Grid Dynamics Holdings, Inc. (GDYN) 2026 Q1 法說會逐字稿

(audio in progress) earnings conference call. I'm Cary Savas, Director of Branding and Communications. Joining us on the call today are CEO, Leonard Livschitz; CFO, Anil Doradla; CTO, Eugene Steinberg; and SVP, Global Head of Partnerships and Marketing, Rahul Bindlish.

（音訊進行中）財報電話會議。我是 Cary Savas，品牌與傳播總監。今天與我們一同參與電話會議的有執行長 Leonard Livschitz；財務長 Anil Doradla；技術長 Eugene Steinberg；以及全球合作夥伴與行銷主管資深副總裁 Rahul Bindlish。

Following the prepared remarks, we will open the call to your questions. Please note that today's conference call is being recorded.

在預先準備的發言之後，我們將開放提問。請注意，今天的電話會議將會錄音。

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. 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.

在開始之前，我想提醒各位，今天的討論將包含前瞻性陳述。這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）提交之其他文件中所述的風險與不確定性影響。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.

現在我把電話會議交給我們的執行長 Leonard。

Thank you, Cary. Good afternoon, everyone, and thank you for joining us today. We started 2026 with solid execution, delivering Q1 revenue of $104.1 million that was higher than our guidance range and ahead of market expectations. This performance reflects continued strength in our business model and validates our focus on AI-led transformation and high-value enterprise engagements.

謝謝你，Cary。各位下午好，感謝今天加入我們。我們以穩健的執行力展開 2026 年，第一季營收達 1.041 億美元，高於我們的指引區間，亦優於市場預期。此一表現反映我們商業模式持續強勁，並驗證我們聚焦於 AI 驅動轉型與高價值企業級合作的策略。

Three trends stood out this quarter, a meaningful and growing contribution from AI revenue, a structural shift in vertical mix toward technology and financial services, and our top customers are undergoing meaningful vendor consolidation with Grid Dynamics emerging as a clear beneficiary.

本季有三項趨勢特別突出：AI 營收的貢獻顯著且持續成長；產業組合正結構性地轉向科技與金融服務；以及我們的主要客戶正在進行具實質意義的供應商整併，而 Grid Dynamics 明顯成為受益者。

Last quarter, we called 2026 a pivotal year for the accelerating adoption of our AI offerings. Our first quarter results support that conviction with AI revenue reaching 29.3% of total company revenue, growing nearly 60% year-over-year. Given this concentration and growth trajectory, AI practice has become the core of our business, fundamentally reshaping our offerings, our talent development and our client relationships. I'm confident we are well positioned to further accelerate AI revenues in 2026.

上一季我們提到，2026 年將是我們 AI 產品加速採用的關鍵一年。第一季的結果支持這項信念：AI 營收已達公司總營收的 29.3%，年增近 60%。在此一占比與成長軌跡之下，AI 業務已成為我們的核心，從根本上重塑我們的產品與服務、人才培育以及客戶關係。我有信心，我們已做好準備在 2026 年進一步加速 AI 營收成長。

For the first time, our top 5 accounts are entirely outside of retail, reflecting meaningful diversification into technology and financial services, sectors where AI adoption is accelerating and our capabilities are highly differentiated. This group includes two leading global technology companies, a global fintech leader, a US-based global bank and a leading financial institution. What makes this group notable is that each of these customers has undergone meaningful vendor consolidation and Grid Dynamics has emerged as a clear beneficiary. This positions us to capture greater market share in 2026 and beyond.

我們的前五大客戶首次全部來自零售以外的產業，反映我們在科技與金融服務領域的顯著多元化；這些產業的 AI 採用正在加速，而我們的能力具高度差異化。這個客戶群包括兩家全球領先的科技公司、一家全球金融科技領導者、一家以美國為基地的全球性銀行，以及一家領先的金融機構。值得注意的是，這些客戶都進行了具實質意義的供應商整併，而 Grid Dynamics 明顯成為受益者。這使我們得以在 2026 年及未來取得更高的市場份額。

Additionally, we have been actively engaged in AI initiatives across all five customers, with some of our largest and most strategic programs driven by this group. Our size and AI technology focus are strategic advantages in a rapidly changing environment. Large enterprises are increasingly seeking highly capable, nimble partners like Grid Dynamics, who can move quickly and deliver meaningful AI outcomes rather than relying on incumbent global system integrators burdened by legacy delivery models.

此外，我們已在這五家客戶中全面積極參與 AI 計畫，其中一些最大且最具策略性的專案即由這個客戶群所推動。在快速變動的環境中，我們的規模與 AI 技術聚焦是策略性優勢。大型企業愈來愈尋求像 Grid Dynamics 這樣能力強、反應敏捷的合作夥伴，能快速行動並交付具實質意義的 AI 成果，而非依賴受制於傳統交付模式的既有全球系統整合商。

In many ways, headcount leverage is no longer a competitive moat and differentiation comes from the main knowledge, AI capabilities and ability to rapidly scale relevant expertise.

