customer base. Our Q1 results, together with our outlook for the year, demonstrate that the diversification we plan for is now happening in practice. This year, we expect our largest customer to represent a decreasing percentage of total revenue even as our absolute dollar revenue with that customer expands. With our largest customer, we continue to grow as we diversify into more organizations and more AI workflows and partner with them on their flagship next-generation AI program. At the same time, growth outside that account is accelerating even faster.

In Q1, revenue from our other big tech customers in the aggregate grew 453% year-over-year. We believe this represents one of the strongest forms of customer diversification the company can deliver. The largest account continues to grow in absolute dollars, while the rest of the customer base grows even faster.

I will now turn the call over to Rahul to discuss where we see the market going, how our strategy comports with our market thesis and how our execution milestones offer proof that our strategy is enabling us to win.

Thank you, Jack, and good afternoon, everyone. It's great to be with you today, especially in a quarter where we have so much progress to share. I'll start with the market in which we believe we today have our strongest strategic position, the AI Innovation Labs and Frontier model builders. We define this as roughly 20 organizations globally that are developing the most advanced foundation models, including the major U.S. labs and sovereign-backed assets.

We are seeing real accelerating momentum across this customer set. We believe this is because we are aligned with where Frontier AI is going. Our conviction is straightforward. AI is moving from text to multimodal from one-shot answer to multistep reasoning from passive assistance to autonomous agents and ultimately, from purely digital tasks to embodied intelligence and robotics, autonomous systems and physical AI applications. Each step along that trajectory makes data engineering more specialized, evaluation more demanding and expert judgment more important.

That is exactly the work Innodata has been preparing for. We have deliberately moved up the stack towards high-quality pretraining data, expert graded reasoning data, agent trajectories, evaluation infrastructure and trust and safety services. The clearest evidence that this strategy is working is now showing up in our revenue.

I'll start with the major Q1 set of new engagements Jack just described. This customer is using us across the life cycle of frontier model development. We are producing high-quality text-based pretraining data at scale, including STEM data sets across physics, mathematics, chemistry, engineering, biology. These are the kinds of expert grade data used to teach models to reason at graduate and PhD levels.

On post training, we are working on data sets for advanced reasoning, creative writing and agent improvement. This customer chose us because our delivery infrastructure combines deep subject matter expertise, a global expert network, leading data scientists and engineers and secure physical infrastructure that allows us to operationalize large complex data requirements. That combination is hard to assemble, harder to scale and increasingly central to what Frontier Labs need. We are seeing the same thing playing out across the broader Frontier Lab customer base.

We are pleased to announce that a large hyperscaler just selected us to become its global trust and safety partner for evaluating models before they're released into production. We were selected because of a differentiated view of how frontier models should be tested holistically for safety, reliability and real-world readiness. We anticipate that our initial statement of work will lead approximately $3 million of potential annual run rate revenue with likely further expansion.

At another company, one of the world's largest cloud and commerce companies, we have moved from execution partner to strategic partner. We believe we have line of sight on approximately $7 million of total contract value across the customers' trust and safety and responsible AI programs, most of which we believe will start later this year and on more than $8 million of total contract value across AI and safety, scale data generation, global responsible AI testing and physical AI.

Physical AI is an important element of our broader thesis. As AI moves into the real world, the data, testing and safety requirements become more complex and more mission-critical. We will talk more about this later in today's call. We're also seeing strong traction in potential 7-figure opportunity with several of Asia's leading tech companies and a major European frontier AI lab. Our customer base is broadening and the pattern is consistent. Relationships start with a focused initial use case, we execute well and work expands and becomes more specialized. We read every day about the significant AI capital investment our customers are making towards physical infrastructure, data centers, networking and compute.

Infrastructure alone does not create usable AI systems. AI labs also require model training, evaluation, safety and continued improvement throughout the AI life cycle. This is the work we do. It is iterative, deeply embedded and structurally compounding. With each new cycle, we learn more about the customer stack, evaluation [indiscernible], security posture and model improvement priorities. This institutional knowledge, we believe becomes an asset that compounds and makes us more valuable over time.

Reuters recently reported that Morgan Stanley now expects AI-related CapEx by the 5 major U.S. hyperscalers to top $800 billion this year and to reach $1.1 trillion next year. Goldstein meanwhile estimates cumulative AI infrastructure spend could reach $7.6 trillion by 2031. While those estimates are not our revenue forecast, they underscore the scale of the ecosystem being built around AI and speak to the scale of the specialized data, evaluation and safety infrastructure that will be required to make that capital productive.

The Frontier Labs ambitions increasingly extend into robotics, intelligent devices, complex reasoning and real-world scenarios, all of which create more complex data and evaluation requirements. In fact, that same trajectory thesis also explains why we are investing in both federal and enterprise markets. As the application of AI moves from chatbots to digital agents to embodied intelligence, we expect federal and government-aligned customers to become meaningful long-term growth vectors.

On the strength of our conviction, we launched our federal practice last September, and it continues to gain market traction. Our engagement with Palantir is generating strong customer feedback in computer vision, and we have initiated work with a major federal systems integrator. We were also just selected as a finalist for potentially significant award. We believe making it this far in the selection validates the suitability of mission-critical regulated AI work.

