GDYN

Looking back on it services & consulting stocks’ Q2 earnings, we examine this quarter’s best and worst performers, including Grid Dynamics (NASDAQ:GDYN) and its peers. IT Services & Consulting companies stand to benefit from increasing enterprise demand for digital transformation, AI-driven automation, and cybersecurity resilience. Many enterprises can't attack these topics alone and need IT services and consulting on everything from technical advice to implementation. Challenges in meeting these needs will include finding talent in specialized and evolving IT fields. While AI and automation can enhance productivity, they also threaten to commoditize certain consulting functions. Another ongoing challenge will be pricing pressures from offshore IT service providers, which have lower labor costs and increasingly equal access to advanced technology like AI. The 8 it services & consulting stocks we track reported a satisfactory Q2. As a group, revenues were in line with analysts’ consensus estimates while next quarter’s revenue guidance was 0.7% below. Thankfully, share prices of the companies have been resilient as they are up 9.6% on average since the latest earnings results. With engineering centers across the Americas, Europe, and India serving Fortune 1000 companies, Grid Dynamics (NASDAQ:GDYN) provides technology consulting, engineering, and analytics services to help large enterprises modernize their technology systems and business processes. Grid Dynamics reported revenues of $108.2 million, up 7% year on year. This print exceeded analysts’ expectations by 1.6%. Overall, it was a strong quarter for the company with full-year revenue guidance beating analysts’ expectations and EPS in line with analysts’ estimates. Grid Dynamics scored the fastest revenue growth and highest full-year guidance raise among its peers. Unsurprisingly, the stock is up 10.2% since reporting and currently trades at $7.78. We think Grid Dynamics is a good business, but is it a buy today? Read our full report here, it’s free. With over 2,500 research experts guiding organizations through complex technology landscapes, Gartner (NYSE:IT) provides research, advisory services, and conferences that help executives make better decisions about technology and other business priorities. Gartner reported revenues of $1.68 billion, flat year on year, outperforming analysts’ expectations by 1.8%. The business had an exceptional quarter with a beat of analysts’ EPS estimates. Gartner achieved the biggest analyst estimate beat of the whole group. The market seems happy with the results as the stock is up 21.4% since reporting. It currently trades at $183.96. Is now the time to buy Gartner? Access our full analysis of the earnings results here, it’s free. With a workforce of approximately 774,000 people serving clients in more than 120 countries, Accenture (NYSE:ACN) is a professional services firm that helps organizations transform their businesses through consulting, technology, operations, and digital services. Accenture reported revenues of $18.72 billion, up 5.6% year on year, in line with analysts’ expectations. It was a slower quarter as it posted revenue guidance for next quarter missing analysts’ expectations. Accenture delivered the weakest guidance update in the group. Interestingly, the stock is up 6.1% since the results and currently trades at $177.78. Read our full analysis of Accenture’s results here. Evolving from its roots in IT staffing to become a high-end technology consulting powerhouse, Everforth (EFOR) provides specialized IT consulting services and staffing solutions to Fortune 1000 companies and U.S. federal government agencies. Everforth reported revenues of $1.01 billion, down 1.3% year on year. This number topped analysts’ expectations by 1.6%. It was an exceptional quarter as it also recorded an impressive beat of analysts’ EPS guidance for next quarter estimates and a beat of analysts’ EPS estimates. Everforth achieved the highest guidance raise of the whole group. The stock is up 40.5% since reporting and currently trades at $32.88. Read our full, actionable report on Everforth here, it’s free. Born from IBM's managed infrastructure services business in a 2021 spinoff, Kyndryl (NYSE:KD) is the world's largest IT infrastructure services provider that designs, builds, and manages technology environments for enterprise customers. Kyndryl reported revenues of $3.62 billion, down 3.3% year on year. This print came in 0.7% below analysts’ expectations. In spite of that, it was a very strong quarter as it produced a beat of analysts’ EPS estimates. The stock is down 5.7% since reporting and currently trades at $13.86. Read our full, actionable report on Kyndryl here, it’s free. Over the past year, investors have been forced to repeatedly answer the same question: what is the market’s biggest risk? The answer has changed several times, and each shift has reshaped market leadership. Late in 2025 and early 2026, artificial intelligence became the market’s primary uncertainty. Investors questioned whether AI would erode software pricing power and weaken competitive moats as AI made it easier to replicate once-differentiated products. By the spring, technology took a back seat to geopolitics. The U.S. conflict with Iran briefly became the market’s dominant narrative, raising concerns about oil prices, inflation, and global growth. But as energy markets remained orderly and fears of a prolonged supply disruption faded, investors quickly turned their focus back to fundamentals. Want to invest in winners with rock-solid fundamentals? Check out our Top 6 Stocks and add them to your watchlist. These companies are poised for growth regardless of the political or macroeconomic climate.

Image source: The Motley Fool. Thursday, July 30, 2026 at 4:30 p.m. ET Chief Executive Officer - Leonard Livschitz Chief Revenue Officer - Vasily Sizov Chief Operating Officer - Yury Gryzlov Anil Doradla Eugene Steinberg Leonard Livschitz: Good afternoon, everyone, and thank you for joining us today. We delivered a solid second quarter. Consolidated revenue of $108.2 million, above the high end of our guidance range and ahead of Wall Street expectations, with non-GAAP earnings of $14.7 million, which also is beating consensus. As you may recall from my last quarter commentary, there were three areas I highlighted. First, improving revenue trends, especially with key accounts in the areas of technology and financial services. Second, our AI adoption and growth. Third, improving profitability trends. I'm happy to report that on all three fronts, our execution is solid and we're seeing the benefits. Growing top accounts relationships, continued AI momentum with expanded capabilities in robotic and physical AI, and solid progress toward our 300 basis point margin expansion commitment. For the second consecutive quarter, our top accounts are in technology and financial services. Technology and financial services now define our more strategic customer relationships and those precisely the sectors where AI adoption is moving fastest and where our capabilities are the most differentiated. Our top accounts continue to drive our growth. Several delivered double-digit quarter-over-quarter growth with standout performances. There are no incremental gains. They reflect expanding programs, deeper program adoption, and the compounding effect of our capabilities that keep finding the opportunity inside each client's organization. Several of these clients are now embedding our GAIN platform as core infrastructure in their own operations, not just a project tool, but as a sustained capability. This is a fundamentally different and more durable commercial relationship than what we've had two years ago. AI revenue reached 30.7% of the total company revenue in the second quarter, growing 54.6% year-over-year and crossing the 30% threshold for the first time. Two consecutive quarters of the year-over-year growth over 50% tells us something important. This is not a spike. It's a sustained shift. The trajectory is clear, and we intend to build on it. Driving this strong performance is a combination of multiple factors. Our GAIN platforms are winning wider enterprise adoption. Our clients continue to transition enterprise AI workloads from pilots to production. Our engineers are more deeply embedded inside client organizations. Bottom line, we're winning entirely new programs that gives us confidence in growth ahead. AI-first delivery is now the default, not the aspiration. Fixed price is a preferred approach on new RFP responses. The productivity and margin gains are real. We're executing well and delivering projects successfully. Our focus on executing larger AI platforms is aligned with significant progress we're making in upskilling our engineering talent. By the end of October, we plan to have 90% of our engineers trained on AI SDLC. Our GAIN platforms have expanded LLM partnerships meaningfully this quarter. We're now working with several of the world's leading AI companies, including the top four frontier providers with whom we're under commercial agreements. This approach ensures our GAIN platforms stay aligned with the leading AI platforms with broader reach across our enterprise client base. GAIN remains the backbone through which we bring AI capabilities to market. Its partner depth makes it stronger every quarter. Our client relationships are evolving, too. Clients who came to us for platform deployments now ask us to stay. They want us to be involved in advisory execution ongoing operations. This meaningful shift is opening a growth vector that did not exist in our model two years ago. On the partnership front, partner influence revenue reached 19.1% of the company total revenue in the second quarter. That was driven primarily by our three core hyperscaler relationships with Google Cloud, AWS, and Microsoft Azure. A growing proportion of that revenue is coming from AI engagements. We are running agentic AI workshops across our Google Vertex AI search customer base, converting search engagement into broader agentic commerce programs. We extended our Google partnership in banking and financial services, closing our first joint win this quarter at a leading global bank. We're deepening our AWS relationship around application modernization and agentic AI in CPG manufacturing and financial services. Our NVIDIA partnership is gaining momentum across both agentic AI and physical AI. Our longer-term target remains 25%-30% partner influence revenue, and we're confident of achieving this target. Last quarter, I introduced our physical AI capabilities and our first commercial engagements in the space. Physical AI requires a deep understanding of multiple disciplines that include modeling real-world robotics movements, digital twins, verification in simulators, and integration with hardware systems. Our active programs span humanoid robotics for pharmaceutical intralogistics, autonomous driving stacks for construction equipment, and policy control platforms for manufacturing clients. We signed a strategic partnership with Doosan, a leading robotics manufacturer this quarter, elevating our NVIDIA relationship and opening an engineering office in Dresden, Germany, to support our European manufacturing clients. Grid Dynamics enhanced robotics offering by welcoming Ekumen, a leading robotics engineering team that joined us in May. Their expertise resides in a Robot Operating System, a foundational open source standard that powers the vast majority of the world's industrial robots. Over the past decade, the company has built an invaluable list of some of the world's most respected robotics companies. Grid Dynamics brings advanced AI modeling, policy control, and enterprise-scale delivery capability. Ekumen brings deep knowledge of the foundational software layer that robot manufacturers depend on. Together, the combination is formidable, spanning the full stack from the foundational software layer through simulation, hardware integration, and enterprise-scale deployment. We believe no other service company in the market today matches this combined footprint and technical depth. Now, let me pass on to Vasily Sizov, Chief Revenue Officer, who will expand on key business aspects of Grid Dynamics client engagements. Vasily? Vasily Sizov: Thank you, Leonard. Let me begin with three demand trends we observed during the quarter. First, clients are prioritizing AI investments that deliver clear, measurable business outcomes. Second, as clients move from isolated use cases to enterprise-scale initiatives, they realize that the underlying technology layers must be modernized to support AI adoption. Third, clients increasingly recognize that successful AI transformation requires more than technology alone, driving interest in AI process consulting, performance benchmarking, and change management. These trends align closely with our strategy and the capabilities we are building. Let me discuss each of them in more detail. First, the demand environment remains constructive, with clients directing AI investments toward practical application with tangible business impact. We are seeing particular interest in AI-enabled automation that improves operating efficiency, scalability, and speed. Importantly, these investments are increasingly moving beyond experimentation, with clients deploying AI capabilities into production to automate complex manual processes, improve customer service, reduce operating costs, and create new sources of revenue. Second, as clients move from isolated AI use cases toward enterprise-scale transformation, they are finding that their data, application, and core platforms must be modernized and made AI-ready. As a result, AI adoption is creating broader demand across the underlying technology landscape. This trend aligns closely with our core expertise in data engineering, application modernization, cloud and platform engineering and reinforces the relevance of these capabilities in the era of AI. Third, we are seeing growing demand for AI process consulting, performance benchmarking, and change management as clients focus on converting AI investments into measurable business value. They need to identify the business processes where AI re-engineering can create the greatest value, establish clear performance baselines, redesign those processes, build the technical enablers, and drive enterprise-wide adoption. We have been deliberately strengthening these capabilities to help clients realize measurable value from AI across the enterprise. These trends are reflected in our client work. Let me highlight a few engagements from the quarter that demonstrate how these capabilities are being applied in practice. For a leading food service distribution company, we built and deployed an AI-powered product credit claims platform that automatically validates customer claims against photographic evidence. The platform cross-checks product, manufacturer label, and shipping label images against the claim's reason code in real time, replacing a fully manual salesperson-mediated review process. In performance testing, the system processed approximately 400 claims supported by 1,000 images end to end in under 15 seconds per claim. The capability is now live in production. The client has approved a long-term roadmap to further enhance the system and extend automated decision-making into more advanced credit adjudication scenarios. For a leading home improvement retailer, Grid Dynamics enabled next-day delivery by designing and deploying a high-load service that modernized the retailer's logistics operations. The solution includes an AI-powered routing capability that assigns fragile items to the appropriate vehicle types, eliminating hundreds of delivery errors each week. As a result, the solution cut average delivery time by more than half from three and a half days, and is expected to support up to half a billion dollars in incremental annual revenue for the client. For a global technology company, we modernized large-scale data processing infrastructure, migrating more than 1,000 data pipelines to a serverless execution model. This reduced idle compute capacity, reduced infrastructure costs, and improved scalability. Our proprietary AI-powered automation accelerated the migration and established a reusable delivery approach that is now being applied across broader initiatives at this client. Now let me turn the call to Yury Gryzlov, our Chief Operating Officer. Yury Gryzlov: Thank you, Vasily. Let me build on the physical AI and robotics work Leonard introduced. Physical AI needs a full technology stack, and we operate across everything between the robot and the enterprise. The devices themselves come from our hardware partners. At the foundation is the Robot Operating System, ROS and ROS2, the open source layer the majority of the world's modern robots are built on, connecting the hardware to everything above it. Through Ekumen, we're not just users of it, we are among its maintainers and the founding member of the alliance that governs it. On the top of that sits the intelligence, the AI models that let a robot perceive its surroundings, generate its own motion, and handle real-world variability. We design and validate that in simulation before it ever runs on a real robot. Our own platform, Incarnum, our GAIN Platform for Physical AI, is where enterprises bring it all together, building manipulation and inspection workflows, deploying those models, and monitoring robotic lines with digital twins. What unifies it is our focus on the enterprise, expanding this capability to the companies that have robots deployed at scale. Here are a few examples that illustrate our work across the stack. For a leading manufacturer of construction and mining equipment, we are building a next-generation stack for autonomous driving, loading, and excavation. We're helping them design the platform, onboard the first use cases, and add capabilities like policy-based control. What began as our first commercial physical AI engagement is now a multi-year program across several regions. With Ekumen, we've proven two arm manipulation, grasping and assembly, trained entirely in simulation and then run reliably on a real robot. Bridging that gap from simulation to the physical robot is one of the hardest problems in the field. Humanoids are the next step. A leading life sciences company is piloting humanoid robots for intralogistics, moving and repacking containers of chemicals, work that was out of reach only a couple of years ago and is now possible thanks to new AI models that generate motion. We provide the platform those robots run on, working with Wandelbots and on NVIDIA's stack. The customer calls it a lighthouse project for their industry, and it's the opening step in a much wider program. We're also building the channels to scale. This quarter, we announced a