在許多方面，人力規模的槓桿已不再是競爭護城河；差異化來自於核心知識、AI 能力，以及快速擴充相關專業的能力。

We're not a systems integrator. We're a product-centric engineering company focused on solving the most complex mission-critical challenges for Fortune 1000 clients with a deliberate emphasis on driving revenue-generating capabilities, not just cost optimization. As enterprises migrate to our custom-developed solutions, the advantage shifts to partners who can build sophisticated production-grade software from concept to deployment. This is precisely what Grid Dynamics does.

我們不是系統整合商。我們是一家以產品為中心的工程公司，專注為《財富》1000 大客戶解決最複雜、最關鍵任務的挑戰，並刻意強調推動創造營收的能力，而不僅僅是成本最佳化。當企業遷移至我們客製開發的解決方案時，優勢將轉向那些能從概念到部署打造成熟、可上線量產等級軟體的合作夥伴。這正是 Grid Dynamics 所做的事。

AI meaningfully expanding Grid Dynamics addressable market. For example, AI-native SDLC and agentic coding fundamentally changed the economics of delivering services. With delivery time and cost compressing, we can take on larger client initiatives that were previously out of our reach. Also, AI is unlocking a wave of legacy modernization that was not previously economically viable. For years, replacing core legacy infrastructure was considered too expensive, time-consuming and risky. AI lowers these barriers.

AI 正在大幅擴大 Grid Dynamics 的可服務市場（TAM）。例如，AI 原生的 SDLC 與代理式（agentic）程式撰寫，從根本上改變了服務交付的經濟性。隨著交付時間與成本被壓縮，我們能承接過去超出我們能力範圍的更大型客戶計畫。此外，AI 正在引爆一波過去在經濟上不可行的舊系統現代化需求。多年來，替換核心舊有基礎架構被認為成本過高、耗時且風險大。AI 降低了這些門檻。

At the leading home improvement retailer, the infrastructure for global operations is based on legacy mainframe platforms. Modernizing the legacy mainframe platform was considered risky, and required specialized and expensive talent. Using AI agents, Grid Dynamics delivered a full modernization program within the time line and budget. Grid Dynamics expertise is now extending into physical AI.

在一家領先的居家修繕零售商，其全球營運的基礎架構建立在舊式大型主機（mainframe）平台之上。大型主機舊平台的現代化被視為高風險，且需要稀缺且昂貴的專業人才。透過 AI 代理，Grid Dynamics 在既定時程與預算內交付了完整的現代化計畫。Grid Dynamics 的專業能力如今也正延伸至實體 AI（physical AI）。

In CPG & Manufacturing, enterprises are turning to self-learning robotics and AI technologies to drive operating efficiencies. Our GAIN platform for physical AI makes intelligent robotics more accessible and economically viable. In the first quarter, we closed our first commercial engagement in physical AI with a heavy equipment manufacturer. We're enabling their mining equipment with intelligent autonomous capabilities.

在消費性包裝商品（CPG）與製造業領域，企業正轉向自我學習機器人與 AI 技術以提升營運效率。我們用於實體 AI 的 GAIN 平台，使智慧機器人更易取得且在經濟上更可行。第一季，我們與一家重型設備製造商完成了首筆實體 AI 的商業合作案。我們正為其採礦設備導入智慧自主能力。

We're building the company around AI. Four pillars define this transformation: AI native delivery, productized engineering, AI consulting, and internal AI automation. The first pillar, AI native delivery, marks a fundamental shift in how we work from human-led workflows to AI agent-driven, spec-based executions across our fixed bid engagements. The economics are compelling and adoption is accelerating. Early indicators point to material productivity gains in select workflows and a structurally different cost base.

我們正以 AI 為核心打造公司。四大支柱定義了這項轉型：AI 原生交付、產品化工程、AI 顧問服務，以及內部 AI 自動化。第一個支柱「AI 原生交付」標誌著我們工作方式的根本轉變：從以人為主的流程，轉向在固定報價（fixed bid）專案中，以 AI 代理驅動、以規格為基礎的執行。其經濟效益極具吸引力，採用速度也在加快。初步指標顯示，在部分工作流程中生產力有顯著提升，且成本結構將出現結構性變化。

In Q1, at our global bank, our autonomous AI workflows analyzed 150 green production applications and uncovered latent defects across systems, including test, and coding and correct behavior. By expanding validated behavior coverage to greater than 70%, we reduced false confidence in system integrity and mitigated production security and regulatory risk.

