Good afternoon, everyone. Welcome to Grid Dynamics First Quarter 2026 Earnings Conference Call. I'm Cary Savas, Director of Branding and Communications. Joining us on the call today are CEO, Leonard Livschitz; CFO, Anil Doradla; CTO, Eugene Steinberg; and SVP, Global Head of Partnerships and Marketing, Rahul Bindlish. Following the prepared remarks, we will open the call to your questions. Please note that today's conference call is being recorded. Before we begin, I'd like to remind everyone that today's discussion will contain forward-looking statements. This includes our business and financial outlook and the answers to some of your questions. Such statements are subject to the risks and uncertainty as described in the company's earnings release and other filings with the SEC. During this call, we will discuss certain non-GAAP measures of our performance. GAAP to non-GAAP financial reconciliations and supplemental financial information are provided in the earnings press release and the 8-K filed with the SEC. You can find all the information I just described in the Investor Relations section of our website. I now turn the call over to Leonard, our CEO.

Thank you, Cary. Good afternoon, everyone, and thank you for joining us today. We started 2026 with solid execution, delivering Q1 revenue of $104.1 million that was higher than our guidance range and ahead of market expectations. This performance reflects continued strength in our business model and validates our focus on AI-led transformation and high-value enterprise engagements. Three trends stood out this quarter, a meaningful and growing contribution from AI revenue, a structural shift in vertical mix toward technology and financial services, and our top customers are undergoing meaningful vendor consolidation with Grid Dynamics emerging as a clear beneficiary. Last quarter, we called 2026 a pivotal year for the accelerating adoption of our AI offerings. Our first quarter results support that conviction with AI revenue reaching 29.3% of total company revenue, growing nearly 60% year-over-year. Given this concentration and growth trajectory, the AI practice has become the core of our business, fundamentally reshaping our offerings, our talent development and our client relationships. I'm confident we are well positioned to further accelerate AI revenues in 2026. For the first time, our top 5 accounts are entirely outside of retail, reflecting meaningful diversification into technology and financial services, sectors where AI adoption is accelerating and our capabilities are highly differentiated. This group includes 2 leading global technology companies, a global fintech leader, a U.S.-based global bank and a leading financial institution. What makes this group notable is that each of these customers has undergone meaningful vendor consolidation and Grid Dynamics has emerged as a clear beneficiary. This positions us to capture greater market share in 2026 and beyond. Additionally, we have been actively engaged in AI initiatives across all 5 customers, with some of our largest and most strategic programs driven by this group. Our size and AI technology focus are strategic advantages in a rapidly changing environment. Large enterprises are increasingly seeking highly capable, nimble partners like Grid Dynamics, who can move quickly and deliver meaningful AI outcomes rather than relying on incumbent global system integrators burdened by legacy delivery models. In many ways, headcount leverage is no longer a competitive moat and differentiation comes from domain knowledge, AI capabilities and ability to rapidly scale relevant expertise. We're not a systems integrator. We're a product-centric engineering company focused on solving the most complex mission-critical challenges for Fortune 1000 clients with a deliberate emphasis on driving revenue-generating capabilities, not just cost optimization. As enterprises migrate to our custom-developed solutions, the advantage shifts to partners who can build sophisticated production-grade software from concept to deployment. This is precisely what Grid Dynamics does. AI is meaningfully expanding Grid Dynamics' addressable market. For example, AI-native SDLC and agentic coding fundamentally changed the economics of delivering services. With delivery time and cost compressing, we can take on larger client initiatives that were previously out of our reach. Also, AI is unlocking a wave of legacy modernization that was not previously economically viable. For years, replacing core legacy infrastructure was considered too expensive, time-consuming and risky. AI lowers these barriers. At a leading home improvement retailer, the infrastructure for global operations is based on legacy mainframe platforms. Modernizing the legacy mainframe platform was considered risky, and required specialized and expensive talent. Using AI agents, Grid Dynamics delivered a full modernization program within the timeline and budget. Grid Dynamics' expertise is now extending into physical AI. In CPG & Manufacturing, enterprises are turning to self-learning robotics and AI technologies to drive operating efficiencies. Our GAIN platform for physical AI makes intelligent robotics more accessible and economically viable. In the first quarter, we closed our first commercial engagement in physical AI with a heavy