Grid Dynamics Holdings, Inc. (GDYN) 2026 Q2 法說會逐字稿

Good afternoon, everyone. Welcome to Grid Dynamics second-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; CRO, Vasily Sizov; COO, Yury Gryzlov; CTO Anil Doradla; CTO, Eugene Steinberg; and CFO, Anil Doradla. Following the prepared remarks, we will open the call to your questions.

各位下午好。歡迎參加 Grid Dynamics 2026 年第二季財報電話會議。我是 Cary Savas，品牌與傳播總監。目前各位與會者將以僅收聽模式參與。今天與我們一同出席電話會議的有：執行長 Leonard Livschitz；首席營收長（CRO）Vasily Sizov；營運長（COO）Yury Gryzlov；技術長（CTO）Anil Doradla；技術長（CTO）Eugene Steinberg；以及財務長（CFO）Anil Doradla。在預先準備的發言之後，我們將開放提問。

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.

請注意，今天的電話會議將被錄音。在開始之前，我想提醒各位，今天的討論將包含前瞻性陳述。其中包括我們的業務與財務展望，以及對各位部分問題的回答。此類陳述受公司財報新聞稿及向美國證券交易委員會（SEC）提交的其他文件中所述之風險與不確定性影響。

During this call, we will discuss certain non-GAAP measures of our performance. GAAP to non-GAAP financial reconciliations and supplemental financial information are provided in the earnings press release and the 8-K filed with the SEC. You can find all the information I just described in the Investor Relations section of our website.

在本次電話會議中，我們將討論若干非 GAAP 的績效衡量指標。GAAP 與非 GAAP 財務調節表及補充財務資訊已載於財報新聞稿以及向 SEC 提交的 8-K 文件中。您可在我們網站的投資人關係（Investor Relations）專區找到我剛才提到的所有資訊。

I now turn the call over to Leonard, our CEO.

現在我把電話交給我們的執行長 Leonard。

Thank you, Cary. 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.

謝謝你，Cary。各位下午好，感謝大家今天加入我們。我們第二季交出穩健的成績。合併營收為 1.082 億美元，高於我們指引區間的上緣並優於華爾街預期；非 GAAP 盈利為 1,470 萬美元，同樣超出市場共識。各位或許還記得我在上季評論中強調的三個面向。

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'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.

第一，營收趨勢改善，尤其是在科技與金融服務領域的關鍵客戶。第二，我們的 AI 採用與成長。第三，獲利能力趨勢改善。我很高興向各位報告，在這三個面向上，我們的執行都很扎實，並且正在看到成效。包括：深化與主要客戶的合作關係、在機器人與實體 AI（physical AI）擴展能力下持續推進 AI 動能，以及朝向我們承諾的 300 個基點毛利率擴張目標取得穩健進展。

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.

連續第二個季度，我們的主要客戶集中在科技與金融服務領域。科技與金融服務如今界定了我們更具策略性的客戶關係，而這些正是 AI 採用速度最快、且我們能力差異化最明顯的產業。我們的主要客戶持續驅動成長。其中數家在季度對季度（QoQ）呈現兩位數成長，表現突出。這些並非零星的增量。它們反映了方案擴大、方案採用更深入，以及我們能力的複利效應——持續在每位客戶組織內部找到新的機會。

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.

其中數位客戶如今正將我們的 GAIN 平台嵌入其自身營運中作為核心基礎設施，不僅是專案工具，而是一項可持續的能力。這與兩年前我們所擁有的商業關係相比，本質上更不同、也更具持久性。第二季 AI 營收占公司總營收的 30.7%，年增 54.6%，並首次跨越 30% 的門檻。

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.

連續兩個季度年增率超過 50% 告訴我們一件重要的事。這不是短期尖峰。而是一個持續性的轉變。趨勢非常清楚，我們也打算在此基礎上持續加碼。

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.

推動這項強勁表現的是多項因素的組合。我們的 GAIN 平台正在贏得更廣泛的企業級採用。客戶持續將企業 AI 工作負載從試點（pilot）轉向正式上線（production）。我們的工程師更深度地嵌入客戶組織內部。歸根結底，我們正在贏得全新的方案，這讓我們對未來成長更有信心。

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.

以 AI 為優先的交付如今已是預設，而非願景。在新的 RFP 回覆中，固定價格（fixed price）是偏好的方式。生產力與毛利率的提升是真實可見的。我們執行良好，並成功交付專案。我們聚焦於執行更大型的 AI 平台，亦與我們在工程人才技能提升（upskilling）方面取得的顯著進展相一致。

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.

我們計畫在 10 月底前，讓 90% 的工程師完成 AI SDLC（軟體開發生命週期）訓練。本季我們的 GAIN 平台在 LLM 合作夥伴方面有顯著擴展。我們目前正與多家全球領先的 AI 公司合作，其中包括前四大前沿（frontier）供應商，且我們已與其簽訂商業協議。此一作法可確保我們的 GAIN 平台與領先的 AI 平台保持一致，並在我們的企業客戶群中擁有更廣泛的觸及。GAIN 仍是我們將 AI 能力推向市場的核心骨幹。其合作夥伴深度使其每一季都更強大。

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.

我們的客戶關係也在演進。原本因平台部署而找上我們的客戶，現在會要求我們留下來。他們希望我們持續參與顧問服務、執行與日常營運。這項重要轉變正在開啟一條成長向量，而這在兩年前的商業模式中並不存在。

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.

在合作夥伴方面，第二季由合作夥伴影響（partner influence）帶動的營收占公司總營收的 19.1%。這主要由我們與三大核心超大規模雲端（hyperscaler）夥伴的關係所驅動：Google Cloud、AWS 與 Microsoft Azure。其中愈來愈高比例的營收來自 AI 專案。我們正在 Google Vertex AI 搜尋客戶群中舉辦代理式 AI（agentic AI）工作坊，將搜尋相關合作轉化為更廣泛的代理式商務（agentic commerce）方案。

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% to 30% partner influence revenue, and we're confident of achieving this target.

我們在銀行與金融服務領域延伸了與 Google 的合作，本季在一家全球領先銀行完成首個聯合得標案。我們正在 CPG 製造與金融服務領域，圍繞應用程式現代化與代理式 AI，深化與 AWS 的合作關係。我們與 NVIDIA 的合作在代理式 AI 與實體 AI 兩方面都正加速累積動能。我們的長期目標仍是讓合作夥伴影響營收占比達到 25% 至 30%，我們有信心達成此目標。

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.

上季我介紹了我們的實體 AI 能力，以及在該領域的首批商業合作案。實體 AI 需要對多個學科有深入理解，包括：真實世界機器人動作建模、數位分身（digital twins）、在模擬器中的驗證，以及與硬體系統的整合。我們目前的專案涵蓋：用於製藥廠內部物流（intralogistics）的人形機器人、用於工程建設設備的自動駕駛堆疊（stack），以及面向製造業客戶的策略控制（policy control）平台。本季我們與領先的機器人製造商 Doosan 簽署策略合作夥伴關係，進一步提升我們與 NVIDIA 的合作層級，並在德國德勒斯登（Dresden）設立工程辦公室，以支援我們的歐洲製造業客戶。Grid Dynamics 於 5 月迎來領先的機器人工程團隊 Ekumen 加入，進一步強化我們的機器人產品與服務。他們的專長在於機器人作業系統（Robot Operating System），這是一項基礎性的開源標準，為全球絕大多數工業機器人提供動力。

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.