第一季，在我們的全球性銀行客戶中，我們的自主 AI 工作流程分析了 150 個綠色生產（green production）應用程式，並在各系統中發現潛在缺陷，包括測試、程式撰寫與正確行為等面向。透過將已驗證行為的覆蓋率擴大至 70% 以上，我們降低了對系統完整性的錯誤信心，並緩解了上線環境的資安與法規風險。

The second pillar, productized engineering, focused on converting our repeatable IP into AI native platform-based offering under the GAIN platforms. GAIN consists of four domain-specific platforms spanning from Agentic AI Commerce, SDLC, Risk and Compliance, and Physical AI. Our engineers increasingly operate as forward deployed specialists composing and customizing these platforms to each client's specific environment, data and workflows. The result is deeper differentiation and stronger client retention.

第二個支柱「產品化工程」聚焦於將我們可重複使用的智慧財產（IP）轉化為 AI 原生、以平台為基礎的產品與服務，並納入 GAIN 平台體系。GAIN 由四個特定領域平台組成，涵蓋代理式 AI 商務、SDLC、風險與合規，以及實體 AI。我們的工程師愈來愈以「前線部署」的專家角色運作，針對每位客戶的特定環境、資料與工作流程來組合並客製化這些平台。其結果是更深的差異化與更強的客戶留存。

A good example is that what we achieved in one of the world's largest food distributors. Our client sales associates were spending hours on manual research and proposal preparation for their restaurant clients. We developed AI agents that compressed the preparation process to minutes while improving the quality of the reports. Our efforts resulted in 50% reduction in preparation time and 18% increase in monthly spend for the targeted accounts.

一個很好的例子是我們在全球最大食品經銷商之一所達成的成果。客戶的業務人員過去需要花數小時進行人工研究，並為其餐飲客戶準備提案。我們開發了 AI 代理，將準備流程壓縮到數分鐘，同時提升報告品質。我們的努力使準備時間降低 50%，並讓目標客戶群的每月支出提升 18%。

The third pillar is AI consulting. As companies undergo AI transformation, existing business workflows must be evaluated and reimagined for agentic world. Clients are seeking out domain knowledge and deep understanding of AI and data. As a leading global fintech company, our engagement focused on development of AI agents which automate enterprise workflows. Early efforts with our Forward Deployed Engineers embedded inside the client organization have identified inefficiencies and deployed AI agents to automate, optimize and scale the process with a human in the loop, resulting in 15% productivity improvement.

第三個支柱是 AI 顧問服務。當企業進行 AI 轉型時，既有的業務工作流程必須被評估，並在代理式（agentic）世界中重新構想。客戶正在尋求產業領域知識，以及對 AI 與資料的深度理解。以一家全球領先的金融科技公司為例，我們的合作聚焦於開發可自動化企業工作流程的 AI 代理。我們的前線部署工程師（Forward Deployed Engineers）嵌入客戶組織內的早期工作，已識別出效率低落之處，並部署 AI 代理在「人類在迴路（human in the loop）」的前提下進行自動化、最佳化與規模化，帶來 15% 的生產力提升。

The fourth pillar is tied to adapting AI for our internal operations. Over the past several months, we have been adopting AI tools both off-the-shelf and internally developed in enhancing our productivity and efficiency. This includes areas such as recruitment, RFP responses, knowledge management and HR. With recruitment, we have seen a 2 times productivity improvement in terms of number of applicants we can process. With RFPs, we have increased the number of responses by 50% without growing headcount.

第四個支柱與將 AI 調整並導入我們的內部營運相關。在過去幾個月中，我們已採用現成以及內部開發的 AI 工具，以提升生產力與效率。這涵蓋招募、RFP 回覆、知識管理與人力資源等領域。在招募方面，就可處理的應徵者數量而言，我們的生產力提升了 2 倍。在 RFP 方面，在不增加人力編制的情況下，我們將回覆數量提高了 50%。

With knowledge management, our responses to employee questions improved from hours to minutes. And with HR, multiple initiatives are being rolled out, and we expect more than 20% operational improvement.

在知識管理方面，我們對員工問題的回應時間從數小時縮短到數分鐘。在人力資源方面，多項計畫正在推行中，我們預期營運改善將超過 20%。

Q1 project highlights. Our vertical execution in the first quarter is best illustrated by a few, notable client engagements.

第一季專案亮點。我們在第一季的垂直領域執行成果，最能透過幾個值得關注的客戶合作案例來說明。

TMT. For a global technology company operating large-scale manufacturing environments, Grid Dynamics designed and validated a unified manufacturing intelligence platform to replace fragmented, manual data flows. The solution is projected to reduce data discovery and reporting cycle times by over 95%. It also lays the foundation for enterprise-wide operational intelligence.

