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Presentation

Cary Savas: 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.

Leonard Livschitz: 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, 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 the main knowledge, AI capabilities and ability to rapidly scale relevant expertise.

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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 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 the 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 time line 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, and 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, focused on converting our repeatable IP into AI native platform-based offering under the GAIN platforms. GAIN consists of 4 domain-specific platforms spanning from 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 that what we achieved in 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 50% reduction in preparation time and 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 agentic world. Clients are seeking out 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 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 in enhancing 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 manufacturers’ 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 responses time, restoring the platform’s ability to support real-time replenishment decisions at the 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?

Rahul Bindlish: Thank you, Leon. Good afternoon, everyone. Partnerships are now a key component of how we go-to-market. Our partner inference revenues have grown to 19.1% of total company revenue in quarter 1, underscoring the value of our ecosystem-driven approach in the agentic era. The majority of our partner inference 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 marketplace 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 service 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 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 2 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 3 distinct phases. The initial engagement was a first project delivered through a co-sell motion with Google Cloud and built on 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, 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.

Anil Doradla: 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.

Cary Savas: [Operator Instructions] First question comes from Puneet Jain of JPMorgan.

Puneet Jain: 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 like Forward Deployed Engineers. Can you train your current employees to be FTEs? Or do you have to change your hiring mix to be able to offer GAIN platform to your customers?

Leonard Livschitz: Let me try to unpack some of your questions. It’s a lot than one. But let’s go backwards, probably a little bit easier. So let’s start with engineering talent and Forward Deployed Engineers. Majority of the people who we deploy, obviously, are internally trained. We have a large number, substantial large number of very technically educated people who we internally build our services and promotions and train them in the models. And it’s led by our R&D organization, so you see Eugene is going to give you some more comments, which combining with retraining the delivery organization brings the talent. Obviously, when we bring the talent from the market, it still needs to be structured so they’re going to be able to adapt Grid Dynamics GAIN platforms approach.

The GAIN platforms approach is really what makes us different. So rather than talking about a very specific model for each individual customers, let me explain a little bit in the words what these new platforms means for the contracts. So basically, we developed a lot of tools over time. And even in the last Board meeting, we introduced lots and lots of different names. And now we’re maturing to the point that we can offer a suite of solutions to the client where we actually define a kind of a combination of Grid Dynamics IP and open available sources into the total solution. And the total solutions which we offer are driven by adoption of the engineers and agents in the form of the guidance, where we expect the return on investment for the client.

So answering your question, the number of non-T&M projects — and because there is a lot, there is a tokenization, there is offering of the fixed bid, there is a performance related. They are significantly increased and they continue to increase. And you will actually see that as we continue to answer your questions today because that model itself requires not only training the FD engineers, but adapting the internal processes and the program management and delivery team to actually control a proper engagement in a different venue. So answering your question, definitely, there is a big shift toward non-T&Ms. 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 number looks flat.

But if you again unpack that number, you will see a significantly higher contribution on the engineering workforce because some of them require an additional training and reclassification before we deploy them to the clients. But the good news is, overall, we have a very strong vector where we are building our position with adopting our clients, new models related to the GAIN platforms.

Puneet Jain: Got it. No, it’s a big change. And so it seems like you’re already doing a lot of hard work that’s involved. Let me ask Anil. So the guidance, like the full year on top line, so it does imply like a mid single digit growth even in the lower half, mid single digit average sequential growth in 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?

Anil Doradla: So there are 2 or 3 factors here. Leonard, do you want to talk about pipeline, then I can take it.

Leonard Livschitz: Well, I will answer the easy part. And then Anil will dive you a little bit of the numbers. There are 2 parts of the confidence level we have. The number one, the demand has grown substantially. So we have the record number of demand. And I’m avoiding the word number of engineering demand because, again, we’re talking about the teams, the platforms, the offering, but overall demand, the vector is very steep right now. That’s a subjective factor because, again, this could happen, it may not happen or whatever, but it’s a good news. It’s a record high. The more interesting factor is, and Anil will dive into the financial estimates, we are facing a larger, as I mentioned in the previous comment to you, number of non-T&M projects.

