Grid Dynamics Holdings, Inc.

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. And third, improving profitability trends. I am happy to report that on all three fronts our execution is solid and we are 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 commitments. For the second consecutive quarter, our top accounts are in technology and financial services. Technology and financial services now define our most 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 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 game 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. were 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 as DLC. Our game 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 game 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. as we are deepening our AWS relationship around application modernization and agendic AI in CPG manufacturing and financial services. Our NVIDIA partnership is gaining momentum across both agendic AI and physical AI. Our longer-term target remains 25% to 30% partner influence revenue and we are 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 opened an engineering office in Dresden, Germany, to support our European manufacturing clients. Grid Dynamics Enhanced Robotics, offering by Welcoming Acumen, 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. Equiment 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?

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 modern 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 a thousand images end-to-end in under 15 seconds per claim. The capability is now live in production, and 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 3.5 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.

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 Acumen, we are 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. and our own platform Incarno, our game 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 are helping them design the platform on board 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 Ecumen, we've proven two-arm manipulation, grasping an 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 intra-logistics, 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 Vandalbots 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 are 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 robotics 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. Acumen'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 game platform for AI as DLC, 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 are 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 AI-native engineering scaling inside the world's largest banks, this is the frontier work that keeps grid dynamics differentiated. Over to you, Eugene.