TMT（科技、媒體與電信）。針對一家在大規模製造環境中營運的全球科技公司，Grid Dynamics 設計並驗證了一個統一的製造智慧平台，以取代分散且仰賴人工的資料流程。該解決方案預計可將資料探索與報表週期時間縮短超過 95%。同時也為全企業範圍的營運智慧奠定基礎。

CPG & Manufacturing. Grid Dynamics built and deployed a unified agentic AI platform for a leading global CPG manufacturer, creating the shared infrastructure required to develop, govern and scale AI agents consistently across the enterprise. Running on a major cloud platform, the solution serves as an operational backbone for AI-driven transformation across the manufacturers' supply chain, consumer and commercial domains, the highest complexity, highest impact areas of the business.

CPG 與製造業。Grid Dynamics 為一家全球領先的 CPG（消費性包裝商品）製造商建置並部署了一個統一的代理式（agentic）AI 平台，打造在全企業範圍內一致開發、治理與擴展 AI 代理所需的共享基礎設施。該解決方案運行於主要雲端平台之上，作為 AI 驅動轉型的營運骨幹，涵蓋該製造商的供應鏈、消費者與商業領域——這些是業務中複雜度最高、影響力最大的區域。

Automotive part retailer. For a leading global retailer, Grid Dynamics led the end-to-end modernization of a mission-critical inventory and replenishment platform, migrating from legacy on-premise infrastructure to a cloud-native environment. The program delivered over 70% reduction in infrastructure costs and approximately 40% improvement in core responses time, restoring the platform's ability to support real-time replenishment decisions at the global scale.

汽車零件零售商。針對一家全球領先的零售商，Grid Dynamics 主導一個關鍵任務的庫存與補貨平台端到端現代化，將基礎設施從傳統內部部署（on-premise）遷移至雲端原生環境。該計畫使基礎設施成本降低超過 70%，核心回應時間改善約 40%，恢復平台在全球規模下支援即時補貨決策的能力。

At a premier global multi-brand restaurant company, Grid Dynamics deployed an AI coding harness to replace the manual QA workflows that struggle to keep pace with frequent enterprise changes across web and mobile. AI agents continuously simulate customer behavior and adapt automatically to UI modifications in real time, eliminating testing bottlenecks without human intervention. The platform has reduced testing time by approximately 50%.

在一家頂尖的全球多品牌餐飲公司，Grid Dynamics 部署了一套 AI 程式碼測試框架（coding harness），以取代難以跟上企業級 Web 與行動端頻繁變更的人工 QA 工作流程。AI 代理可持續模擬顧客行為，並即時自動適應 UI 修改，在無需人工介入的情況下消除測試瓶頸。該平台將測試時間縮短約 50%。

With that, I will hand over to Rahul Bindlish, Global Head of Partnerships and Marketing, who will share some of the exciting initiatives currently underway and give you a closer look at where Grid Dynamics is headed. Rahul?

接下來，我將交給全球合作夥伴與行銷主管 Rahul Bindlish，他將分享目前正在推進的一些令人振奮的計畫，並讓各位更深入了解 Grid Dynamics 的發展方向。Rahul？

Partnerships are now a key component of how we go-to-market. Our partner inference revenues have grown to 19.1% of total company revenue in quarter 1, underscoring the value of our ecosystem-driven approach in the agentic era. The majority of our partner inference revenue is driven by Google Cloud, AWS, and Microsoft Azure, our three core hyperscaler relationships. They are an active go-to-market channel for our platforms and services. Our go-to-market strategy is aligned with the AI strategy described by Leonard in his comments.

合作夥伴關係如今已成為我們市場進入（go-to-market）方式的關鍵組成部分。第一季我們的合作夥伴帶動之推論（inference）收入已成長至公司總收入的 19.1%，凸顯在代理式時代，我們以生態系驅動方法的價值。我們的合作夥伴推論收入主要由 Google Cloud、AWS 與 Microsoft Azure 所帶動，這三者是我們三大核心超大規模雲端供應商（hyperscaler）合作關係。它們是我們平台與服務的積極市場進入通路。我們的 go-to-market 策略與 Leonard 在評論中所描述的 AI 策略一致。

We will be deploying all our platforms on the marketplace of hyperscalers. Our GAIN platform for risk and compliance is now listed on both Google Cloud Marketplace and AWS marketplace. Enterprises searching for production grade capabilities in this domain within those ecosystems will find Grid Dynamics IP directly, increasing our sales pipelines.