This work force is defined by a different estimate, how do we qualify the revenue based on this project in which point. So when we unpack the number, we are a bit more conservative, which we’re going to guide this particular quarter or the next quarter because now it becomes a little bit more of a financial exercise. The work has been signed. The work is going on, but Anil probably give you a little bit better feedback. But the summary for you, the takeaway for me, 2 parts, significantly higher number of the pipeline and a very large number of the non-T&M project, which require a little bit more financial attention, how we guide the numbers for the near future for the next couple of months.

Anil Doradla: No, look, I mean, Leonard, you pretty much hit it. Let me kind of build upon that. Leonard and the team in our prepared remarks talked about a fundamental transformation on how we’re moving. And the word you will see again and again is a platform. Now the historical approach we all know is that you take the engineer, you have a certain T&M rate, you multiply it by hours, days; and the formula, as you know, is very linear. We’re transitioning. We’re seeing that. Rahul is leading the way from a partnership and Eugene is leading the way, obviously, on the CTO. We’ve introduced all these new products and platforms, and we’re working on monetization. Now there are stages of monetization. There’s upfront, that will get start off small.

There’s greater stickiness with these engineers. And as our clients become comfortable with both our products as well as our engineers in this new model, that’s when we start seeing a lot more monetization there. So when we started looking at these numbers, the obviously, revenue recognition is a key component to it, right? And we’re taking, think of it as baby steps right now. We see the pipeline. I look at year-to-date from January 1 through now, compare that with last year, really good. I look at some of these initiatives we’re working on, on AI, really good. But the question will be, how do we time it? Is it a linear timing or nonlinear timing? So from that context, for the full year, we’re keeping it. Now let’s see the couple of quarters.

Does it turn out much stronger because we have some of the recognitions or not. So we’re still experimenting with this. We’re working through it. So the optics of it looks slightly different from what you can see underneath from a business point of view.

Leonard Livschitz: Let me add one more factor, because it could be a bit 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 a question, how can you grow such a steep increase of profitability on relatively modest increase of revenue? So this gives you a little bit more a story that we look at the new projects we’ve been awarded to us — as Rahul was mentioning in his statement — at a different margin profile than the current business. We just don’t want to run ahead of the time and do all the financial qualification of that until we see the results. But we are very confident in the progress we’re about to make.

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

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

Margaret Nolan: 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?

Leonard Livschitz: I think the best way to start is with the person who is responding to that. I think, Rahul, you have a perfect opportunity to tell how you build the business continue to grow. So please go ahead.

Rahul Bindlish: Yes. Thanks for that question, Maggie. Like 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. And we are well on our path to achieve that. In fact, I would say we are tracking slightly ahead when we look at our internal goals to achieve that. And with GAIN platforms being deployed on the hyperscaler marketplaces, we’ll probably see acceleration of that partner inference revenues in the future quarters.

Leonard Livschitz: Let me just add one more color maybe on this. Rahul, a bit kind of mentioned in his prepared remarks, but it’s important because, again, it’s new. So we talked with Puneet about the new model of the business. Now we talk a little bit different model of engagement with our partners. In the past, we’ve basically been talking about hyperscalers. And that was a very consistent is, frankly, the influence revenue generated with these partnerships. Now we start adding, especially with the physical AI, some interesting new level of partnerships. And monetization is a little bit lower yet, but we see a substantial growth because now we’re adding into with the heavy hitters in the industry because it adds more addressable market.

The other element, which is kind of getting also related to our GAIN platforms, it’s a consultancy part. So now we’re also getting partnerships with some of the business organizations which are asking us to become the lead technology implementation partner, which is adding a little bit more of the flavor from transition from the business conceptual idea to implementation related to specific AI platforms. As you know, business leaders are a little bit more cautious about spending the budget because you can spend a lot of money on experimentation. So they would like to seek some clarity where they would have a confidence that the investment is not going to be not just risky, but send them to wrong direction. And Grid Dynamics is becoming the partner of that, their consultancy work.