equipment manufacturer. We're enabling their mining equipment with intelligent autonomous capabilities. We're building the company around AI. Four pillars define this transformation: AI-native delivery, productized engineering, AI consulting, and internal AI automation. The first pillar, AI-native delivery, marks a fundamental shift in how we work from human-led workflows to AI agent-driven, spec-based executions across our fixed bid engagements. The economics are compelling and adoption is accelerating. Early indicators point to material productivity gains in select workflows and a structurally different cost base. In Q1, at our global bank, our autonomous AI workflows analyzed 150 green production applications and uncovered latent defects across systems, including test, coding and correct behavior. By expanding validated behavior coverage to greater than 70%, we reduced false confidence in system integrity and mitigated production security and regulatory risk. The second pillar, productized engineering, focuses on converting our repeatable IP into AI-native platform-based offerings under the GAIN platforms. GAIN consists of 4 domain-specific platforms spanning Agentic AI Commerce, SDLC, Risk and Compliance, and Physical AI. Our engineers increasingly operate as forward deployed specialists composing and customizing these platforms to each client's specific environment, data and workflows. The result is deeper differentiation and stronger client retention. A good example is what we achieved with one of the world's largest food distributors. Our client sales associates were spending hours on manual research and proposal preparation for their restaurant clients. We developed AI agents that compressed the preparation process to minutes while improving the quality of the reports. Our efforts resulted in a 50% reduction in preparation time and an 18% increase in monthly spend for the targeted accounts. The third pillar is AI consulting. As companies undergo AI transformation, existing business workflows must be evaluated and reimagined for an agentic world. Clients are seeking domain knowledge and deep understanding of AI and data. As a leading global fintech company, our engagement focused on development of AI agents which automate enterprise workflows. Early efforts with our Forward Deployed Engineers embedded inside the client organization have identified inefficiencies and deployed AI agents to automate, optimize and scale the process with a human in the loop, resulting in a 15% productivity improvement. The fourth pillar is tied to adapting AI for our internal operations. Over the past several months, we have been adopting AI tools both off-the-shelf and internally developed to enhance our productivity and efficiency. This includes areas such as recruitment, RFP responses, knowledge management and HR. With recruitment, we have seen a 2x productivity improvement in terms of number of applicants we can process. With RFPs, we have increased the number of responses by 50% without growing headcount. With knowledge management, our responses to employee questions improved from hours to minutes. And with HR, multiple initiatives are being rolled out, and we expect more than 20% operational improvement. Q1 project highlights. Our vertical execution in the first quarter is best illustrated by a few notable client engagements. TMT. For a global technology company operating large-scale manufacturing environments, Grid Dynamics designed and validated a unified manufacturing intelligence platform to replace fragmented, manual data flows. The solution is projected to reduce data discovery and reporting cycle times by over 95%. It also lays the foundation for enterprise-wide operational intelligence. CPG & Manufacturing. Grid Dynamics built and deployed a unified agentic AI platform for a leading global CPG manufacturer, creating the shared infrastructure required to develop, govern and scale AI agents consistently across the enterprise. Running on a major cloud platform, the solution serves as an operational backbone for AI-driven transformation across the manufacturer's supply chain, consumer and commercial domains, the highest complexity, highest impact areas of the business. Automotive part retailer. For a leading global retailer, Grid Dynamics led the end-to-end modernization of a mission-critical inventory and replenishment platform, migrating from legacy on-premise infrastructure to a cloud-native environment. The program delivered over 70% reduction in infrastructure costs and approximately 40% improvement in core response time, restoring the platform's ability to support real-time replenishment decisions at global scale. At a premier global multi-brand restaurant company, Grid Dynamics deployed an AI coding harness to replace the manual QA workflows that struggle to keep pace with frequent enterprise changes across web and mobile. AI agents continuously simulate customer behavior and adapt automatically to UI modifications in real time, eliminating testing bottlenecks without human intervention. The platform has reduced testing time by approximately 50%. With that, I will hand over to Rahul Bindlish, Global Head of Partnerships and Marketing, who will share some of the exciting initiatives currently underway and give you a closer look at where Grid Dynamics is headed. Rahul?

