Grid Dynamics Holdings, Inc.

Good afternoon, everyone. Welcome to Grid Dynamics' third quarter 2026 earnings conference call. I'm Cary 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, and CTO Eugene Steinberg. 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. Gap to non-gap financial reconciliations and supplemental financial information are provided in the earnings press release and the 8-K filed with the SEC. You can find all the information I just described in the investor relations section of our website. I now turn the call over to Leonard, our CEO.

Thank you, Gary. Good afternoon, everyone, and thank you for joining us today. Q3 2026 was a pivotal quarter with record financial results. Revenue of $114.3 million is the highest in our history and came in above the high end of our guidance range and well ahead of Wall Street expectations. Non-GAAP EBITDA was $22.9 million, also a record. Our efforts are paying off. We are growing faster, more profitable, and in the verticals and applications where AI enterprise spend is centered. We entered the fourth quarter with confidence and with a scalable client demand, setting a positive backdrop for 2027. In the third quarter, our AI revenues grew by 45% on a year-over-year basis. This marks the third consecutive quarter where our AI revenue has grown over 45% on a year-over-year basis and is now approaching 32% of our total revenue. Companies that recently initiated AI exploratory projects are now signing multi-quarter production programs with us. Last quarter, I set a goal. 90% of our engineers trained on AI SDLC by the end of October. I'm happy to report we're currently at 93% and continue to expand. Green Dynamics has always been a technology-first engineering company and investing in our people ahead of client demand and simply how we operate. Going forward, I'm optimistic about our revenue growth trajectory and that optimism is grounded in the structure, not sentiment. As we look in 2027, We expect to revert to healthy double-digit growth and sustain that momentum over the following years. Our AI revenue is compounding on an even larger base. As it approaches and exceeds 40% of our total revenue, the growth rate of the overall business will increasingly reflect the growth rate of the AI practice. Our AI-based GAINS solutions continue to grow. We generate more revenue per headcount at an accelerated pace. We expand our physical AI and capital markets offerings. And our hyperscaler and NVIDIA partnerships create a cost-sell motion that complements our direct sales. Altogether, they are the reasons to be optimistic. We committed to 300 basis points of margin expansion in the fourth quarter of 2026 over the fourth quarter 2025, and we exceeded our goal a quarter early. The structured drivers are A greater mix of AI projects, higher billing rates, optimal location strategy execution, and disciplined OPEX management. These margins represent a new norm and in 2027 will continue to execute on the same operating framework. One of the most important trends in our business this year is the growing revenue per employee. In the third quarter, we exceeded $100,000 for the first time. Let me remind you that this is based on a total headcount and not just billable engineers. In the second quarter, revenue grew 7% year-over-year with headcount declined 3.5%, a divergence of more than 10 percentage points. In the third quarter, the gap widened further. Revenue grew 9.7% year-over-year while headcount declined A divergence of 18 percentage points. We believe this is one of the strongest headcount to revenue decoupling metrics in the IT services industry today. These diversions reflect systematic changes across how we run our operations and deliver to our clients. Internally, AI tools are transforming every non-engineering function. Recruitment, RFP Responses, Knowledge Management, Finance, and HR. We are processing more candidates, responding to more proposals, and managing more client relationships with the same or fewer people. Doing more with less is becoming the signature of every AI native program we run. Beyond operations, the shift toward platform-based, fixed-price and output-based engagements means we are monetizing intellectual property alongside the headcount. When clients engage with our game platforms, they get a combination of talent, processes and IP. As such component of our revenue grows, the link between headcount and revenue weakens by design. In a sector where headcount and revenue have historically moved in lockstep, grid dynamics is demonstrating that the two can and should be decoupled. Now about AI-native delivery. 12 months ago, AI-native delivery was the direction we were heading toward. Today is how we deliver. A few examples. At a leading global technology company, we secured a large AI-native fixed-price project. At one of the largest food distribution companies in the world, we delivered a production solution and have the planned time with 40% fewer engineers. At a leading global technology platform, an agendic solution reduced defect review time from 30 days to 4 hours. Our GAIN platforms are deployed across multiple active projects and our embedded engineers arrive with codified IP that clients are paying premium for. The commercial headline is clear. The AI native model is validating its economics. Our CTO will take you deeper into the technology progress in a moment. Our vertical diversification continues to reshape the composition of our business. Technology and financial services now dominate our top account relationships. The retail exposure that once defined our revenue mix is now smaller and we are less dependent on them for growth. The growth engine of the business is firmly in the sector where AI adoption is accelerating and this is precisely where we have concentrated our investments and our talents over the past few years. Our capital markets practice is growing rapidly with expanding teams across US, UK and India. In manufacturing and supply chain, a leading global energy infrastructure company selected us for AI-native application modernization. A major automotive part retailer is expanding its modernization program. And a leading global construction and industrial equipment manufacturer is advancing from a discovery to implementation autonomous system program. In physical AI, our first inter-logistics humanoid deployment is on track for delivery at the global pharmaceutical company. The last couple of years have been a period of transition and execution as the company focused on growing revenue, enabling AI expertise and improving margins. During this time, our targeted approach to capability-driven acquisition in capital market and robotics serve as well. We will enter 2027 in a stronger position. The business is growing, Marjorie Expanding and strategic clarity we have built around AI native delivery, productized engineering and physical AI gives us a sharper lens for evaluating acquisition targets. We are acquiring capability, domain knowledge and engineering depth that either accelerates an existing practice or opens a new one. In 2027, our focus is on building a pipeline of larger and more strategically impactful opportunities. After two years of disciplined execution, we are ready to acquire larger companies in 2027. During the quarter, Grid Dynamics delivered some notable projects. For a leading global technology company, Grid Dynamics built the Data Foundation for the launch of a new subscription package that brings together multiple online services as a bundle. The new system enables accurate tracking of the entire subscriber lifecycle, including acquisition, retention, churn, and transition between subscription offerings, while ensuring reliable revenue attribution and Engagement Analytics across multiple services. Delivered in time for the global launch, the engagement involves modernizing interconnected production data pipelines, ensuring data accuracy at scale, and establishing a scalable foundation for additional subscription models. The solution provides the critical analytics and financial reporting capabilities needed to support the successful launch of the new bundle offering across more than 100 markets, serving millions of subscribers worldwide. For a leading US-based global bank, Grid Dynamics continued expanding the enterprise AI platform that powers AI-driven productivity and automation across the organization. This quarter, we delivered new capabilities that enable employees to launch long-running AI agents directly from their AI productivity workspace, as well as a centralized service analytics platform that provides management with detailed visibility and control over AI adoption, access permissions, and costs. These innovations enable employees to delegate complex time-consuming tasks to AI agents, freeing up their time to focus on higher-value work, while giving management greater transparency and control over AI operations at scale. These enhancements extend the capability of an AI workspace used daily by more than 180,000 employees across the bank. For a leading global payment company, Grid Dynamics modernized its core financial crime prevention platform, introducing machine learning fraud detection models, self-service anti-money laundering capability, and agentic AI tools for sanctions research and alert prioritization. The modernized platform now screens approximately 14 million cross-border transactions annually for financial crime, which is 20% increase year-over-year, while reducing screening time per transaction by nearly 70%. Compliance false positives dropped by more than 90%, significantly reducing unnecessary manual reviews. AI Power Automation saves compliance analysts more than 1200 hours annually while self-service capability enabled teams to launch new AML rules in minutes instead of weeks. The platform also achieved 99.99% availability supporting reliable continuous payment process. For an advanced humanoid robotics company, we're leading simulation effort using NVIDIA ISAAC SIM and Google DeepMind MuJoCo. We are focused on advanced physics-based simulation to refine complex articulated movements for industrial applications such as welding automation. Leveraging our broader robotics capabilities, our technical scope quickly expanded into autonomy and manipulation architecture. Next, we plan to extend our engagement by helping the robots better perceive their surroundings and develop more dynamic movements. With that, let me turn the call over to our CTO, who will present our exciting technology offerings and AI-driven innovations. Eugene.