strategic partnership with Doosan Robotics, a global leader in collaborative robots deployed across 45 countries. It's a full stack collaboration. Our platform, plus the foundational AI components, integration services, and engineering around it, paired with Doosan's cobots and our combined global reach. Together, we can provide what traditional robotic software can't: dual arm assembly, inspection of complex geometry parts, and packing of deformable items. It sits alongside our elevated NVIDIA partnership, and we are in active talks with several more hardware and software vendors. Considering the economics of software services in this space and our positioning, we are confident that we have a material market advantage. Reliable performance in the physical world takes engineers who understand simulation, control, and hardware variability, working through problems that have no templated solution, and so can't be easily automated. This combination is hard to assemble. Ekumen's decade of foundational robotics depth, together with our strength in AI modeling, simulation, and enterprise delivery. We don't believe another services company matches it today. Closing that gap isn't a matter of hiring a team. It's years of hard-won experience, which we are now putting to work for our customers. In summary, robotics and physical AI is a growing market, measured in the trillions over the coming decade. Our expanded capability is helping us capitalize on the early traction we saw last year, reflected in a rapidly growing pipeline from both existing customers and new logos. Another important part of my update is tied to our capital markets focus, where a similar pattern is playing out in software rather than robots. As our banking clients push agentic AI deep into their engineering, the hard part is no longer producing code, it's doing it safely with quality, security, and control they can provide to a regulator. This quarter, that showed up most sharply around security. Banks want the speed of frontier models and AI-generated code without introducing new vulnerabilities. Our answer is spec-driven agentic engineering led by Allium, part of our GAIN platform for AI SDLC, and it's exhibiting real traction across our banking clients. The clearest example is at one of the world's largest banks, where Allium is being used to build new tools as part of a bank-wide initiative to modernize business operations. Working across London, New York, and India, we're bringing specification-driven development to both new and existing systems, starting with tools for AI-assisted productivity and extending to agents that automate operational work. Taken together, physical AI reaching the enterprise and the AI-native engineering scaling inside the world's largest banks, this is the frontier work that keeps Grid Dynamics differentiated. Over to you, Eugene. Eugene Steinberg: Thank you, Yury. Good afternoon. Last year, I described our AI strategy through three horizons. This quarter, I'll describe them by maturity, what has reached scale and what is beginning to scale. Horizon one, scaled, AI-first modernization and the agentic platform. Modernization remains the foundation of our business, AI is changing how the work gets done. Agents can now accelerate work across most of the modernization life cycle, particularly code generation and testing. The remaining work, business acceptance, production scaling, and complex coordination still depends on human judgment and accountability. An agent can write a code. A person still makes a call and stands behind it. We have invested in a set of GAIN tools that support this life cycle. Rosetta governs how agents operate. Allium analyzes legacy systems to create reliable specifications for their replacements. SpecFlow, our latest open source contribution, uses those specifications to support autonomous feature implementation. Rosetta has progressed from its first lighthouse clients to larger engagements across retail, financial services, and manufacturing. At a Fortune 30 U.S. home improvement retailer, approximately 550 of the client's engineers are working with the platform. In one program, seven COBOL services were moved to a modern technology stack with approximately 90% of the code generated by agents. All seven services entered production this quarter. The client already had capable engineers and access to many of the same AI tools we use. What it needed from us was domain knowledge, governance, and control, the capabilities that turn powerful agents into dependable enterprise systems. This productivity is helping us expand client relationships. It is also creating opportunities to use more fixed price and outcome-based commercial models when the scope and accountability are clearly defined. Allium also reached an important milestone this quarter. It is being piloted across five major banks and has begun moving into its first commercial banking engagements. Allium analyzes legacy code to help establish reliable functional specifications for replacement systems. It also supports controlled migration and rollback, reducing the operational risk of moving critical applications onto modern platforms. At one major North American bank, a one-hour GAIN demonstration in February led to a signed contract in April. The bank was managing 150 applications with limited test coverage and a growing security backlog. We translated identified issues into failing tests inside the bank's own tooling, allowing its engineers to independently reproduce and assess each finding. At another Tier 1 bank, this same approach is supporting a security modernization program spanning more than 100,000 systems. This part of the modernization work co-funded by the client's cloud provider. Our differentiation is not limited to code generation. Our agents can also incorporate context such as security advisories, dependencies, and upstream changes. That broader context helps identify problems that code-only tools can miss and provides the traceability and evidence regulated enterprises expect. We deliberately make selected GAIN platforms open source. The immediate objective is adoption and technical credibility, not software license revenue. Open code allows engineering leaders to evaluate our capabilities directly and strengthen our position when client needs help deploying those capabilities at enterprise scale. The same pattern applies to data. Enterprise AI cannot deliver reliable results without accessible, well-governed data. That is increasing demand for data platform modernization. Our new AI data migration accelerator, released this quarter, is already being deployed in data lake modernization program for a global consumer products manufacturer. The second scaled component of Horizon One is GAIN Agentic Runtime. Enterprise agents need access to trusted data. Evidence that their behavior is controlled, and governance over operating costs. For a global payment client, we brought these capabilities together as shared services, with the retrieval layer now supporting 25 enterprise consumers. We also converted the client's dispute architecture, including fraud, chargebacks, and KYC, to configuration-driven workloads. A common foundation now supports four use cases. By automating much of this configuration, the program rebuilt a decade of business logic in just six months and reduced integration and release cycle times by 96%. At our largest banking client, an internal platform built with our support now centralizes the registration, governance, and operation of AI agents across the organization. The client reports regular adoption by more than 80% of its employees across more than 80 markets. As adoption grows, we are also developing the operational tooling needed to govern and support the platform at that scale. Across these engagements, the pattern is consistent. AI accelerates production, but enterprise value comes from the domain knowledge, governance, and accountability required to put it all into production responsibly. Horizon Two: scaling. Harness engineering and physical AI. Horizon Two covers capabilities that are moving from research and internal validation towards repeatable client deployment. The first is agentic harness engineering. Traditional agentic workflows are most effective when the task and sequence of step are already known. Harnesses are designed for more dynamic work, situations in which an agent must select tools, adjust its approach, and respond to new information while remaining with defined controls. The harness provides those controls. It records what the agent did, tests its output, manages exceptions, and introduces human review where accountability requires it. This allows enterprises to apply agents to more complex work without giving up oversight. During the second quarter, our India engineering center developed nine harness-based solutions. Following our client zero approach, we are testing them first with our own operations. The objective is to establish evidence of reliability, define the necessary controls, and improve the solutions before introducing them into client environments. The second area is physical AI and robotics. We are investing here because the engineering challenge is fundamentally different from conventional software development. A coding agent can generate software and test it in digital environment. A physical system must also operate safely and reliably in the real world. It must account for geometry, motion, changing conditions, and the behavior of physical environment. Validation, therefore, has to take place both in simulation and on hardware. A language model alone cannot close this loop. Our research is focused on bringing physics, geometry, simulation, and continuous validation into the agent's operating environment. That is also the strategic rationale for the robotics engineering team we acquired in May. Members of this team have long contributed to core infrastructure in the Robot Operating System ecosystem, with particular expertise in simulation and validation. Their capabilities are now contributing to GAIN for Physical AI, our platform built on Incarnum. During the quarter, we released three new components: tools for composing robotic policies, a continuous improvement loop, and sandbox environment for control testing. We are beginning to validate the platform through early client and partner deployments. A leading life cycle company is piloting humanoid robots in its warehouse operations using our platform. Separately, a robotics partner has incorporated the platform into its own offering, creating a distribution channel for our physical AI technology. Horizon 2 is not yet the same maturity as our modernization and agentic platform business. Our focus now is to demonstrate repeatability, convert technical validation into production deployments, and establish scalable commercial models. The opportunity is to build differentiated intellectual property in areas where success requires not only generating software but providing how that software behaves in the physical world. Across both horizons, the pattern is clear. The cost of producing software is falling, while the value of governing it, validating it, and taking responsibility for it in production is increasing. This quarter, more components of GAIN moved from tools and pilots into broader enterprise adoption. At the same time, our investments in agentic harnesses and physical AI progressed from research towards controlled client deployments. As these capabilities mature, they allow us to reuse more of our engineering, deploy solutions faster, and take greater responsibility for measurable outcomes. Our advantage is not simply that our agents can generate code. It is that we combine those agents with domain knowledge, operational controls, and the engineering discipline required to make them dependable at enterprise scale. That is where we believe durable value will be created in the agentic era, and where Grid Dynamics is positioned to lead. Anil, over to you. Anil Doradla: Thanks, Eugene. Good afternoon, everyone. Second quarter came in at $108.2 million, slightly above the higher end of our guidance range of $106 million-$108 million. That represents 7% year-over-year growth, including de minimis contributions from Ekumen. Non-GAAP EBITDA was $14.7 million or 13.6% of revenues and was closer to the high end of our $14 million-$15 million guidance range. Looking at the performance of our verticals, TMT remained our largest vertical and accounted for 31.8% of total revenues for the quarter, with a growth of 11.7% sequentially and 36.4% on a year-over-year basis. The growth was primarily driven by our largest technology customers. We continue to benefit from vendor consolidation at these customers, which has driven increased wallet share across new and existing programs. Retail contributed 26.5% of total revenues in the second quarter of 2026. The vertical was flat in absolute dollars on a year-over-year basis and grew 3.1% sequentially. The sequential growth was supported by demand from key accounts, including a major specialty retailer. Our finance vertical accounted for 22.9% of total revenues in the quarter and grew 1.2% on a sequential basis. Within this vertical, we witnessed solid demand from our fintech service engagements, including increased contributions from a major payments network, which helped offset the successful completion of engagements with insurance and data analytics and consumer credit reporting clients in North America. Looking ahead to the remainder of 2026, we remain bullish on our growth outlook within this vertical. CPG and manufacturing represented 10.9% of quarterly revenues and grew 2.1% on a sequential basis and 4.2% on a year-over-year basis. Within this vertical, we are witnessing robust demand from a leading wholesale food distributor, along with growth from some of our manufacturing customers. Turning to our remaining verticals, our other vertical contributed 6% of our second quarter revenues, while healthcare and pharma contributed for 1.9% of our revenues for the quarter. We ended the second quarter with a total headcount of 4,838, down from 4,964 employees in the first quarter of 2026 and from 5,013 in the second quarter of 2025. We continue to rationalize our overall headcount as well as align our skill sets and geographic mix. At the end of the second quarter of 2026, our total U.S. headcount was 379, or 7.8% of our company's total headcount versus 7.2% in the year-ago quarter. Our non-U.S. headcount, located in Europe, Americas, and India, was 4,459, or 92.2%. In the second quarter, revenues from our top five and top 10 customers were 43.5% and 61.5% respectively, versus 37.5% and 57.3% in the same period a year ago respectively. Moving to the income statement, our GAAP gross profit during the quarter was $39.6 million, or 36.6%, compared to $36.2 million, or 34.8%, in the first quarter of 2026 and $34.5 million or 34.1% in the year-ago quarter. On a non-GAAP basis, our gross profit was $40 million, or 36.9%, compared to $36.7 million, or 35.3%, in the first quarter of 2026 and $35.1 million or 34.7% in the year-ago quarter. On a year-over-year basis, the increase in the gross margin percentage was primarily driven by revenue growth outpacing delivery cost. On a sequential basis, the increase in gross margin percentage was due to a combination of working time and improved resource utilization. Non-GAAP EBITDA during the second quarter that excluded interest income, expenses, provisions for income taxes, depreciation and amortization, stock-based compensation, restructuring, expenses related to geographic reorganization, and transaction and other related costs was $14.7 million, or 13.6% of revenues, versus $12.5 million or 12% of revenues in the first quarter of 2026 and was up from $12.7 million or 12.6% in the year-ago quarter. The sequential and year-over-year growth in EBITDA was largely due to a combination of higher revenues and strong operating leverage across our non-engineering overhead. Our GAAP net income in the second quarter was $2.9 million, or $0.03 per share, based on a diluted share count of 83 million shares, compared to the first quarter net loss of $1.5 million or a loss of $0.02 per share based on a diluted share count of 84.7 million and net income of $5.3 million or $0.06 per share based on 86.4 million diluted shares in the year-ago quarter. On a non-GAAP basis, in the second quarter, our non-GAAP net income was $9 million or $0.11 per share based on 83 million diluted shares compared to the first quarter non-GAAP net income of $7.5 million or $0.09 per share based on 85.9 million diluted shares, and $8.3 million or $0.10 per share based on 86.4 million diluted shares in the year-ago quarter. On June 30, 2026, our cash and cash equivalents totaled $298.4 million, down from $327.5 million on March 31, 2026. Since our first quarter earnings call, we repurchased approximately 2.6 million shares for a total consideration of $17.3 million. Cumulatively, since our board authorized the 50 million share repurchase program, we have repurchased approximately 4.4 million shares for a total of $30.8 million, reflecting our continued confidence in the long-term value of the business. Coming to the third quarter guidance, we expect revenues to be in the range of $112 million-$114 million. We expect our third quarter non-GAAP EBITDA to be in the range of $16.5 million-$17.5 million. For the third quarter, we expect our basic share count to be in the range of 81 million-82 million shares and our diluted share count to be in the range of 83 million-84 million shares. For 2026, we're maintaining our full year revenue outlook of $435 million-$465 million. That concludes my prepared remarks. We are now ready to take questions. Carrie? Mayank Tandon: Great. Thank you. Congrats on the quarter, Leonard and Anil. Anil Doradla: Thank you, Mayank. Mayank Tandon: There was a lot of detail around AI. Just to step back, Leonard, could you maybe talk about the AI efforts and the implications for both growth and profitability over the next, say, 12, 24 months? Maybe you can help reassure investors that AI will actually be a net positive for you, because there's still a lot of skeptics out there that think it's going to be a net negative over time. Leonard Livschitz: Right. Thank you, Mayank. It's a pretty comprehensive question. If I answer all of the parts, there'll be probably nothing left for the other end. I'll try to be concise in terms of the key elements. Then we can talk a little bit more in detail. First of all, we are reaching many aspects of AI implementations. We talked about it in the past. We're adding those features now. We're talking about directly or indirectly about forward deployed engineers. We make announcements. We train a substantial number of the people in the workforce, and these people are basically driving a new way of