Thank you, Leon. Good afternoon, everyone. Partnerships are now a key component of how we go-to-market. Our partner-influenced revenues have grown to 19.1% of total company revenue in Q1, underscoring the value of our ecosystem-driven approach in the agentic era. The majority of our partner-influenced revenue is driven by Google Cloud, AWS, and Microsoft Azure, our 3 core hyperscaler relationships. They are an active go-to-market channel for our platforms and services. Our go-to-market strategy is aligned with the AI strategy described by Leonard in his comments. We will be deploying all our platforms on the marketplaces of hyperscalers. Our GAIN platform for risk and compliance is now listed on both Google Cloud Marketplace and AWS Marketplace. Enterprises searching for production-grade capabilities in this domain within those ecosystems will find Grid Dynamics IP directly, increasing our sales pipelines. We also have joint sales motions with the hyperscalers to accelerate deal closures. That is a fundamentally different way to win business compared to traditional services and sales. This is the first deployment in a deliberate rollout. We are moving additional platforms onto the marketplaces of every major hyperscaler. It also deepens our co-sell relationships with these partners. Our GAIN platforms plus Forward Deployed Engineers model is a new approach to go-to-market with the hyperscalers. The platform creates the entry point, our engineers deliver the value realization. Enterprises see this clearly and the first few engagement wins reflect their willingness to pay for it. Each platform we bring to market addresses a specific business pain point with domain-specific IP. This changes the sales dynamics in a way that matters for our growth model. When we lead with a vertical-specific platform, whether that is agentic commerce, compliance or physical AI, we enter a client conversation with a validated solution for a specific business problem. Sales cycles compress, conversion rates improve and initial contracts expand faster because the platform's value is visible to both the business buyer and the technical evaluator. This vertical specificity is what makes our co-sell relationships with Google, AWS and Azure productive. Grid Dynamics' technical depth and domain knowledge, combined with the hyperscalers' cloud infrastructure, is what allows us to win engagements against competition. Our AI revenue acceleration is the output of that combination. We are also expanding our partnership with NVIDIA by porting our solutions onto their software stack. Our GAIN platform for physical AI is built on NVIDIA's stack, including Omniverse, and we are taking it to market with NVIDIA for manufacturing and CPG companies. Industrial AI in manufacturing environments requires simulation fidelity and sensor integration that generic AI infrastructure does not support. Building on NVIDIA's stack positions us to address that requirement and enables joint go-to-market with NVIDIA into a customer segment where the demand for production-grade physical AI is accelerating. We have also expanded our partnership ecosystem in the AI consulting space, entering into relationships with specialized firms in business process mining and organizational change management. Effective enterprise AI deployment is more than just a technology problem. Clients who deploy agentic workflows are simultaneously reengineering the processes those agents replace and managing the organizational change that follows. By integrating specialized process mining and change management partners into our delivery model, we extend the value that Grid Dynamics offers from platform and engineering, through to adoption and measurable ROI capture. There are two more trends worth noting. Many of the engagements that we are winning through partner channels are extending beyond the initial project. When an AI project delivers clear ROI and our clients are seeing this at scale, the relationship does not close, it expands. Clients return for more use cases, projects and programs. That pattern is visible in our retention data and in the expansion of existing hyperscaler co-sell accounts. At one of the largest food distributors in North America, that pattern played out across three distinct phases. The initial engagement was a first project delivered through a co-sell motion with Google Cloud and built on the GAIN platform for agentic commerce. The platform search capabilities were in production within weeks. The client retained Grid Dynamics immediately following go-live to extend the program, using our catalog enrichment solution built on the same platform to improve the quality of the search results. We are now in the third phase, the development of an agentic platform for the client's commercial operations with the first use case targeting sales efficiency already in production. The margin profile of AI engagements, especially those built on GAIN platforms, is meaningfully different from the traditional services pipeline. When we win through a joint sales motion, clients are buying a validated solution at a fixed commercial structure. That changes the margin profile to higher gross margins than our blended services average. The GAIN platforms plus Forward Deployed Engineers model is not just an acquisition strategy. It's a retention and margin expansion strategy too. With that, I'll hand it to Anil to walk through the financials.