我們將把所有平台部署到各大 hyperscaler 的市集（marketplace）上。我們用於風險與合規的 GAIN 平台目前已同時上架於 Google Cloud Marketplace 與 AWS Marketplace。在這些生態系中尋找此領域具備生產等級能力的企業，將可直接找到 Grid Dynamics 的智慧財產（IP），進而擴大我們的銷售管線。

We also have joint sales motions with the hyperscalers to accelerate deal closures. That is a fundamentally different way to win business compared to traditional service and sales. This is the first deployment in a deliberate rollout. We are moving additional platforms onto the marketplaces of every major hyperscaler. It also deepens our co-sell relationships with these partners.

我們也與 hyperscaler 進行聯合銷售推進（joint sales motions），以加速成交結案。相較於傳統服務與銷售，這是一種根本不同的贏單方式。這是我們有計畫推進之分階段上線（rollout）的第一個部署。我們正把更多平台移上每一家主要 hyperscaler 的市集。這也進一步深化我們與這些合作夥伴的共同銷售（co-sell）關係。

Our GAIN platforms plus Forward Deployed Engineers model is a new approach to go-to-market with the hyperscalers. The platform creates the entry point, our engineers deliver the value realization. Enterprises see this clearly and the first few engagement wins reflect their willingness to pay for it.

我們的 GAIN 平台加上前線部署工程師（Forward Deployed Engineers）模式，是與 hyperscaler 共同 go-to-market 的新方法。平台創造切入點，而我們的工程師負責交付價值落地。企業客戶清楚看見這一點，而最初幾個合作案的勝出也反映出客戶願意為此付費。

Each platform we bring to market addresses a specific business pain point with domain-specific IP. This changes the sales dynamics in a way that matters for our growth model. When we lead with a vertical-specific platform, whether that is agentic commerce, compliance or physical AI, we enter a client conversation with a validated solution for a specific business problem. Sales cycles compress, conversion rates improve and initial contracts expand faster because the platform's value is visible to both the business buyer and the technical evaluator. This vertical specificity is what makes our co-sell relationships with Google, AWS and Azure productive.

我們推向市場的每一個平台，都以特定領域的 IP 來解決明確的業務痛點。這會以對我們成長模型至關重要的方式改變銷售動態。當我們以垂直領域專屬平台作為切入點——無論是代理式商務、合規或實體 AI——我們進入客戶對話時，已帶著針對特定業務問題的已驗證解決方案。由於平台價值對業務採購方與技術評估者都清晰可見，銷售週期縮短、轉換率提升，且初始合約擴張更快。這種垂直領域的專一性，正是我們與 Google、AWS 與 Azure 的共同銷售關係能夠高效運作的原因。

Grid Dynamics technical depth and domain knowledge, combined with the hyperscalers cloud infrastructure, is what allows us to win engagements against competition. Our AI revenue acceleration is the output of that combination.

Grid Dynamics 的技術深度與領域知識，結合 hyperscaler 的雲端基礎設施，使我們能在競爭中贏得合作案。我們 AI 收入加速的成果，正是這種組合的產出。

We are also expanding our partnership with NVIDIA by porting our solutions onto their software stack. Our GAIN platform for physical AI is built on NVIDIA stack, including Omniverse, and we are taking it to market with NVIDIA for manufacturing and CPG companies.

我們也正透過將解決方案移植到 NVIDIA 的軟體堆疊上，擴大與 NVIDIA 的合作夥伴關係。我們用於實體 AI 的 GAIN 平台建構於 NVIDIA 堆疊之上（包括 Omniverse），並與 NVIDIA 一同推向製造業與 CPG 企業市場。

Industrial AI in manufacturing environments requires simulation fidelity and sensor integration that generic AI infrastructure does not support. Building on NVIDIA's stack positions us to address that requirement and enables joint go-to-market with NVIDIA into a customer segment where the demand for production-grade physical AI is accelerating.

製造環境中的工業 AI 需要高擬真度的模擬與感測器整合，而通用 AI 基礎設施並不支援這些需求。建立在 NVIDIA 堆疊之上，使我們能滿足該需求，並可與 NVIDIA 共同 go-to-market，切入一個對生產等級實體 AI 需求正加速成長的客戶族群。

We have also expanded our partnership ecosystem in the AI consulting space, entering into relationships with specialized firms in business process mining and organizational change management. Effective enterprise AI deployment is more than just a technology problem. Clients who deploy agentic workflows are simultaneously reengineering the processes those agents replace and managing the organizational change that follows. By integrating specialized process mining and change management partners into our delivery model, we extend the value that Grid Dynamics offers from platform and engineering, through to adoption and measurable ROI capture.