In Q1, Innodata Federal in concert with the robotics and computer vision practice gained traction with several U.S. government research agencies and specialized AI vendors. As we previously reported, we were awarded a prime contract position under the Missile Defense Agency's Shield program, part of the broader golden dome strategy, positioning us to compete for future task orders as programs scale. We believe these are early proof points showing that the embodied AI portion of our thesis is already beginning to monetize in the federal market. We are encouraged by the White House AI Action Plan released in July 2025 that identified more than 90 federal policy actions to accelerate AI adoption, infrastructure, evaluation and government use.

The same thesis applies to enterprise AI. In enterprises, we anticipate an exploding need for data engineering. This quarter, we had active programs across major hyperscalers, networking and consumer Internet customers, covering use cases across customer service, data center operations, financial operations, legal workflows and intelligent content delivery.

Much of the work we are doing involves building and deploying agents, and we see firsthand the huge business impact these autonomously acting agentic systems will likely have for our customers. At the same time, we observed the gap that exists between the business value they want to extract with agents and the means by which they gain confidence that the agents are working as intended. To address this gap, we have built an evaluation and observability platform, which we released this quarter in beta.

Our platform is a control plane for agent systems. It helps enterprises evaluate agent behavior, inspect traces, monitor live performance, that regressions early and maintain audit trails and production. Over time, it allows experts to supervise larger and more complex workloads with fewer resources and to optimize agent token consumption.

I'm thrilled to report that just last Friday, we signed our first major platform opportunity, a $1 million engagement with one of our hyperscaler customers. We also now have 15 other companies actively evaluating the platform. Equally exciting, we are in discussions with 2 leading hyperscalers about becoming channel partners to distribute our platform to their customers. This could be a game changer, potentially enabling us to scale the platform in a manner that would not be possible with a direct sales force alone.

External market data supports the enterprise thesis. Citigroup recently raised its global AI market forecast to more than $4.2 trillion by 2030 with roughly $1.9 trillion tied to enterprise AI. Before I turn the call back over to Jack, I want to emphasize something. Each of these 3 vectors, Innovation Labs, federal and government-aligned customers and enterprise AI is a multi-customer business with its own structural tailwinds. Together, they form a diversified growth thesis and gives us confidence to anticipate both additional upside as 2026 unfolds and continued growth in 2027 and beyond.

Okay, Jack, I'll turn the call back to you now.

Thanks, Rahul. I'm going to take the next few minutes to connect the progress Rahul just described to how we believe our business model can flex over time at both the gross margin line and the adjusted EBITDA line.

On gross margin, we see the opportunity for expansion as we develop capabilities that decouple revenue growth from linear headcount growth. One example is off-the-shelf data sets, where we retain the IP rights, enabling us to resell the same data set to multiple customers. We are increasingly using this model for data sets that have proven particularly effective at solving specific model training goals. The economics can be attractive, advancing our long-term objective of adding more software leveraged offerings to the mix. Our Q1 margins benefited from this offering, and we expect our Q2 margins to benefit as well.

A second example is platforms. Rahul discussed the important milestone we achieved in Q1 with the launch of our agent observability platform. Beyond that, we have built platforms that generate data pipelines for agent optimization and adversarial simulation. These are proprietary technologies for generating synthetic data in a highly novel way, enabling scaled human judgment to be applied more efficiently, more consistently and across larger workloads, translating to more revenue for us with fewer people.

Turning to adjusted EBITDA. Our results show that operating leverage is inherent in our business. In Q1, revenue grew 54% year-over-year, while adjusted EBITDA grew approximately 96%. Put differently, adjusted EBITDA grew roughly 1.8x faster than revenue. That is operating leverage by definition. Now the reason is structural. Each incremental program builds on the same core operating infrastructure, so the marginal cost of adding the next program is meaningfully lower than the cost of building that capability from scratch.

As revenue growth accelerates, we expect this operating leverage to remain an important feature of the model. The reinvestment we are making in the business supports both of these leverage points. On go-to-market, we are adding talent to improve account penetration and market reach and putting in place compelling channel partnerships. On product and research, we have meaningfully expanded our internal research team over the last several quarters, attracting senior scientists and engineers from leading AI labs and top universities. This investment helps us continue to differentiate as we move up the value chain toward evaluation, agent reliability, alignment, risk sensitive control and synthetic data.

I want to highlight one specific milestone that captures the kind of research organization we are building. One of our researchers, Esther Derman, recently had 2 papers accepted at the 2026 International Conference on Machine Learning, or ICML. ICML is one of the most prestigious AI research venues in the world. One of Ester's papers received the so-called Spotlight designation, which places it at the very pinnacle of AI research.

Put that in context, ICML reported that 23,918 submissions entered review for 2026, which interestingly was twice the number from the year before. Of this close to 24,000 papers, just 6,352 or 26.6% were accepted, and of that, a mere 536 or 2.2% were selected as spotlight papers. Esther's accepted papers focused on model-based off-line reinforcement learning and risk-sensitive reinforcement learning. The spotlight paper is on risk sensitive reinforcement learning.