在過去十年中，該公司建立了一份極具價值的名單，涵蓋全球最受尊敬的一些機器人公司。Grid Dynamics 帶來先進的 AI 建模、策略控制，以及企業級規模的交付能力。Ekumen 帶來機器人製造商所依賴之基礎軟體層的深厚知識。兩者結合極具競爭力，涵蓋從基礎軟體層到模擬、硬體整合，以及企業級規模部署的全堆疊能力。我們相信，當今市場上沒有其他服務公司能匹敵這種結合後的版圖與技術深度。

Now, let me pass on to Vasily Sizov, Chief Revenue Officer, who will expand on key business aspects of Grid Dynamics client engagements. Vasily?

現在，讓我把時間交給首席營收長 Vasily Sizov，他將進一步說明 Grid Dynamics 客戶合作案的關鍵業務面向。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 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.

謝謝你，Leonard。我先從本季我們觀察到的三個需求趨勢談起。第一，客戶正優先投入能帶來清晰、可衡量商業成果的 AI 投資。第二，隨著客戶從零散的使用案例走向企業級規模的計畫，他們意識到必須將底層技術層現代化，才能支援 AI 的導入。第三，客戶愈來愈認知到成功的 AI 轉型不僅僅是技術本身，因而帶動對 AI 流程顧問、績效基準衡量與變革管理的興趣。這些趨勢與我們的策略以及我們正在打造的能力高度一致。我將更詳細地逐一說明。

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.

第一，需求環境仍具建設性，客戶正將 AI 投資導向具體可落地、且能帶來實質商業影響的應用。我們看到市場對 AI 驅動的自動化特別感興趣，因其可提升營運效率、可擴展性與速度。重要的是，這些投資正愈來愈多地超越實驗階段，客戶將 AI 能力部署到生產環境，用以自動化複雜的人工流程、改善客戶服務、降低營運成本，並創造新的營收來源。第二，當客戶從零散的 AI 使用案例走向企業級轉型時，他們發現其資料、應用與核心平台必須現代化並具備 AI 就緒能力。因此，AI 的導入正在底層技術版圖上創造更廣泛的需求。此趨勢與我們在資料工程、應用現代化、雲端與平台工程方面的核心專長高度契合，也強化了在 AI 時代這些能力的相關性。

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.

第三，隨著客戶聚焦於將 AI 投資轉化為可衡量的商業價值，我們看到對 AI 流程顧問、績效基準衡量與變革管理的需求正在增加。他們需要找出哪些業務流程透過 AI 重新設計能創造最大價值、建立清晰的績效基準、重新設計流程、建置技術使能要素，並推動全企業範圍的採用。我們一直有意識地強化這些能力，以協助客戶在全企業範圍內從 AI 實現可衡量的價值。這些趨勢也反映在我們的客戶專案中。

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, 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.

我來重點分享本季幾個合作案，展示這些能力如何在實務中落地應用。針對一家領先的餐飲服務配送公司，我們建置並部署了一個 AI 驅動的產品折讓/退款（credit）索賠平台，可自動依據照片證據驗證客戶申訴。該平台會即時將產品、製造商標籤與運輸標籤影像，與索賠原因代碼進行交叉比對，取代原本完全由業務人員介入的人工審核流程。在效能測試中，系統端到端處理約 400 件索賠、並支援 1,000 張影像，每件索賠耗時不到 15 秒。該能力目前已在生產環境上線，且客戶已核准一份長期路線圖，以進一步強化系統，並將自動化決策延伸至更進階的折讓/退款審理情境。

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 $500 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.

針對一家領先的居家修繕零售商，Grid Dynamics 透過設計並部署高負載服務、現代化其物流營運，協助實現隔日送達。該解決方案包含 AI 驅動的路由能力，可將易碎品分配至適當的車輛類型，每週消除數百起配送錯誤。因此，該方案將平均配送時間從 3.5 天縮短超過一半，並預期可為客戶帶來每年最高達 5,000 億美元的新增營收。針對一家全球科技公司，我們將大規模資料處理基礎設施現代化，將超過 1,000 條資料管線遷移至無伺服器（serverless）執行模型。此舉降低了閒置運算容量、降低基礎設施成本，並提升可擴展性。我們自有的 AI 驅動自動化工具加速了遷移，並建立可重複使用的交付方法，目前正應用於該客戶更廣泛的計畫中。

Now let me turn the call to Yury Gryzlov, our Chief Operating Officer.

現在我把電話會議交給我們的營運長 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.

謝謝你，Vasily。我接續 Leonard 介紹的實體 AI 與機器人相關工作。實體 AI 需要完整的技術堆疊，而我們涵蓋從機器人到企業端之間的所有環節。裝置本體由我們的硬體合作夥伴提供。在基礎層是機器人作業系統 Robot Operating System（ROS 與 ROS2），這是全球多數現代機器人所採用的開源層，將硬體與其上層的一切連接起來。透過 Ekumen，我們不只是使用者，我們也是其維護者之一，並且是治理該系統聯盟的創始成員。在其之上是智慧層，也就是讓機器人能感知周遭環境、生成自身動作並處理真實世界變異的 AI 模型。在模型真正運行於實體機器人之前，我們會先在模擬環境中完成設計與驗證。

Our own platform, Incarno, 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.

我們自有的平台 Incarno，也就是我們用於實體 AI 的 GAIN 平台，是企業將一切整合在一起的地方：建置操作（manipulation）與檢測（inspection）工作流程、部署模型，並透過數位分身（digital twins）監控機器人生產線。其核心一致性在於我們對企業端的聚焦，將這項能力擴展到已大規模部署機器人的公司。以下是幾個例子，說明我們如何橫跨整個技術堆疊提供服務。針對一家領先的建築與礦業設備製造商，我們正在打造用於自動駕駛、裝載與挖掘的下一代技術堆疊。我們協助其設計平台、導入首批使用案例，並加入如基於策略（policy-based）的控制等能力。這項原本作為我們第一個商業化實體 AI 合作案的專案，如今已成為跨多個地區、為期多年的計畫。透過 Ekumen，我們已驗證雙手臂操作、抓取與組裝，可完全在模擬中訓練，並在實體機器人上可靠運行。將模擬與實體機器人之間的落差銜接起來，是此領域最困難的問題之一。

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.

人形機器人（humanoids）是下一步。一家領先的生命科學公司正在試點以人形機器人進行廠內物流（intralogistics），搬運並重新包裝化學品容器；這類工作在幾年前仍難以實現，如今因能生成動作的新 AI 模型而成為可能。我們提供這些機器人運行的平台，並與 Wandelbots 合作，同時基於 NVIDIA 的技術堆疊。客戶稱其為該產業的燈塔專案（lighthouse project），也是更大規模計畫的起點。

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.

我們也在建立可規模化的通路。本季，我們宣布與 Doosan Robotics 建立策略夥伴關係；Doosan Robotics 是協作型機器人（cobots）領域的全球領導者，產品已部署於 45 個國家。這是一項全堆疊合作。我們的平台加上基礎 AI 元件、整合服務與相關工程能力，搭配 Doosan 的協作機器人與我們雙方的全球觸及。我們能共同提供傳統機器人軟體難以做到的能力：雙手臂組裝、複雜幾何零件的檢測，以及可變形物品的包裝。這也與我們升級的 NVIDIA 夥伴關係相輔相成，且我們正與更多硬體與軟體供應商進行積極洽談。

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.