So I think it’s another really important difference from the past.

Margaret Nolan: 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?

Rahul Bindlish: 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 there, we have seen many of these customers consolidating. And the other thing is that in some of them, we have now become a preferred vendor. We were always there, but now as they were consolidating, we reached the preferred vendor status. With the TMT, there are 2 nuances to the movement. There’s obviously our work with them, what we’re doing. They know what AI is, and they appreciate us. It’s a very interesting thing. The smartest technology customers are the one who are seeking our AI capabilities and more, which is a little counterintuitive, right? But the other interesting thing that is going on with these customers is that there’s a hyperscaler relationship too.

So on both fronts, we are seeing a lot of activity. Now every quarter, there might be some negatives moving there, but the trajectory is very strong as we get consolidated as we’re one of the few vendors, as we’ve got a clean sheet with many of these new stakeholders and we augment that with some of the hyperscaler growth that is going on.

Leonard Livschitz: But I think the important color, very specific color for you, Maggie, is that Anil mentioned about selection being a preferred vendor. We’re not talking about generic preferred niche vendor anymore. The AI proliferation equalize the supply base. In other words, there is — the size does not provide advantage to some of the largest vendors. The capability of deploying AI solution at scale has been determined as a vital part. And being a smaller company and being able to transition faster remember, again, the very first question from Puneet — how quickly we can train people. It’s amount of quality work with those specialized teams, which determine our awards on the business side. And with the TMT, it’s definitely the #1 followed right now with the financial clients. We’ll talk a little bit more about others as time comes. But the top 5, top 6 clients, we are in the driver seat for AI deployments.

Cary Savas: The next question comes from Surinder Thind of Jefferies.

Surinder Thind: When we think about the non-time and materials model, how do we think about the incremental risk that you’re taking on? Obviously, over the past decade, 2 decades, we moved in that direction because projects got bigger, they got more complex. There is maybe greater uncertainty about scope or changes in scope. How does that work in the new model? Because if you’re looking at an outcome-based or fixed price token usage, like where is the risk in the model for you guys? Or how are you guys addressing that?

Leonard Livschitz: Surinder, I will actually have Eugene Steinberg, our CTO, to start talking because she is a bit of an architect of the system. And uncertainty has 2 prongs. One of them is a risk level, the second one is a reward level. And I will let Eugene talk about the coexist on both and how we handle it. Please, Eugene.

Eugene Steinberg: Yes. Of course, when you are taking a fixed price project, you always have to balance risk versus reward. So on the risk standpoint, the main risks in the fixed price projects are coming from uncertainty. Uncertainty is coming usually from understanding of the requirements and finding gaps in the requirements of the project. We are using very actively our AI agents and our specific game, Rosetta framework, to uncover all the uncertainties in the requirements and clarify with our sources ahead of time during the presale phase, and that builds us a very strong confidence in the understanding of what needs to be done. During implementation, we are very actively using always AI coding assistance and our GAIN Rosetta framework, helping to accelerate the delivery of a project and building the buffer for any unknown unknowns, which usually happen in those projects.

Anil Doradla: So let me just add one thing to what Eugene just said. So Surinder, you know you’ve been in the IT industry, and this is a risk not unique to Grid. It’s a universal risk. All I’ll add is a couple of additions to what Eugene said. The first thing is that when you scope out projects, if you don’t have a deep understanding of the project or as Eugene says, the risk, it’s a problem. Now when I look back at the history over the last 5 years, historically, we were a T&M shop. We moved towards fixed price. And actually, during those first year or 2 of our fixed price, we learned a lot. We have committed mistakes in the past. This is the pre-AI era, and we worked. As a matter of fact, there were times when our fixed price project margins were comparable with our T&M, and I always went back to the team what’s going on.

So we learned. Now when you look at our fixed price margins pre-AI, they’re higher than our T&M. And those learnings are now moving into our AI. So we really know what we’re doing. I think what we’ve learned is that if you don’t understand the problem that you’re dealing with and you don’t have a technological know-how, you’re absolutely right, there is a heightened level of risk. We’ll always have that risk. But as Leonard pointed out, there’s a reward component too with that.