Both areas map directly to problems our customers are working to solve, how to train AI systems efficiently and how to make AI systems behave reliably in environments where the cost of failure is high. We are excited about Esther's accomplishment, and we expect more achievements like this from the team in the quarters ahead. The depth of research talent we are building is becoming a meaningful competitive advantage.

In our last call, I said we are entering a golden age of innovation at Innodata. Today, I'll reiterate that even more strongly. We are building proprietary technologies that allow us to construct unique data sets, measurably improve model performance and bring Agentic systems to production readiness. Raul and I are focused on some highly creative ways to translate this innovation into the strongest possible economic outcome for Innodata and its shareholders. We expect to provide additional updates on this as the year progresses.

I will now turn the call over to Marissa, who will walk through the numbers.

Thank you, Jack, and good afternoon, everyone. Revenue for Q1 2026 was $90.1 million, up 54% year-over-year and 24% sequentially from $72.4 million in Q4 2025. This exceeded analyst consensus of $76.5 million by approximately $13.6 million or 18%.

Adjusted gross profit was $42.6 million, representing adjusted gross margin of 47%, that was 6 percentage points higher than Q4 and 7 percentage points above our externally communicated 40% target. Adjusted EBITDA was $25 million or 28% of revenue. This exceeded analyst consensus of $10.4 million by approximately 139% and represented a 6-point margin expansion from Q4.

Net income for the quarter was $14.9 million. Fully diluted earnings per share was $0.42 compared with consensus of $0.08. Our effective tax rate for the quarter was approximately 14%, below our long-term target range of 23% to 25%, primarily reflecting tax benefits recognized during the quarter.

We ended the quarter with $117.4 million in cash, up $35.1 million from $82.2 million at year-end 2025. The increase reflects continued strong profitability, disciplined working capital management and customer prepayments related to our pretraining programs. We remain fully undrawn against our Wells Fargo credit facility, which we successfully renewed and expanded during the quarter from $30 million to $50 million on the 3-year term. We believe the expanded facility reflects our increased scale, profitability and balance sheet strength.

As Jack noted, we are raising our 2026 revenue growth guidance to approximately 40% or more. We continue to view that guidance as prudent. As Jack mentioned, there are several potential large programs we have not included in our forecast. As timing and scope gets finalized, we'll adjust our forecast accordingly.

One reporting note, effective this quarter, we are reporting our financial results as a single operating segment. We previously reported 3 operating segments: BDS, Agility and Synodex. The shift to single segment reporting reflects the transformation of our business, strategy and operating model, driven by our focus on Agentic AI technologies and by the increasingly integrated way we manage and deliver our services.

Thank you, everyone, for joining us today. Operator, please open the line for questions.

[Operator Instructions]. We'll take the first question from George Sutton, Craig-Hallum.

Great results, guys. I did miss the first few minutes, Jack, so I apologize if this is redundant, but I wondered if you can go in a little more detail on the $51 million contract that you announced today. Just give us a sense of the timing of that, the potential broadening of that over time or into next year, for example?

Sure. Thank you, George. Very excited about that win. It's a very significant win for us from a dollar value perspective, but in addition to that, what's even more exciting is that we now believe we've got another growth partner of significance. It's pretty clear to us that we expect this customer to be our second largest customer this year, which is very meaningful. There are active conversations going on with the customer about things that are not in that $51 million, other things that we can be doing with them.

The work that we're doing goes across pretraining, mid-training, post-training activities as well as evaluation. They're seeing us as a full-service shop, and they're very much leaning into several of our later or latest innovations, which is also tremendously exciting. They're a very large company. They're one of the big techs, and we're excited about the partnership.

I wondered if we could just think through even 12 months ago, 18 months ago, when the vast majority of your work seemed to be on the post-train side. Now we're talking a much broader set of use cases. You're talking about trust and safety and robotics and federal and the new platform evaluation and observability. Can you just give us a sense of how different the scope of what you're working on is today versus then? I assume that could only increase from here.

Yes. I mean -- so -- and we mentioned the term a couple of times in the prepared remarks, we talked about our strategic trajectory. I think that's like really super critical. Our hypothesis all along has been that these tools are going from one-shot answers to multistep reasoning engines that's giving way to autonomous agents, which are giving way to embodies intelligence.

What's critical is along that categorical vector path, if you will. The thing that will propel that along and what will become -- where companies will have even -- we predict even more voracious appetites for data is making that journey across that trajectory. At the same time, on the other axis, you think of it like a quality vector. It will be the data mixes and the quality of data that determines within any one of those categories, how well the AI is performing.

Strategically, we're working on 2 things. We're working on what are the data sets that are going to be required, what are the data capabilities that will be required in order to move along that vector of capabilities and then what does the data look like? How do we create more interesting data mixes and higher quality data that helps our partners achieve the quality that they're seeking within any one of those categories. Whether it's pretraining, mid-training, post-training evaluations, safety, to us, it's what is required in that category and what's required at the point in time as determined by research in order to achieve the best results.

One last question. Obviously, a quarter ago, we built in a fair amount of investment that you were making in sales and marketing and R&D, and you meaningfully exceeded any expectations we had on the EBITDA line. This was not the quarter we were expecting a good EBITDA progress. Can you just talk about what those investments yielded you, what they might yield you going forward?