implementing our solutions because, as we tend to get more focused on a fixed bid and fixed budget projects, it helps us to identify not only the execution of the various modernization projects, but also create a technology consulting. That's with respect of the people and why it's accretive to us. When it comes to agentic AI, a part of the, again, implementation of the suite of our solutions, we are driving our customers to adopt our GAIN platform model. All the elements of the model are driven by internal tested and developments, but also tailored to our customer needs. They will need to adapt the solution where they see the most fit for themselves, but also we guide them through the process to create the best ROI for that. That's the second part. Before I talk about the physical AI, I want to bring, to address your point in terms of net positive versus net negative. If you look at the increased growth in just these two areas, that substantially exceeds the, some of the aged businesses which would eventually drop out. Because the gloom and doom from many facets were about that engineering and consultancy is less relevant. Moreover, people would say it's easier to train FTEs. We embrace FTEs. We embrace our clients. At the same time, as many of the leaders in the industry saying, we can do more work, we can do more engagements, which we prove with all the listed examples. I'm not going to go through all of them because we have a lot of people who can give you more details on that. As a consolidated effort, as we go today through further discussions, we will demonstrate on specific examples where this accretiveness works. I want to emphasize forward deploy engineering and agentic AI. The third part which is also super critical for us, which actually drives the adoption and partnership enhancement of our relationship to the next level, is actually our preparation for physical AI work. We not just made a small acquisition. We not just made a announcement about opening additional robotics labs. We've been working with our clients for a long enough time to understand what it means for their own platform, what it means for their application and solutions from various world, from industrial, from modern machineries to logistics companies, to even work in industrialization of various new solutions. The material side, the remuneration for the physical AI is still to come, but now we have an evidence of substantial players looking at Grid Dynamics, again, in a leadership role by expanding our capabilities to the practical world of their usage. This is pretty much a summary, and then, of course, we'll go in more in detail. Mayank Tandon: That's very helpful, and sorry if you can't see me. I'm having an issue with my video. You can help with that. I'll try to get that fixed eventually. Just a very quick follow-up, Anil, for you. In terms of the guide, just want to get a sense of the visibility that you have today versus last quarter. What I mean by that is the pipeline now converting faster? Have you seen evidence of that? Does that maybe give you more confidence in the sustainability of growth acceleration once we get beyond fiscal 2026 into fiscal 2027? Anil Doradla: You're talking about next year. Let's talk about this year, and then we'll get to next year. You go into the second half, Mayank, you see, if you look at our visibility and our second half, there are a couple of factors. Number 1, remember the 85/10/5? Most of our revenue comes from customers who've been with us for 2 years and beyond. That formula more or less stays well intact. That you're seeing in the top 5, top 10 customers, right? Because most of the absolute dollar and year-over-year growth is coming there. That stays intact. As you go into the second half, there are 3 layers, as you go. First is the working time. Second half is higher than the first half. Second thing is that the billable headcount. We're seeing new programs kicking in. Maybe without addressing your pipeline question directly, indirectly is that, yes, we're seeing an increased billable headcount as we go into the second half. The 3rd thing is that we are planning some acquisitions. All these 3 add up to layers. When you look into 2027, I think I'll let the business guys chime in here, but from my point of view, I see 2 things that are very interesting. Number 1, the relationships that we're having with our technology customers, our financial customers, our top 10 and 20 customers, is going deeper and deeper. Things that we've not done, we're doing. Application modernization programs, which we've not done, we're addressing. The addressable market that we're going after is larger. I overhear these conversations week after week. Which leads me to believe as you go into 2027, if we continue winning at the rate that we're winning, it should play out incrementally past it. I don't know, Vasily or Yury, whether you want to add anything to that. Vasily Sizov: Yeah. Let me chime in. Yes, I would say that our position with most of our biggest clients has been strengthening over the last few years, through vendor consolidation. What we see is that we should benefit in the coming years, from this consolidation, which means bigger programs would come our way, by customers cutting loose, the long tail of vendors which are no longer relevant. Given our strong technology positioning in agentic AI, which is a very hot topic for most of our customers, we are really well-positioned to benefit from that. Mayank Tandon: Terrific. Thank you so much. Congrats. Anil Doradla: Thank you. Vasily Sizov: Thank you. Anil Doradla: Thank you, Mayank. The next questions come from Bryan Bergin, Janney Securities. Go ahead, Bryan. Bryan Bergin: Hey, y'all. Good afternoon. Thanks. Taking the questions here. Maybe just to start, a follow-up on that last question as it relates to that second half, more of a near-term question. Just as it relates to, you give us 3Q guide, implied 4Q is still a decent ramp. Are you seeing a broadening of momentum in other sectors? You're obviously doing quite well in technology. Are you seeing a broadening of momentum elsewhere that gives you that confidence? As it relates to potentially some M&A requirements, any way you can share with us how you're thinking about maybe the organic contribution remaining versus any needed M&A that you have to go get? Anil Doradla: Right. Bryan, let me point out that as you know, there's a certain seasonality in our business, right? As we go into Q3, Q4, that's well established. As I said, there are 3 levels at which we're operating. Number 1 is just the working times of the second half of the year, and you guys know it's better. Second thing is that the billable headcount and the trends are positive, and all our prepared commentary should lead you to include that. The third thing is that there is a certain amount of acquisition, and we do have a pipeline. It varies. I always joke, right? An acquisition is not done till the money is transferred to their bank, right? We've seen acquisitions that we thought are not going to happen, they happen. We've seen acquisitions that were locked and loaded, and we just are not able to. If you look at that second half, I don't want to comment too much upon Q4 other than saying that, look, we have a seasonal pattern for the year. As we go from the low end of our full-year guide to the high end of the guide, the first component of working time stays intact. The second component of billable headcount, we have variable calculations. The third component perhaps picks up a little bit more is the acquisitions. Bryan Bergin: Okay. Understood. My follow-up is a margin and a tie-in with the delivery model question. You reiterate the confidence in the 300 basis point expansion, that's good to hear. I'm just curious how much of this margin improvement is coming from structural changes, automation, and efficiencies in the delivery versus traditional cost control cutting measures. I think it's notable you had 7% revenue growth while headcount was down three. I know you're saying you're going to add billable headcount, but is there a lasting change in this delivery model? Just maybe talk about that AI-driven efficiency and delivery that you're seeing. Anil Doradla: Right. There are three, four parts of this question. Let me take the first part, and then when it comes to some of the AI trends, I'll pass it on. When you look at what we set out to do, we said that on a year-over-year, we're going to deliver 300 basis points margins on a Q4 by Q4 on a year-over-year basis. Part of that effort is efficiency. It's just the way we're organized. As you know, we've ramped from a handful of countries to 19 countries. We've got many incorporated entities. There's a little bit of a efficiency that we brought in, and some of those are one-time, but we operate at a certain level, right? The second part that we are seeing here is we're embracing a little bit more change in the way we're doing business, whether it's AI, whether it's fixed price, whether it's embracing more tools. That is creating a certain level of, I would say, it's not so visible now, but over time, you'll see a non-linearity perhaps that is in. The movement that you've seen on the headcount right now was largely driven by efficiency improvements on non-engineering headcount. People should not worry. It's not that we let go some billable headcount. No, it's just non-engineering, non-billable headcount. We cleaned it up. From this point onwards, beyond the 300 basis points that you'll have from Q4 to Q4 as you go into 2027, there is a plan for us to leverage more of these tools. There is a plan of bringing a certain level of non-linearity. We have the plans. The clients have to accept it, and we have to proceed with that. Go ahead, Leonard. Leonard Livschitz: Yeah. Let me share a couple of things. First of all, just to complete answer on the very first question of yours about diversification of the platforms. I think it's very critical to understand that this is not overnight we suddenly diversified verticals. First and foremost, we've been in the payment system, we've been in financial service, we've been industrial modernization. The second of all is, you can actually see from the previous comments about us, what Vasily said, replacing some incumbent vendors is because we're playing in a big boys league, in a higher level. In the past, there were always couple top guys and a couple mid-level vendors. Now we only compete with the top guys. The reason being is, I think AI adoption and technology implementation equalize the field a bit. We've always been prepared for the big tasks and a big program, big transformational solutions. We also gained a reputation of this consultancy part. As we get more admittance to the bigger projects, inevitably, what happen with that, it's a better visibility, better projection, better position. We're saving with the tools, we're adding more capabilities, and we're looking back and we say, "What of these internal systems which we had for a long time are less efficient?" We accepted to live on a world of uncertainty. That's very important. We don't see the world changing so dramatically, we'll go back immediately to the luxury of being very consolidated in a very few locations. We adding not only India, and we adding investment into India and the technology capability, but also LatAm. As we do more, we create a global platform internally to optimize this efficiency. It's a cost structure, it's performance-based, it's tooling, it's removing redundancies from the past. I hope, Bryan, I covered a lot. Puneet Jain: Hey, thanks for taking my question. How are your AI and robotics partnership different from your traditional hyperscaler relationships like with Google, AWS, Microsoft Azure, that generate much of your 19% of partnership revenue? The partnerships you got with NVIDIA, model companies, do they differ or do they offer a different revenue trajectory potential or client ownership structure than your other partnerships? Vasily Sizov: All right. Thank you so much for the question, Puneet. Let me address this question. We definitely value our relationships with NVIDIA, and believe that's a great partnership to build a pipeline of future opportunities on. As you understand, right now, the industry, the manufacturing is going through a massive transformation and new tools like agentic AI or physical AI definitely brings new technology to more traditional manufacturing. We see this as a great opportunity to build a new pipeline of opportunities, a new type of engagements which would help us to transform those manufacturers on a bigger scale. Just an example. For example, right now we have an active engagements with one of the world's largest industrial equipment manufacturer on building an agentic AI platform which allows to manage the fleet of autonomous vehicles and deploy physical AI capabilities on the edge devices. We see more and more interest to such opportunities. It's definitely one of the top priorities for us to grow. Puneet Jain: Got it. I'd like to follow up on the prior question, specifically around headcount. I noticed your non-U.S. headcount was down despite the Ekumen, which probably contributed employees in Argentina. The U.S. headcount, by comparison, was up on sequential basis. Should we expect this remix to continue as you do more AI-based services? Will that require more on-site headcount or U.S. headcount compared to in the past? If that's true, what does that mean for margin and change management within your employee base? Leonard Livschitz: Very good. Puneet, what you said, it's music to Eugene's ear because he's been the one who is architecting the acceleration of some of the U.S.-based presence, both from the technology office perspective, but also from the technology consultancy with the clients. I'm not saying there's more shift toward onshoring as a trend. I think if you look back pre-COVID days, our onshore presence between onshore technology people and as well as some of the offshoring engineers who would come on long-term projects, reached almost close to 20%. It's never been so low. When the onshoring presence pulled back due to an ability to work directly with the clients, a lot of work has been going on offshoring. We're not saying that work is no longer relevant, but there are more and more demand to presence on premise with the clients to work together on these complex cases because the rapid change of transformation sometimes catches the clients a little bit through uncertainty, right? We talk about 2 basic approaches to their mental and budgetary resolution of the projects. One of them is more like a status quo. Let's see and tell what's going to happen. They don't need as much of onshoring presence, and some of them demand very rapid acceleration, but they're concerned with some of the spendings, as you know, around tokens and other things which definitely create the pressure. That's where our headcount onshoring technology-wise is coming. As I mentioned to Bryan, some of the reduction of offshoring headcount comes from non-engineering and non, I would say, forward-looking specialties. There is a difference between the headcount and contribution of this headcount. From the budget perspective, it's a little bit less clear that these people were extremely expensive, but just the infrastructure of all these people would no longer be needed for us to serve the markets better. To answer your question, we do see some additional growth of onshoring. The ability of us to prove that our margin expansion will continue to grow is vastly driven how much of the fixed bid, fixed budget projects we can adapt, how much of our internal developed tools are accepted by the clients, how much of the nonlinear value we're bringing to the party, and I think we're quite growing with those elements. Just to conclude on that from my side, and if people want to add, I think you picked the right trend. I don't think the legacy some of the people are a sign for concern because majority of them come from the Central Eastern Europe. I think this is all by the book. We are really moving forward with a clear plan on continue to have margin improvement. Puneet Jain: Got it. Thank you. Anil Doradla: Thank you, Puneet. Matt Dezort: Great. Thanks, guys. Congrats on the results. I wanted to see if you could double-click on this new consultancy practice that you're talking about. Can you discuss more of how you see this business developing? I know you talked about activities like change management, but what sort of opportunities are you seeing in the pipeline build there? Who are you going up against in these bake-offs, and how is the competitive environment different from your traditional work, maybe? Leonard Livschitz: Yeah. I will start very briefly, and then Vasily will actually expand on it. Matt, there are two parts of it. The first part is there's no change of our purpose. Consultancy has always been a part of our DNA. Nothing is earth-shattering because our clients consider us to be a technology consultants, and that's why we're able to compete against the big firms. What has changed is the distribution of that kind of offering. Just the previous question with Puneet was about onshoring presence, right? People who we hire, they're extremely technical, but they're also customer-oriented. That kind of work, very important because we are expanding the purpose of consultancy from pure technology consultancies, and now adding, AI infrastructure consultancies, hardware selection consultancy, tool selection consultancy, and to some extent getting more into the sacred world of business consultancy. Listen. Vasily Sizov: Yes. Think about business consultancy as a natural extension of our technology enabler build-out capabilities. Essentially, the focus of the customers is shifting from just creation of a system, but for creation of a systems to change business processes they have. Therefore, they would like to analyze first which business processes are the best candidates to improve, which value is hidden there, then to build a technical enabler to reveal this value, and then adopt that technical enabler on the enterprise wide scale. That's exactly where the focus of our consultancy is, not only to create the technical enabler, but also to help get all the value on the enterprise scale from this change. That's the essence. Right now we have several active engagements on that, specifically on the front of consulting change management, which goes along with technical enablers, and we see this opportunity ahead of for a great growth in the future. Matt Dezort: That makes sense. Eugene Steinberg: I can add to that many of our customers observe a performance and productivity of our delivery teams using our GAIN platforms. They become interested, and they want those platforms and those methodologies inside their own software