Thank you, Leonard. Good afternoon. Leonard described revenue decoupling from the headcount. Let me show you the engineering behind this process. The mechanism is transition to fixed price projects and AI native delivery for billable work and doing more with less by using AI for our internal productivity. It all comes down to one idea. The software factory powered by our GAIN platforms. On every client project, we start by building a software factory, which is a client-specific harness for general-purpose AI coding agents. Since this factory does much of the heavy lifting, this allows a smaller team to deliver a large scope. Most importantly, it makes change cheaper. Such a factory approach makes projects shorter and more predictable. At the end of the day, this is what allows us to commit to a fixed-price project in a phase of scope uncertainty. Every fixed-price project starts half understood and half specified. But much of the detail only emerged during delivery, including problems nobody could predict. In traditional delivery, this risk is covered by padding the timeline and team size. The factory changes this situation. First and foremost, it makes change significantly cheaper and faster. When something unexpected comes up, the factory rebuilds affected components quickly and reliably, so that the project becomes more flexible and easier to manage and control. While AI agents provide the execution capacity, engineers provide specs, exercise judgment, and ensure verification and control. This is why we can bid with an aggressive timeline and a smaller team while still delivering profitably. Here is an example. An AI bid manager assistant for a major food distributor. Previously, every RFP bid required hundreds of hours from dozens of specialists. Now, the AI assistant matches the RFP request with the distributor catalog and best supply offers. The first phase of this program is now in production with the goal to achieve faster, cheaper, and more competitive bits. We are building this program with a factory approach. The work runs in several work streams of four to five engineers each. These factory teams are measured to deliver twice as much progress as conventional AI assistant teams on the same program. When we build a software factory, our gain platforms, like Rosetta or Allium, provide reusable engineering components and tools. After the project is done, we carry the learnings back to those platforms to continually compound knowledge. The critical aspect of AI delivery is security. Clients want the speed of AI-native delivery without adding new vulnerabilities. and new agentic threats require an adequate agentic response. Traditional security tools check code after it is written. We go further by shifting security left. Our AI agents work inside the development process itself, so security checks happen while the code is being specified and developed. Specifically in capital markets, banks want correctness they can prove in front of regulators. This is where we deploy Allium. It helps to clearly specify functionality and find and fix vulnerabilities before we might leak into the release. We apply the same factory approach inside Grid Dynamics. It contributes to the productivity gains Leonard described outside client work. Our internal tools team can deliver more with less and growing numbers of agentic applications and workflows already run in the Grid Dynamics environment. We drink our own champagne. Additionally, they continue to build up physical AI capabilities, pipeline, and projects. Next-generation robots are trained on a mix of real-world examples and simulation. This is where Grid Dynamics fits perfectly. They bring deep robotics research, simulation experience, and AI engineering that runs reliably at enterprise scale. We are working with our clients and partners to take AI-controlled robots from labs to the production line. That is the work behind the two programs Leonard mentioned. One is the autonomous vehicle program of the industry equipment maker. The other is the humanoid robot for the pharmaceutical company moving materials inside its facilities. Both programs are growing. Grid Dynamics continues to expand its robotics partnership network, adding three robotics partners in the third quarter. Together, we participate in top-tier industry events, like GTC in Berlin, actively building our physical AI and robotics business. The Grid Dynamics team is proud of the progress we are making on AI transformation and we are excited about the journey ahead. Anil, over to you.