我們也在 AI 顧問服務領域擴大合作夥伴生態系，與專精於業務流程探勘（process mining）與組織變革管理的公司建立合作關係。有效的企業 AI 部署不僅僅是技術問題。導入代理式工作流程的客戶，會同時重塑被這些代理取代的流程，並管理隨之而來的組織變革。透過將專業的流程探勘與變革管理夥伴整合進我們的交付模式，我們把 Grid Dynamics 所提供的價值從平台與工程，延伸到採用落地與可衡量的投資報酬（ROI）實現。

There are two more trends worth noting. Many of the engagements that we are winning through partner channels are extending beyond the initial project. When an AI project delivers clear ROI and our clients are seeing this at scale, the relationship does not close, it expands. Clients return for more use cases, projects and programs. That pattern is visible in our retention data and in the expansion of existing hyperscaler co-sell accounts. At one of the largest food distributors in North America, that pattern played out across three distinct phases. The initial engagement was a first project delivered through a co-sell motion with Google Cloud and built on GAIN platform for agentic commerce.

還有兩個值得注意的趨勢。我們透過合作夥伴通路贏得的許多合作案，正在延伸到初始專案之外。當 AI 專案帶來明確的 ROI，且客戶在規模化落地中看見成效時，關係不會結束，而是擴大。客戶會回來尋求更多使用案例、專案與計畫。這種模式在我們的留存數據，以及既有 hyperscaler 共同銷售帳戶的擴張中都清楚可見。在北美最大的食品分銷商之一，這種模式分三個不同階段展開。最初的合作是透過與 Google Cloud 的共同銷售推進所交付的第一個專案，並建構於用於代理式商務的 GAIN 平台之上。

The platform search capabilities were in production within weeks. The client retained Grid Dynamics immediately following go-live to extend the program, using our catalog enrichment solution built on the same platform to improve the quality of the search results. We are now in the third phase, the development of an agentic platform for the client's commercial operations with the first use case targeting sales efficiency already in production.

該平台的搜尋能力在數週內即上線投入生產。客戶在上線後立即續聘 Grid Dynamics 以延伸計畫，使用我們建構於同一平台之上的目錄豐富化（catalog enrichment）解決方案，提升搜尋結果品質。我們目前已進入第三階段：為客戶的商業營運開發代理式平台，其中第一個以提升銷售效率為目標的使用案例已在生產環境中運行。

The margin profile of AI engagements, especially those built on GAIN platforms, is meaningfully different from the traditional services pipeline. When we win through a joint sales motion, clients are buying a validated solution at a fixed commercial structure. That changes the margin profile, higher gross margins than our blended services average. The GAIN platforms plus Forward Deployed Engineers model is not just an acquisition strategy. It's a retention and margin expansion strategy too.

AI 專案（尤其是建立在 GAIN 平台上的專案）的毛利結構，與傳統服務管線有顯著不同。當我們透過聯合銷售模式贏得案子時，客戶購買的是已驗證的解決方案，且採固定的商業結構。這會改變毛利結構，使毛利率高於我們服務業務的綜合平均水準。GAIN 平台加上前線部署工程師（Forward Deployed Engineers）模式，不僅是一項併購策略。它同時也是一項留存與毛利擴張策略。

With that, I'll hand it to Anil to walk through the financials.

接下來，我把時間交給 Anil，請他帶大家看財務表現。

Thanks, Rahul. Good afternoon, everyone. We recorded the first quarter revenues of $104.1 million, slightly above the higher end of our guidance range of $103 million to $104 million. Our revenues grew 3.7% on a year-over-year basis.

謝謝你，Rahul。各位下午好。我們第一季營收為 1.041 億美元，略高於我們指引區間 1.03 億至 1.04 億美元的上緣。我們的營收年增 3.7%。

Non-GAAP EBITDA was $12.5 million or 12% of revenues and was at the midpoint of our $12 million to $13 million guidance range. In the first quarter, there was a negative impact from FX fluctuations on a year-over-year basis. We are exposed to a currency basket across Europe, Latin America and India. While we utilize both natural hedges and an active hedging program, the net impact on a year-over-year basis on our EBITDA was a headwind of approximately $1.2 million.

非 GAAP EBITDA 為 1,250 萬美元，占營收 12%，落在我們 1,200 萬至 1,300 萬美元指引區間的中點。第一季，外匯波動對年對年比較造成負面影響。我們曝險於涵蓋歐洲、拉丁美洲與印度的多種貨幣組合。雖然我們同時採用自然避險與主動避險計畫，但年對年比較下，外匯對 EBITDA 的淨影響約為 120 萬美元的不利因素。

As Leonard highlighted, our top customers are global technology and financial enterprises. And this is by design. Our growth strategy is deliberately focused on verticals where AI adoption is accelerating and our capabilities are highly differentiated. In the first quarter, revenue breakdown reflects this redistribution with meaningful diversification into our TMT and financial verticals.