factory, and we are helping them to establish the tools, methodology, and change management, which is required to GAIN the similar productivities in their broader organization. Matt Dezort: That's a good segue, Eugene, for my follow-up on GAIN adoption and just the S-curve that implies. As you accelerate GAIN rollout, how should we think about that adoption curve and pure AI revenue? Is it likely to scale linearly, or you're talking about wallet share gains from AI, is there a way we could see some exponential growth, and how could you drive a more sharper inflection in that AI penetration with GAIN? Eugene Steinberg: What is interesting about our GAIN strategy is that we are consolidating all our IP from multiple accounts, from multiple practices under the same umbrella, and AI helps us to do that very, very rapidly and quickly. Our embedded engineers, forward-deployed engineers, are all tasked to bring back the learnings, the ideas, what works and what not works back to the GAIN platform. Part of the GAIN platform is also open source that helps to drive the insights from the broader community and put the GAIN platforms in front of many leaders. At this point in time, we observe a growth of the direct revenue from our GAIN platform, but much more important, we observe a growth of the overall connection and expansion of our relationships inside our accounts and the new accounts, which are driven by these platforms. We see many of the inbound interests and conversations which result in new leads, new opportunities, and new converted business from GAIN platform. This is what is happening right now. Leonard Livschitz: Good. Matt Dezort: Thank you, guys. Surinder Thind: Thank you, guys. I'd like to start with a question just around this idea of there's a bit more excitement around moving from proof of concept to maybe the actual implementation projects. That commentary seems to be a bit more universal. From your perspective, can you maybe talk about what the revenue journey for that looks like? Meaning how big a proof of concept project would be if it's a few hundred thousand dollars, does that become a $2 million project, or what's kind of the range of outcomes that we can expect here as we think about more of those proof of concepts coming and how that would impact the growth rate? Leonard Livschitz: Surinder. Again, I will give you a little bit of a high level, and I think because it's all revenue touched, Vasily will give you a little bit more color. There are different proof of concept. The definition of proof of concept could be quite stretched, both from intent and then dollars associated with that and follow-ups. When we looked at proof of concepts as a result of our partnerships, for example, that resulted in some of the very meaningful programs where the customer embraced not only our partner solution, but our offering, which was in conjunction with these partnerships. When we look today and specifically at the suite of GAIN productivity, it's actually very interesting. Eugene mentioned about inbound interest. As you know, for us, for Grid Dynamics and our size and capabilities, visibility is very critical. The customers would reach to us with something we still call proof of concept, but those are substantial projects because the measurement of proof of concept, sometimes driven today not by the amount of dollars, could be quite more substantial in many cases, but the time to implement. The whole short-term engagement definition, which used to be followed or preceded by the proof of concept, become the proof of concept itself, and then it's a major rollout. This has conceptually changed the definition of proof of concept and revenue associated with it, but I'm sure that Vasily will give some more details. Vasily Sizov: Yes. Many customers start definitely with implementation of some smaller pieces of business cases. Leonard Livschitz: Sure Vasily Sizov: which have tangible business results in order to demonstrate it for their boards, for their management, and then using that as an example, essentially request more investment into that, which eventually gets converted into platform build-out, to build AI harness and et cetera. This trend definitely persists. That's what we see with our customers. I can state that for platforms work, this work essentially is much more sticky and longer-term in nature than the POCs. Having built the platform, of course, there is a growing appetite to build more and more business cases on top of this platform, so it grows like a snowball, and that actually is what's reflected in our pipeline. Yury Gryzlov: I think I just wanted to add that it also depends on the industry, right? We've mentioned today about physical AI and robotics. Definitely there is a lot of proof of concept in those areas, but at the same time, it also depends on how deep you are in your relationship with the customer and other programs around, outside even of those areas, right? That's where those proof of concept could be actually quite significant. Sometimes it could be just maybe a few weeks of small engagement, sometimes it could be six months plus. Going back to the GAIN model and our platforms in the GAIN, I think this is where also we try to condense this knowledge, right, and a way to speed up this implementation as much as possible for a customer. That also contributes to the ratio of the proof of concept revenue versus the longer engagement implementation revenue. Leonard Livschitz: Just to summarize for Surinder. The POCs associated with FDE consultancy, GAIN model modernization subjects, and agentic AI as overall are very substantial from GetGo. The physical AI part is what traditionally would call proof of concept because it's such an innovative way to modernize modern productivity and interface between human robotics. These type of POCs are more traditional way, and their revenue will follow with the scale which you would typically expect from POCs. Surinder Thind: Cool. Then maybe thinking about the data and AI practice and the really high growth rate that we're seeing there, the 30% of revenues. Can you help me understand what's going on in the other 70%? When I do the math, I get to roughly about a 10% decline in that other 70% of revenues. How much of that is just cannibalization by the data and AI component? Because I assume every new piece of work probably falls into that bucket. Then is there components that are maybe in that legacy, I'll call it legacy bucket for lack of a better word, other elements to that, such as pricing compression or just other factors to think about? Anil Doradla: Very good question, Surinder. I'm actually going to make it much simpler. It's not even that complex. In our case, when you look at our business trends, from time to time we might see a significant customer, I'm loosely using that word, maybe a top 30 customer, top 40 customer, right, have some changes. Maybe there's a change in strategy or a big project is completed or something changes, and we can have some volatility there. If you go back over the past couple of quarters and look back at Leonard's commentary, he talked about sensitivity of brick-and-mortar retail, for example. You create some volatilities there. Very simply put, if I were to extract some of these volatilities there, we would see a better growth pattern. Second point is, your question is whether there is cannibalization. What we see is, Eugene and Vasily can back me up on this, when we get into our clients, especially in our top 20 clients, we're going deeper and deeper. All the work is incremental AI work that we typically see. The third thing that we see here is on the pricing. We are not seeing pricing pressures. As a matter of fact, when you go into the AI world, obviously there's a premium. When you look at what we are doing over the past couple of years, and I look at a certain grade in a certain country, and whether there's pricing pressures, the answer is absolutely no. We're not seeing that. Now, we can argue whether there's a pricing increase. That's a different story, but there's no pricing declines. Vasily, I don't know whether you want to- Vasily Sizov: Yes. I think it's a question of semantics, right? The cost per project or cost per functionality or piece of scope is definitely getting reduced because of the higher productivity and shortening the timelines for the delivery. That's kind of what's happening. That leads to more work and more projects rather than the reduction. I think that's a very important color. Leonard Livschitz: Finally, you can look at the revenue per person. Again, we need to have some history to prove that trend will continue to grow. We're not trying to defend legacy. I think Anil was quite clear that some businesses fall off, right? You're absolutely right. The AI content brings more new business. There is one element which is not there, is us trying to retain some legacy business and compressing our margin. That's just not part of it. Surinder Thind: Cool. It does sound like there's a definitional component here, right? To your earlier point of how you divide up the buckets and trying to look at it collectively, plus all of the noise of project starts and stops and things like that. I appreciate that. Thank you. Anil Doradla: Thank you, sir. Vasily Sizov: Thank you. Leonard Livschitz: Our top accounts are expanding. Our AI programs are moving consistently from pilot to enterprise scale deployment, and our platform portfolio is deepening both organically and through the capabilities we have added in robotics and physical AI. I'm confident in the second half of 2026, the strategy is working, the momentum is building. Before you buy stock in Grid Dynamics, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Grid Dynamics wasn’t one of them. The 10 stocks that made the cut are built for long-term growth and could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you’d have $386,727!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $1,232,139!* That performance is why people listen. 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Grid Dynamics (GDYN) Q2 2026 Earnings Call Transcript was originally published by The Motley Fool

Interested in Grid Dynamics Holdings, Inc.? Here are five stocks we like better. Grid Dynamics exceeded its Q2 guidance, reporting revenue of $108.2 million, up 7% year over year, and non-GAAP EBITDA of $14.7 million. AI-related revenue rose 54.6% and reached 30.7% of total revenue, as customer projects increasingly moved from pilots into production. Growth was supported by expanded relationships with major technology and financial-services customers, including Google Cloud, AWS and Microsoft Azure. The company is also expanding into physical AI and robotics through partnerships and programs involving humanoid robots, autonomous equipment and manufacturing workflows. Profitability improved as gross margins rose and headcount declined to 4,838, while management forecast Q3 revenue of $112 million to $114 million and maintained its full-year 2026 revenue outlook of $435 million to $465 million. Grid Dynamics also repurchased roughly 2.6 million shares during the quarter. Buy the Dip? These Earnings Misses Offer Long-Term Upside Grid Dynamics (NASDAQ:GDYN) reported second-quarter revenue and profitability above its guidance range, as the technology services company pointed to expanding demand for artificial intelligence programs, deeper relationships with major technology and financial-services customers, and progress in physical AI and robotics. Revenue for the second quarter totaled $108.2 million, up 7% from a year earlier and slightly above the company’s guidance range of $106 million to $108 million. Non-GAAP EBITDA was $14.7 million, or 13.6% of revenue, near the high end of its $14 million to $15 million outlook. → Why SK hynix Could Be the Best AI Chip Stock to Buy Now The Top 5 Analysts Ranked by MarketBeat and Stocks They Cover The company said AI-related revenue reached 30.7% of total revenue, surpassing the 30% threshold for the first time. AI revenue increased 54.6% year over year, following another quarter of growth above 50%. Management said its largest accounts, particularly in technology and financial services, continued to drive growth through expanded programs and broader adoption of its GAIN AI platform. The company said some clients are embedding GAIN into their operations as an ongoing capability rather than using it only for individual projects. → Microsoft Just Flipped the AI Spending Narrative Overnight Vasily Sizov, Grid Dynamics’ chief revenue officer, said customers are directing AI spending toward applications with measurable business outcomes, including automation, operating-cost reductions, customer-service improvements and new revenue opportunities. “These investments are increasingly moving beyond experimentation,” Sizov said, describing a shift toward production deployments that automate complex manual processes and improve scalability and speed. → Carrier Earnings Could Send the Stock to a New All-Time High He said enterprise-scale AI adoption is also creating demand for data engineering, cloud services, application modernization and platform engineering, as clients update underlying systems to support AI workloads. Customers are also seeking help with process consulting, performance benchmarking and change management, according to Sizov. Grid Dynamics highlighted several customer engagements, including an AI-powered credit-claims platform for a food-service distributor. The platform validates claims against photographic evidence and, in testing, processed about 400 claims supported by 1,000 images in under 15 seconds per claim. The system is now live, and the client approved a longer-term roadmap for additional automation. For a home-improvement retailer, the company deployed logistics software supporting next-day delivery and AI-based vehicle routing for fragile items. Grid Dynamics said the system reduced average delivery time by more than half from three and a half days and is expected to support up to $500 million in incremental annual revenue for the customer. Partner-influenced revenue was 19.1% of second-quarter revenue, driven primarily by relationships with Google Cloud, Amazon Web Services and Microsoft Azure. Grid Dynamics reiterated its longer-term goal for partner-influenced revenue to account for 25% to 30% of company revenue. The company said it expanded its Google Cloud work in banking and financial services, including its first joint win at a global bank. It also cited growing AWS activity in application modernization and agentic AI, while its NVIDIA relationship is gaining traction in agentic and physical AI. Physical AI, which combines AI models with robotics, simulation, hardware integration and enterprise deployments, remains an emerging focus. The company said its active programs include humanoid robotics for pharmaceutical intralogistics, autonomous-driving systems for construction equipment and manufacturing policy-control platforms. During the quarter, Grid Dynamics announced a strategic partnership with robotics manufacturer Doosan and opened an engineering office in Dresden, Germany, to support European manufacturing clients. It also added Ekumen, a robotics engineering team that joined the company in May and brings expertise in the Robot Operating System software ecosystem. Yury Gryzlov, chief operating officer, said the company’s physical AI platform, Incarno, is designed to help enterprises build robotic workflows, deploy models and monitor robotic lines using digital twins. He said a construction and mining equipment manufacturer engagement has expanded into a multiyear program across several regions. Chief Financial Officer Anil Doradla said the company’s GAAP gross margin rose to 36.6% from 34.8% in the first quarter and 34.1% a year earlier. Non-GAAP gross margin increased to 36.9%, supported by revenue growth outpacing delivery costs, improved resource utilization and working-time benefits. GAAP net income was $2.9 million, or $0.03 per diluted share, compared with a net loss of $1.5 million in the prior quarter. Non-GAAP net income rose to $9 million, or $0.11 per diluted share, from $7.5 million, or $0.09 per share, in the first quarter. Total headcount ended the quarter at 4,838, down from 4,964 in the first quarter and 5,013 a year earlier. Doradla said reductions were focused on non-engineering and non-billable roles as the company rationalizes its workforce and geographic footprint. Technology, media and telecommunications remained the largest vertical, representing 31.8% of quarterly revenue and growing 36.4% year over year. Retail accounted for 26.5% of revenue, finance represented 22.9%, and consumer packaged goods and manufacturing made up 10.9%. Revenue concentration increased, with the top five customers accounting for 43.5% of second-quarter revenue and the top 10 accounting for 61.5%. For the third quarter, Grid Dynamics forecast revenue of $112 million to $114 million and non-GAAP EBITDA of $16.5 million to $17.5 million. The company maintained its full-year 2026 revenue outlook of $435 million to $465 million. Cash and cash equivalents totaled $298.4 million as of June 30, down from $327.5 million at the end of the first quarter. Since its prior earnings call, the company repurchased approximately 2.6 million shares for $17.3 million. Since authorization of its $50 million repurchase program, it has bought back about 4.4 million shares for $30.8 million. Management said it expects second-half growth to be supported by seasonally higher working time, increased billable headcount, new programs and potential acquisitions. The company also reiterated its commitment to 300 basis points of margin expansion on a fourth-quarter year-over-year basis. Grid Dynamics (NASDAQ: GDYN) is a digital engineering and technology services company that helps enterprises accelerate their digital transformation initiatives. The company specializes in designing and implementing scalable, cloud-native solutions that leverage advanced analytics, machine learning and artificial intelligence to optimize operations, enhance customer experiences and drive revenue growth. Its technology expertise spans e-commerce platforms, modern data architectures, DevOps and automation, as well as custom application development across a range of industries including retail, financial services, high tech and automotive. Key service offerings include cloud migration and modernization, data engineering and analytics, AI/ML-driven insights, digital commerce and omnichannel solutions. This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest reporting and unbiased coverage. Please send any questions or comments about this story to [email protected]. The article "Grid Dynamics Q2 Earnings Call Highlights" was originally published by MarketBeat. View MarketBeat's top stocks for July 2026.