Leonard Livschitz: Yes. And I just want to close on that with one simple statement. In my prepared remarks, I mentioned clearly that Grid Dynamics is not a system integrator. We are a product-centric engineering company. And that actually gives us the higher level of confidence that we take on the projects, we have a higher probability of success. So Eugene was mentioning Rosetta, another methodology we’re using. It’s all part of the GAIN platforms. Now the outcomes on a greater scale, Surinder, will be seen as we will propagate more and more results of this work. So it’s not about how much money we generate in the project, but how much rate of growth we’re going to see in this project going forward. Right now, at the size that we have and the scale of the tasks, we are training not only the models, but our customers, how to react on gradual, I would say, continuation of the development and approaching the goals.

So it’s very, very important for the fixed bid for us to make sure we have intermediary goals because the approximation of the work and deliver results have to be iterative process. And that’s very important. So we’re improving not only our technology capability, but our project management relationship with the clients as well.

Surinder Thind: Maybe just a quick related follow-on. Any color or commentary on the delta between kind of the fixed price margins that you’re able to achieve currently and what you’re achieving on the time and materials side?

Anil Doradla: Sure. So when I look at — now it varies quite a bit, right? So I’ll throw a number out and somewhere in the ZIP code. I have seen the contribution margins when we get to some of our AI work somewhere in the 60-plus range too. Now I mean, not every project is a 60%. Otherwise, we would have been a 60% gross margin, but this is a contribution margin and then obviously, you have to offset by some of the overhead. In general, if you look at most of our AI work, it is higher margins. If you look at the deltas between our T&M business and non-T&M business, there is a delta. So we see non-T&M in general being higher. And then when you look at AI business portions of the business, we do see some outliers, very positive outliers.

Surinder Thind: Ultimately, what does this mean from a gross margin perspective? There’s obviously the near term that you’re able to handle from both managing headcount. But can you talk about where utilization is relative to your headcount goals and how we should think about the evolution over not just next quarter, but the next 12 to 24 months? Because it sounds like there’s a big opportunity here, and I just want to make sure I understand the component that you control through managing headcount and utilization versus the component that’s ultimately going to roll out as a result of just the revenue mix itself.

Anil Doradla: Very good question. So the way I look at, Surinder, your question is there is what I call the near to intermediate areas of focus, which is part of our 300 bps margin expansion, right, Q4 to Q4, and you’re already seeing that, right? Then there’s a more fundamental question that you’re asking is what is this pricing model and what is the margin model. So that is a more evolutionary thing that will not happen overnight, that has a more longer term. And that is what we are all working on as we work on these AI platforms. The whole GAIN — as a finance guy, if you really look at what I tell Rahul from a GAIN platform and Eugene, who’s always excited about technology is, what does it do to the margins and what does it do to the stickiness and what does it do to the growth?

I mean, that’s what it really boils down to, right? And our long-term model is to embed GAIN platforms with our customers — that is just not human capital, but it’s agents and actually IP — create more stickiness, move towards a more fixed price model, which should result in a higher margin structure. Now what is that finally going to end up being? It’s work in progress.

Leonard Livschitz: Yes. So I think Anil gave you a lot of financial guidance. Let me break it down to a couple of key elements, which I gauge the business. So there are 3 elements, obviously, adoption of AI in terms of the efficiency of the business, the marginality of the business. But there’s a third factor, which you guys use quite often, which is not totally irrelevant. I think it’s quite appropriate. It’s the revenue per person. So utilization of the test becomes more driven by the revenue per person increase. And there are 2 parts of it. On an overall EBITDA margin on a net margin, this is the fourth pillar of the platform, how internally we utilize it. But that doesn’t help with the growth of the business. With the growth of the business, it comes actually with the idea that we are going to have repeatable and kind of reusable IP intelligence of our platforms.

