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Operator: Good day, everyone, and welcome to the Innodata Inc. 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 R. Agress. Please go ahead.

Amy R. Agress: Thank you, operator. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack S. Abuhoff, Chairman and CEO of Innodata Inc.; Rahul Singhal, President and Chief Revenue Officer; and Marissa B. Espineli, Interim CFO. Also on the call today is Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. You will 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 will then take questions from analysts. Before we get started, I would like to remind everyone that during this call, we will be making forward-looking statements, which are predictions, projections, 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 and the Risk Factors section of our 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.

Jack S. Abuhoff: Thank you, and good afternoon, everyone. Q1 was a record quarter for Innodata Inc., 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 three 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 one 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 ten 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 will 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 $51 million of revenue this year. Twelve months ago, our revenue from this customer was zero. 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 are 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 planned 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 diversify into more organizations and more AI workflows, and we partner with them on their flagship next-generation AI program. But at the same time, growth outside that account is accelerating even faster. In Q1, revenue from our other 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.

Rahul Singhal: Thank you, and good afternoon, everyone. It is great to be with you today, especially in a quarter where we have so much progress to share. I will start with the market in which we believe we today have a 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 answers 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 Inc. has been preparing for. We have deliberately moved up the stack toward high-quality pretraining data, expert-weighted 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 will 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 datasets across physics, mathematics, chemistry, engineering, and biology.

These are the kinds of expert data used to teach models to reason at graduate and PhD level. On post-training, we are working on datasets 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 the work frontier labs need. We are seeing the same thing play out across the broader frontier labs 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 are 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 performance. We anticipate that our initial statement of work will lead to 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 [inaudible] million dollars of total contract value across the customer’s trust and safety and responsible AI programs, most of which we believe will start later this year, including approximately $8 million of total contract value for trust and safety in data generation, global responsible AI testing, and physical AI [inaudible].

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 are also seeing strong traction in potential seven-figure opportunities at 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 customers are making toward physical infrastructure—data centers, networking, and compute. But infrastructure alone does not create usable AI systems.

AI labs also require model training, evaluation, safety, and continual improvements for 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 blueprints, 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 five major U.S. hyperscalers to top $800 billion this year and to reach $1.1 trillion next year. Olmuth’s team, 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 meet that capital product.

The frontier labs’ ambitions increasingly extend to 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 and embodied intelligence, we expect federal and government-aligned customers to become meaningful long-term growth vectors. On the strength of our conviction, we launched a 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 a potentially significant award. We believe making it this far in the selection validates the suitability of Innodata Inc. for mission-critical regulated AI work. In Q1, Innodata Inc. 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 Whitehound 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 hyperscaler, networking, and consumer internet customers, helping 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 that autonomously acting agentic systems will likely have for our customers.

At the same time, we observe 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 agentic systems. It helps enterprises evaluate agent behavior, inspect traces, monitor live performance, catch regressions early, and maintain audit trails in production. Over time, it allows experts to supervise larger and more complex workloads with fewer resources and to optimize agent token consumption. I am proud to report that just last Friday, we signed a 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 two 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 salesforce alone. External market data supports the enterprise piece. 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 three 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 give us confidence to anticipate both additional upside as 2026 unfolds and continued growth in 2027 and beyond. Okay, Jack. I will turn the call back to you now.

Jack S. Abuhoff: Thanks, Rahul. I am 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 datasets, where we retain the IP rights, enabling us to resell the same dataset to multiple customers. We are increasingly using this model for datasets 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 1.8 times faster than revenue. That is operating leverage by definition. 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 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 two 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 Esther’s papers received the so-called Spotlight designation, which places it at the very pinnacle of AI research. To put that in context, ICML reported that 23,918 submissions entered review for 2026, which 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 offline 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 were entering a golden age of innovation at Innodata Inc.