Thanks, Rahul. Good afternoon, everyone. We recorded the first quarter revenues of $104.1 million, slightly above the higher end of our guidance range of $103 million to $104 million. Our revenues grew 3.7% on a year-over-year basis. Non-GAAP EBITDA was $12.5 million or 12% of revenues and was at the midpoint of our $12 million to $13 million guidance range. In the first quarter, there was a negative impact from FX fluctuations on a year-over-year basis. We are exposed to a currency basket across Europe, Latin America and India. While we utilize both natural hedges and an active hedging program, the net impact on a year-over-year basis on our EBITDA was a headwind of approximately $1.2 million. As Leonard highlighted, our top customers are global technology and financial enterprises. And this is by design. Our growth strategy is deliberately focused on verticals where AI adoption is accelerating and our capabilities are highly differentiated. In the first quarter, revenue breakdown reflects this redistribution with meaningful diversification into our TMT and financial verticals. Looking at the performance of our verticals, TMT became our largest vertical and accounted for 29.5% of total revenues for the quarter with growth of 30.3% on a year-over-year basis. The growth was primarily driven by a combination of our largest technology customers as well as new customers. Retail contributed 28.4% of total revenues in the first quarter of 2026. The finance vertical accounted for 23.5% of total revenues in the quarter, and we witnessed strong demand from our banking and fintech customers. For the remainder of 2026, we are bullish on our outlook with our banking and fintech customers. Turning to the remaining verticals. CPG & Manufacturing represented 9.4% of quarterly revenues. In the quarter, we witnessed growth from our manufacturing customers in North America and new engagements in Europe. The Other vertical contributed 7.1% of first quarter revenues. And finally, Healthcare and Pharma contributed 2.1% of our revenues for the quarter. We ended the first quarter with a total headcount of 4,964, up from 4,961 employees in the fourth quarter of 2025 and from 4,926 in the first quarter of 2025. We continue to rationalize our overall headcount as we align our skill sets and geographic mix. At the end of the first quarter of 2026, our total U.S. headcount was 353 or 7.1% of the company's total headcount versus 7.2% in the year-ago quarter. Our non-U.S. headcount located in Europe, Americas and India was 4,611 or 92.9%. In the first quarter, revenues from our top 5 and top 10 customers were 40.8% and 59.7%, respectively, versus 35.6% and 56.6% in the same period a year ago, respectively. Moving to the income statement. Our GAAP gross profit during the quarter was $36.2 million or 34.8% compared to $36.1 million or 34% in the fourth quarter of 2025 and $37 million or 36.8% in the year-ago quarter. On a non-GAAP basis, our gross profit was $36.7 million or 35.3% compared to $36.6 million or 34.5% in the fourth quarter of 2025 and $37.6 million or 37.4% in the year-ago quarter. On a year-over-year basis, the decline in the gross margin was from a combination of FX headwinds and higher cost structures across our delivery locations. Non-GAAP EBITDA during the first quarter that excluded interest income expense, provisions for income taxes, depreciation and amortization, stock-based compensation, restructuring, expenses related to geographic reorganization and transaction and other related costs was $12.5 million or 12% of revenues versus $13.7 million or 12.9% of revenues in the fourth quarter of 2025 and was down from $14.6 million or 14.5% in the year-ago quarter. The sequential and year-over-year decline in EBITDA was largely due to a combination of FX headwinds and higher operating costs. Our GAAP net loss in the first quarter was $1.5 million or a loss of $0.02 per share based on a diluted share count of 84.7 million shares compared to the fourth quarter net income of $0.3 million or breakeven per share based on diluted share count of 86.4 million and net income of $2.9 million or $0.03 per share based on 87.8 million diluted shares in the year-ago quarter. On a non-GAAP basis, in the first quarter, our non-GAAP net income was $7.5 million or $0.09 per share based on 85.9 million diluted shares compared to the fourth quarter non-GAAP net income of $8.7 million or $0.10 per share based on 86.4 million diluted shares and $10 million or $0.11 per share based on 87.8 million diluted shares in the year-ago quarter. On March 31, 2026, our cash and cash equivalents totaled $327.5 million, down from $342.1 million on December 31, 2025. Since our fourth quarter earnings call, we repurchased approximately 1.8 million shares for a total consideration of $11.5 million. Since our Board authorized the $50 million share repurchase program, we have repurchased approximately 2 million shares for a total of $13.5 million, reflecting our continued confidence in the long-term value of the business. M&A continues to take priority in our capital allocation strategy. We are committed to augmenting our organic business with acquisitions that strategically enhance our capabilities, geographic presence and industry verticals. Coming to the second quarter guidance. We expect revenues to be in the range of $106 million to $108 million. We expect our second quarter non-GAAP EBITDA to be in the range of $14 million to $15 million. For Q2 2026, we expect our basic share count to be in the range of 84 million to 85 million and our diluted share count to be in the range of 85 million to 86 million. For the full year 2026, we're maintaining our revenue outlook of $435 million to $465 million. That concludes my prepared remarks. We're ready to take your questions.

Operator instructions. First question comes from Puneet Jain of JPMorgan.

So Leonard, thanks for sharing updates on the GAIN framework. As these platforms become increasingly integrated in your delivery, could you talk about the impact it has on overall operations, say, like are these necessarily fixed price contracts? Do clients pay for tokens like for LLMs or are they bundled in your overall services? You talked about Forward Deployed Engineers. Can you train your current employees to be FDEs? Or do you have to change your hiring mix to be able to offer the GAIN platform to your customers?