Yes. We talked a little bit about today the potential of channel partnerships with our observability platform. We talked about other platforms that we have that help make agents perform better and make them safer. We talked about off-the-shelf data sets. Those are all things that we've been working on within our R&D labs and that we're continuing to work on.

Then there are some other things that we're starting to work on, some things that I think we'll be announcing maybe as early as next quarter, actually. We see a tremendous ROI that we're getting from our R&D organization. We're thrilled with the people that we've got. We're thrilled with the output that we're getting. What we're seeing is that's enabling us to move along the trajectory that I described to be a little bit ahead of where our customers need us to be and to increasingly be a thought partner to our customers to bring them new ideas, to encourage them to come to us with their problems, not just their orders. That's huge for our business.

Next up is Allen Klee from Maxim Group.

Congratulations. In terms of following up a little bit on one of the last questions of the investments that you're making to grow, you did talk about how you're going to get some better margins from certain things you're deploying. Is there a way to think about like as we go through the year and specifically next quarter, should we think that there -- for some reason next quarter, there would be a more than normal jump in investment expenses? Or is there any reason why there might be a timing that revenue might not be what would normally track?

Yes, I don't think that you should anticipate a step change in investment at this point. We're comfortable and we're getting a great return on what we're doing today. It will increment that up. We certainly don't see it flatlining. It will continue to increase. I think the enormous operating leverage in the model will enable us to do that without having to take a big hit on profitability. I think that we're able to really pull off the hat trick here, both revenue growth, margin growth and innovation growth as we move along the trajectory of helping models get smarter and helping them achieve extraordinary levels of intelligence.

I might have missed something that was said when there was a discussion on the segment. Are you still breaking out the 3 segments? If you are, could you provide what the revenues were for each one? Or is this all getting combined now?

It's all getting combined now. We're reporting on a consolidated basis. We ran the tests for segment reporting and made the determination that it's more -- that it's appropriate for us now to be reporting on a consolidated basis. Within the Synodex and Agility platforms, we're doing some really interesting things helping to think through -- and everybody has probably been reading about where software going, is software becoming service.

We're doing some things to enable that to take place. We see enormous opportunities for Agentic technologies within those businesses and potentially the ability to transform them. We're managing them differently. We're not thinking about small incremental improvements in revenue. We're thinking about fundamental step changes in the purpose of those businesses and what they can achieve for customers.

Then when you were talking about the Frontier lab, could you maybe just give an example of what is being provided?

Sorry, Frontier labs generally or any specific frontier lab? I'm not sure I'm following the question.

I'm just trying to understand a little more of like what specific area of what you provide that this is adding to.

Sure. If you take some of the wins that we were describing on our call today for the large $51 million contract, we're providing what's called pretraining, mid-training and post-training data. Soon we anticipate providing evals as well. 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, on another one of the wins that we talked about or 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. 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.

Up next is Hamed Khorsand from BWS Financial.

Just first question is, was there anything of onetime 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?

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. I think that it was a strong quarter. I think next quarter is going to be a strong quarter. I think the quarters after that are going to be good. We're providing -- we're not providing quarter-by-quarter revenue guidance because the fact is that things do start and stop. 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. We're doing some things now increasingly that are of an ongoing nature.

No, I don't think you should think of the quarter as aberrational at all. 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. Yes, we're excited about the year.

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? I mean you're talking about something that might happen in the future as far as the Agentic and the valuations and so forth.

No, these are things we're doing today. I mean, the thing that doesn't change is 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. 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.

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

Thanks, operator. Yes, to wrap up, Q1 '26 was a record quarter for Innodata across all the key metrics that we're reporting, 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.

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 them 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 2 ICML 2026 paper acceptances and her 1 Spotlight designation, really exciting stuff. We launched our valuation and observability platform in beta in the quarter, and no sooner did we launch, and we closed a $1 million opportunity with one of the world's largest hyperscalers around that platform. 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. Yes, I thank everybody for being on the journey with us.

Once again, everyone, that does conclude today's conference. We would like to thank you all for your participation today. You may now disconnect.

Good afternoon, ladies and gentlemen, and welcome to the Innodata to Report Fourth Quarter and Fiscal Year 2025 Results Conference Call. [Operator Instructions] 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 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 data sets that improve long context reasoning and 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 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.

The second area 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 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 data sets 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 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 data sets. 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 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 data sets 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 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 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 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 data sets 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 data sets for next-generation robotic data sets, including egocentric data. Egocentric data captures the world from the robots 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 data sets 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 data sets. 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. I'll now turn the call over to Marissa, who will go through the numbers.

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 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.

[Operator Instructions] 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 data sets that are used for valuation, data sets 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 exactly that same methodology. 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 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 we're starting to see increasingly hybrid human/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, 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 and 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.

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 time line 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?

So there are pipelines, but we're -- 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.

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 more 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. 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.