考量此領域軟體服務的經濟性以及我們的定位，我們有信心具備實質的市場優勢。要在真實物理世界中達到可靠表現，需要理解模擬、控制與硬體變異性的工程師，去解決沒有制式模板、因此也不易自動化的問題。這樣的組合很難建立。Ekumen 十年累積的機器人基礎深度，加上我們在 AI 建模、模擬與企業級交付方面的強項。我們不認為目前有其他服務公司能與之匹敵。縮小這個差距不是靠招募一個團隊就能做到。那是多年艱苦累積的經驗，而我們現在正將其投入為客戶創造價值。

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.

總結而言，機器人與實體 AI 是一個正在成長的市場，未來十年以兆美元規模衡量。我們擴大的能力正協助我們把握去年看到的早期動能，這反映在來自既有客戶與新客戶標誌（new logos）的管線快速成長。

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.

我更新內容的另一個重要部分與我們對資本市場的聚焦相關，在那裡，類似的模式正在軟體領域上演，而非機器人。當我們的銀行客戶將代理式 AI 深入導入其工程體系時，困難之處不再是產出程式碼，而是如何在具備品質、安全性與可控性、並能向監管機構交代的前提下安全地完成。本季，這一點在安全性方面表現得最為明顯。銀行希望享有前沿模型與 AI 生成程式碼的速度，同時不引入新的弱點。我們的答案是由 Allium 主導、以規格驅動的代理式工程；它是我們用於 AI SDLC 的 GAIN 平台的一部分，並且在我們的銀行客戶中展現出實質的動能。

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.

最明確的例子是在全球最大的銀行之一，Allium 正被用於建置新工具，作為全行推動營運現代化計畫的一部分。我們跨倫敦、紐約與印度協作，將規格驅動開發導入新系統與既有系統，先從 AI 輔助生產力工具開始，並延伸到可自動化營運工作的代理。綜合來看，實體 AI 進入企業，以及 AI 原生工程在全球最大銀行內部擴大規模，這些前沿工作正是讓 Grid Dynamics 保持差異化的關鍵。

Over to you, Eugene.

交給你了，Eugene。

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.

謝謝你，Yury。各位下午好。去年，我以三個地平線來描述我們的 AI 策略。本季，我將以成熟度來描述它們：哪些已達到規模化，哪些正開始規模化。地平線一：已規模化——AI 優先的現代化與代理式平台。現代化仍是我們業務的基礎，AI 正在改變工作完成的方式。代理現在可以加速現代化生命週期中大多數工作，尤其是程式碼生成與測試。剩餘的工作——業務驗收、上線後的規模化，以及複雜協調——仍然依賴人類的判斷與責任承擔。代理可以寫程式碼。但仍需要人做出決策並為其負責。我們已投資一套支援此生命週期的 GAIN 工具。Rosetta 規範代理如何運作。Allium 分析舊有系統，為其替代方案建立可靠的規格。

And 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 US 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.

而 SpecFlow——我們最新的開源貢獻——利用這些規格來支援自主的功能實作。Rosetta 已從最初的燈塔客戶（lighthouse clients）推進到零售、金融服務與製造業的更大型專案。在一家《財富》美國前 30 大的居家修繕零售商，約有 550 名客戶工程師正在使用該平台。在其中一個計畫中，七個 COBOL 服務被遷移到現代技術堆疊，約 90% 的程式碼由代理生成。這七個服務本季全部已進入生產環境。

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.

該客戶本來就擁有能力出色的工程師，也能取得我們使用的許多相同 AI 工具。他們需要我們提供的是領域知識、治理與控制——也就是把強大的代理轉化為可靠企業系統的能力。這種生產力正協助我們擴大客戶關係。當範疇與責任歸屬清楚界定時，它也創造了更多採用固定價格與成果導向商業模式的機會。

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.

Allium 本季也達成一個重要里程碑。它正在五家主要銀行進行試點，並已開始進入其首批商業銀行的實際專案。Allium 會分析舊有程式碼，以協助建立替代系統的可靠功能規格。它也支援受控的遷移與回滾，降低將關鍵應用移轉到現代平台的營運風險。

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.

在一家北美主要銀行，2 月一次一小時的 GAIN 示範在 4 月就促成簽約。該銀行管理 150 個應用程式，測試覆蓋率有限，且安全性待辦（backlog）持續增加。我們將已識別的問題轉換為該銀行自有工具中的失敗測試，讓其工程師能獨立重現並評估每一項發現。在另一家第一級（Tier 1）銀行，同樣的方法正支援一項涵蓋超過 100,000 個系統的安全現代化計畫。這部分現代化工作由客戶的雲端供應商共同出資。

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.

我們的差異化不僅限於程式碼生成。我們的代理也能納入安全公告、相依性與上游變更等脈絡資訊。更廣的脈絡有助於識別僅靠程式碼工具可能遺漏的問題，並提供受監管企業所期待的可追溯性與證據。我們刻意將部分 GAIN 平台開源。短期目標是採用率與技術可信度，而非軟體授權收入。

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.

開源程式碼讓工程領導者能直接評估我們的能力，並在客戶需要協助將這些能力以企業規模部署時，強化我們的定位。同樣的模式也適用於資料。若沒有可存取且治理良好的資料，企業 AI 無法交付可靠結果。這正在提高對資料平台現代化的需求。我們本季發布的新 AI 資料遷移加速器，已開始部署於一家全球消費品製造商的資料湖現代化計畫中。

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.

地平線一的第二個已規模化組成是 GAIN Agentic Runtime。企業級代理需要存取可信資料。需要其行為受控的證據，以及對營運成本的治理。針對一位全球支付客戶，我們將這些能力整合為共享服務，目前檢索層已支援 25 個企業內部使用者（consumers）。我們也將客戶的爭議處理架構（包含詐欺、拒付與 KYC）轉換為配置驅動的工作負載。一個共同基礎如今支援四個使用案例。透過自動化大量配置工作，該計畫在短短六個月內重建了十年的業務邏輯，並將整合與發布週期時間縮短了 96%。在我們最大的銀行客戶，一個在我們支援下建置的內部平台，現已集中管理全組織 AI 代理的註冊、治理與運行。客戶回報其員工中超過 80% 在超過 80 個市場中持續採用。

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.

隨著採用率提升，我們也在開發在此規模下治理與支援平台所需的營運工具。在這些專案中，模式一致。AI 加速產出，但企業價值來自將其負責任地導入生產環境所需的領域知識、治理與責任承擔。

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.

地平線二：規模化中。Harness 工程與實體 AI。地平線二涵蓋正從研究與內部驗證，走向可重複的客戶部署的能力。第一項是代理式 harness 工程。傳統的代理式工作流程在任務與步驟順序已知時最有效。Harness 則是為更動態的工作而設計：在這些情境中，代理必須選擇工具、調整方法，並在保持既定控制的同時回應新資訊。Harness 提供這些控制。它會記錄代理做了什麼、測試其輸出、管理例外情況，並在責任要求時引入人工審查。

This allows enterprises to apply agents to more complex work without giving up oversight. During the second quarter, our 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.

這讓企業能在不放棄監督的前提下，將代理應用於更複雜的工作。第二季期間，我們的工程中心開發了九個以 harness 為基礎的解決方案。依循我們的 client zero 方法，我們先在自身營運中測試它們。目標是在導入客戶環境之前，建立可靠性的證據、界定必要的控制，並改進這些解決方案。

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.