Let me try to unpack some of your questions. It's more than one question, but let's go backwards; that might be a little easier. So let's start with engineering talent and Forward Deployed Engineers. The majority of the people we deploy are internally trained. We have a substantially large number of very technically educated people whom we internally develop and train in the models. It's led by our R&D organization, so you will hear Eugene give you more comments, which, combined with retraining the delivery organization, brings the talent. Obviously, when we bring talent from the market, it still needs to be structured so they can adapt to Grid Dynamics' GAIN platforms approach. The GAIN platforms approach is really what makes us different. Rather than talking about a very specific model for each individual customer, we can offer a suite of solutions to the client where we define a combination of Grid Dynamics IP and open-source components into the total solution. The total solutions we offer are driven by adoption of the engineers and agents in the form of guidance, where we expect the return on investment for the client. To answer your question, the number of non-T&M projects—and because there are tokenization options, fixed-bid offerings and performance-related contracts—has significantly increased and continues to increase. You will actually see that as we continue to answer questions today because that model itself requires not only training the Forward Deployed Engineers, but adapting internal processes and program management and delivery teams to control a proper engagement in a different model. So to answer your question, there is a big shift toward non-T&M. The training and rollout of our engineering force is going very successfully. You haven't seen right now from the absolute number of employees how the dynamics of the headcount has changed yet because the number looks flat. But if you unpack that number, you will see a significantly higher contribution from the engineering workforce because some of them require additional training and reclassification before we deploy them to clients. But the good news is, overall, we have a very strong vector where we are building our position with clients adopting new models related to the GAIN platforms.

Got it. No, it's a big change. And so it seems like you're already doing a lot of the hard work that's involved. Let me ask Anil. So the guidance, like the full year on top line, implies mid-single-digit growth even in the lower half, mid-single-digit average sequential growth in the second half to hit the lower half of the guidance. So what drives the confidence or the visibility on achievement of this guidance for the full year?

So there are two or three factors here. Leonard, do you want to talk about pipeline, then I can take it?

I will answer the easier part, and then Anil will dive into the numbers. There are two parts of the confidence level we have. Number one, demand has grown substantially. We have a record level of demand. I'm avoiding the exact engineering-demand terminology because we're talking about teams, platforms, and offerings, but overall demand is very strong and the vector is very steep right now. That's a subjective factor, but it's good news. The more interesting factor is, as I mentioned earlier, the larger number of non-T&M projects. This work force is defined by a different way we qualify revenue for projects. So when we unpack the number, we're a bit more conservative in guiding the near term because it becomes a bit more of a financial exercise. The work has been signed and is going on, but Anil will give you a bit better feedback. The summary for you: two parts—significantly higher pipeline and a very large number of non-T&M projects, which require more financial attention in how we guide numbers for the near future.

No, look, Leonard, you pretty much hit it. Let me build upon that. Leonard and the team in our prepared remarks talked about a fundamental transformation and the word you will see again and again is platform. Historically, you take the engineer, you have a certain T&M rate, you multiply by hours and days; the formula is very linear. We're transitioning. We're seeing that Rahul is leading the way from a partnership perspective and Eugene is leading the way on the CTO side. We've introduced new products and platforms, and we're working on monetization. There are stages of monetization: there's an upfront stage that will start off small, then greater stickiness with these engineers. As our clients become comfortable with both our products and our engineers in this new model, that's when we start seeing a lot more monetization. When we look at these numbers, revenue recognition is a key component. Think of it as baby steps right now. We see the pipeline: year-to-date from January 1 through now compared to last year is really good. I look at the initiatives we're working on in AI—really good. But the question will be timing: is it linear or nonlinear? So from that context, for the full year, we're keeping our outlook. Let's see over the next couple of quarters whether it turns out much stronger because of recognition timing. We're still experimenting and working through it. So the optics may look slightly different from what you can see underneath from a business point of view.

Let me add one more factor because it could be missed from the first point of view. We also guide substantially better margins. So if you look at the delta between Q1 and Q2, you may ask how you can grow such an increase in profitability on a relatively modest increase in revenue. This story is that the new projects we've been awarded—as Rahul mentioned—have a different margin profile than our current business. We just don't want to run ahead of ourselves and complete all the financial qualification until we see the results, but we are very confident in the progress we expect to make.