Well, I'm not going to say it's next quarter necessarily, but I think very soon we're going to be seeing quarters that from a revenue perspective are beating what our revenue was for an entire year 3 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 like 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. So I think this -- I think we'll be seeing that. I think we'll see evidence of that in the first quarter. I think we'll continue to see evidence of that through 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. 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. There are things that we complete and we're starting new things. And by following the path of innovation by 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 they're -- that are the emerging requirements. Again, 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 we're -- when we look at 2026, we see a huge growth year. 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. And it promises to be a tremendous year on all of those fronts.

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.

Good afternoon, ladies and gentlemen, and welcome to the Innodata Reports Third Quarter 2025 Results Conference Call. [Operator Instructions] This call is being recorded on November 6, 2025. I would now like to turn the conference over to Amy Agress. Please go ahead.

Thank you, Michael. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata; Rahul Singhal, President and Chief Revenue Officer; and Marissa Espineli, Interim CFO. Also on the call today is Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. We'll hear from Jack first, who will provide perspective about the business, followed by remarks from Rahul, and then Marissa 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 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 reconciliation 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. 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 analyst 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 8 big tech customers we talked about recently on these calls, we are currently forecasting 6 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 the 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 evaluation 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 8 customers, we landed in Q3 or expect to finalize shortly 5 additional big techs. We believe all 5 of these new big techs are poised to contribute meaningfully to our 2026 growth. Three of these new 5, 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 2 of these are global leaders in commerce, cloud and AI. Now let's turn to our new 2025 initiatives, 6 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 pretraining data at scale. Now pretraining 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 pretraining data. Models that trained on higher-quality pretraining corpora 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 pretraining data. So we invested about $1.3 million to build new capabilities to create high-quality pretraining 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 5 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 pretraining 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 announced 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 the 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 life cycle, 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 that 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 3 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. Clarke, 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 and 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 million 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 that 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 task 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 multistep 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 6 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 start-ups that have raised tens of millions of 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 are 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 2 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 Clarke, who retired four-star Army General and former Commander of U.S. 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 5 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.

Thank you, Jack. I'm honored to step into this expanded role. Many of you may have seen Time Magazine recently ranked Innodata #24 on the inaugural list of America's Top 500 Growth Leaders for 2026, recognizing companies that "Capture trends and stay ahead of time." That mindset, seeing what's next and acting fast is core to who we are now.

You are seeing the results of that today. We are deepening relationships with both existing and new Silicon Valley customers while delivering quick successes across the 6 investment areas Jack just outlined and 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 begun -- recently begun providing model safety and remediation solutions that leverage the working 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 SaaS software companies and one of the 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, there are 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 Marissa. And after her remarks, we'll be available to take your questions.

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. Adjusted gross 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, reflecting 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 carryforward 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.

Now for Q&A. Our first question comes from Allen Klee with Maxim Group.

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

Allen, it's a great question. I think the contracts that we -- when we talk about 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, 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.

Okay. 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?

So that would be year-over-year, and that would be incremental in 2025 versus 2024.

Got it. And then with your largest customer, I think you've mentioned now more than once of 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?

So the relationship is strong and the business is stable. I think as you'll see, 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 come into -- with that customer. We haven't really baked that into anything yet because we're not sure of what the ramp-up would be, but it's certainly very significant relative to next year.

Our next question comes from George Sutton with Craig-Hallum.

Quite an update, and congrats both Jack and Rahul for your expanded roles. Relative to the verbal comment, Jack, with your largest customer, I assume that would just run through an existing statement of work, so you could take that business on relatively quickly?

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.

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?

Yes. So I think the point that we were making is that the timing for us starting this practice is ideal. The federal government has clearly communicated the strategic emphasis that they're putting on AI and AI enablement, both in the DoD, the IC and even civilian agencies.

So you have that -- on top of that, they're recognizing that 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 2 things in combination, the prioritization that the government is placing on AI, again, spanning the entirety of federal on the one hand and then on the 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.

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

Yes, 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 Gen AI solutions into our customer base, and we hope to expand this service in the future.

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

Thank you. Yes, 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 analyst expectations. Looking out ahead to 2026, we see the potential for continued transformative growth powered by deepening relationships among the Mag 7 and other Silicon Valley leaders.

And we see that growth coming from 2 sources. First, the continued expansion we're driving with existing and new customers. And then secondly, the strong returns we're beginning to see from our recent investments. Today, I talked about 6 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 pretraining data wins, $42 million that signed, $26 million that we believe gets signed very soon. The $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, a $6.5 million verbal confirmation of the 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.

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

Good day, ladies and gentlemen, and welcome to the Innodata to Report Second Quarter 2025 Results Conference Call. [Operator Instructions] This call is being recorded on Thursday, July 31, 2025.

I would now like to turn the conference over to Amy Agress. Please go ahead.

Thank you, Sergio. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata; and Mariss Espineli, Interim CFO. Also on the call today is Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. We'll hear from Jack first, who will provide perspective about the business, and then Mariss will follow with a review of our results for the second 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 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'll now turn the call over to Jack.

Thank you, Amy, and good afternoon, everyone. Thank you for joining us. We're very pleased to report that Q2 2025 was another outstanding quarter for Innodata. We beat analysts' expectations across the board on key metrics; revenue, adjusted EBITDA, net income and fully diluted EPS. Revenue grew 79% year-over-year to $58.4 million and adjusted EBITDA grew 375% to $13.2 million, reflecting the operating leverage that's inherent in our model. We also continue to strengthen our balance sheet.