Image source: The Motley Fool. Thursday, July 30, 2026 at 4:30 p.m. ET Chief Executive Officer - Leonard Livschitz Chief Revenue Officer - Vasily Sizov Chief Operating Officer - Yury Gryzlov Anil Doradla Eugene Steinberg Leonard Livschitz: Good afternoon, everyone, and thank you for joining us today. We delivered a solid second quarter. Consolidated revenue of $108.2 million, above the high end of our guidance range and ahead of Wall Street expectations, with non-GAAP earnings of $14.7 million, which also is beating consensus. As you may recall from my last quarter commentary, there were three areas I highlighted. First, improving revenue trends, especially with key accounts in the areas of technology and financial services. Second, our AI adoption and growth. Third, improving profitability trends. I'm happy to report that on all three fronts, our execution is solid and we're seeing the benefits. Growing top accounts relationships, continued AI momentum with expanded capabilities in robotic and physical AI, and solid progress toward our 300 basis point margin expansion commitment. For the second consecutive quarter, our top accounts are in technology and financial services. Technology and financial services now define our more strategic customer relationships and those precisely the sectors where AI adoption is moving fastest and where our capabilities are the most differentiated. Our top accounts continue to drive our growth. Several delivered double-digit quarter-over-quarter growth with standout performances. There are no incremental gains. They reflect expanding programs, deeper program adoption, and the compounding effect of our capabilities that keep finding the opportunity inside each client's organization. Several of these clients are now embedding our GAIN platform as core infrastructure in their own operations, not just a project tool, but as a sustained capability. This is a fundamentally different and more durable commercial relationship than what we've had two years ago. AI revenue reached 30.7% of the total company revenue in the second quarter, growing 54.6% year-over-year and crossing the 30% threshold for the first time. Two consecutive quarters of the year-over-year growth over 50% tells us something important. This is not a spike. It's a sustained shift. The trajectory is clear, and we intend to build on it. Driving this strong performance is a combination of multiple factors. Our GAIN platforms are winning wider enterprise adoption. Our clients continue to transition enterprise AI workloads from pilots to production. Our engineers are more deeply embedded inside client organizations. Bottom line, we're winning entirely new programs that gives us confidence in growth ahead. AI-first delivery is now the default, not the aspiration. Fixed price is a preferred approach on new RFP responses. The productivity and margin gains are real. We're executing well and delivering projects successfully. Our focus on executing larger AI platforms is aligned with significant progress we're making in upskilling our engineering talent. By the end of October, we plan to have 90% of our engineers trained on AI SDLC. Our GAIN platforms have expanded LLM partnerships meaningfully this quarter. We're now working with several of the world's leading AI companies, including the top four frontier providers with whom we're under commercial agreements. This approach ensures our GAIN platforms stay aligned with the leading AI platforms with broader reach across our enterprise client base. GAIN remains the backbone through which we bring AI capabilities to market. Its partner depth makes it stronger every quarter. Our client relationships are evolving, too. Clients who came to us for platform deployments now ask us to stay. They want us to be involved in advisory execution ongoing operations. This meaningful shift is opening a growth vector that did not exist in our model two years ago. On the partnership front, partner influence revenue reached 19.1% of the company total revenue in the second quarter. That was driven primarily by our three core hyperscaler relationships with Google Cloud, AWS, and Microsoft Azure. A growing proportion of that revenue is coming from AI engagements. We are running agentic AI workshops across our Google Vertex AI search customer base, converting search engagement into broader agentic commerce programs. We extended our Google partnership in banking and financial services, closing our first joint win this quarter at a leading global bank. We're deepening our AWS relationship around application modernization and agentic AI in CPG manufacturing and financial services. Our NVIDIA partnership is gaining momentum across both agentic AI and physical AI. Our longer-term target remains 25%-30% partner influence revenue, and we're confident of achieving this target. Last quarter, I introduced our physical AI capabilities and our first commercial engagements in the space. Physical AI requires a deep understanding of multiple disciplines that include modeling real-world robotics movements, digital twins, verification in simulators, and integration with hardware systems. Our active programs span humanoid robotics for pharmaceutical intralogistics, autonomous driving stacks for construction equipment, and policy control platforms for manufacturing clients. We signed a strategic partnership with Doosan, a leading robotics manufacturer this quarter, elevating our NVIDIA relationship and opening an engineering office in Dresden, Germany, to support our European manufacturing clients. Grid Dynamics enhanced robotics offering by welcoming Ekumen, a leading robotics engineering team that joined us in May. Their expertise resides in a Robot Operating System, a foundational open source standard that powers the vast majority of the world's industrial robots. Over the past decade, the company has built an invaluable list of some of the world's most respected robotics companies. Grid Dynamics brings advanced AI modeling, policy control, and enterprise-scale delivery capability. Ekumen brings deep knowledge of the foundational software layer that robot manufacturers depend on. Together, the combination is formidable, spanning the full stack from the foundational software layer through simulation, hardware integration, and enterprise-scale deployment. We believe no other service company in the market today matches this combined footprint and technical depth. Now, let me pass on to Vasily Sizov, Chief Revenue Officer, who will expand on key business aspects of Grid Dynamics client engagements. Vasily? Vasily Sizov: Thank you, Leonard. Let me begin with three demand trends we observed during the quarter. First, clients are prioritizing AI investments that deliver clear, measurable business outcomes. Second, as clients move from isolated use cases to enterprise-scale initiatives, they realize that the underlying technology layers must be modernized to support AI adoption. Third, clients increasingly recognize that successful AI transformation requires more than technology alone, driving interest in AI process consulting, performance benchmarking, and change management. These trends align closely with our strategy and the capabilities we are building. Let me discuss each of them in more detail. First, the demand environment remains constructive, with clients directing AI investments toward practical application with tangible business impact. We are seeing particular interest in AI-enabled automation that improves operating efficiency, scalability, and speed. Importantly, these investments are increasingly moving beyond experimentation, with clients deploying AI capabilities into production to automate complex manual processes, improve customer service, reduce operating costs, and create new sources of revenue. Second, as clients move from isolated AI use cases toward enterprise-scale transformation, they are finding that their data, application, and core platforms must be modernized and made AI-ready. As a result, AI adoption is creating broader demand across the underlying technology landscape. This trend aligns closely with our core expertise in data engineering, application modernization, cloud and platform engineering and reinforces the relevance of these capabilities in the era of AI. Third, we are seeing growing demand for AI process consulting, performance benchmarking, and change management as clients focus on converting AI investments into measurable business value. They need to identify the business processes where AI re-engineering can create the greatest value, establish clear performance baselines, redesign those processes, build the technical enablers, and drive enterprise-wide adoption. We have been deliberately strengthening these capabilities to help clients realize measurable value from AI across the enterprise. These trends are reflected in our client work. Let me highlight a few engagements from the quarter that demonstrate how these capabilities are being applied in practice. For a leading food service distribution company, we built and deployed an AI-powered product credit claims platform that automatically validates customer claims against photographic evidence. The platform cross-checks product, manufacturer label, and shipping label images against the claim's reason code in real time, replacing a fully manual salesperson-mediated review process. In performance testing, the system processed approximately 400 claims supported by 1,000 images end to end in under 15 seconds per claim. The capability is now live in production. The client has approved a long-term roadmap to further enhance the system and extend automated decision-making into more advanced credit adjudication scenarios. For a leading home improvement retailer, Grid Dynamics enabled next-day delivery by designing and deploying a high-load service that modernized the retailer's logistics operations. The solution includes an AI-powered routing capability that assigns fragile items to the appropriate vehicle types, eliminating hundreds of delivery errors each week. As a result, the solution cut average delivery time by more than half from three and a half days, and is expected to support up to half a billion dollars in incremental annual revenue for the client. For a global technology company, we modernized large-scale data processing infrastructure, migrating more than 1,000 data pipelines to a serverless execution model. This reduced idle compute capacity, reduced infrastructure costs, and improved scalability. Our proprietary AI-powered automation accelerated the migration and established a reusable delivery approach that is now being applied across broader initiatives at this client. Now let me turn the call to Yury Gryzlov, our Chief Operating Officer. Yury Gryzlov: Thank you, Vasily. Let me build on the physical AI and robotics work Leonard introduced. Physical AI needs a full technology stack, and we operate across everything between the robot and the enterprise. The devices themselves come from our hardware partners. At the foundation is the Robot Operating System, ROS and ROS2, the open source layer the majority of the world's modern robots are built on, connecting the hardware to everything above it. Through Ekumen, we're not just users of it, we are among its maintainers and the founding member of the alliance that governs it. On the top of that sits the intelligence, the AI models that let a robot perceive its surroundings, generate its own motion, and handle real-world variability. We design and validate that in simulation before it ever runs on a real robot. Our own platform, Incarnum, our GAIN Platform for Physical AI, is where enterprises bring it all together, building manipulation and inspection workflows, deploying those models, and monitoring robotic lines with digital twins. What unifies it is our focus on the enterprise, expanding this capability to the companies that have robots deployed at scale. Here are a few examples that illustrate our work across the stack. For a leading manufacturer of construction and mining equipment, we are building a next-generation stack for autonomous driving, loading, and excavation. We're helping them design the platform, onboard the first use cases, and add capabilities like policy-based control. What began as our first commercial physical AI engagement is now a multi-year program across several regions. With Ekumen, we've proven two arm manipulation, grasping and assembly, trained entirely in simulation and then run reliably on a real robot. Bridging that gap from simulation to the physical robot is one of the hardest problems in the field. Humanoids are the next step. A leading life sciences company is piloting humanoid robots for intralogistics, moving and repacking containers of chemicals, work that was out of reach only a couple of years ago and is now possible thanks to new AI models that generate motion. We provide the platform those robots run on, working with Wandelbots and on NVIDIA's stack. The customer calls it a lighthouse project for their industry, and it's the opening step in a much wider program. We're also building the channels to scale. This quarter, we announced a strategic partnership with Doosan Robotics, a global leader in collaborative robots deployed across 45 countries. It's a full stack collaboration. Our platform, plus the foundational AI components, integration services, and engineering around it, paired with Doosan's cobots and our combined global reach. Together, we can provide what traditional robotic software can't: dual arm assembly, inspection of complex geometry parts, and packing of deformable items. It sits alongside our elevated NVIDIA partnership, and we are in active talks with several more hardware and software vendors. Considering the economics of software services in this space and our positioning, we are confident that we have a material market advantage. Reliable performance in the physical world takes engineers who understand simulation, control, and hardware variability, working through problems that have no templated solution, and so can't be easily automated. This combination is hard to assemble. Ekumen's decade of foundational robotics depth, together with our strength in AI modeling, simulation, and enterprise delivery. We don't believe another services company matches it today. Closing that gap isn't a matter of hiring a team. It's years of hard-won experience, which we are now putting to work for our customers. In summary, robotics and physical AI is a growing market, measured in the trillions over the coming decade. Our expanded capability is helping us capitalize on the early traction we saw last year, reflected in a rapidly growing pipeline from both existing customers and new logos. Another important part of my update is tied to our capital markets focus, where a similar pattern is playing out in software rather than robots. As our banking clients push agentic AI deep into their engineering, the hard part is no longer producing code, it's doing it safely with quality, security, and control they can provide to a regulator. This quarter, that showed up most sharply around security. Banks want the speed of frontier models and AI-generated code without introducing new vulnerabilities. Our answer is spec-driven agentic engineering led by Allium, part of our GAIN platform for AI SDLC, and it's exhibiting real traction across our banking clients. The clearest example is at one of the world's largest banks, where Allium is being used to build new tools as part of a bank-wide initiative to modernize business operations. Working across London, New York, and India, we're bringing specification-driven development to both new and existing systems, starting with tools for AI-assisted productivity and extending to agents that automate operational work. Taken together, physical AI reaching the enterprise and the AI-native engineering scaling inside the world's largest banks, this is the frontier work that keeps Grid Dynamics differentiated. Over to you, Eugene. Eugene Steinberg: Thank you, Yury. Good afternoon. Last year, I described our AI strategy through three horizons. This quarter, I'll describe them by maturity, what has reached scale and what is beginning to scale. Horizon one, scaled, AI-first modernization and the agentic platform. Modernization remains the foundation of our business, AI is changing how the work gets done. Agents can now accelerate work across most of the modernization life cycle, particularly code generation and testing. The remaining work, business acceptance, production scaling, and complex coordination still depends on human judgment and accountability. An agent can write a code. A person still makes a call and stands behind it. We have invested in a set of GAIN tools that support this life cycle. Rosetta governs how agents operate. Allium analyzes legacy systems to create reliable specifications for their replacements. SpecFlow, our latest open source contribution, uses those specifications to support autonomous feature implementation. Rosetta has progressed from its first lighthouse clients to larger engagements across retail, financial services, and manufacturing. At a Fortune 30 U.S. home improvement retailer, approximately 550 of the client's engineers are working with the platform. In one program, seven COBOL services were moved to a modern technology stack with approximately 90% of the code generated by agents. All seven services entered production this quarter. The client already had capable engineers and access to many of the same AI tools we use. What it needed from us was domain knowledge, governance, and control, the capabilities that turn powerful agents into dependable enterprise systems. This productivity is helping us expand client relationships. It is also creating opportunities to use more fixed price and outcome-based commercial models when the scope and accountability are clearly defined. Allium also reached an important milestone this quarter. It is being piloted across five major banks and has begun moving into its first commercial banking engagements. Allium analyzes legacy code to help establish reliable functional specifications for replacement systems. It also supports controlled migration and rollback, reducing the operational risk of moving critical applications onto modern platforms. At one major North American bank, a one-hour GAIN demonstration in February led to a signed contract in April. The bank was managing 150 applications with limited test coverage and a growing security backlog. We translated identified issues into failing tests inside the bank's own tooling, allowing its engineers to independently reproduce and assess each finding. At another Tier 1 bank, this same approach is supporting a security modernization program spanning more than 100,000 systems. This part of the modernization work co-funded by the client's cloud provider. Our differentiation is not limited to code generation. Our agents can also incorporate context such as security advisories, dependencies, and upstream changes. That broader context helps identify problems that code-only tools can miss and provides the traceability and evidence regulated enterprises expect. We deliberately make selected GAIN platforms open source. The immediate objective is adoption and technical credibility, not software license revenue. Open code allows engineering leaders to evaluate our capabilities directly and strengthen our position when client needs help deploying those capabilities at enterprise scale. The same pattern applies to data. Enterprise AI cannot deliver reliable results without accessible, well-governed data. That is increasing demand for data platform modernization. Our new AI data migration accelerator, released this quarter, is already being deployed in data lake modernization program for a global consumer products manufacturer. The second scaled component of Horizon One is GAIN Agentic Runtime. Enterprise agents need access to trusted data. Evidence that their behavior is controlled, and governance over operating costs. For a global payment client, we brought these capabilities together as shared services, with the retrieval layer now supporting 25 enterprise consumers. We also converted the client's dispute architecture, including fraud, chargebacks, and KYC, to configuration-driven workloads. A common foundation now supports four use cases. By automating much of this configuration, the program rebuilt a decade of business logic in just six months and reduced integration and release cycle times by 96%. At our largest banking client, an internal platform built with our support now centralizes the registration, governance, and operation of AI agents across the organization. The client reports regular adoption by more than 80% of its employees across more than 80 markets. As adoption grows, we are also developing the operational tooling needed to govern and support the platform at that scale. Across these engagements, the pattern is consistent. AI accelerates production, but enterprise value comes from the domain knowledge, governance, and accountability required to put it all into production responsibly. Horizon Two: scaling. Harness engineering and physical AI. Horizon Two covers capabilities that are moving from research and