So the utilization part comes with the utilization of humans and IP capital. So it’s a new formula, which is really — will be gauged in my opinion, which I’m going to drive the company — is increased revenue per person. Now saying that, there’s another factor, right? It’s Europe versus India versus U.S. local consultancy. Different categories of different regions create a different ratio between revenue and the margin. And I’m telling my team, it’s irrelevant. The revenue per person as a guidance for utilization has to grow everywhere. The new ability to create game-based platforms Forward Deployed Engineers and the models should drive the efficiency as we already see in the early adoption regardless of the regions and the traditional T&M models, which are not going to be as much used as we go forward.

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

Bryan Bergin: Maybe just at a high level to start on client sentiment. Just given the war in Iran, anything you can comment on how the conversation with enterprises has progressed over the last 2 months here? And just more recently as well, anything in recent weeks that’s different?

Rahul Bindlish: Yes, I can do that. Thanks for that question, Bryan. So there are clear trends, Bryan, that we are seeing with our clients. Number one is whereas last year, there was clearly clients who were looking at AI projects as POCs and trying to progress them into projects. Clearly, this year, there are production projects being invested in clients across the industries, very consistent. Second trend we are seeing is with AI, it is driving more projects and programs even for application modernization and data platforms. So we are seeing our pipeline grow in those 2 areas as well. Third, very clearly we are saying — whereas the last year, they were the early adopters of AI, now we are seeing a wave of fast followers. That is increasing really our pipeline as well as, in some ways, our total addressable market.

Anil Doradla: Bryan, coming to your point, the Iran war, to me, at least when I look at the business, it’s a non-event at this stage, right, in the third place.

Leonard Livschitz: Yes, I would say I would not really comment right now because the situation is very fluid there. We don’t conduct the business in an area of the direct impact. So it’s very hard to say that. The secondary impact on the business, again, it’s negligible. I think that we had a huge impact continuing to the impact of the Russian invasion to Ukraine, right? That’s much more dear to us. I don’t think we’re affected as much. But the global world has changed more with the conflict of Middle East and obviously conflict between Russia and Ukraine. And there are various factors. I mean, look, ultimately, the peace and resolution is the benefit for everyone. But how the peace is going to be achieved is very important.

Right now, we’re just plugging alone. And in our business model and our customer relationship, there is no detriment. There are some positive movements related to their retooling, especially in the manufacturing space because there are obviously more demand for manufacturing of certain type of products. If we talk about our digital twin approach and about our physical AI approach, we’re gaining momentum. But I would hate to say that it’s really driven specifically by the individual event. But we definitely see the shift of manufacturing to the much higher retooling and scaling the production. And one of them is related to the traditional manufacturing. One of them is related to more semiconductor manufacturing.

Bryan Bergin: Second question here, just as it relates to kind of the AI productivity conversation, just coming out of a lot of the larger traditional SIs, the conversation around productivity, pricing compression for them became more pronounced here in recent weeks. I fully understanding you’re not competing in many of the places that they are. But just how are the enterprise conversations for you in engagements that are not transitioning under the game framework as far as that type of a dynamic?

Eugene Steinberg: So how the conversations are going in the framework — so in this case, very often, we still enjoy significant productivity improvements from AI. I can give you some examples. So we just completed a project with one of the wealth management client of ours. And this is where we deployed AI agent across the CA pipelines in one of their large business units. So there, we saw 3x to 6x productivity improvements in the creation of the test coverage. And that allowed us to go wide in this customer and increase our stickiness and increase our reach to all business units of these customers going forward. That proved that we can do more with less resources and this differentiates us across other vendor base of this customer.

Anil Doradla: Yes. So let me add a couple of statements to what Eugene just said. So the question is really how is the pricing environment right now beyond the AI. So AI obviously has its own dynamics, and I will put that aside. When I look at the business, I look at a couple of very interesting things. One is that I do not see clients coming and asking that now that same engineer give me a big discount now. I’m not seeing that. Now we can argue whether I’m seeing a premium or more premium, that’s second question. But we’re not seeing any pricing pressures. Number two is that in our case, tied to Leonard’s opening comments, we’ve seen a lot of vendor consolidation over the last 18 months. Very interesting thing about vendor consolidation, it’s good news and not so good news.