Cash increased from $56.6 million at the end of Q1 to $59.8 million at the end of Q2. And a few days after quarter close, we collected an additional $8 million that typically would have been received by June 30. Our $30 million credit facility remains undrawn, giving us flexibility to support future growth.

Our business momentum continues to accelerate. As a result, we are raising our full year 2025 revenue growth guidance to 45% or more organic revenue growth, up from the 40% we communicated last quarter. Our forecast reflects significant new deals that have been finalized since our last call as well as several deals that we believe are highly likely to close in the near term. We have a robust pipeline that includes significant dollar values positioning us for strong second half of the year. Many of these deals are not incorporated in our forecast, leaving room for possible further increases.

Demand for our services is strong and accelerating, and we are seeing success across a diversity of existing and new customers. I'll talk about our largest customer first. We recently won several new projects with our largest customer and we have others in pipeline that are not yet included in our forecast, but which we think are reasonably likely. Several of these new projects are under the second SOW we reported signing with this customer last quarter. We believe that the second SOW potentially gives us access to an even larger generative AI revenue pool with this customer.

With another big tech customer, we were recently awarded a number of significant engagements, and we have additional significant engagements in late-stage pipeline, enabling us to forecast $10 million of revenue from this customer in the second half of this year. It is worth noting that we did just $200,000 of revenue with this customer over the entire trailing 12-month period. So this is a very significant upswing that we believe will inure to our benefit significantly next year. These are just 2 examples. There are more.

The traction we are now seeing is exhilarating. We have built a marquee set of customers whose trust we have worked hard to earn and whose demand for our capabilities is expanding. Our big tech customers are at an all-out race towards super intelligence and autonomy, which we believe will be driven to a large degree by high-quality complex training data. We believe we are ideally situated to supply them with this high-quality complex training data. Moreover, we believe we are ideally situated to help them test models, diagnose performance issues and prescribe data mixes required to improve performance. This is a frontier area.

We believe that the future of LLM improvements lies not only in scale data, but in smart data, knowing exactly what kinds of post-training data are required to achieve specific improvements in factuality, safety, coherence and reasoning. At the same time, we are positioning ourselves to help enterprises build and manage AI that can act autonomously, often referred to as Agentic AI. This will require simulation training data to capture how humans process multivariant problems. It will also require sophisticated trust and safety monitoring and management.

We believe agent-based AI is going to serve as the cornerstone technology that unlocks the full value of large language models and generative AI for enterprises. Moreover, we believe that progress on Agentic AI is likely to soon result in a ChatGPT moment for robotics. Within the next several years, we believe Agentic AI will be served at the edge in hardware devices with which we will commonly interact in many respects in our lives. We believe the market for simulation data services and evaluation services to drive Agentic AI and robotics is likely to dwarf the market for frontier model post-training data.

Our growth opportunities are significant and multidimensional. We intend to invest in ways that we believe will enable us to continue our growth path over the next several years. These include short-cycle high-return growth initiatives like custom annotation pipelines, verticalized agent development and expanded global delivery, strategic platform development, especially for LLM testing, safety and real-world deployment. Also advisory and integration services for enterprises building AI native systems, expansion into new domains such as multi-agent systems and robotics and expansion into new markets.

We believe now is the time to lean in investing in capabilities that can compound value over the next decade. This year, we intend to substantially increase investments, most of which will be expensed while at the same time beating 2024 adjusted EBITDA. In the second quarter, we incurred approximately $1.4 million of operating expenses that we think of as investments. This largely consisted of new hires in delivery, product innovation, go-to-market expansion and talent acquisition.

At the heart of this performance is a simple truth. We are deeply aligned with the most significant technological invention of our era, generative AI. Across the entire life cycle of generative AI model training from pretraining to post-training to evaluation to safety, we're delivering the services that unlock the performance of Gen AI models.

I'll now turn the call over to Mariss to go over the financial results, after which Mariss, Aneesh and I will be available to take questions from analysts.

Thank you, Jack, and good afternoon, everyone. Revenue for Q2 2025 reached $58.4 million, representing a year-over-year increase of 79% and demonstrating strong continuing momentum. Adjusted gross margin was 43% for the quarter, up 32% in Q2 of last year. Our adjusted EBITDA for Q2 2025 was $13.2 million or 23% of revenue compared to $2.8 million or 9% of revenue in the same quarter last year.

Net income was $7.2 million in the second quarter, up from loss of $14,000 in the same period last year. In Q2, we were able to utilize the benefit of accumulated net operating losses or NOLCO to partially offset our tax liability. Looking ahead to the coming quarters, barring any changes in the tax environment, we expect our tax rate to be approximately 27% to 28%.

Our cash position at the end of Q2 2025 was $59.8 million, reflecting a sequential increase of about $3.2 million, shaped by strong profitability and disciplined cash management. As Jack mentioned, we collected an additional $8 million in early July that in ordinary course would have likely been collected in Q2. We still have not drawn down on our $30 million Wells Fargo credit facility. The amount drawable under this facility at any point in time is determined based on borrowing base formula.