internal validation towards repeatable client deployment. The first is agentic harness engineering. Traditional agentic workflows are most effective when the task and sequence of step are already known. Harnesses are designed for more dynamic work, situations in which an agent must select tools, adjust its approach, and respond to new information while remaining with defined controls. The harness provides those controls. It records what the agent did, tests its output, manages exceptions, and introduces human review where accountability requires it. This allows enterprises to apply agents to more complex work without giving up oversight. During the second quarter, our India engineering center developed nine harness-based solutions. Following our client zero approach, we are testing them first with our own operations. The objective is to establish evidence of reliability, define the necessary controls, and improve the solutions before introducing them into client environments. The second area is physical AI and robotics. We are investing here because the engineering challenge is fundamentally different from conventional software development. A coding agent can generate software and test it in digital environment. A physical system must also operate safely and reliably in the real world. It must account for geometry, motion, changing conditions, and the behavior of physical environment. Validation, therefore, has to take place both in simulation and on hardware. A language model alone cannot close this loop. Our research is focused on bringing physics, geometry, simulation, and continuous validation into the agent's operating environment. That is also the strategic rationale for the robotics engineering team we acquired in May. Members of this team have long contributed to core infrastructure in the Robot Operating System ecosystem, with particular expertise in simulation and validation. Their capabilities are now contributing to GAIN for Physical AI, our platform built on Incarnum. During the quarter, we released three new components: tools for composing robotic policies, a continuous improvement loop, and sandbox environment for control testing. We are beginning to validate the platform through early client and partner deployments. A leading life cycle company is piloting humanoid robots in its warehouse operations using our platform. Separately, a robotics partner has incorporated the platform into its own offering, creating a distribution channel for our physical AI technology. Horizon 2 is not yet the same maturity as our modernization and agentic platform business. Our focus now is to demonstrate repeatability, convert technical validation into production deployments, and establish scalable commercial models. The opportunity is to build differentiated intellectual property in areas where success requires not only generating software but providing how that software behaves in the physical world. Across both horizons, the pattern is clear. The cost of producing software is falling, while the value of governing it, validating it, and taking responsibility for it in production is increasing. This quarter, more components of GAIN moved from tools and pilots into broader enterprise adoption. At the same time, our investments in agentic harnesses and physical AI progressed from research towards controlled client deployments. As these capabilities mature, they allow us to reuse more of our engineering, deploy solutions faster, and take greater responsibility for measurable outcomes. Our advantage is not simply that our agents can generate code. It is that we combine those agents with domain knowledge, operational controls, and the engineering discipline required to make them dependable at enterprise scale. That is where we believe durable value will be created in the agentic era, and where Grid Dynamics is positioned to lead. Anil, over to you. Anil Doradla: Thanks, Eugene. Good afternoon, everyone. Second quarter came in at $108.2 million, slightly above the higher end of our guidance range of $106 million-$108 million. That represents 7% year-over-year growth, including de minimis contributions from Ekumen. Non-GAAP EBITDA was $14.7 million or 13.6% of revenues and was closer to the high end of our $14 million-$15 million guidance range. Looking at the performance of our verticals, TMT remained our largest vertical and accounted for 31.8% of total revenues for the quarter, with a growth of 11.7% sequentially and 36.4% on a year-over-year basis. The growth was primarily driven by our largest technology customers. We continue to benefit from vendor consolidation at these customers, which has driven increased wallet share across new and existing programs. Retail contributed 26.5% of total revenues in the second quarter of 2026. The vertical was flat in absolute dollars on a year-over-year basis and grew 3.1% sequentially. The sequential growth was supported by demand from key accounts, including a major specialty retailer. Our finance vertical accounted for 22.9% of total revenues in the quarter and grew 1.2% on a sequential basis. Within this vertical, we witnessed solid demand from our fintech service engagements, including increased contributions from a major payments network, which helped offset the successful completion of engagements with insurance and data analytics and consumer credit reporting clients in North America. Looking ahead to the remainder of 2026, we remain bullish on our growth outlook within this vertical. CPG and manufacturing represented 10.9% of quarterly revenues and grew 2.1% on a sequential basis and 4.2% on a year-over-year basis. Within this vertical, we are witnessing robust demand from a leading wholesale food distributor, along with growth from some of our manufacturing customers. Turning to our remaining verticals, our other vertical contributed 6% of our second quarter revenues, while healthcare and pharma contributed for 1.9% of our revenues for the quarter. We ended the second quarter with a total headcount of 4,838, down from 4,964 employees in the first quarter of 2026 and from 5,013 in the second quarter of 2025. We continue to rationalize our overall headcount as well as align our skill sets and geographic mix. At the end of the second quarter of 2026, our total U.S. headcount was 379, or 7.8% of our company's total headcount versus 7.2% in the year-ago quarter. Our non-U.S. headcount, located in Europe, Americas, and India, was 4,459, or 92.2%. In the second quarter, revenues from our top five and top 10 customers were 43.5% and 61.5% respectively, versus 37.5% and 57.3% in the same period a year ago respectively. Moving to the income statement, our GAAP gross profit during the quarter was $39.6 million, or 36.6%, compared to $36.2 million, or 34.8%, in the first quarter of 2026 and $34.5 million or 34.1% in the year-ago quarter. On a non-GAAP basis, our gross profit was $40 million, or 36.9%, compared to $36.7 million, or 35.3%, in the first quarter of 2026 and $35.1 million or 34.7% in the year-ago quarter. On a year-over-year basis, the increase in the gross margin percentage was primarily driven by revenue growth outpacing delivery cost. On a sequential basis, the increase in gross margin percentage was due to a combination of working time and improved resource utilization. Non-GAAP EBITDA during the second quarter that excluded interest income, expenses, provisions for income taxes, depreciation and amortization, stock-based compensation, restructuring, expenses related to geographic reorganization, and transaction and other related costs was $14.7 million, or 13.6% of revenues, versus $12.5 million or 12% of revenues in the first quarter of 2026 and was up from $12.7 million or 12.6% in the year-ago quarter. The sequential and year-over-year growth in EBITDA was largely due to a combination of higher revenues and strong operating leverage across our non-engineering overhead. Our GAAP net income in the second quarter was $2.9 million, or $0.03 per share, based on a diluted share count of 83 million shares, compared to the first quarter net loss of $1.5 million or a loss of $0.02 per share based on a diluted share count of 84.7 million and net income of $5.3 million or $0.06 per share based on 86.4 million diluted shares in the year-ago quarter. On a non-GAAP basis, in the second quarter, our non-GAAP net income was $9 million or $0.11 per share based on 83 million diluted shares compared to the first quarter non-GAAP net income of $7.5 million or $0.09 per share based on 85.9 million diluted shares, and $8.3 million or $0.10 per share based on 86.4 million diluted shares in the year-ago quarter. On June 30, 2026, our cash and cash equivalents totaled $298.4 million, down from $327.5 million on March 31, 2026. Since our first quarter earnings call, we repurchased approximately 2.6 million shares for a total consideration of $17.3 million. Cumulatively, since our board authorized the 50 million share repurchase program, we have repurchased approximately 4.4 million shares for a total of $30.8 million, reflecting our continued confidence in the long-term value of the business. Coming to the third quarter guidance, we expect revenues to be in the range of $112 million-$114 million. We expect our third quarter non-GAAP EBITDA to be in the range of $16.5 million-$17.5 million. For the third quarter, we expect our basic share count to be in the range of 81 million-82 million shares and our diluted share count to be in the range of 83 million-84 million shares. For 2026, we're maintaining our full year revenue outlook of $435 million-$465 million. That concludes my prepared remarks. We are now ready to take questions. Carrie? Mayank Tandon: Great. Thank you. Congrats on the quarter, Leonard and Anil. Anil Doradla: Thank you, Mayank. Mayank Tandon: There was a lot of detail around AI. Just to step back, Leonard, could you maybe talk about the AI efforts and the implications for both growth and profitability over the next, say, 12, 24 months? Maybe you can help reassure investors that AI will actually be a net positive for you, because there's still a lot of skeptics out there that think it's going to be a net negative over time. Leonard Livschitz: Right. Thank you, Mayank. It's a pretty comprehensive question. If I answer all of the parts, there'll be probably nothing left for the other end. I'll try to be concise in terms of the key elements. Then we can talk a little bit more in detail. First of all, we are reaching many aspects of AI implementations. We talked about it in the past. We're adding those features now. We're talking about directly or indirectly about forward deployed engineers. We make announcements. We train a substantial number of the people in the workforce, and these people are basically driving a new way of implementing our solutions because, as we tend to get more focused on a fixed bid and fixed budget projects, it helps us to identify not only the execution of the various modernization projects, but also create a technology consulting. That's with respect of the people and why it's accretive to us. When it comes to agentic AI, a part of the, again, implementation of the suite of our solutions, we are driving our customers to adopt our GAIN platform model. All the elements of the model are driven by internal tested and developments, but also tailored to our customer needs. They will need to adapt the solution where they see the most fit for themselves, but also we guide them through the process to create the best ROI for that. That's the second part. Before I talk about the physical AI, I want to bring, to address your point in terms of net positive versus net negative. If you look at the increased growth in just these two areas, that substantially exceeds the, some of the aged businesses which would eventually drop out. Because the gloom and doom from many facets were about that engineering and consultancy is less relevant. Moreover, people would say it's easier to train FTEs. We embrace FTEs. We embrace our clients. At the same time, as many of the leaders in the industry saying, we can do more work, we can do more engagements, which we prove with all the listed examples. I'm not going to go through all of them because we have a lot of people who can give you more details on that. As a consolidated effort, as we go today through further discussions, we will demonstrate on specific examples where this accretiveness works. I want to emphasize forward deploy engineering and agentic AI. The third part which is also super critical for us, which actually drives the adoption and partnership enhancement of our relationship to the next level, is actually our preparation for physical AI work. We not just made a small acquisition. We not just made a announcement about opening additional robotics labs. We've been working with our clients for a long enough time to understand what it means for their own platform, what it means for their application and solutions from various world, from industrial, from modern machineries to logistics companies, to even work in industrialization of various new solutions. The material side, the remuneration for the physical AI is still to come, but now we have an evidence of substantial players looking at Grid Dynamics, again, in a leadership role by expanding our capabilities to the practical world of their usage. This is pretty much a summary, and then, of course, we'll go in more in detail. Mayank Tandon: That's very helpful, and sorry if you can't see me. I'm having an issue with my video. You can help with that. I'll try to get that fixed eventually. Just a very quick follow-up, Anil, for you. In terms of the guide, just want to get a sense of the visibility that you have today versus last quarter. What I mean by that is the pipeline now converting faster? Have you seen evidence of that? Does that maybe give you more confidence in the sustainability of growth acceleration once we get beyond fiscal 2026 into fiscal 2027? Anil Doradla: You're talking about next year. Let's talk about this year, and then we'll get to next year. You go into the second half, Mayank, you see, if you look at our visibility and our second half, there are a couple of factors. Number 1, remember the 85/10/5? Most of our revenue comes from customers who've been with us for 2 years and beyond. That formula more or less stays well intact. That you're seeing in the top 5, top 10 customers, right? Because most of the absolute dollar and year-over-year growth is coming there. That stays intact. As you go into the second half, there are 3 layers, as you go. First is the working time. Second half is higher than the first half. Second thing is that the billable headcount. We're seeing new programs kicking in. Maybe without addressing your pipeline question directly, indirectly is that, yes, we're seeing an increased billable headcount as we go into the second half. The 3rd thing is that we are planning some acquisitions. All these 3 add up to layers. When you look into 2027, I think I'll let the business guys chime in here, but from my point of view, I see 2 things that are very interesting. Number 1, the relationships that we're having with our technology customers, our financial customers, our top 10 and 20 customers, is going deeper and deeper. Things that we've not done, we're doing. Application modernization programs, which we've not done, we're addressing. The addressable market that we're going after is larger. I overhear these conversations week after week. Which leads me to believe as you go into 2027, if we continue winning at the rate that we're winning, it should play out incrementally past it. I don't know, Vasily or Yury, whether you want to add anything to that. Vasily Sizov: Yeah. Let me chime in. Yes, I would say that our position with most of our biggest clients has been strengthening over the last few years, through vendor consolidation. What we see is that we should benefit in the coming years, from this consolidation, which means bigger programs would come our way, by customers cutting loose, the long tail of vendors which are no longer relevant. Given our strong technology positioning in agentic AI, which is a very hot topic for most of our customers, we are really well-positioned to benefit from that. Mayank Tandon: Terrific. Thank you so much. Congrats. Anil Doradla: Thank you. Vasily Sizov: Thank you. Anil Doradla: Thank you, Mayank. The next questions come from Bryan Bergin, Janney Securities. Go ahead, Bryan. Bryan Bergin: Hey, y'all. Good afternoon. Thanks. Taking the questions here. Maybe just to start, a follow-up on that last question as it relates to that second half, more of a near-term question. Just as it relates to, you give us 3Q guide, implied 4Q is still a decent ramp. Are you seeing a broadening of momentum in other sectors? You're obviously doing quite well in technology. Are you seeing a broadening of momentum elsewhere that gives you that confidence? As it relates to potentially some M&A requirements, any way you can share with us how you're thinking about maybe the organic contribution remaining versus any needed M&A that you have to go get? Anil Doradla: Right. Bryan, let me point out that as you know, there's a certain seasonality in our business, right? As we go into Q3, Q4, that's well established. As I said, there are 3 levels at which we're operating. Number 1 is just the working times of the second half of the year, and you guys know it's better. Second thing is that the billable headcount and the trends are positive, and all our prepared commentary should lead you to include that. The third thing is that there is a certain amount of acquisition, and we do have a pipeline. It varies. I always joke, right? An acquisition is not done till the money is transferred to their bank, right? We've seen acquisitions that we thought are not going to happen, they happen. We've seen acquisitions that were locked and loaded, and we just are not able to. If you look at that second half, I don't want to comment too much upon Q4 other than saying that, look, we have a seasonal pattern for the year. As we go from the low end of our full-year guide to the high end of the guide, the first component of working time stays intact. The second component of billable headcount, we have variable calculations. The third component perhaps picks up a little bit more is the acquisitions. Bryan Bergin: Okay. Understood. My follow-up is a margin and a tie-in with the delivery model question. You reiterate the confidence in the 300 basis point expansion, that's good to hear. I'm just curious how much of this margin improvement is coming from structural changes, automation, and efficiencies in the delivery versus traditional cost control cutting measures. I think it's notable you had 7% revenue growth while headcount was down three. I know you're saying you're going to add billable headcount, but is there a lasting change in this delivery model? Just maybe talk about that AI-driven efficiency and delivery that you're seeing. Anil Doradla: Right. There are three, four parts of this question. Let me take the first part, and then when it comes to some of the AI trends, I'll pass it on. When you look at what we set out to do, we said that on a year-over-year, we're going to deliver 300 basis points margins on a Q4 by Q4 on a year-over-year basis. Part of that effort is efficiency. It's just the way we're organized. As you know, we've ramped from a handful of countries to 19 countries. We've got many incorporated entities. There's a little bit of a efficiency that we brought in, and some of those are one-time, but we operate at a certain level, right? The second part that we are seeing here is we're embracing a little bit more change in the way we're doing business, whether it's AI, whether it's fixed price, whether it's embracing more tools. That is creating a certain level of, I would say, it's not so visible now, but over time, you'll see a non-linearity perhaps that is in. The movement that you've seen on the headcount right now was largely driven by efficiency improvements on non-engineering headcount. People should not worry. It's not that we let go some billable headcount. No, it's just non-engineering, non-billable headcount. We cleaned it up. From this point onwards, beyond the 300 basis points that you'll have from Q4 to Q4 as you go into 2027, there is a plan for us to leverage more of these tools. There is a plan of bringing a certain level of non-linearity. We have the plans. The clients have to accept it, and we have to proceed with that. Go ahead, Leonard. Leonard Livschitz: Yeah. Let me share a couple of things. First of all, just to complete answer on the very first question of yours about diversification of the platforms. I think it's very critical to understand that this is not overnight we suddenly diversified verticals. First and foremost, we've been in the payment system, we've been in financial service, we've been industrial modernization. The second of all is, you can actually see from the previous comments about us, what Vasily said, replacing some incumbent vendors is because we're playing in a big boys league, in a higher level. In the past, there were always couple top guys and a couple mid-level vendors. Now we only compete with the top guys. The