第二個領域是實體 AI 與機器人。我們在此投資，因為其工程挑戰在本質上與傳統軟體開發不同。編碼代理可以生成軟體並在數位環境中測試。而實體系統還必須在真實世界中安全且可靠地運作。它必須考量幾何、運動、變動的條件，以及物理環境的行為。因此，驗證必須同時在模擬與硬體上進行。僅靠語言模型無法閉合這個迴路。

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 robotics [operating system] ecosystem, with particular expertise in simulation and validation. Their capabilities are now contributing to GAIN for Physical AI, our platform built on Incarno.

我們的研究聚焦於將物理、幾何、模擬與持續驗證帶入代理的作業環境。這也是我們於 5 月收購機器人工程團隊的策略性理由。該團隊成員長期為機器人[作業系統]生態系的核心基礎設施做出貢獻，並在模擬與驗證方面具備專長。他們的能力如今正為 Physical AI 的 GAIN 做出貢獻——這是我們建立在 Incarno 之上的平台。

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.

在本季度，我們發布了三個新元件：用於組合機器人策略的工具、持續改進迴路，以及用於控制測試的沙盒環境。我們正透過早期客戶與合作夥伴的部署開始驗證該平台。一家領先的生命週期公司正在其倉儲作業中使用我們的平台試點人形機器人。另外，一家機器人合作夥伴已將該平台納入其自身產品中，為我們的實體 AI 技術建立了一個分銷通路。

Horizon Two 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.

第二地平線（Horizon Two）尚未達到我們現代化與代理式平台業務同等的成熟度。我們目前的重點是證明可重複性，將技術驗證轉化為生產部署，並建立可擴展的商業模式。機會在於於那些成功不僅需要生成軟體、還需要確保該軟體在物理世界中如何表現的領域，打造具差異化的智慧財產。

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.

跨越兩個地平線，趨勢很清楚。生產軟體的成本正在下降，而對其進行治理、驗證並在生產環境中承擔責任的價值正在上升。本季度，GAIN 的更多元件從工具與試點走向更廣泛的企業採用。同時，我們在代理式 harness 與實體 AI 的投資也從研究推進到受控的客戶部署。隨著這些能力成熟，我們得以重複利用更多工程成果、更快部署解決方案，並對可衡量的成果承擔更大的責任。

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.

我們的優勢不僅僅在於代理能生成程式碼。而在於我們將這些代理與領域知識、營運控制，以及使其能在企業規模下可靠運作所需的工程紀律結合起來。我們相信，這正是代理式時代將創造持久價值之處，也是 Grid Dynamics 有望領先的位置。

Anil, over to you.

Anil，交給你。

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 to $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 to $15 million guidance range.

謝謝你，Eugene。各位下午好。第二季營收為 1.082 億美元，略高於我們指引區間 1.06 億至 1.08 億美元的上緣。這代表年增 7%，其中包含 Ekumen 的極小貢獻。非 GAAP EBITDA 為 1,470 萬美元，約占營收 13.6%，接近我們 1,400 萬至 1,500 萬美元指引區間的高端。

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.

從各垂直產業的表現來看，TMT 仍是我們最大的垂直領域，本季度占總營收 31.8%，季增 11.7%，年增 36.4%。成長主要由我們最大的科技客戶帶動。我們持續受惠於這些客戶的供應商整併，推動我們在新舊專案中的錢包份額（wallet share）提升。

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.

零售在 2026 年第二季貢獻總營收的 26.5%。該垂直領域以絕對金額計年比持平，季增 3.1%。季增主要由關鍵客戶的需求支撐，其中包括一家大型專業零售商。

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.

本季度我們的金融垂直領域占總營收 22.9%，季增 1.2%。在此垂直領域內，我們看到金融科技服務專案需求穩健，包括來自一家主要支付網路的貢獻增加，這有助於抵消我們在北美與保險、資料分析以及消費者信用報告客戶之專案順利結案所帶來的影響。展望 2026 年剩餘期間，我們對該垂直領域的成長前景仍然樂觀。

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.

民生消費品（CPG）與製造業占季度營收的 10.9%，季增 2.1%，年增 4.2%。在此垂直領域內，我們看到一家領先的批發食品分銷商帶來強勁需求，同時部分製造業客戶也帶來成長。

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 US headcount was 379, or 7.8% of our company's total headcount versus 7.2% in the year-ago quarter. Our non-US headcount, located in Europe, Americas, and India, was 4,459, or 92.2%. In the second quarter, revenues from our top 5 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.

再看其餘垂直領域，「其他」垂直領域貢獻我們第二季營收的 6%，而醫療保健與製藥貢獻本季度營收的 1.9%。我們在第二季結束時的總員工數為 4,838 人，低於 2026 年第一季的 4,964 人，也低於 2025 年第二季的 5,013 人。我們持續精簡整體人力，同時調整技能組合與地理配置。截至 2026 年第二季末，我們在美國的員工總數為 379 人，占公司總人數的 7.8%，高於去年同期的 7.2%。我們的非美國員工位於歐洲、美洲與印度，共 4,459 人，占比 92.2%。第二季來自前五大與前十大客戶的營收占比分別為 43.5% 與 61.5%，相較去年同期分別為 37.5% 與 57.3%。

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.

接著看損益表，本季度 GAAP 毛利為 3,960 萬美元，毛利率 36.6%，相較 2026 年第一季為 3,620 萬美元、毛利率 34.8%，以及去年同期為 3,450 萬美元、毛利率 34.1%。以非 GAAP 基礎計算，我們的毛利為 4,000 萬美元，毛利率 36.9%，相較 2026 年第一季為 3,670 萬美元、毛利率 35.3%，以及去年同期為 3,510 萬美元、毛利率 34.7%。以年比來看，毛利率提升主要由於營收成長速度快於交付成本。以季比來看，毛利率提升則來自可計費工時（working time）與資源利用率改善的綜合作用。

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.

第二季非 GAAP EBITDA（排除利息收入、利息費用、所得稅費用準備、折舊與攤銷、股份基礎給付、重組、與地理重組相關費用，以及交易與其他相關成本）為 1,470 萬美元，約占營收 13.6%；相較 2026 年第一季為 1,250 萬美元、占營收 12%，也高於去年同期的 1,270 萬美元、占營收 12.6%。EBITDA 的季增與年增主要來自較高的營收，以及我們在非工程類間接費用上的強勁營運槓桿。

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.

我們第二季的 GAAP 淨利為 290 萬美元，或每股 0.03 美元，係以稀釋後股數 8,300 萬股計算；相較之下，第一季為淨損 150 萬美元或每股虧損 0.02 美元（以稀釋後股數 8,470 萬股計算），而去年同期則為淨利 530 萬美元或每股 0.06 美元（以稀釋後股數 8,640 萬股計算）。以非 GAAP 基礎計，第二季非 GAAP 淨利為 900 萬美元或每股 0.11 美元（以稀釋後股數 8,300 萬股計算），相較第一季非 GAAP 淨利 750 萬美元或每股 0.09 美元（以稀釋後股數 8,590 萬股計算），以及去年同期非 GAAP 淨利 830 萬美元或每股 0.10 美元（以稀釋後股數 8,640 萬股計算）。

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.

截至 2026 年 6 月 30 日，我們的現金及約當現金合計為 2.984 億美元，較 2026 年 3 月 31 日的 3.275 億美元下降。自第一季財報電話會議以來，我們回購約 260 萬股，總對價為 1,730 萬美元。累計而言，自董事會核准 5,000 萬股回購計畫以來，我們已回購約 440 萬股，總額 3,080 萬美元，反映我們對公司長期價值的持續信心。

Coming to the third quarter guidance, we expect revenues to be in the range of $112 million to $114 million. We expect our third quarter non-GAAP EBITDA to be in the range of $16.5 million to $17.5 million. For the third quarter, we expect our basic share count to be in the range of 81 million to 82 million shares and our diluted share count to be in the range of 83 million to 84 million shares. For 2026, we're maintaining our full-year revenue outlook of $435 million to $465 million.