Today, I will reiterate that even more strongly. We are building proprietary technologies that allow us to construct unique datasets, measurably improve model performance, and bring agentic systems to production readiness. Rahul and I are focused on some highly creative ways to translate this innovation into the strongest possible economic outcome for Innodata Inc. 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.

Marissa B. Espineli: 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 a three-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 get finalized, we will adjust our forecast accordingly. One reporting note: effective this quarter, we are reporting our financial results as a single operating segment. We previously reported three operating segments—PDF, 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. We will now open the call for questions. Operator, please open the line for questions.

Operator: Thank you. If you would like to ask a question today, please proceed as instructed. We will take the first question from George Frederick Sutton with Craig-Hallum.

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George Frederick Sutton: Thank you. 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 into 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?

Jack S. Abuhoff: Sure. Thank you, George. We are very excited about that win. It is a very significant win for us from a dollar value perspective. But in addition to that, what is even more exciting is that we now believe we have another growth partner of significance. It is pretty clear to us that we expect this customer to be our second-largest customer this year, which is very meaningful. There are 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 are doing goes across pretraining, mid-training, and post-training activities, as well as evaluation. They are seeing us as a full-service shop, and they are very much leaning into several of our latest innovations, which is also tremendously exciting. They are a very large company—one of the big techs—and we are excited about the partnership.

George Frederick Sutton: Super. 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-training side, and now we are talking a much broader set of use cases. You are talking about trust and safety and robotics and federal and the new platform for evaluation and observability. Can you give us a sense of how different the scope of what you are working on is today versus then? And I assume that could only increase from here.

Jack S. Abuhoff: Yes. We mentioned the term a couple of times in the prepared remarks—we talked about our strategic trajectory, and I think that is really critical. Our hypothesis all along has been that these tools are going from one-shot answers to multistep reasoning engines, that is giving way to autonomous agents, which are giving way to embodied intelligence. What is critical is that along that categorical vector path, the thing that will propel progress—and where companies will have, we predict, even more voracious appetites for data—is making that journey across that trajectory. At the same time, on the other axis, you can think of it like a quality vector. It will be the data mixes and the quality of data that determine, within any one of those categories, how well the AI is performing.

Strategically, we are working on two things: what are the datasets and data capabilities required 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 help our partners achieve the quality they are seeking within any one of those categories, whether it is pretraining, mid-training, post-training, evaluations, or safety. To us, it is about what is required in that category and what is required at that point in time, as determined by research, in order to achieve the best results.

George Frederick Sutton: Got it. One last question. 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 product growth. Can you talk about what those investments yielded you and what they might yield going forward?

Jack S. Abuhoff: We talked today about 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 datasets. Those are all things that we have been working on within our R&D labs and that we are continuing to work on. There are some other things that we are starting to work on—some things that I think we will be announcing maybe as early as next quarter. We see a tremendous ROI from our R&D organization. We are thrilled with the people we have and with the output we are getting. What we are seeing is that this enables us to move along the trajectory 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 is huge for our business.

George Frederick Sutton: Thank you very much for the thoughts.

Operator: Next up is Allen Robert Klee from Maxim Group.

Allen Robert Klee: Hi. Congratulations. Following up on the last question about the investments you are making to grow—you talked about how you will get better margins from certain things you are deploying—but is there a way to think about seasonality as we go through the year, and specifically next quarter? Should we think that, for some reason next quarter, there would be a more-than-normal jump in investment expenses, or is there any reason why the timing might mean revenue does not track as it normally would? Thank you.

Jack S. Abuhoff: We do not anticipate a step change in investment at this point. We are comfortable, and we are getting a great return on what we are doing today. We will increment that up—we certainly do not see it flatlining—but the enormous operating leverage in the model will enable us to do that without having to take a big hit on profitability. We are able to pull off the hat trick here: 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.

Allen Robert Klee: Thank you. I might have missed something that was said when there was a discussion on the segments. Are you still breaking out the three segments, and if you are, could you provide what the revenues were for each one? Or is this all getting combined now?