reason being is, I think AI adoption and technology implementation equalize the field a bit. We've always been prepared for the big tasks and a big program, big transformational solutions. We also gained a reputation of this consultancy part. As we get more admittance to the bigger projects, inevitably, what happen with that, it's a better visibility, better projection, better position. We're saving with the tools, we're adding more capabilities, and we're looking back and we say, "What of these internal systems which we had for a long time are less efficient?" We accepted to live on a world of uncertainty. That's very important. We don't see the world changing so dramatically, we'll go back immediately to the luxury of being very consolidated in a very few locations. We adding not only India, and we adding investment into India and the technology capability, but also LatAm. As we do more, we create a global platform internally to optimize this efficiency. It's a cost structure, it's performance-based, it's tooling, it's removing redundancies from the past. I hope, Bryan, I covered a lot. Puneet Jain: Hey, thanks for taking my question. How are your AI and robotics partnership different from your traditional hyperscaler relationships like with Google, AWS, Microsoft Azure, that generate much of your 19% of partnership revenue? The partnerships you got with NVIDIA, model companies, do they differ or do they offer a different revenue trajectory potential or client ownership structure than your other partnerships? Vasily Sizov: All right. Thank you so much for the question, Puneet. Let me address this question. We definitely value our relationships with NVIDIA, and believe that's a great partnership to build a pipeline of future opportunities on. As you understand, right now, the industry, the manufacturing is going through a massive transformation and new tools like agentic AI or physical AI definitely brings new technology to more traditional manufacturing. We see this as a great opportunity to build a new pipeline of opportunities, a new type of engagements which would help us to transform those manufacturers on a bigger scale. Just an example. For example, right now we have an active engagements with one of the world's largest industrial equipment manufacturer on building an agentic AI platform which allows to manage the fleet of autonomous vehicles and deploy physical AI capabilities on the edge devices. We see more and more interest to such opportunities. It's definitely one of the top priorities for us to grow. Puneet Jain: Got it. I'd like to follow up on the prior question, specifically around headcount. I noticed your non-U.S. headcount was down despite the Ekumen, which probably contributed employees in Argentina. The U.S. headcount, by comparison, was up on sequential basis. Should we expect this remix to continue as you do more AI-based services? Will that require more on-site headcount or U.S. headcount compared to in the past? If that's true, what does that mean for margin and change management within your employee base? Leonard Livschitz: Very good. Puneet, what you said, it's music to Eugene's ear because he's been the one who is architecting the acceleration of some of the U.S.-based presence, both from the technology office perspective, but also from the technology consultancy with the clients. I'm not saying there's more shift toward onshoring as a trend. I think if you look back pre-COVID days, our onshore presence between onshore technology people and as well as some of the offshoring engineers who would come on long-term projects, reached almost close to 20%. It's never been so low. When the onshoring presence pulled back due to an ability to work directly with the clients, a lot of work has been going on offshoring. We're not saying that work is no longer relevant, but there are more and more demand to presence on premise with the clients to work together on these complex cases because the rapid change of transformation sometimes catches the clients a little bit through uncertainty, right? We talk about 2 basic approaches to their mental and budgetary resolution of the projects. One of them is more like a status quo. Let's see and tell what's going to happen. They don't need as much of onshoring presence, and some of them demand very rapid acceleration, but they're concerned with some of the spendings, as you know, around tokens and other things which definitely create the pressure. That's where our headcount onshoring technology-wise is coming. As I mentioned to Bryan, some of the reduction of offshoring headcount comes from non-engineering and non, I would say, forward-looking specialties. There is a difference between the headcount and contribution of this headcount. From the budget perspective, it's a little bit less clear that these people were extremely expensive, but just the infrastructure of all these people would no longer be needed for us to serve the markets better. To answer your question, we do see some additional growth of onshoring. The ability of us to prove that our margin expansion will continue to grow is vastly driven how much of the fixed bid, fixed budget projects we can adapt, how much of our internal developed tools are accepted by the clients, how much of the nonlinear value we're bringing to the party, and I think we're quite growing with those elements. Just to conclude on that from my side, and if people want to add, I think you picked the right trend. I don't think the legacy some of the people are a sign for concern because majority of them come from the Central Eastern Europe. I think this is all by the book. We are really moving forward with a clear plan on continue to have margin improvement. Puneet Jain: Got it. Thank you. Anil Doradla: Thank you, Puneet. Matt Dezort: Great. Thanks, guys. Congrats on the results. I wanted to see if you could double-click on this new consultancy practice that you're talking about. Can you discuss more of how you see this business developing? I know you talked about activities like change management, but what sort of opportunities are you seeing in the pipeline build there? Who are you going up against in these bake-offs, and how is the competitive environment different from your traditional work, maybe? Leonard Livschitz: Yeah. I will start very briefly, and then Vasily will actually expand on it. Matt, there are two parts of it. The first part is there's no change of our purpose. Consultancy has always been a part of our DNA. Nothing is earth-shattering because our clients consider us to be a technology consultants, and that's why we're able to compete against the big firms. What has changed is the distribution of that kind of offering. Just the previous question with Puneet was about onshoring presence, right? People who we hire, they're extremely technical, but they're also customer-oriented. That kind of work, very important because we are expanding the purpose of consultancy from pure technology consultancies, and now adding, AI infrastructure consultancies, hardware selection consultancy, tool selection consultancy, and to some extent getting more into the sacred world of business consultancy. Listen. Vasily Sizov: Yes. Think about business consultancy as a natural extension of our technology enabler build-out capabilities. Essentially, the focus of the customers is shifting from just creation of a system, but for creation of a systems to change business processes they have. Therefore, they would like to analyze first which business processes are the best candidates to improve, which value is hidden there, then to build a technical enabler to reveal this value, and then adopt that technical enabler on the enterprise wide scale. That's exactly where the focus of our consultancy is, not only to create the technical enabler, but also to help get all the value on the enterprise scale from this change. That's the essence. Right now we have several active engagements on that, specifically on the front of consulting change management, which goes along with technical enablers, and we see this opportunity ahead of for a great growth in the future. Matt Dezort: That makes sense. Eugene Steinberg: I can add to that many of our customers observe a performance and productivity of our delivery teams using our GAIN platforms. They become interested, and they want those platforms and those methodologies inside their own software factory, and we are helping them to establish the tools, methodology, and change management, which is required to GAIN the similar productivities in their broader organization. Matt Dezort: That's a good segue, Eugene, for my follow-up on GAIN adoption and just the S-curve that implies. As you accelerate GAIN rollout, how should we think about that adoption curve and pure AI revenue? Is it likely to scale linearly, or you're talking about wallet share gains from AI, is there a way we could see some exponential growth, and how could you drive a more sharper inflection in that AI penetration with GAIN? Eugene Steinberg: What is interesting about our GAIN strategy is that we are consolidating all our IP from multiple accounts, from multiple practices under the same umbrella, and AI helps us to do that very, very rapidly and quickly. Our embedded engineers, forward-deployed engineers, are all tasked to bring back the learnings, the ideas, what works and what not works back to the GAIN platform. Part of the GAIN platform is also open source that helps to drive the insights from the broader community and put the GAIN platforms in front of many leaders. At this point in time, we observe a growth of the direct revenue from our GAIN platform, but much more important, we observe a growth of the overall connection and expansion of our relationships inside our accounts and the new accounts, which are driven by these platforms. We see many of the inbound interests and conversations which result in new leads, new opportunities, and new converted business from GAIN platform. This is what is happening right now. Leonard Livschitz: Good. Matt Dezort: Thank you, guys. Surinder Thind: Thank you, guys. I'd like to start with a question just around this idea of there's a bit more excitement around moving from proof of concept to maybe the actual implementation projects. That commentary seems to be a bit more universal. From your perspective, can you maybe talk about what the revenue journey for that looks like? Meaning how big a proof of concept project would be if it's a few hundred thousand dollars, does that become a $2 million project, or what's kind of the range of outcomes that we can expect here as we think about more of those proof of concepts coming and how that would impact the growth rate? Leonard Livschitz: Surinder. Again, I will give you a little bit of a high level, and I think because it's all revenue touched, Vasily will give you a little bit more color. There are different proof of concept. The definition of proof of concept could be quite stretched, both from intent and then dollars associated with that and follow-ups. When we looked at proof of concepts as a result of our partnerships, for example, that resulted in some of the very meaningful programs where the customer embraced not only our partner solution, but our offering, which was in conjunction with these partnerships. When we look today and specifically at the suite of GAIN productivity, it's actually very interesting. Eugene mentioned about inbound interest. As you know, for us, for Grid Dynamics and our size and capabilities, visibility is very critical. The customers would reach to us with something we still call proof of concept, but those are substantial projects because the measurement of proof of concept, sometimes driven today not by the amount of dollars, could be quite more substantial in many cases, but the time to implement. The whole short-term engagement definition, which used to be followed or preceded by the proof of concept, become the proof of concept itself, and then it's a major rollout. This has conceptually changed the definition of proof of concept and revenue associated with it, but I'm sure that Vasily will give some more details. Vasily Sizov: Yes. Many customers start definitely with implementation of some smaller pieces of business cases. Leonard Livschitz: Sure Vasily Sizov: which have tangible business results in order to demonstrate it for their boards, for their management, and then using that as an example, essentially request more investment into that, which eventually gets converted into platform build-out, to build AI harness and et cetera. This trend definitely persists. That's what we see with our customers. I can state that for platforms work, this work essentially is much more sticky and longer-term in nature than the POCs. Having built the platform, of course, there is a growing appetite to build more and more business cases on top of this platform, so it grows like a snowball, and that actually is what's reflected in our pipeline. Yury Gryzlov: I think I just wanted to add that it also depends on the industry, right? We've mentioned today about physical AI and robotics. Definitely there is a lot of proof of concept in those areas, but at the same time, it also depends on how deep you are in your relationship with the customer and other programs around, outside even of those areas, right? That's where those proof of concept could be actually quite significant. Sometimes it could be just maybe a few weeks of small engagement, sometimes it could be six months plus. Going back to the GAIN model and our platforms in the GAIN, I think this is where also we try to condense this knowledge, right, and a way to speed up this implementation as much as possible for a customer. That also contributes to the ratio of the proof of concept revenue versus the longer engagement implementation revenue. Leonard Livschitz: Just to summarize for Surinder. The POCs associated with FDE consultancy, GAIN model modernization subjects, and agentic AI as overall are very substantial from GetGo. The physical AI part is what traditionally would call proof of concept because it's such an innovative way to modernize modern productivity and interface between human robotics. These type of POCs are more traditional way, and their revenue will follow with the scale which you would typically expect from POCs. Surinder Thind: Cool. Then maybe thinking about the data and AI practice and the really high growth rate that we're seeing there, the 30% of revenues. Can you help me understand what's going on in the other 70%? When I do the math, I get to roughly about a 10% decline in that other 70% of revenues. How much of that is just cannibalization by the data and AI component? Because I assume every new piece of work probably falls into that bucket. Then is there components that are maybe in that legacy, I'll call it legacy bucket for lack of a better word, other elements to that, such as pricing compression or just other factors to think about? Anil Doradla: Very good question, Surinder. I'm actually going to make it much simpler. It's not even that complex. In our case, when you look at our business trends, from time to time we might see a significant customer, I'm loosely using that word, maybe a top 30 customer, top 40 customer, right, have some changes. Maybe there's a change in strategy or a big project is completed or something changes, and we can have some volatility there. If you go back over the past couple of quarters and look back at Leonard's commentary, he talked about sensitivity of brick-and-mortar retail, for example. You create some volatilities there. Very simply put, if I were to extract some of these volatilities there, we would see a better growth pattern. Second point is, your question is whether there is cannibalization. What we see is, Eugene and Vasily can back me up on this, when we get into our clients, especially in our top 20 clients, we're going deeper and deeper. All the work is incremental AI work that we typically see. The third thing that we see here is on the pricing. We are not seeing pricing pressures. As a matter of fact, when you go into the AI world, obviously there's a premium. When you look at what we are doing over the past couple of years, and I look at a certain grade in a certain country, and whether there's pricing pressures, the answer is absolutely no. We're not seeing that. Now, we can argue whether there's a pricing increase. That's a different story, but there's no pricing declines. Vasily, I don't know whether you want to- Vasily Sizov: Yes. I think it's a question of semantics, right? The cost per project or cost per functionality or piece of scope is definitely getting reduced because of the higher productivity and shortening the timelines for the delivery. That's kind of what's happening. That leads to more work and more projects rather than the reduction. I think that's a very important color. Leonard Livschitz: Finally, you can look at the revenue per person. Again, we need to have some history to prove that trend will continue to grow. We're not trying to defend legacy. I think Anil was quite clear that some businesses fall off, right? You're absolutely right. The AI content brings more new business. There is one element which is not there, is us trying to retain some legacy business and compressing our margin. That's just not part of it. Surinder Thind: Cool. It does sound like there's a definitional component here, right? To your earlier point of how you divide up the buckets and trying to look at it collectively, plus all of the noise of project starts and stops and things like that. I appreciate that. Thank you. Anil Doradla: Thank you, sir. Vasily Sizov: Thank you. Leonard Livschitz: Our top accounts are expanding. Our AI programs are moving consistently from pilot to enterprise scale deployment, and our platform portfolio is deepening both organically and through the capabilities we have added in robotics and physical AI. I'm confident in the second half of 2026, the strategy is working, the momentum is building. Before you buy stock in Grid Dynamics, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Grid Dynamics wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. 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As with all our articles, The Motley Fool does not assume any responsibility for your use of this content, and we strongly encourage you to do your own research, including listening to the call yourself and reading the company's SEC filings. Please see our Terms and Conditions for additional details, including our Obligatory Capitalized Disclaimers of Liability. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy. Grid Dynamics (GDYN) Q2 2026 Earnings Call Transcript was originally published by The Motley Fool