接著談第三季財測，我們預期營收將落在 1.12 億至 1.14 億美元區間。我們預期第三季非 GAAP EBITDA 將落在 1,650 萬至 1,750 萬美元區間。第三季我們預期基本股數約為 8,100 萬至 8,200 萬股，稀釋後股數約為 8,300 萬至 8,400 萬股。針對 2026 年，我們維持全年營收展望 4.35 億至 4.65 億美元不變。

That concludes my prepared remarks. We are now ready to take questions. Cary?

以上是我事先準備的發言。我們現在準備開始回答問題。Cary？

Thank you, Anil. (Operator Instructions)

謝謝你，Anil。(接線員指示)

Mayank Tandon, Needham.

Needham 的 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.

關於 AI 有很多細節。先退一步看，Leonard，你能否談談你們在 AI 方面的努力，以及在未來例如 12、24 個月對成長與獲利能力的影響？也許你可以幫助投資人更安心，AI 對你們實際上會是淨正面影響，因為外界仍有不少懷疑者認為長期來看會是淨負面。

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.

好的。謝謝你，Mayank。這是一個相當全面的問題。如果我把每個部分都回答完，可能就不剩什麼給其他人了。我會盡量就關鍵要點簡潔說明。之後我們可以再更深入地談一些細節。

So 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. So that's with respect of the people and why it's accretive to us.

首先，我們正在觸及 AI 落地的多個面向。我們過去也談過。我們現在正在加入這些功能。我們也直接或間接談到前線派駐工程師（forward deployed engineers）。我們會發布相關公告。我們訓練了相當多的員工，而這些人基本上正在推動一種新的解決方案導入方式；因為當我們更聚焦於固定報價、固定預算的專案時，這有助於我們不僅辨識各項現代化專案的執行，也能創造技術顧問服務。以上是就人才面向而言，以及為何這對我們具有增益（accretive）。

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. So 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.

談到代理式 AI（agentic AI），作為我們解決方案套件導入的一部分，我們正推動客戶採用我們的 GAIN 平台模型。該模型的所有要素都來自內部測試與開發，同時也會依客戶需求量身打造。因此，客戶需要在他們認為最適合自身的地方調整解決方案；同時，我們也會在過程中引導他們，以創造最佳的投資報酬率（ROI）。這是第二部分。

And 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 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.

在我談到實體 AI（physical AI）之前，我想先回應你提到的「淨正面」與「淨負面」的問題。如果你看這兩個領域帶來的成長提升，其幅度大幅超過一些較老的業務；那些老業務最終可能會因為各方的悲觀論調而逐步淡出，因為有人認為工程與顧問服務的相關性降低。此外，也有人會說訓練全職員工（FTE）更容易。我們擁抱 FTE。我們也擁抱我們的客戶。

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. So as a consolidated effort, as we go today through further discussions, we will demonstrate on specific examples where this accretiveness works, but I want to emphasize forward deploy engineering and agentic AI.

同時，正如產業中許多領導者所說，我們可以承接更多工作、進行更多專案合作；我們也用所有列舉的案例證明了這點。我不會逐一細講，因為我們有很多同仁可以提供你更多細節。因此，作為整體性的努力，隨著我們今天進一步討論，我們會用具體案例展示這種增益如何發揮作用；但我想強調的是前線派駐工程與代理式 AI。

And 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 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. So 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.

第三部分同樣對我們至關重要，實際上也把我們關係的採用度與合作夥伴關係提升到下一個層級，那就是我們為實體 AI 工作所做的準備。我們不只是做了一筆小型收購。我們不只是宣布要開設更多機器人實驗室。我們與客戶合作的時間已足夠長，能理解這對他們自身平台意味著什麼、對他們的應用與解決方案意味著什麼；涵蓋各種領域，從工業、現代化機械到物流公司，甚至包括各種新解決方案的工業化落地。因此，實體 AI 的「實質面」與報酬（remuneration）仍有待後續顯現，但我們現在已看到有相當多的重要玩家在關注 Grid Dynamics；我們透過把能力擴展到其實際使用場景，再次展現領導角色。

This is pretty much a summary, and then, of course, we'll go in more in detail.

以上大致是摘要，當然我們之後會再更深入說明。

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?

這非常有幫助，也抱歉如果你看不到我。我的視訊出了點問題。你們可以協助處理。我會試著最後把它修好。再追問一個很快的問題，Anil，想請教你。就指引（guidance）而言，我想了解你們目前的能見度相較上一季如何。我的意思是，現在的管線（pipeline）是否轉換得更快？你們是否看到這方面的證據？這是否會讓你們對成長加速的可持續性更有信心，當我們走過 2026 會計年度、進入 2027 會計年度之後？

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 one, remember the 85/10/5? Most of our revenue comes from customers who've been with us for two years and beyond. That formula more or less stays well intact. And 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. So that stays intact.

你談的是明年。我們先談今年，然後再談到明年。Mayank，當你進入下半年時，你會看到，如果你看我們的能見度以及我們的下半年，有幾個因素。第一，還記得 85/10/5 嗎？我們大部分的營收來自與我們合作兩年以上的客戶。那個公式大致上仍然維持得很完整。而你在前 5 大、前 10 大客戶那邊也看得到，對吧？因為絕對金額以及年增的成長大多來自那裡。所以這一點仍然不變。

As you go into the second half, there are three layers, as you go. First is the working time. Second half is higher than the first half. Second thing is that the billable headcount. So we're seeing new programs kicking in. So 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.

當你進入下半年時，會有三個層面。第一是工作時間。下半年高於上半年。第二件事是可計費人力。所以我們看到新的專案開始啟動。所以也許不直接回答你關於 pipeline 的問題，間接來說，是的，隨著進入下半年，我們看到可計費人力在增加。

And the third thing is that we are planning some acquisitions. So all these three add up to layers. Now when you look into 2027, I think I'll let the business guys chime in here, but from my point of view, I see two things that are very interesting.

第三件事是我們正在規劃一些併購。所以這三件事加總起來，形成多個層次。現在當你看 2027 年時，我想我會讓業務端的同事在這裡補充，但從我的角度來看，我看到兩件非常有意思的事。

Number one, 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. And 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.

第一，我們與科技客戶、金融客戶，以及前 10 大與前 20 大客戶的關係，正在變得越來越深。以前我們沒做過的事情，我們正在做。以前我們沒做過的應用程式現代化專案，我們正在著手處理。我們所鎖定的可服務市場（addressable market）更大。我每週都會聽到這些對話，這讓我相信，當你進入 2027 年時，如果我們能持續以目前的速度贏得案子，它應該會在此之後以漸進方式延續發酵。

But I don't know, Vasily or Yury, whether you want to add anything to that.

但我不知道 Vasily 或 Yury，你們是否想補充什麼。

Yeah. Let me chime in. So 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, very hot topic for most of our customers, we are really well-positioned to benefit from that.