如 Leonard 所強調，我們的主要客戶是全球科技與金融企業。這是刻意的布局。我們的成長策略有意聚焦在 AI 採用加速、且我們能力高度差異化的垂直領域。第一季的營收結構反映了這樣的重新配置，並在 TMT 與金融垂直領域實現了有意義的多元化。

Looking at the performance of our verticals, TMT became our largest vertical and accounted for 29.5% of total revenues for the quarter with growth of 30.3% on a year-over-year basis. The growth was primarily driven by a combination of our largest technology customers as well as new customers. Retail contributed 28.4% of total revenues in the first quarter of 2026. The finance vertical accounted for 23.5% of total revenues in the quarter, and we witnessed strong demand from our banking and fintech customers. For the remainder of 2026, we are bullish on our outlook with our banking and fintech customers.

從各垂直領域的表現來看，TMT 成為我們最大的垂直領域，本季占總營收 29.5%，年增 30.3%。成長主要來自我們最大科技客戶的貢獻以及新客戶的加入。零售在 2026 年第一季占總營收 28.4%。金融垂直領域本季占總營收 23.5%，我們看到來自銀行與金融科技客戶的強勁需求。對於 2026 年剩餘期間，我們對銀行與金融科技客戶的展望保持樂觀。

Turning to the remaining verticals. CPG & Manufacturing represented 9.4% of quarterly revenues. In the quarter, we witnessed growth from our manufacturing customers in North America and new engagements in Europe. The Other vertical contributed 7.1% of first quarter revenues. And finally, Healthcare and Pharma contributed 2.1% of our revenues for the quarter.

接著看其餘垂直領域。民生消費品（CPG）與製造業占本季營收 9.4%。本季我們看到北美製造業客戶帶來的成長，以及在歐洲的新專案合作。其他（Other）垂直領域占第一季營收 7.1%。最後，醫療保健與製藥占本季營收 2.1%。

We ended the first quarter with a total headcount of 4,964, up from 4,961 employees in the fourth quarter of 2025 and from 4,926 in the first quarter of 2025. We continue to rationalize our overall headcount as we align our skill sets and geographic mix. At the end of the first quarter of 2026, our total US headcount was 353 or 7.1% of the company's total headcount versus 7.2% in the year ago quarter. Our non-US headcount located in Europe, Americas and India was 4,611 or 92.9%.

第一季結束時，我們總員工數為 4,964 人，高於 2025 年第四季的 4,961 人，也高於 2025 年第一季的 4,926 人。我們持續精簡並合理化整體人力配置，以配合技能組合與地理分布的調整。截至 2026 年第一季末，美國員工總數為 353 人，占公司總員工數 7.1%，相較去年同期為 7.2%。非美國員工分布於歐洲、美洲與印度，共 4,611 人，占 92.9%。

In the first quarter, revenues from our top 5 and top 10 customers were 40.8% and 59.7%, respectively, versus 35.6% and 56.6% in the same period a year ago, respectively.

第一季，前五大與前十大客戶的營收占比分別為 40.8% 與 59.7%，相較去年同期分別為 35.6% 與 56.6%。

Moving to the income statement. Our GAAP gross profit during the quarter was $36.2 million or 34.8% compared to $36.1 million or 34% in the fourth quarter of 2025 and $37 million or 36.8% in the year ago quarter. On a non-GAAP basis, our gross profit was $36.7 million or 35.3% compared to $36.6 million or 34.5% in the fourth quarter of 2025 and $37.6 million or 37.4% in the year ago quarter. On a year-over-year basis, the decline in the gross margin was from a combination of FX headwinds and higher cost structures across our delivery locations.

接著看損益表。本季 GAAP 毛利為 3,620 萬美元，毛利率 34.8%，相較 2025 年第四季為 3,610 萬美元、毛利率 34%，去年同期為 3,700 萬美元、毛利率 36.8%。以非 GAAP 計算，本季毛利為 3,670 萬美元，毛利率 35.3%，相較 2025 年第四季為 3,660 萬美元、毛利率 34.5%，去年同期為 3,760 萬美元、毛利率 37.4%。年對年毛利率下滑，主要是外匯不利因素與各交付據點成本結構上升的綜合影響。

Non-GAAP EBITDA during the first quarter that excluded interest income expense, provisions for income taxes, depreciation and amortization, stock-based compensation, restructuring, expenses related to geographic reorganization and transaction and other related costs was $12.5 million or 12% of revenues versus $13.7 million or 12.9% of revenues in the fourth quarter of 2025 and was down from $14.6 million or 14.5% in the year ago quarter. The sequential and year-over-year decline in EBITDA was largely due to a combination of FX headwinds and higher operating costs.