So it seems like you are at the cusp of that monetization and that drives the confidence.

The next set of questions comes from Maggie Nolan of William Blair.

I wanted to ask about your partner revenue that crossed 19% of revenue. So where do you anticipate that going? And to what extent do you expect that to be a positive margin driver for the company?

I think the best way to start is with the person who is responding to that. Rahul, you have a perfect opportunity to tell us how you plan to build the business and continue to grow. Please go ahead.

Thanks for that question, Maggie. As you have seen, partnerships have become one of our key go-to-market channels, and it will continue to be. We have a long-term goal to get to about 25% to 30% of our revenues being influenced by partnerships. We are well on our path to achieve that. In fact, I would say we are tracking slightly ahead of our internal goals to achieve that. With GAIN platforms being deployed on the hyperscaler marketplaces, we'll probably see acceleration of partner-influenced revenues in future quarters.

Let me add one more color. Rahul mentioned this in his prepared remarks, but it's important because it's new. In the past, we talked about hyperscalers as an influence; that was consistent in terms of influenced revenue generated with those partnerships. Now we are adding, especially with physical AI, some interesting new levels of partnerships, and monetization is a bit lower yet we see substantial growth because we're adding heavy hitters in the industry and that expands the addressable market. Another element related to our GAIN platforms is the consulting part. We are also getting partnerships with business organizations that are asking us to become the lead technology implementation partner, which helps transition business conceptual ideas into implementation related to specific AI platforms. Business leaders are cautious about spending on experimentation. They want clarity that investments are moving in the right direction, and Grid Dynamics is becoming that technology partner. It's another important difference from the past.

On the TMT growth, do you think that's durable into the back half of the year? To what extent was that driven by concentration with particular clients? And what's the visibility into those clients that drove that?

Yes, Maggie, that's clearly a highlight and it's super exciting. Not only the TMT, but if you look at some of our financial clients, we have seen many of these customers consolidating suppliers. In some of them, we have now become a preferred vendor. We were always present, but as they consolidated, we reached preferred vendor status. With TMT, there are two nuances. One is our work—these customers know what AI is, and they appreciate us. The smartest technology customers are the ones seeking our AI capabilities, which is a little counterintuitive. The other thing is that these customers often have hyperscaler relationships too, so on both fronts we are seeing activity. Every quarter there might be some fluctuations, but the trajectory is strong as they consolidate and we become one of the few vendors and augment that with hyperscaler growth.

An important color for you, Maggie, is that we're not talking about being a generic preferred niche vendor anymore. AI proliferation is equalizing the supply base. Size does not necessarily provide an advantage for the largest vendors. The ability to deploy AI solutions at scale is becoming the critical differentiator, and being a smaller company that can transition faster is an advantage. How quickly we can train people and deliver high-quality work with specialized teams determines our awards on the business side. With TMT, it's currently the number one followed closely by financial clients. For our top clients, we believe we are in the driver's seat for AI deployments.

The next question comes from Surinder Thind of Jefferies.

When we think about the non-time-and-materials model, how do we think about the incremental risk that you're taking on? Over the past decade or two, projects got bigger and more complex and there's greater uncertainty about scope or changes in scope. How does that work in the new model? If you're looking at an outcome-based or fixed price token usage, where is the risk in the model for you guys? How are you addressing that?

Surinder, I'll have Eugene Steinberg, our CTO, start because he is an architect of this system. Uncertainty has two prongs: risk and reward. Eugene will talk about how we manage both.

Of course, when you take a fixed-price project, you always have to balance risk versus reward. On the risk side, the main risks in fixed-price projects come from uncertainty—usually gaps in understanding requirements. We are actively using our AI agents and our Rosetta framework to uncover uncertainties in requirements and clarify them during the presale phase, which builds strong confidence in what needs to be done. During implementation, we are always using AI coding assistance and the GAIN Rosetta framework to accelerate delivery and build buffers for unknowns that usually appear. That helps mitigate risk in fixed-price engagements.

Let me add one thing to what Eugene said. Surinder, you've been in IT and this risk isn't unique to Grid—it's universal. When you scope projects, if you don't have a deep understanding of the project, it's a problem. Historically, we were a T&M shop and moved toward fixed-price. During the first years of fixed-price, we learned a lot and made mistakes. That was the pre-AI era. At times our fixed-price project margins were comparable to T&M, and we learned from that. Pre-AI, our fixed-price margins were higher than T&M. Those learnings are now moving into our AI work. If you don't understand the problem and lack technological know-how, yes, risk is heightened. We'll always have that risk. But as Leonard pointed out, there's a reward component too.