Jack S. Abuhoff: It is all getting combined now. We are reporting on a consolidated basis. We ran the tests for segment reporting and made the determination that it is appropriate for us now to be reporting on a consolidated basis. Within the Synodex and the Agility platform, we are doing some really interesting things helping to think through where software is going—people have been reading about whether software is becoming service. We are 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 are managing them not for small incremental improvements in revenue but for potential step changes in the purpose of those businesses and what they can achieve for customers.

Allen Robert Klee: Okay. Thank you. And when you were talking about the frontier labs, could you maybe give an example of what is being provided?

Jack S. Abuhoff: Sorry, Allen—did you mean frontier labs generally or any specific frontier lab? I am not sure I am following the question.

Allen Robert Klee: I am just trying to understand a little more of the specific areas of what you provide that this is adding to.

Jack S. Abuhoff: Sure. If you take some of the wins that we were describing on our call today—for the large $51 million contract—what we call pretraining, mid-training, and post-training data. Soon we anticipate providing evals as well. You can think of those as classifications of data required in order to train and fine-tune large language models. For another customer we talked about, we are providing trust and safety services. We are evaluating models, testing them, isolating areas where they are underperforming, and prescribing the data mixes required 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; and creating datasets required for physical AI.

You can think of physical AI as embodied intelligence or robots. It is really along the full spectrum of capabilities required by the foundation model builders from a data perspective in order to support their products.

Allen Robert Klee: That is great.

Jack S. Abuhoff: Thank you so much.

Operator: Up next is Hamed Khorsand from BWS Financial.

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

Jack S. Abuhoff: I would say both. There are things that we are doing that we will not be doing next quarter; there are things we are going to be doing next quarter that we did not do this quarter. But it was a strong quarter. I think next quarter is going to be a strong quarter, and I think the quarters after that are going to be good. We are 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 do not necessarily dovetail perfectly. But we have more and more things going on, and that tends to even things out. We are also doing some things now, increasingly, that are of an ongoing nature. So, no, I do not think you should think of the quarter as an aberration at all.

As we move through the year, there are going to be things that we are doing increasingly that are driven by innovation and are going to be margin accretive and margin supporting. We are excited about the year.

Hamed Khorsand: Has the composition of revenue changed at all, or is the scope of work still the same? And when you are talking about something that might happen in the future as far as the agentic and the evaluations and so forth—

Jack S. Abuhoff: These are things we are doing today. The thing that does not 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 is not changing. What does change is, as the models and the capabilities seek to do more and perform better, the mix of what we do does change. But that is our job: to stay research led and to ensure that we are a little bit ahead of where our customers need us to be.

Hamed Khorsand: Okay.

Jack S. Abuhoff: Thank you.

Operator: At this time, there are no further questions. I would like to hand the call back to Mr. Jack S. Abuhoff for any additional or closing remarks.

Jack S. Abuhoff: Thanks, operator. To wrap up, Q1 2026 was a record quarter for Innodata Inc. across all the key metrics that we are reporting—revenue, adjusted gross profit, adjusted EBITDA, and cash. We delivered 54% revenue growth, we expanded margins meaningfully, and we generated significant cash without having to draw on our 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 prudent. We see potential upside as additional programs that are not in that forecast convert and scale. A big tech customer that generated no revenue for us 12 months ago is now on track to become our second-largest customer this year.

Our customer concentration is improving in the very best possible way—faster growth from the broader customer base, while our large customer continues to grow in absolute dollars. We are also continuing to innovate at an increasingly rapid pace. The strength of our research bench is showing up in customer outcomes and in external recognition, like Esther’s two ICML 2026 paper acceptances and her one Spotlight designation—really exciting stuff. We launched our evaluation and observability platform in beta in the quarter, and no sooner did we launch than we closed a $1 million opportunity with one of the world’s largest hyperscalers around that platform. We are really excited about what lies ahead. We are confident that 2026 is going to be an exciting and tremendous year for the company.

Thank you, everybody, for being on the journey with us.

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

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