The good news is that they go from hundreds to dozens. The bad news is that, okay, they say that you’re one of the chosen one, give me a little bit of a discount for the next year or so, something like that, right? So we’ve gone through that. So I would say maybe that would be the closest thing I could come to. But the team does a very good job when it comes to new customers, new logos. They’re very particular. We have a very strong discipline in terms of ensuring that the margins come in. It’s with our well-established customers. And there, we’re seeing some of these trends.

Leonard Livschitz: You have a very clear example now.

Rahul Bindlish: Yes. I just want to add a couple of points there, Bryan. Number one, productivity improvement in the industry is still being shown at individual developer level. When you translate that into projects, especially brownfield projects where majority of our business is, where you are integrating into legacy systems, that productivity at a project level actually falls down to significantly lower numbers, right? So from that perspective, there is less pressure because you are executing projects and programs and not providing individual engineers. At the same time, when we have examples of consistently showing productivity improvements, we are able to go back to our customers and grab more business. So it becomes expansion of a business strategy rather than play on the margin or the rate.

Leonard Livschitz: I think let me just conclude. In a good environment people talk about their side cases and I kind of summarize from the global business positioning. So what I see, and this is quite promising because when I personally meet with the leaders or clients and usually, when you go to the top, the conversations on the overall spendings, and the priorities and budgets come quite clearly as a critical path, especially when those leaders coming from technology organizations, which depend to show concrete results to their business leaders. They are much more focused on productivity in terms of the overall return to the clients. Remember, we talked about this in the past. So you agree with business people on ROI on a total budget versus outcome and then you go to the VMO, and VMO breaks it down by the rate per person.

We are getting right now in a budget discussion overall projects, where the budgets are driven by the fixed bid by the deliverables. And that model, that productivity conversation usually goes on a deployment of the measurable results before somebody starts looking at productivity, because when are you going to ask productivity if it’s a total budget being agreed between both sides. So this environment a little bit better. But before when Surinder was talking about, he acknowledged, obviously, the question of the risk of the model. But that risk is not related directly to productivity anymore at those new adapted businesses.

Bryan Bergin: I’ve got one last one for Rahul here since he’s on the call. Just Rahul beyond the major hyperscalers, as you think ahead, what other types of partner ecosystems are you focused on?

Rahul Bindlish: So I think there are going to be at least 3 categories. I already spoke about NVIDIA. I do expect that partnership to take off from here. The second category would be specialized partners. I talked about on the AI consulting area. But I do expect as technology evolves, there are more specialized AI firms that we will start to partner with, potentially even the likes of your LLM providers, right, as their strategies evolve. The third category is what Leonard had talked about. We are starting to see interest from large consulting business consulting companies who are looking for technology partners to enable capabilities that they want their clients to have, right? And that’s the third very interesting partnership area that I see us progressing with.

Leonard Livschitz: This is immediate. This is we’re developing right now.

Rahul Bindlish: This is we’re developing right now, yes.

Cary Savas: The next questions come from Mayank Tandon of Needham.

Mayank Tandon: I don’t know if there’s much to ask. But I’ll go ahead anyway, give it a shot.

Anil Doradla: Mayank, we expect you to be the best questions.

Mayank Tandon: I’m sorry, I’m running out of questions here. But I guess just very quickly, just to keep the call on schedule. The question I had was around your visibility. I think you talked about that earlier, Anil. In terms of the revenue, how much of the business would you say is sold versus you have to still go out and win? So what is sort of potentially at risk versus what you already have in the bag in terms of your guidance?

Anil Doradla: So you recall, Mayank, we have had a very traditional model or a well-established model about 85%, 10% and 5%, right, where 85% of our revenue in any given year comes from customers who have been with us 2 years and beyond, 10% comes from over the last 12 months and 5% comes from new. That framework more or less continues to be intact. There might be some variations, especially as we ramp some of these new customers. So the way — I look at it through this lens. Now when you look at our whole guidance philosophy and when you look at our whole outlook philosophy, what we know well is potentially where we have some of these downside risks, right? I mean, we’re dealing with these customers and these are big customers, and we have some sense of what we do.

