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 Mariz will provide a review of our results for the first quarter. We'll take questions from analysts. Before we get started, I'd like to remind everyone that during this call, we will be making forward-looking statements which are predictions, projections, or other statements about future events. These statements are based on current expectations, assumptions, and estimates, and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements.
Factors that could cause these results to differ materially are set forth in today's earnings press release in the Risk Factors section of our Form 10-K, 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 Agress. Good afternoon, everyone. Q1 was a record quarter for Innodata. 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, 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 zero. 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 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 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 a 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 efforts. 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 1-shot answers to multi-step 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 pre-training 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 pre-training data at scale, including STEM datasets 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 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 what frontier labs need. We are seeing the same thing playing it 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 billion 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 customer's 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 agent safety, game 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 and potential seven-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 continual 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 rubrics, 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. Goldman's team meanwhile estimates cumulative AI infrastructure spend could reach $7.6 trillion by 2031. 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 ambition 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 strategic 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 for 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 hyperscaler, 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 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 believe 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 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 our 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 turn the call back to you now.
Thanks, Rahul. I'm gonna 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. 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 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 interestingly, was twice the number from the year before. Of this close to 24,000 papers, just 6,352, or 26.6%, were accepted. 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. 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. Rahul 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 Espineli, 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%-25%, primarily reflecting tax benefit 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 pre-training 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 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'll adjust our forecast accordingly. 1 reporting note. Effective this quarter, we are reporting our financial results as a single operating segment. We previously reported 3 operating segments, DDS, 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.
Thank you. Everyone, if you would like to ask a question today, please press star 1 on your telephone keypad. We'll take the 1st question from George Sutton, Craig-Hallum.
Thank you. Great results, guys. I did miss the first few minutes, Jack, so I apologize if this is redundant. 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.
Sure. Thank you, George. Yeah, very excited about that win. It's a very significant win for us from a dollar value perspective. In addition to that, what's even more exciting is that we now believe, you know, we've got another growth partner of significance. It's pretty clear to us that, you know, we expect this customer to be our second largest customer this year, which, you know, 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, you know, pre-training, mid-training, post-training activities, as well as evaluation.
you know, they're seeing us as, you know, 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.
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'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 observability. Can you just kind of 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.
Yeah, I mean, we mentioned the term a couple of times in the prepared remarks. We talked about our strategic trajectory, and I think that's, like, really super critical. You know, our hypothesis, you know, all along has been that, you know, these tools are going from, you know, one-shot answers to, you know, multi-step reasoning engines that's giving way to autonomous agents, which are giving way to, you know, embodied intelligence. What's critical is along that categorical vector path, if you will, the thing that will propel that along and what will become, you know, 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 is, you know, you can think of it like a quality vector.
It'll be the data mixes and the quality of data that determines within any one of those categories how well the AI is performing. Strategically, you know, we're working on two 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 pre-training, mid-training, post-training evaluations, you know, safety, to us, you know, 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.
Gotcha. 1 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, 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?
You know, we talked a little bit 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 data sets. You know, those are all things that we've been working on, you know, within our R&D labs and that we're continuing to work on. There are some other things that we're starting to work on. Some things that I think we'll be, you know, announcing maybe as early as next quarter, actually. You know, 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.
You know, 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.
Super. Thank you very much for the thoughts.
Next up is Allen Klee from Maxim Group.
Hi. 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 kind of, 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? Is there any reason why there might be a timing that revenue might not be kind of what it would normally track? Thank you.
Yeah, no, I don't think that you should anticipate a step change in, you know, investment at this point. You know, 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'll continue to increase. I think, you know, the enormous operating leverage in the model will enable us to do that without having to take a big hit on profitability. You know, I think that we're able to, you know, really pull off a hat trick here, you know. Both, you know, revenue growth, margin growth, and, you know, innovation growth as we move along the trajectory of, you know, helping models get smarter and helping them achieve, you know, extraordinary levels of intelligence.
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? If you are, could you provide what the revenues were for each one? Is this all getting combined now?
It's all getting combined now. We're reporting on a consolidated basis. You know, we kind of ran the tests for segment reporting and made the determination that it's appropriate for us now to be reporting on a consolidated basis. You know, within the Synodex and Agility platforms, we're doing some really interesting things, helping to think through, you know, and everybody's probably been reading about, you know, where's software going. You know, is software becoming service? We're doing some things, you know, 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, you know, fundamental step changes in the purpose of those businesses and what they can achieve for customers.
Okay. Thank you. When you were talking about the frontier lab, could you maybe just give an example of what is being provided? Thank you.
Sorry, Allen. 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 this is adding to.
Sure. If you take some of the wins that we were describing on our call today, excuse me. For the large, you know, $51 million contract, we're providing what's called pre-training, mid-training, and post-training data. Soon, we anticipate providing evals as well. You can think of those as all, you know, classifications of data that's required in order to train and fine-tune large language models. In terms of, you know, 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, you know, another one of the wins that we talked about or the soon-to-be wins, scaled, you know, 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.
That's great. Thank you so much.
Up next is Hamed Khorsand from BWS Financial.
Hi. Just for first question is, was there anything of one time nature in the first quarter results as far as the revenue is concerned or, you know, should we expect this to be a good baseline going forward?
I'd say both. You know, there are things that we're doing that we won't be doing next quarter. There are things we're gonna be doing next quarter that we're not doing this quarter. You know, I think that it was a strong quarter. I think next quarter is gonna be a strong quarter. I think, you know, the quarters after that are gonna be good. You know, we're not providing quarter by quarter revenue guidance because the fact is that things do start and stop. You know, when we talk about the phases of training a model, those don't necessarily dovetail perfectly. 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, you know, of an ongoing nature.
No, I don't think you should think of the quarter as aberrational at all. I think that, you know, as we move through the year, there are gonna be things that we're doing increasingly that are driven by innovation, that are gonna be margin accretive, margin supporting. Yeah, we're excited about the year.
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 evaluations and so forth.
No, these are things that we're doing today. When I mean, the thing that doesn't change is, you know, our mission for the company. Our mission is to be the data partner to foundation model builders, and to be the, you know, 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.
Okay. Thank you.
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. To wrap up, Q1 2026 was a record quarter for Innodata across all the key metrics that we're reporting. You know, 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 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 largest customer continues to grow in absolute dollars. We're also continuing to innovate at a increasingly rapid pace. The strength of our research bench is showing up in customer outcomes and in external recognition like Esther's, you know, two ICML 2026 paper acceptances and her one spotlight designation. Really exciting stuff. We launched our Agent Observability platform in beta in the quarter. No sooner did we launch than we closed a $1 million opportunity with one of the world's largest hyperscalers around that platform. We're, you know, we're really excited about what lies ahead. We're confident that 2026 is gonna be an exciting and tremendous year for the company. Yeah, 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.