Revenues of $108.2 million and Record AI Revenues of 30.7% SAN RAMON, Calif., July 30, 2026--(BUSINESS WIRE)--Grid Dynamics Holdings, Inc. (Nasdaq: GDYN) ("Grid Dynamics" or the "Company"), a leader in enterprise-level AI and digital transformation, today announced results for the quarter ended June 30, 2026. Second Quarter 2026 Revenues Performance We are pleased to report second quarter 2026 revenues of $108.2 million, slightly above the high end of our guidance range of $106.0 million to $108.0 million that we provided in April 2026. Our Technology, Media and Telecom ("TMT") vertical was our principal growth driver, representing 31.8% of the second quarter revenues. TMT revenues increased 36.4% year-over-year, and 11.7% sequentially, driven by strong demand from our largest technology customers. Retail remained our second-largest vertical, contributing 26.5% of total revenues for the second quarter of 2026 driven by robust demand from key accounts. The Finance vertical contributed 22.9% of the second quarter revenues, supported by ongoing demand from our financial services engagements. Our Consumer Packaged Goods ("CPG") and Manufacturing vertical represented 10.9% of quarterly revenues and increased 2.1% sequentially. Lastly, the Healthcare and Pharma, and Other verticals contributed 1.9% and 6.0% of the total second quarter revenues, respectively. "We delivered another solid quarter. Revenue came in above the high end of our guidance range. AI revenue crossed 30% of total company revenue for the first time and grew over 50% year-over-year for a second consecutive quarter. Our AI-based GAIN platforms are winning wider enterprise adoption as clients transition enterprise AI workloads from pilots to production. Our top accounts continue to drive our growth. Several of them delivered double-digit quarter-over-quarter growth. Additionally, Technology and Financial services verticals now define our most strategic customer relationships. These are precisely the sectors where AI adoption is moving the fastest and where our capabilities are the most differentiated. We also strengthened our Physical AI capabilities with the addition of Ekumen, giving us end-to-end depth from robotics software through enterprise-scale deployment. Margin expansion continues to be a top priority. Productivity gains from AI-Native Delivery are showing up in our results. So is disciplined cost-control execution. Our second quarter performance, and our third quarter outlook, confirm we are on track to deliver our 300 basis point margin commitment," said Leonard Livschitz, Chief Executive Officer. Second Quarter 2026 Financial Highlights Total revenues were $108.2 million, up 3.9% on a sequential and 7.0% on a year-over-year basis. GAAP gross profit was $39.6 million, or 36.6% of revenues, compared to $34.5 million, or 34.1% of revenues, in the second quarter of 2025. Non-GAAP gross profit was $40.0 million, or 36.9% of revenues, compared to $35.1 million, or 34.7% of revenues, in the second quarter of 2025. GAAP net income was $2.9 million, or $0.03 per share, based on 83.0 million diluted weighted-average common shares outstanding in the second quarter of 2026, compared to $5.3 million, or $0.06 per share, based on 86.4 million diluted weighted-average common shares outstanding, in the second quarter of 2025. Non-GAAP net income was $9.0 million, or $0.11 per diluted share, based on 83.0 million diluted weighted-average common shares outstanding in the second quarter of 2026, compared to $8.3 million, or $0.10 per diluted share, based on 86.4 million diluted weighted-average common shares outstanding, in the second quarter of 2025. Non-GAAP EBITDA (earnings before interest, taxes, depreciation, amortization, other income and expenses, fair value adjustments, stock-based compensation, transaction and transformation-related costs, and restructuring costs as well as geographic reorganization expenses), a non-GAAP metric, was $14.7 million, compared to $12.7 million in the second quarter of 2025. See "Non-GAAP Financial Measures" and "Reconciliation of Non-GAAP Information" below for a discussion of our non-GAAP measures. Cash Flow and Other Metrics Cash provided by operating activities was $14.5 million for the six months ended June 30, 2026, compared to $23.7 million for the six months ended June 30, 2025. Cash and cash equivalents totaled $298.4 million as of June 30, 2026, compared to $342.1 million as of December 31, 2025. Total headcount was 4,838 as of June 30, 2026, compared with 5,013 as of June 30, 2025. Financial Outlook Third Quarter The Company expects revenues in the third quarter of 2026 to be in the range of $112.0 to $114.0 million. Non-GAAP EBITDA in the third quarter of 2026 is expected to be between $16.5 and $17.5 million. For the third quarter of 2026, we expect our basic share count to be in the 81 - 82 million range and diluted share count to be in the 83 - 84 million range. Full Year The Company expects full-year 2026 revenues to be in the range of $435.0 to $465.0 million. Grid Dynamics is not able, at this time, to provide GAAP targets for net income/(loss) for the third quarter of 2026 because of the difficulty of estimating certain items excluded from Non-GAAP EBITDA that cannot be reasonably predicted, such as interest income, taxes, other income/(expenses), fair-value adjustments, geographic reorganization expenses, restructuring expenses, transaction-related costs and charges related to stock-based compensation expense. The effect of these excluded items may be significant. Conference Call and Webcast Grid Dynamics will host a video conference call at 4:30 p.m. ET on Thursday, July 30, 2026 to discuss its second quarter financial results. Investors and other interested parties can access a webcast of the video conference call on the Investor Relations section of the Company’s website at https://www.griddynamics.com/investors. A replay will also be available after the call at https://www.griddynamics.com/investors with the passcode $Q2@2026. About Grid Dynamics Grid Dynamics (Nasdaq: GDYN) is a premier AI transformation partner for the Fortune 1000. We combine deep AI expertise with proven enterprise-scale delivery to help clients identify where to invest in AI, build systems that work at scale, and capture real business value from AI deployments. A key differentiator for Grid Dynamics is our nearly two decades of technology leadership and pioneering enterprise AI expertise. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India. To learn more about Grid Dynamics, please visit https://www.griddynamics.com. Follow us on LinkedIn. Non-GAAP Financial Measures To supplement the financial measures presented in this Grid Dynamics press release in accordance with generally accepted accounting principles in the United States ("GAAP"), the Company also presents non-GAAP measures of financial performance. A "non-GAAP financial measure" refers to a numerical measure of Grid Dynamics historical or future financial performance or financial position that is included in (or excluded from) the most directly comparable measure calculated and presented in accordance with GAAP. Grid Dynamics provides certain non-GAAP measures as additional information relating to its operating results as a complement to results provided in accordance with GAAP. The non-GAAP financial information presented herein should be considered in conjunction with, and not as a substitute for or superior to, the financial information presented in accordance with GAAP and should not be considered a measure of liquidity and profitability. Grid Dynamics has included these non-GAAP financial measures because they are financial measures used by Grid Dynamics’ management to evaluate Grid Dynamics’ core operating performance and trends, to make strategic decisions regarding the allocation of capital and new investments and are among the factors analyzed in making performance-based compensation decisions for key personnel. Grid Dynamics believes the use of non-GAAP financial measures, as a supplement to GAAP measures, is useful to investors in that they eliminate items that are either not part of core operations or do not require a cash outlay, such as stock-based compensation expense. Grid Dynamics believes these non-GAAP measures provide investors and other users of its financial information consistency and comparability with its past financial performance and facilitate period to period comparisons of operations. Grid Dynamics believes these non-GAAP measures are useful in evaluating its operating performance compared to that of other companies in its industry, as they generally eliminate the effects of certain items that may vary for different companies for reasons unrelated to overall operating performance. There are significant limitations associated with the use of non-GAAP financial measures. Further, these measures may differ from the non-GAAP information, even where similarly titled, used by other companies and therefore should not be used to compare our performance to that of other companies. Grid Dynamics compensates for these limitations by providing investors and other users of its financial information a reconciliation of non-GAAP measures to the related GAAP financial measures. Grid Dynamics encourages investors and others to review its financial information in its entirety, not to rely on any single financial measure, and to view its non-GAAP measures in conjunction with GAAP financial measures. Please see the reconciliation of non-GAAP financial measures to the most directly comparable GAAP measures attached to this release. Forward-Looking Statements This communication contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, that are not historical facts, and involve risks and uncertainties that could cause actual results of Grid Dynamics to differ materially from those expected and projected. These forward-looking statements can be identified by the use of forward-looking terminology, including the words "believes," "estimates," "anticipates," "expects," "intends," "plans," "may," "will," "potential," "projects," "predicts," "continue," or "should," or, in each case, their negative or other variations or comparable terminology. These forward-looking statements include, without limitation, the quotations of management, the section titled "Financial Outlook," and statements concerning Grid Dynamics’s expectations with respect to future performance, particularly in light of the macroeconomic and geopolitical environment, including the Russian invasion of Ukraine. Factors that may cause such differences include, but are not limited to: (i) Grid Dynamics operates in a rapidly evolving industry, which makes it difficult to evaluate future prospects and may increase the risk that it will not continue to be successful; (ii) Grid Dynamics may be unable to effectively manage its growth or achieve anticipated growth, particularly as it expands into new geographies, which could place significant strain on Grid Dynamics’ management personnel, systems and resources; (iii) Grid Dynamics’ revenues are highly dependent on a limited number of clients and industries, and any decrease in demand for outsourced services in these industries, or in general as a result of artificial intelligence ("AI") or other technologies, may reduce Grid Dynamics’ revenues and adversely affect Grid Dynamics’ business, financial condition and results of operations; (iv) macroeconomic conditions, inflationary pressures, the risk of recession, the impact of tariffs and other factors impacting world trade, and the geopolitical climate, including the Russian invasion of Ukraine and the conflict with Iran, have and may continue to materially adversely affect our stock price, business operations, overall financial performance and growth prospects; (v) Grid Dynamics’ revenues are highly dependent on clients primarily located in the United States, and any economic downturn in the United States or in other parts of the world, including Europe or disruptions in the credit markets may have a material adverse effect on Grid Dynamics’ business, financial condition and results of operations; (vi) Grid Dynamics faces intense and increasing competition; (vii) Grid Dynamics’ failure to successfully attract, hire, develop, motivate and retain highly skilled personnel could materially adversely affect Grid Dynamics’ business, financial condition and results of operations; (viii) failure to adapt to rapidly changing technologies, methodologies and evolving industry standards, including those relating to AI, may have a material adverse effect on Grid Dynamics’ business, financial condition and results of operations; (ix) issues relating to the use of AI technologies may result in reputational harm or liability, (x) security breaches and other incidents could expose us to liability and cause our business and reputation to suffer; (xi) failure to successfully deliver contracted services or causing disruptions to clients’ businesses may have a material adverse effect on Grid Dynamics’ reputation, business, financial condition and results of operations; (xii) risks and costs related to acquiring and integrating other companies; (xiii) risks relating to the global regulatory environment as well as legal proceedings and other claims, (xiv) risks related to the new and rapidly challenging AI business and (xv) other risks and uncertainties indicated in Grid Dynamics filings with the SEC. Grid Dynamics cautions that the foregoing list of factors is not exclusive. Grid Dynamics cautions readers not to place undue reliance upon any forward-looking statements, which speak only as of the date made. Grid Dynamics does not undertake or accept any obligation or undertaking to release publicly any updates or revisions to any forward-looking statements to reflect any change in its expectations or any change in events, conditions or circumstances on which any such statement is based. Further information about factors that could materially affect Grid Dynamics, including its results of operations and financial condition, is set forth under the "Risk Factors" section of the Company’s annual report on Form 10-K filed March 5, 2026 and in other periodic filings Grid Dynamics makes with the SEC. View source version on businesswire.com: https://www.businesswire.com/news/home/20260730673583/en/ Contacts Grid Dynamics Investor Relations: [email protected]

Grid Dynamics (GDYN) came out with quarterly earnings of $0.11 per share, beating the Zacks Consensus Estimate of $0.1 per share. This compares to earnings of $0.1 per share a year ago. These figures are adjusted for non-recurring items. This quarterly report represents an earnings surprise of +10.00%. A quarter ago, it was expected that this company would post earnings of $0.08 per share when it actually produced earnings of $0.09, delivering a surprise of +12.5%. Over the last four quarters, the company has surpassed consensus EPS estimates three times. Grid Dynamics, which belongs to the Zacks Computers - IT Services industry, posted revenues of $108.16 million for the quarter ended June 2026, surpassing the Zacks Consensus Estimate by 1.39%. This compares to year-ago revenues of $101.1 million. The company has topped consensus revenue estimates four times over the last four quarters. The sustainability of the stock's immediate price movement based on the recently-released numbers and future earnings expectations will mostly depend on management's commentary on the earnings call. Grid Dynamics shares have lost about 24.7% since the beginning of the year versus the S&P 500's gain of 6.9%. While Grid Dynamics has underperformed the market so far this year, the question that comes to investors' minds is: what's next for the stock? There are no easy answers to this key question, but one reliable measure that can help investors address this is the company's earnings outlook. Not only does this include current consensus earnings expectations for the coming quarter(s), but also how these expectations have changed lately. Empirical research shows a strong correlation between near-term stock movements and trends in earnings estimate revisions. Investors can track such revisions by themselves or rely on a tried-and-tested rating tool like the Zacks Rank, which has an impressive track record of harnessing the power of earnings estimate revisions. Ahead of this earnings release, the estimate revisions trend for Grid Dynamics was unfavorable. While the magnitude and direction of estimate revisions could change following the company's just-released earnings report, the current status translates into a Zacks Rank #4 (Sell) for the stock. So, the shares are expected to underperform the market in the near future. You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. It will be interesting to see how estimates for the coming quarters and the current fiscal year change in the days ahead. The current consensus EPS estimate is $0.12 on $112.19 million in revenues for the coming quarter and $0.43 on $438.3 million in revenues for the current fiscal year. Investors should be mindful of the fact that the outlook for the industry can have a material impact on the performance of the stock as well. In terms of the Zacks Industry Rank, Computers - IT Services is currently in the top 34% of the 250 plus Zacks industries. Our research shows that the top 50% of the Zacks-ranked industries outperform the bottom 50% by a factor of more than 2 to 1. Taboola.com Ltd. (TBLA), another stock in the same industry, has yet to report results for the quarter ended June 2026. The results are expected to be released on August 5. This company is expected to post quarterly earnings of $0.13 per share in its upcoming report, which represents a year-over-year change of +30%. The consensus EPS estimate for the quarter has remained unchanged over the last 30 days. Taboola.com Ltd.'s revenues are expected to be $500.4 million, up 7.5% from the year-ago quarter. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report Grid Dynamics Holdings, Inc. (GDYN) : Free Stock Analysis Report Taboola.com Ltd. (TBLA) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research

Image source: The Motley Fool. April 30, 2026, 4:30 p.m. ET Chief Executive Officer — Leonard Livschitz Chief Financial Officer — Anil Doradla Global Head of Partnerships and Marketing — Rahul Bindlish Chief Technology Officer — Eugene Steinberg 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. 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. Before you buy stock in Grid Dynamics, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Grid Dynamics wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you’d have $504,832!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you’d have $1,223,471!* Now, it’s worth noting Stock Advisor’s total average return is 971% — a market-crushing outperformance compared to 202% for the S&P 500. Don't miss the latest top 10 list, available with Stock Advisor, and join an investing community built by individual investors for individual investors. See the 10 stocks » *Stock Advisor returns as of May 1, 2026. This article is a transcript of this conference call produced for The Motley Fool. While we strive for our Foolish Best, there may be errors, omissions, or inaccuracies in this transcript. As with all our articles, The Motley Fool does not assume any responsibility for your use of this content, and we strongly encourage you to do your own research, including listening to the call yourself and reading the company's SEC filings. Please see our Terms and Conditions for additional details, including our Obligatory Capitalized Disclaimers of Liability. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy. Grid Dynamics (GDYN) Q1 2026 Earnings Transcript was originally published by The Motley Fool

Grid Dynamics (GDYN) came out with quarterly earnings of $0.09 per share, beating the Zacks Consensus Estimate of $0.08 per share. This compares to earnings of $0.11 per share a year ago. These figures are adjusted for non-recurring items. This quarterly report represents an earnings surprise of +9.09%. A quarter ago, it was expected that this company would post earnings of $0.09 per share when it actually produced earnings of $0.1, delivering a surprise of +11.11%. Over the last four quarters, the company has surpassed consensus EPS estimates two times. Grid Dynamics, which belongs to the Zacks Computers - IT Services industry, posted revenues of $104.1 million for the quarter ended March 2026, surpassing the Zacks Consensus Estimate by 0.90%. This compares to year-ago revenues of $100.42 million. The company has topped consensus revenue estimates four times over the last four quarters. The sustainability of the stock's immediate price movement based on the recently-released numbers and future earnings expectations will mostly depend on management's commentary on the earnings call. Grid Dynamics shares have lost about 37.9% since the beginning of the year versus the S&P 500's gain of 4.2%. While Grid Dynamics has underperformed the market so far this year, the question that comes to investors' minds is: what's next for the stock? There are no easy answers to this key question, but one reliable measure that can help investors address this is the company's earnings outlook. Not only does this include current consensus earnings expectations for the coming quarter(s), but also how these expectations have changed lately. Empirical research shows a strong correlation between near-term stock movements and trends in earnings estimate revisions. Investors can track such revisions by themselves or rely on a tried-and-tested rating tool like the Zacks Rank, which has an impressive track record of harnessing the power of earnings estimate revisions. Ahead of this earnings release, the estimate revisions trend for Grid Dynamics was unfavorable. While the magnitude and direction of estimate revisions could change following the company's just-released earnings report, the current status translates into a Zacks Rank #4 (Sell) for the stock. So, the shares are expected to underperform the market in the near future. You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. It will be interesting to see how estimates for the coming quarters and the current fiscal year change in the days ahead. The current consensus EPS estimate is $0.10 on $107.85 million in revenues for the coming quarter and $0.43 on $444.24 million in revenues for the current fiscal year. Investors should be mindful of the fact that the outlook for the industry can have a material impact on the performance of the stock as well. In terms of the Zacks Industry Rank, Computers - IT Services is currently in the top 26% of the 250 plus Zacks industries. Our research shows that the top 50% of the Zacks-ranked industries outperform the bottom 50% by a factor of more than 2 to 1. Serve Robotics Inc. (SERV), another stock in the same industry, has yet to report results for the quarter ended March 2026. The results are expected to be released on May 7. This company is expected to post quarterly loss of $0.65 per share in its upcoming report, which represents a year-over-year change of -306.3%. The consensus EPS estimate for the quarter has been revised 1.6% lower over the last 30 days to the current level. Serve Robotics Inc.'s revenues are expected to be $2.34 million, up 430.7% from the year-ago quarter. Want the latest recommendations from Zacks Investment Research? Today, you can download 7 Best Stocks for the Next 30 Days. Click to get this free report Grid Dynamics Holdings, Inc. (GDYN) : Free Stock Analysis Report Serve Robotics Inc. (SERV) : Free Stock Analysis Report This article originally published on Zacks Investment Research (zacks.com). Zacks Investment Research