I'll reiterate what Jack said, the momentum in our business is nothing short of amazing. We believe we got a tiger by the tail and we're investing with the goal of positioning the company to align with what we project the market needs are going to be over the next few years. In Q2, we incurred approximately $1.4 million of operating costs to build out a variety of technical capabilities to expand our go-to-market as investment toward a future that we believe is truly exciting.

Thank you, everyone. Sergio, we're ready for questions.

[Operator Instructions] Your first question comes from George Sutton from Craig-Hallum.

Nice results. Congratulations. So I wondered if we could talk about during the quarter, your largest competitor, Scale AI was a large majority purchased by Meta. And we've had a few of the large tech companies come out and say they would no longer work with Scale AI. These ostensively would be tech companies that you have statements of work with. So I'm just curious if you can kind of give us the after effect of that acquisition as you've seen it.

George, well, thank you for being on the call. So I guess, first, we congratulate Scale for having delivered a great success for their shareholders. And we believe their success and their valuation is a proof point of the key role that data plays [Technical Difficulty]. Before this, we were and continue to very aggressively outreach to market participants and to market our capabilities.

We have, in light of this stepped up that effort with certain companies and there are certain conversations that are going on and are now planned to be happening over the next couple of months that I think could be very exciting for us. I don't know that I can get into particulars much beyond that, but I'll reiterate that we do see an opportunity to accelerate our market presence.

Okay. And lastly for me, you throw an interesting nugget about robotics and the attachment to hardware, creating significant -- even more significant opportunities than the large language model training. So can you just walk through how you envision that would work for you and just lay out that opportunity?

Sure. So I think that we tend to read about these technologies somewhat as if they exist in isolation. But the reality is that as large language models become more and more competent and able to interpret ambiguous language and have capabilities to plan and articulate multistep responses to problems.

There are technologies that will be added to that capability, enabling those models to invoke external APIs or other tools, enabling for multistep tasks using greater memory and planning capabilities. But when you take that and then you think about deploying that at the edge within devices, what you have is a very capable robot.

So I think what this means for us is there's a whole new set of activities, both to train these devices to fine-tune models and to evaluate their performance that together constitutes a market that I believe will exceed that of post -- creating post-training data and evaluating models for frontier model builders. So it's something we're hugely excited about and intend to be investing very significantly in.

Your next question comes from Allen Klee from Maxim Group LLC.

So when you reported last quarter, you kind of said that you thought revenue might be down around 5% in the second quarter. Your actual number was flat -- up very slightly sequential. So you outperformed. So I'm kind of curious like where did the variance come from?

Sure. I'll start and then, Aneesh, do you want to give any additional color. I think that what we were trying to communicate last quarter is revenue was up -- we were up on a run rate basis from our largest customer, and we were, of course, very happy about that. But we wanted to focus our investors on the guidance that we were giving because there are a lot of pluses, puts and takes that get factored into that guidance.

And underlying the work that we're doing, there are dependencies on engineering teams that we're working hand in glove with. So it's entirely possible that a quarter could be up or down, and that isn't necessarily something that should be extrapolated out and considered locked and loaded permanently.

We weren't anticipating that it would necessarily be down, though, and we're very happy to see that it wasn't. As I said, looking at the largest customer and as well as several -- quite a number actually of other customers, we see an incredible pipeline of opportunity right now. We're very excited about that. And we're only baking into our guidance and our forecast things that we think are highly likely to close within the next really 30 to 60 days. There's a lot beyond that. I think we're going to be winning as well. So I hope that's helpful.

Aneesh, anything you want to add to that?

Yes. I think you framed that correctly, Jack. Just to kind of reiterate, Allen, we're not seeing any slowdown with our largest customer. In Q2, we generated approximately $33.9 million of revenue from this account. And as Jack mentioned, we secured several new projects and have additional opportunities in the pipeline that while not yet included in our forecast appear reasonably likely. So again, we feel very bullish and optimistic of our prospects in the back half of the year and remain very excited.

You highlighted -- one of the things you highlighted was the enterprise and the opportunity there. There's a lot of enterprises out there. I'm just curious how you think about the go-to-market to attack it.

Yes, it's a great question. Well, we're attacking it already. And what we're finding is that the interest in the technology and the opportunities to instantiate it into workflows exist across markets. So naturally, we're looking at the markets where we have the most penetration and the most relationships today. But we're also reaching out to companies in markets where we don't have as much reach and we're finding great receptivity.

So I think the highlight there is that Agentic AI, as it's proven is going to be the catalyst that unlocks enterprise opportunity. And I think that among enterprises that I talk to and more broadly, they're no longer just looking at this like a frontier technology that's interesting to monitor. They're seeing it as a new economic infrastructure that they're going to need to be embracing and they're going to need to be adopting. And I think that we can play a very significant role in that.

When we have conversations with them about the things that we think they need to do and our consultants are working with them to figure out what's the right order of operations and how they gain control of their data in order to harvest these opportunities. We've got a lot of experience, both from working with the large big techs on the frontier model such that we know where things are going and how they can best utilize them and also on all the work we've done historically, taking apart workflows and thinking about how to integrate new technologies into workflows to make them more efficient. So yes, super excited about the opportunities there.

