Grid Dynamics Holdings, Inc.

Good afternoon, everyone. Welcome to Grid Dynamics' first quarter 2026 earnings conference call. I'm Terry Savas, Director of Branding and Communications. At this time, our participants are in listen-only mode. Joining us on the call today are CEO Leonard Livschitz, CFO Anil Doradla, CTO Eugene Steinberg, and SVP Global Head of Partnerships and Marketing, Rahul Bindlish. Following the prepared remarks, We will open the call to your questions. Please note that today's conference call is being recorded. Before we begin, I'd like to remind everyone that today's discussion will contain forward-looking statements. This includes our business and financial outlook and the answers to some of your questions. Such statements are subject to the risks and uncertainty as described in the company's earnings release and other filings with the SEC. During this call, we will discuss certain non-GAAP measures of our performance. Thank you, Gary.

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 continuous strengths 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 Green 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're well-positioned to further accelerate AI revenues in 2026. For the first time, our top five 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 two leading global technology companies, a global fintech leader, a US-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 Grease Analytics has emerged as a clear beneficiary. This position was to capture greater market share in 2026 and beyond. Additionally, we have been actively engaged in AI initiatives across all five 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 mode, 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 optimizations. As enterprises migrate over 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 agent decoding fundamentally change 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. As a leading home improvement retailer, The infrastructure of global operations is based on legacy mainframe platforms. Modernizing this legacy mainframe platform was considered risky and required specialized and expensive talent. Using AI agents, Grid Dynamics delivered a full modernization program within the timeline and budget. Grid Dynamics expertise is now extending into physical AI. In CPG and manufacturing, enterprises are turning to self-learning robotics and AI technologies to drive operating efficiencies. Our game 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 in turn, AI automation. The first pillar, AI native delivery, marks a fundamental shift in how we work. From human-led workflows to AI agent-driven, Thank you for your attention. including past encoding incorrect behavior. By expanding validated behavior coverage to greater than 70%, we reduce false confidence in system integrity and mitigated production security and regulatory risk. The second pillar, productized engineering, focus on converting our repeatable IP into AI-native platform-based offering under the GAIN platform. Gain consists of four 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 with one of the world's largest food distributors. Our client sales associates were spending hours on manual research and proposal preparation for their restaurant clients. We developed AI agents that compressed the preparation process to minutes while improving the quality of the reports. Our efforts resulted in 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 a changing world. Clients are seeking out domain knowledge and deep understanding of AI and data. At 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 these processes 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 adapting AI tools, both on 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 our piece, we have increased the number of responses by 50% with our growing headcount. With knowledge management, our responses to employee questions improve 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 and Manufacturing Green 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 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 the premier global multi-bread restaurant company, Grid Dynamics deployed an AI coding harness to replace the manual QA workflows that struggle to keep pace with frequent interface 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 Partnership and Marketing, who will share some of the exciting initiatives currently underway and give you a closer look at where Grid Dynamics is headed. Rahul?

Thank you, Leonard. Good afternoon, everyone. Partnerships are now a key component of how we go to market. Our partner insurance revenues have grown to 19.1% of total company revenue in quarter one, underscoring the value of our ecosystem-driven approach in the agentic era. The majority of our partner insurance revenue is driven by Google Cloud, AWS, and Microsoft Azure, our three 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 GridDynamics IP directly, increasing our sales by size. We also have joint sales motions with the hyperscalers to accelerate de-closures. That is a fundamentally different way to win business compared to traditional services sales. This is the first deployment in a deliberate rollout. We are moving additional platforms onto the marketplaces of every major hyperscaler. also deepens our co-sale 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 standpoint with Domain Specific IP. This changes the sales dynamic in a way that matters for a 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 coastal relationships with Google, AWS, and Azure productive. With dynamic 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 CPD companies. Industrial AI in manufacturing environments requires simulation fidelity and sensor integration. that GenVic 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 re-engineering the processes those agents replace and managing the organizational change that follows. By integrating specialized process mining and change management partners into our delivery model, we extend the value that grid dynamics offers from platform and engineering through to adoption and measurable ROI capture. There are two more trends worth noting. Many of the engagements that we are winning through partner channels are extending beyond the initial project. When an AI project delivers clear ROI and our clients are seeing this at scale, the relationship does not close, it expands. Clients return for more use cases, projects and programs. That pattern is visible In our retention data and in the expansion of existing hyperscaler co-sell accounts. At one of the largest food distributors in North America, that pattern played out across three distinct phases. The initial engagement was a first project delivered through a co-sell motion with Google Cloud and built on GAIN platform for agent e-commerce. The platform's 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 game 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, then a 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.