I want to close on one statement. In my prepared remarks I mentioned Grid Dynamics is not a systems integrator; we are a product-centric engineering company. That gives us greater confidence to take on projects with a higher probability of success. Eugene mentioned Rosetta—it's all part of the GAIN platforms. Outcomes at scale will be seen as we propagate more results of this work. It's not only about how much money we generate in a project but how much growth we see from it going forward. At our size and scale, we are training not only models but also our customers on how to run iterative development and approach goals. For fixed-bid projects it's very important to have intermediary goals because delivery and results should be iterative. We're improving both our tech capability and project-management relationships with clients.

Maybe a quick follow-on. Any color on the delta between the fixed-price margins you're achieving currently and time-and-materials margins?

It varies, but to give a ballpark, I've seen contribution margins for some AI work in the 60%-plus range. Not every project is 60%; otherwise we'd be at 60% gross margin overall. But contribution margins on certain AI projects can be quite strong. In general, non-T&M is higher margin than T&M, and AI portions of the business include some very positive outliers.

Ultimately, what does this mean for gross margin? There's the near-term you can handle through headcount management. Can you talk about where utilization is relative to headcount goals and how we should think about the evolution over the next 12 to 24 months? I want to understand the component you control through headcount and utilization versus the component driven by revenue mix.

Good question. I look at near- to intermediate-term initiatives as part of our 300-basis-point margin expansion from Q4 to Q4, which you're already seeing. There's a more fundamental, evolutionary change in pricing and margin models that won't happen overnight. That's what we're working on with our AI platforms. From a finance perspective, what Rahul and Eugene are building with GAIN platforms should increase stickiness and move toward more fixed-price models, which should result in a higher margin structure over time. What that finally ends up being is a work in progress.

Anil gave good financial guidance. Let me break down three elements I gauge the business by: adoption of AI in terms of efficiency, the margin profile, and revenue per person. Utilization becomes more driven by revenue per person increases. There are two parts: internal efficiency improvements and the creation of repeatable IP and platforms that increase revenue per person. This is a new formula for the company: increased revenue per person driven by reusability of IP and agents. Different regions and engagement types have distinct revenue-to-margin ratios, but the revenue-per-person metric should grow everywhere as we propagate Forward Deployed Engineers and platform-based approaches.

The next set of questions comes from Bryan Bergin of TD Cowen.

Maybe just at a high level to start on client sentiment. Given the war in Iran, anything you can comment on how conversations with enterprises have progressed over the last two months? And anything in recent weeks that's different?

There are clear trends we are seeing with our clients. Number one, whereas last year clients were looking at AI projects as proofs of concept and trying to progress them, this year there are production projects being invested in across industries, very consistently. Second, AI is driving more projects and programs for application modernization and data platforms, so we are seeing our pipeline grow in those areas as well. Third, while last year was about early adopters, now we're seeing a wave of fast followers, which increases both our pipeline and total addressable market.

Bryan, regarding the Iran war, to me at least when I look at the business, it's effectively a non-event at this stage.

I wouldn't comment extensively because the situation is fluid. We don't conduct business in areas of direct impact, so it's hard to say the conflict is directly affecting us. The secondary impact on the business is negligible. The larger impacts we've seen relate to the ongoing conflict in Ukraine, which has had more direct effects. I don't think we're materially affected by the Iran situation in our customer base. There are some positive movements related to retooling in manufacturing, which has increased demand in certain areas like digital twin and physical AI, but I wouldn't attribute that specifically to one event. In general, the global environment is changing due to multiple conflicts, but in our customer relationships there's no large detriment. We are seeing shifts that in some cases increase demand in manufacturing and semiconductor-related areas.

Second question on the AI productivity conversation. Among larger traditional SIs, pricing compression and productivity conversations have become more pronounced. I understand you don't compete in all the same places, but how are enterprise conversations for you in engagements that are not transitioning under the GAIN framework as far as that dynamic?

In engagements outside the GAIN framework, we still enjoy significant productivity improvements from AI. For example, with a wealth management client, we deployed AI agents across their CA pipelines in a large business unit and saw 3x to 6x productivity improvements in creation of test coverage. That allowed us to expand our scope in that customer and increase stickiness across business units. We can do more with fewer resources, and that differentiates us in the vendor base for that customer.