That's great. I'll ask one more, and then I'll jump back in the queue. You highlighted a certain amount of money this quarter that you spent -- operating expenses that you viewed as like investment. Is there any reason to think that the scale of how much you're going to be investing for growth in the second half is going to change meaningfully from where it's been?

Great question, Allen. So we -- as you rightly pointed out, we said we invested about $1.3 million in Q2 across several functional areas, including sales, delivery and product solution capabilities. We anticipate that stepping that up from Q2 to Q3 by approximately another $1.5 million.

And the reason for doing that is we see tremendous opportunity in the space and we want to be able to capitalize on that. So we will be making some incremental investments in sales, delivery, solutioning and product to be able to capitalize on what we think is a very significant opportunity right now.

Your next question comes from Hamed Khorsand from BWS Financial.

So my first question was, could you just talk about why you mentioned organic growth and what your intentions are there?

Sure, Hamed. I think we mentioned it to draw attention to the fact that this is organic growth. I think if you look across companies that are reporting and reporting growth, a lot of them are growing inorganically, and that can be a great strategy for them, but it's a different strategy. And I think our strategy and the kind of growth that we're reporting is a testament to the product set and the capabilities that we've developed.

And from a risk-adjusted basis, I think that's probably a safer bet for investors. So we're very proud of it. We're very proud of what we've been able to accomplish. And looking ahead to how well aligned we are with what we see as today's market opportunities and tomorrow's likely market opportunities, we think that, that organic growth can continue.

And the organic growth that you're seeing in your business, is that coming with any kind of competitive pressures on pricing or you're able to maintain pricing and capture new customers?

It's a robust market. I think that we expect -- well, just expect we do experience, of course, a competitive environment. But what we're seeing is that the most important thing to our customers isn't our price. It's the quality of our data and the extent now to which we can work hand in glove with them in order to help understand model performance, understand model deficiencies, understand use cases and make recommendations about the data sets that are required to remediate or to extend those capabilities.

So it's a holistic service and the investments that they're making are so extraordinary, and there's such a deep desire to win in this race that when we're contributing as well as we are in so many accounts, they become much less price sensitive. Now that having been said, I don't believe that we're the most expensive among our competitive set, but I do think we're among the best. And that's a position that I think if we can sustain that will significantly inure to our benefits from a competitive perspective and a growth perspective.

And lastly, last quarter, you had a series of different customers you were describing and talking about this quarter, I think it sounds a little less. So I'm just trying to understand where are you in terms of those relationships? Have they started up what you were talking about last quarter? And so where do you sit as far as revenue opportunity goes when you look out into year-end '26?

Yes. No, there's actually more opportunity and there's a bigger pipeline today than there was a quarter ago. I just looked at that earnings call and thought that maybe that was a little long and decided stylistically to try to condense it a bit. There's more opportunity. There are things that we talked about last time that have closed and that are now in our forecast. There are things that we're continuing to progress that are real interesting.

By memory, I'm thinking about things we talked about. I think there's only one thing that kind of went dormant a little bit, but everything else is either closed, moving forward well, advancing significantly in discussions and that we feel very bullish about.

Your next question comes from Mr. Allen Klee from Maxim Group LLC.

I just had a follow-up. I thought it was really interesting how you said that you can make the data smarter for the customers to get better results. Could you go into that a little bit?

Sure. So -- there are a lot of different dimensions that we use to look at data and analyze data. Our data science team is rapidly expanding. We end up for engineering teams producing what are the equivalent of -- in many cases, the equivalent of white papers with all sorts of mathematical formula and statistical analysis that correlate what we benchmark as a model's performance or identify as a model's deficiency with what data sets are required in order to remediate that.

And what that capability has resulted in is that we're no longer just providing data, but we're -- our status, our role has been elevated to sitting at the table with the data scientists who are building these models and figuring it out with them. The journey is about data. And it's about -- as I said in the prepared remarks, it's about not just scale data, but smart data.

So being able to do all that deep technical scientific analysis of data of model performance of correlating the data that's required in order to achieve the level of performance that's required. In just the last, I'd say, several months, that's become a problem space that we're getting to occupy, and that's tremendously exciting for us.

There are no further questions at this time. I will now turn the call over to Jack Abuhoff for closing remarks. Please go ahead.

Thank you, operator. So Q2 was a high-performing quarter with 79% year-over-year growth, and we're anticipating a strong second half to the year. In the second half, we anticipate potentially winning major new customers, significantly deepening relationships and further broadening our base.

We'll also be continuing to make investments in infrastructure, talent and platforms that we believe are key to continuing our growth trajectory over the years to come. As a result of our successful execution, we're raising our guidance today from 40% to 45% or more organic revenue growth for the year.

And yes, I mean, we're humbled by our good fortunes that scale data, our specialty is, we believe, the [ sine qua non ] of the greatest technological innovation of our lifetimes. And with the runway we see ahead, our goal remains to build Innodata into one of the leading AI services companies for this era.

So thank you all for your continued support, and we look forward to being with you a quarter from now.

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