是的。我來補充一下。所以是的，我會說，透過供應商整併（vendor consolidation），過去幾年我們在多數最大客戶中的地位一直在強化。我們看到的是，未來幾年我們應該能從這種整併中受益，這意味著更大的專案會流向我們，因為客戶會淘汰那些已不再相關的長尾供應商。鑑於我們在 agentic AI（代理式 AI）方面強勁的技術定位——這對大多數客戶而言是非常、非常熱門的主題——我們確實處於非常有利的位置來受益。

Bryan Bergin, TD Securities (sic - Janney Securities)

Bryan Bergin，TD Securities（原文誤植 - Janney Securities）

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?

也許先從上一個問題的追問開始，聚焦在下半年，算是比較短期的問題。就你給我們的 3Q 指引來看，隱含的 4Q 仍然是相當不錯的爬坡。你們是否看到其他產業的動能在擴散？你們在科技領域顯然做得很好。你們是否看到其他領域也出現更廣泛的動能，讓你們有這樣的信心？

And then 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?

另外，關於可能的一些併購需求，你們能否分享一下你們如何看待有機貢獻的延續，與是否需要透過併購來補足之間的取捨？

Right. Bryan, so 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 three levels at which we're operating. Number one is just the working times of the second half of the year, and you guys know it's better.

好的。Bryan，我先指出，如你所知，我們的業務有一定的季節性，對吧？進入 Q3、Q4 時，這點已經很明確。如我所說，我們是在三個層面上運作。第一就是下半年工作時間的因素，而你們也知道那會更好。

Second thing is that the billable headcount and the trends are positive, and all our prepared commentary should lead you to include that. And the third thing is that there is a certain amount of acquisition, and we do have a pipeline. It varies.

第二件事是可計費人力，而趨勢是正向的；我們所有準備好的說明也應該會引導你把這點納入考量。第三件事是會有一定程度的併購，而且我們確實有一個 pipeline。它會有變動。

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.

我總是開玩笑說，對吧？併購在錢匯到對方銀行之前都不算完成，對吧？我們看過一些我們以為不會發生的併購，最後卻發生了。我們也看過一些看似板上釘釘、萬事俱備的併購，但我們就是沒能完成。

So 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. But 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. And the third component perhaps picks up a little bit more is the acquisitions.

所以如果你看下半年，我不想對 Q4 講太多，除了說：你看，我們一年之中有季節性模式。但當我們從全年指引的低端走向高端時，第一個工作時間的組成因素仍然不變。第二個可計費人力的組成因素，我們有一些可變的計算方式。第三個組成因素可能會多一些的，就是併購。

Okay. Understood. My follow-up is a margin and a tie-in with the delivery model question. So 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.

好的。了解。我的追問是關於毛利率，以及與交付模式問題的連結。你們重申對 300 個基點擴張的信心，這點很高興聽到。我只是好奇，這些毛利率改善有多少來自結構性變革、自動化，以及交付端的效率提升，而不是傳統的成本控制、削減措施。我覺得值得注意的是，你們營收成長 7%，但人力卻下降 3%。我知道你們說會增加可計費人力，但這個交付模式是否出現了持久性的改變？能否談談你們看到的 AI 驅動效率與交付方面的情況。

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.

好的。這個問題有三、四個部分。我先回答第一部分，然後談到一些 AI 趨勢時，我會把問題交給其他人。

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. So here'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?

當你看我們一開始設定要做的事時，我們說過，以年對年來看，我們要在 Q4 對 Q4 的年對年基礎上，實現 300 個基點的利潤率提升。其中一部分努力來自效率提升。也就是我們的組織方式。如你所知，我們已從少數幾個國家擴張到 19 個國家。我們有許多法人實體。所以我們導入了一些效率提升，其中有些是一次性的，但我們的營運會維持在某個水準，對吧？

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. And 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.

第二部分是，我們看到我們正在更積極地擁抱一些做生意方式的改變，不論是 AI、不論是固定價格（fixed price）、或是採用更多工具。而這正在創造某種程度的——我會說——現在還不那麼明顯，但隨著時間推移，你可能會看到某種非線性（non-linearity）的效果出現。你目前看到的人力變動，主要是由非工程人力的效率改善所驅動。

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. But 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.

大家不需要擔心。並不是我們裁掉了一些可計費的人力。不是的，這只是非工程、非可計費的人力。我們把它清理掉了。但從現在開始，除了你們從Q4到Q4、一路走向2027所會看到的300個基點之外，我們也有計畫要更充分運用這些工具。我們也有計畫導入一定程度的非線性。

We have the plans. The clients have to accept it, and we have to proceed with that.

我們有這些計畫。客戶必須接受，而我們也必須推進落實。

Go ahead, Leonard.

請繼續，Leonard。

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.

是的。我分享幾點。首先，為了把你第一個問題——關於平台多元化——的回答補完整。我認為很關鍵的一點是，這不是一夕之間我們突然把垂直領域多元化。最重要的是，我們一直都在支付系統、金融服務，以及工業現代化領域。

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.

第二點是，你其實可以從先前對我們的評論中看到，Vasily提到我們正在取代一些既有供應商，原因在於我們是在更高層級、在「大聯盟」裡競爭。過去通常會有幾家頂尖廠商，再加上幾家中型供應商。現在我們只跟頂尖廠商競爭。原因是，我認為AI採用與技術落地在某種程度上拉平了競爭場域。

So we've always been prepared for the big tasks and a big program, big transformational solutions, but 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.

所以我們一直都為大型任務、大型計畫與大型轉型解決方案做好準備，但我們也在顧問服務這一塊建立了口碑。當我們獲得更多進入大型專案的機會時，隨之而來的必然是更好的能見度、更好的預測能力，以及更有利的位置。

So 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.

因此我們透過工具節省成本、增加更多能力，同時回頭檢視：我們長期以來的哪些內部系統效率較低？我們接受在不確定的世界中運作。這非常重要。我們不認為世界會劇烈改變到讓我們立刻回到只在少數地點高度集中、那種「很奢侈」的狀態。

We're adding not only India, and we're 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.

我們不只增加印度，也加大對印度與技術能力的投資，同時也布局拉丁美洲。隨著我們做得更多，我們在內部打造一個全球平台來優化這些效率。

So 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.

所以這涉及成本結構、以績效為基礎的模式、工具化，以及移除過去的重複冗餘。Bryan，希望我涵蓋了很多重點。

Puneet Jain, JPMorgan.

Puneet Jain，摩根大通。

Hey, thanks for taking my question. So 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? So 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?

嗨，謝謝讓我提問。那麼，你們的AI與機器人合作夥伴關係，和你們傳統的超大規模雲端供應商（hyperscaler）合作關係（例如Google、AWS、Microsoft Azure）有何不同？後者貢獻了你們合作夥伴收入的19%。那你們與NVIDIA、模型公司等的合作，是不同嗎？它們是否相較於其他合作夥伴，提供不同的營收成長軌跡潛力或客戶歸屬（ownership）結構？

All right. Thank you so much for the question, Puneet. Let me address this question.

好的。非常感謝你的問題，Puneet。我來回答這個問題。

So 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. And 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.

我們確實非常重視與NVIDIA的關係，並相信這是一個很棒的合作夥伴關係，可用來建立未來機會的管線。如你所知，目前整個產業、製造業正經歷大規模轉型，而像代理式AI（agentic AI）或實體AI（physical AI）這類新工具，確實把新技術帶進更傳統的製造場景。我們把這視為一個很好的機會，去建立新的機會管線與新型態的合作案，幫助我們以更大規模推動這些製造商的轉型。舉個例子。例如目前我們正與全球最大的工業設備製造商之一進行合作，打造一個代理式AI平台，用於管理自動駕駛車隊，並在邊緣裝置上部署實體AI能力。我們看到市場對這類機會的興趣愈來愈高。這絕對是我們優先成長的重點之一。

Got it. I'd like to follow up on the prior question, specifically around headcount. I noticed your non-US headcount was down despite the Ekumen, which probably contributed employees in Argentina. And the US 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 US headcount compared to in the past? If that's true, what does that mean for margin and change management within your employee base?