So when we give our guidance, for example, at least in the short term, we’re taking that into account. When I switch from my short-term guidance to my long-term guidance, I basically switch from a bottoms up to a top down a little bit, right, where I look at the overall pipeline, I look at the forecast, I look at our customer engagements and come up with this. Now if you were to ask me whether I have a number that I believe is at risk, I mean, it’s a whole probabilistic distribution, right, on how I look at it. I would say when I look at the business today versus 3 months ago versus 4 months ago, things are improving. So qualitatively, I would say that things are improving. Now there’s always that risk that we have with any one particular customer due to circumstances or as someone asked a question on the Iran war, there’s a macro issue, consumer-sensitive industries are impacted.

That’s always there. But as we see right now, we feel good about where we see the overall business.

Leonard Livschitz: So let me just give you, as always, direct pointers. After listening to Anil we need some guidance on his guidance. There are 2 areas which I think are very important to understand. Number one, the retail business, which traditionally was the most volatile has been derisked and continues to be derisking because it’s a smaller contribution. It’s not little, but it’s small. So that’s area where the variance of uncertainty you are talking about. But the second risk is actually growing as we’re going to grow the business is how the AI deployments will actually convert into the measurable profits and gain, not Grid Dynamics GAIN platform, but the client gain, right? And that business is growing very fast. So we’re very happy that we can actually forecast a better deployment of these projects.

But again, when we talk about fixed bids, we’re talking about outcome-based, we’re talking about criterion, which before was not that clear, exactly it’s how do you measure that ROI. So this criterion becomes a system of criteria, which is growing more and more of our business. So I would say that the business we project is very certain that we’re substantially derisking with retail. However, I see as we grow macro going forward, we need to make sure we bet on the right partners. And that’s when actually the ecosystem of the partners also evolves. Remember, Bryan’s question, who is going to be the next level of partners besides hyperscalers. And then Rahul mentioned 2 parts, of course, consulting is very clear gain. But then which of the other elements of the LLMs on other substantial guys who will provide us data centers, who provide us the material traffic of these deployments, the cost of these models is going to play a much bigger role.

We are tuned to the system. We’re selected to be preferred in many cases. We’re confident. But the whole dynamics of AI deployed deliverable value, it’s still something we have to prove on a major scale for everyone.

Mayank Tandon: Just to close out, Anil, you mentioned that M&A is still a priority for you. So just wanted to get some context in terms of what you might be looking for. And then, have private companies maybe sort of recognize that valuations have come down a lot and maybe are more inclined to sell versus resisting a potential sale to a company like Grid?

Anil Doradla: Yes. So as you rightly pointed out, yes, we’re very focused, fingers crossed. We hope to close some deals — and most of them are tuck-ins. What we’re looking at right now are tuck-ins from a capability point of view. So obviously, technology has elevated to be very important, data, AI and certain end markets tied to our strategy. So now when it comes to the valuation, you will always have to pay a premium for good companies. For good, capable companies, you will always have to pay some level of premium. But overall, you’re right, they have come in. And things are looking better from a valuation point of view. But at the end of the day, if someone has some true differentiation, you do have to pay.

Leonard Livschitz: The bottom line is, the accretiveness of these acquisitions have been the vital point, and we’re very close to prove to the market we can still come back and do our M&As because, again, you’re right, the appetite for them has been a little bit more modest, but it’s not as critical as our broader net, which we threw around the world related to the 2 elements, really 2 elements: AI-related technologies, especially the cutting-edge technologies, we can benefit more as a congruent business than the particular company on themselves. And the second part is looking for the partnership outside of the traditional path, which we’re enhancing. So stay tuned. We’re in good shape with that.

Cary Savas: Ladies and gentlemen, this concludes the Q&A portion of our call. I will now turn it over to Leonard for closing [Technical Difficulty].

Leonard Livschitz: Q1 2026 is proof that our AI transformation is working. Our 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 road map, including AI-native delivery, productized GAIN platforms, consulting and internal automation. We look forward to updating you next quarter. Thank you.

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