Adding to Eugene: beyond AI, I do not see clients broadly coming to us and asking for large discounts on the same engineer. We're not seeing material pricing pressure. You could argue whether we're seeing a premium, that's a separate question. We have seen vendor consolidation over the last 18 months. The good news is clients are moving from hundreds to dozens of vendors. The challenge is they may ask for some concessions when they designate a set of preferred vendors. But our team is disciplined with new logos and pricing ensures margins come in. For established customers, we do see some of these consolidation trends, but overall pricing has been stable.

A clear example is that productivity improvements are often shown at the individual developer level, but when translated into brownfield projects—where most of our business is, integrating into legacy systems—the project-level productivity improvement is lower. Therefore there's less pressure on rates because we execute projects and programs rather than supply individual engineers. When we do consistently show productivity improvements, we can go back to customers and win more work, so it becomes an expansion strategy rather than a play on margin or rate alone. In budget discussions, business leaders look at ROI on total budget vs. outcome; VMOs break it into rate-per-person. We're seeing more projects where budgets are driven by fixed bids tied to deliverables, which changes how productivity is discussed. So this environment is somewhat favorable for our model.

One last question for Rahul. Beyond the major hyperscalers, as you think ahead, what other types of partner ecosystems are you focused on?

I see at least three categories. First, NVIDIA—we expect that partnership to accelerate, particularly for physical AI. Second, specialized partners in AI consulting, process mining, and organizational change management; as technology evolves there will be more specialized firms to partner with, including potentially LLM providers as strategies evolve. Third, large business consulting firms that are looking for technology partners to enable capabilities for their clients; we're developing relationships in that area now. We're actively developing these partnerships.

The next questions come from Mayank Tandon of Needham.

I don't know if there's much to ask. But I'll go ahead anyway and give it a shot.

Mayank, we expect you to ask the best questions.

I'm sorry, I'm running out of questions here. But I guess just quickly, you talked about visibility earlier, Anil. In terms of revenue, how much of the business would you say is sold versus what you still have to go out and win? What is potentially at risk versus what you already have in the bag in terms of your guidance?

You recall our traditional model is roughly 85% from customers who've been with us more than two years, 10% from the last 12 months and 5% from new customers. That framework more or less continues to be intact, though there may be variations as we ramp new customers. When I look at our guidance and outlook, we take into account potential downside risks with large customers and macro factors. If you ask whether I have a specific number at risk, it's a probabilistic distribution across customers and the macro environment. Qualitatively, compared to three or four months ago, things are improving, and we feel good about the overall business.

A few direct pointers: retail, which was the most volatile, has been derisked as it's a smaller contribution now. As we grow, the other risk is converting AI deployments into measurable client profits and gains, not just Grid Dynamics' GAIN platforms but the client's realized value. This business is growing fast and we can forecast better deployments, but fixed-bid and outcome-based projects require clear measurement criteria. As we expand, choosing the right partners—hyperscalers, LLM providers, and others—will be important because the cost and delivery of models play a larger role. We are tuned to the system and selected as preferred in many cases, and we're confident, but the dynamics of delivering measurable AI value at scale is something we need to prove further.

Just to close out, Anil, you mentioned M&A is still a priority. What might you be looking for? Have private companies recognized valuations have come down and are more inclined to sell versus resisting a sale to a company like Grid?

Yes. We're focused on tuck-ins that augment capabilities, geography and verticals. We're looking at capabilities in technology, data, AI and certain end markets tied to our strategy. Valuations have come down, so things look better from a valuation standpoint. That said, you pay a premium for truly differentiated companies. If someone has real differentiation, you have to pay for it. We're focused on accretive acquisitions and hope to close some deals; many are tuck-ins.

The bottom line is accretiveness of acquisitions is vital. We're close to showing the market we can pursue M&A because appetite has been modest but valuation dynamics are improving. We are prioritizing AI-related technologies and partnerships outside the traditional path. Stay tuned—we're in good shape.

Ladies and gentlemen, this concludes the Q&A portion of our call. I will now turn it over to Leonard for closing. Technical difficulty.

Q1 2026 is proof that our AI transformation is working. Our AI revenue reached 29.3% of total revenue. GAIN has matured from a framework to platforms with Forward Deployed Engineers. Agentic AI solutions are now in production across a range of industry verticals and are generating measurable ROI at commercial scale. The pipeline entering Q2 is the strongest it has ever been. AI consulting and hyperscale partnerships are expanding. We're executing on our strategic roadmap, including AI-native delivery, productized GAIN platforms, consulting and internal automation. We look forward to updating you next quarter. Thank you.