了解。我想延伸追問前一題，特別是關於人力（headcount）。我注意到你們的非美國人力下降了，儘管併購Ekumen應該在阿根廷帶來一些員工。相較之下，你們的美國人力在季比（sequential）基礎上是增加的。隨著你們提供更多以AI為基礎的服務，這種人力結構的重新配置（remix）會持續嗎？這是否會比過去更需要現場人力或美國本土人力？如果是，這對毛利率以及員工隊伍內部的變革管理意味著什麼？

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 US-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.

很好。Puneet，你說的這點對Eugene來說簡直是天籟，因為一直以來他都在規劃加速提升我們在美國的在地布局，不論是從技術辦公室（technology office）的角度，還是從面向客戶的技術顧問服務角度。我不是說整體趨勢會更偏向在岸化（onshoring）。我認為如果回看COVID之前，我們的在岸人力占比——包含在岸技術人員，以及一些會到現場參與長期專案的離岸工程師——幾乎接近20%。從來沒有像現在這麼低。當在岸人力因為能夠直接與客戶遠端合作而回落時，很多工作就轉到離岸來做。

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 two 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. So that's where our headcount onshoring technology-wise is coming.

我們不是說那些工作不再重要，但面對這些複雜案例，客戶愈來愈需要我們到現場一起協作，因為轉型的快速變化有時會讓客戶在不確定性中有點措手不及，對吧？我們談到他們在專案的心態與預算決策上有兩種基本做法。其中一種比較像維持現狀（status quo）。先看看再說，等事情發生再決定。這種情況不需要那麼多在岸人力；而另一部分客戶則要求非常快速的加速，但他們也擔心一些支出——如你所知，像token以及其他項目——這些確實會帶來壓力。因此，我們在技術面增加在岸人力就是從這裡來的。

As I mentioned to Bryan, some of the reduction of offshoring headcount comes from non-engineering and non, I would say, forward-looking specialties. So there is a difference between the headcount and contribution of this headcount. So 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.

如我先前對Bryan提到的，離岸人力的部分減少，主要來自非工程、以及我會說不是面向未來的專業領域。所以人力數量與這些人力的貢獻是有差別的。從預算角度來看，未必能很清楚地說這些人非常昂貴，但為了更好地服務市場，我們不再需要維持支撐這些人力所需的整體基礎設施。

So 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.

所以回答你的問題，我們確實看到在岸人力會有一些額外成長。我們能否證明毛利率擴張會持續，主要取決於：我們能在多大程度上調整固定報價、固定預算的專案；我們內部開發的工具在多大程度上被客戶接受；我們能帶來多少非線性的價值；而我認為我們在這些要素上都在成長。

So 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.

最後我這邊做個總結，如果其他人想補充也可以：我認為你抓到了正確的趨勢。我不認為部分人力的「既有包袱」是需要擔心的訊號，因為其中大多數來自中東歐。我認為這一切都符合規劃、按部就班。我們確實正依照清晰的計畫向前推進，持續改善毛利率。

Matt Dezort, William Blair.

Matt Dezort，William Blair。

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?

恭喜本季成果。我想請你們更深入說明一下你們提到的這個新的顧問服務實務。能否多談談你們如何看待這項業務的發展？我知道你們提到像變革管理之類的活動，但在那邊的商機管線建置上，你們看到哪些類型的機會？你們在這些競標（bake-offs）中主要對上哪些對手？以及相較於你們傳統業務，競爭環境有何不同？

Yeah. So 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.

是的。我先非常簡短地開頭，接著由Vasily再補充展開。Matt，這裡有兩個部分。第一個部分是，我們的使命沒有改變。顧問服務一直是我們DNA的一部分。沒有什麼翻天覆地的變化，因為客戶一直把我們視為科技顧問，這也是我們能與大型公司競爭的原因。

What has changed is the distribution of that kind of offering. And 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. So Vasily?

改變的是這類服務的交付與配置方式。而且前一題Puneet問到的是在岸（onshoring）據點的布局，對吧？我們聘用的人才技術能力非常強，同時也以客戶為導向。這類工作非常重要，因為我們正把顧問服務的範疇從純科技顧問擴大，加入AI基礎設施顧問、硬體選型顧問、工具選型顧問，並在某種程度上更深入到傳統上被視為神聖領域的商業顧問。所以Vasily？

Yeah. 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. So that's the essence.

是的。把商業顧問視為我們科技賦能（technology enabler）建置能力的自然延伸。本質上，客戶的重心正從「只打造一個系統」轉向「打造系統以改變其既有的業務流程」。因此，他們會希望先分析哪些業務流程最適合優化、其中隱藏了哪些價值，接著建置技術賦能來釋放這些價值，然後在全企業範圍內導入並採用該技術賦能。這正是我們顧問服務的重點：不僅打造技術賦能，也協助在企業規模上把這次變革的價值完整實現。這就是核心。

And 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.

目前我們在這方面已有數個進行中的案子，特別是在與技術賦能相伴的變革管理顧問上；我們也看到未來有很好的成長機會。

That makes sense.

了解。

I can add to that 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.

我也補充一下：我們許多客戶觀察到，我們交付團隊使用我們的GAIN平台後，在績效與生產力上有明顯提升。他們因此產生興趣，希望把這些平台與方法論導入到他們自己的軟體工廠（software factory）中；我們正在協助他們建立工具、方法論，以及所需的變革管理，讓他們在更廣泛的組織內也能GAIN到類似的生產力提升。

That's a good segue way, 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，這正好銜接到我接下來關於GAIN採用，以及其所暗示的S曲線的追問。當你們加速推進GAIN的部署時，我們應該如何看待那條採用曲線與純AI營收？它更可能是線性擴張，還是你們提到AI帶來的錢包份額（wallet share）提升，可能出現某種指數型成長？以及你們要如何透過GAIN，讓AI滲透率出現更明顯的拐點？

So 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. And 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. And 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.

我們的GAIN策略有趣之處在於：我們正在把來自多個客戶、多個實務線的所有智慧財產（IP）整合到同一個架構之下，而AI能幫助我們非常快速地完成這件事。我們的嵌入式工程師與前線部署工程師（forward-deployed engineers）都被要求把學到的經驗、想法、哪些有效與哪些無效，回饋到GAIN平台。而GAIN平台的一部分也是開源的，這有助於從更廣泛的社群獲得洞見，並把GAIN平台帶到許多領導者面前。

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. So 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.

就目前而言，我們確實看到GAIN平台帶來的直接營收在成長；但更重要的是，我們看到在既有客戶與新客戶中，由這些平台所驅動的整體連結度提升，以及關係的擴張。因此我們看到許多主動找上門的興趣與對話，進而帶來新的線索、新的機會，並由GAIN平台促成新的成交業務。這就是目前正在發生的情況。

Surinder Thind, Jefferies.

Surinder Thind，Jefferies。

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?

我想先從一個問題開始，關於目前市場對於從概念驗證（proof of concept）走向實際導入專案，似乎有更多興奮感。這樣的評論看起來更具普遍性。從你們的角度，能否談談這樣的營收路徑會是什麼樣子？也就是說，如果一個概念驗證專案規模是幾十萬美元，它是否會變成一個200萬美元的專案？或是我們在看到更多概念驗證出現時，可能期待的結果區間大概是什麼？以及這會如何影響成長率？

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.

Surinder。我先從較高層次回答一點；因為這牽涉到營收的各種觸點，Vasily會再補充更多細節。

So 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.

概念驗證有不同類型。概念驗證的定義可能被拉得很寬，無論是從意圖、相關金額，以及後續延伸來看都是如此。例如我們回頭看因合作夥伴關係而產生的概念驗證，最後導向一些非常有意義的計畫：客戶不僅採用我們合作夥伴的解決方案，也採用我們與這些合作夥伴共同提供的服務。而如果看今天、特別是GAIN生產力套件，這其實非常有意思。Eugene提到有許多主動找上門的興趣。

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.

如你所知，對我們Grid Dynamics以我們的規模與能力而言，能見度非常關鍵。客戶會帶著一些我們仍稱之為概念驗證的需求來找我們，但那些其實是相當可觀的專案；因為如今衡量概念驗證的標準，有時不再是金額多寡，而是導入所需的時間，在很多情況下反而更為可觀。過去那種短期合作的定義——通常是由概念驗證引導或先行——現在短期合作本身就成了概念驗證，接著就是大規模推廣。這在概念上改變了概念驗證以及其相關營收的定義；不過我相信Vasily會提供更多細節。

Yes. Many customers start definitely with implementation of some smaller pieces of business cases.

是的。許多客戶確實會先從導入一些較小的業務案例片段開始。

Sure.

了解。

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.

這些案例能產生可量化的業務成果，用來向董事會與管理層展示；然後以此作為範例，進一步爭取更多投資，最終轉化為平台建置，例如建立AI harness等等。這個趨勢確實仍在延續。這就是我們在客戶端看到的情況。我也可以說，就平台相關工作而言，這類工作本質上比概念驗證（POC）更具黏著度、也更偏長期。平台建好之後，當然會有越來越強的意願在這個平台之上建立更多、更多的業務案例，因此會像滾雪球一樣成長，而這也正反映在我們的商機管線中。

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.

我想我只是想補充一下，這也取決於產業，對吧？我們今天提到了實體 AI 與機器人。這些領域確實有很多概念驗證（POC），但同時也取決於你與客戶的關係深度，以及在那些領域之外、周邊的其他專案，對吧？這正是那些概念驗證可能實際上相當重要的地方。有時可能只是幾週的小型合作，有時可能是六個月以上。

And 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.

回到 GAIN 模型以及我們在 GAIN 中的平台，我認為這也是我們嘗試把這些知識濃縮起來的地方，對吧，並以此盡可能加快客戶的導入落地。這也會影響概念驗證收入相對於較長期合作之導入實施收入的占比。

So just to summarize for Surinder. The POCs associated with FDE consultancy, GAIN model modernization subjects, and agentic AI as overall are very substantial from get go. 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. So these type of POCs are more traditional way, and their revenue will follow with the scale which you would typically expect from POCs.

所以簡單為 Surinder 總結一下。與 FDE 顧問服務、GAIN 模型現代化主題，以及整體的代理式 AI（agentic AI）相關的 POC，從一開始就非常可觀。實體 AI 的部分，才是傳統上會稱為概念驗證的那種，因為它是一種非常創新的方式，用來現代化提升生產力，以及人與機器人之間的介面互動。因此這類 POC 更偏傳統，其收入也會隨著規模擴大，呈現你通常對 POC 所期待的成長路徑。

And 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. And 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?

接著再想一下資料與 AI 業務（practice）以及我們看到的那個非常高的成長率，也就是營收的 30%。你能幫我理解另外 70% 發生了什麼嗎？我算了一下，另外那 70% 的營收大約是下滑 10%。其中有多少只是被資料與 AI 這個部分所侵蝕（cannibalization）？因為我假設每一個新的工作項目大概都會被歸到那個桶裡。另外，在那個我姑且稱之為「傳統／既有」的桶裡，是否還有其他因素，例如價格壓縮，或其他需要考量的因素？

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. So very simply put, if I were to extract some of these volatilities there, we would see a better growth pattern.

非常好的問題，Surinder。我其實會把它講得更簡單。甚至沒有那麼複雜。以我們的情況來看，當你觀察我們的業務趨勢時，時不時會看到某個重要客戶（我先寬鬆地用這個詞），可能是前 30 大客戶、前 40 大客戶，對吧，出現一些變化。可能是策略改變、某個大型專案結束，或是有些事情改變了，於是我們在那裡會有一些波動。如果你回頭看過去幾個季度並參考 Leonard 的評論，他提到例如實體零售（brick-and-mortar retail）的敏感性。那就會造成一些波動。所以很簡單地說，如果我把其中一些波動因素剔除，我們會看到更好的成長型態。

Second point is, your question is whether there is cannibalization. What we see is, and 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. So all the work is incremental AI work that we typically see.

第二點，你的問題是是否存在互相侵蝕（cannibalization）。我們看到的是——Eugene 和 Vasily 也可以佐證——當我們進入客戶端，特別是在我們前 20 大客戶中，我們正越做越深。因此我們通常看到的都是增量的 AI 工作。

And 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. But 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.

第三點是你提到的定價。我們沒有看到定價壓力。事實上，進入 AI 領域，顯然是有溢價的。但當你看我們過去幾年所做的事，我看某個國家某個職級，是否存在定價壓力，答案是絕對沒有。我們沒有看到那種情況。當然，我們可以討論是否有漲價。那是另一個議題，但沒有價格下滑。

Vasily, I don't know whether you want to --

Vasily，我不知道你是否想要--

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. So I think that's a very important color.

是的。我認為這是語意上的問題，對吧？每個專案的成本、每項功能的成本，或每一段範疇（scope）的成本，確實因為生產力提升以及交付時程縮短而在下降。大概就是這樣。這帶來的是更多工作與更多專案，而不是縮減。所以我認為這是一個非常重要的補充說明。

And finally, you can look at the revenue per person. Again, we need to have some history to prove that that trend will continue to grow. So 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. But 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.

最後，你也可以看人均營收。同樣地，我們需要更多歷史數據來證明這個趨勢會持續成長。所以我們並不是在試圖捍衛既有（legacy）。我想 Anil 已經很清楚地說，有些業務會流失，對吧？你說得完全正確。AI 內容會帶來更多新業務。但有一個不存在的元素是：我們不會為了保留某些既有業務而壓縮我們的利潤率。這不是其中的一部分。

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, so I appreciate that.

聽起來這裡確實有一個定義上的因素，對吧？就像你先前提到的，你如何把不同項目分桶，以及嘗試把它們整體來看，再加上專案啟動與停止等各種雜訊，所以我理解並感謝你的說明。

Ladies and gentlemen, this concludes our Q&A session for today. I will now pass it over to Leonard for closing comments. Leonard?

各位女士先生，今天的問答環節到此結束。我現在把時間交給 Leonard 做結語。Leonard？

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, and the team is executing.

我們的主要客戶帳戶正在擴張。我們的 AI 計畫正穩定地從試點走向企業級規模部署，而我們的平台產品組合也在加深——不論是有機成長，或是透過我們在機器人與實體 AI 方面新增的能力。我對 2026 年下半年充滿信心：策略正在奏效、動能正在累積、團隊正在落實執行。