第一季非 GAAP EBITDA（排除利息收入/費用、所得稅提列、折舊與攤銷、以股份為基礎的薪酬、重組、與地理重整相關費用，以及交易與其他相關成本）為 1,250 萬美元，占營收 12%；相較 2025 年第四季為 1,370 萬美元、占營收 12.9%，也低於去年同期的 1,460 萬美元、占營收 14.5%。EBITDA 的季減與年減，主要是外匯不利因素與營運成本上升的綜合影響。

Our GAAP net loss in the first quarter was $1.5 million or a loss of $0.02 per share based on a diluted share count of 84.7 million shares compared to the fourth quarter net income of $0.3 million or breakeven per share based on diluted share count of 86.4 million and net income of $2.9 million or $0.03 per share based on 87.8 million diluted shares in the year ago quarter.

第一季 GAAP 淨損為 150 萬美元，按稀釋後股數 8,470 萬股計算，每股虧損 0.02 美元；相較之下，第四季 GAAP 淨利為 30 萬美元，按稀釋後股數 8,640 萬股計算，每股損益兩平；去年同期 GAAP 淨利為 290 萬美元，按稀釋後股數 8,780 萬股計算，每股盈餘 0.03 美元。

On a non-GAAP basis, in the first quarter, our non-GAAP net income was $7.5 million or $0.09 per share based on 85.9 million diluted shares compared to the fourth quarter non-GAAP net income of $8.7 million or $0.10 per share based on 86.4 million diluted shares and $10 million or $0.11 per share based on 87.8 million diluted shares in the year ago quarter.

以非 GAAP 計算，第一季非 GAAP 淨利為 750 萬美元，按稀釋後股數 8,590 萬股計算，每股盈餘 0.09 美元；相較第四季非 GAAP 淨利為 870 萬美元、按稀釋後股數 8,640 萬股計算每股盈餘 0.10 美元；去年同期為 1,000 萬美元、按稀釋後股數 8,780 萬股計算每股盈餘 0.11 美元。

On March 31, 2026, our cash and cash equivalents totaled $327.5 million, down from $342.1 million on December 31, 2025.

截至 2026 年 3 月 31 日，我們的現金及約當現金合計為 3.275 億美元，低於 2025 年 12 月 31 日的 3.421 億美元。

Since our fourth quarter earnings call, we repurchased approximately 1.8 million shares for a total consideration of $11.5 million. Since our Board authorized the $50 million share repurchase program, we have repurchased approximately 2 million shares for a total of $13.5 million, reflecting our continued confidence in the long-term value of the business.

自第四季法說會以來，我們回購約 180 萬股，總對價為 1,150 萬美元。自董事會核准 5,000 萬美元的庫藏股回購計畫以來，我們已回購約 200 萬股，總額 1,350 萬美元，反映我們對公司長期價值的持續信心。

M&A continues to take priority in our capital allocation strategy. We are committed to augmenting our organic business with acquisitions that strategically enhance our capabilities, geographic presence and industry verticals.

在我們的資本配置策略中，併購（M&A）仍然是優先事項。我們致力於透過能在策略上強化我們能力、地理布局與產業垂直領域的收購案，來補強有機成長。

Coming to the second quarter guidance. We expect revenues to be in the range of $106 million to $108 million. We expect our second quarter non-GAAP EBITDA to be in the range of $14 million to $15 million. For Q2 2026, we expect our basic share count to be in the range of 84 million to 85 million and our diluted share count to be in the range of 85 million to 86 million. For the full year 2026, we're maintaining our revenue outlook of $435 million to $465 million.

接著說明第二季指引。我們預期營收將落在 1.06 億至 1.08 億美元之間。我們預期第二季非 GAAP EBITDA 將落在 1,400 萬至 1,500 萬美元之間。針對 2026 年第二季，我們預期基本股數約為 8,400 萬至 8,500 萬股，稀釋後股數約為 8,500 萬至 8,600 萬股。針對 2026 全年，我們維持營收展望為 4.35 億至 4.65 億美元。

That concludes my prepared remarks. We're ready to take your questions.

以上是我事先準備的發言。我們準備好回答各位的提問。

(Operator Instructions)

(接線員指示)

Puneet Jain of JP Morgan.

JP Morgan 的 Puneet Jain。

Hey, thanks for taking my question. So Leonard, thanks for sharing updates on the GAIN framework. As these platforms become increasingly integrated in your delivery, could you talk about the impact it has on overall operations, say, like are these necessarily fixed price contracts? Do clients pay for tokens like for LLMs or are they bundled in your overall services?

嗨，謝謝讓我提問。Leonard，謝謝你分享 GAIN 架構的最新進展。隨著這些平台在你們的交付中愈來愈整合，你能談談它對整體營運的影響嗎？例如，這些是否必然是固定價格合約？客戶是像對 LLM 一樣按 token 付費，還是被打包在你們的整體服務中？

You talked about like Forward Deployed Engineers. Can you train your current employees to be FTEs? Or do you have to change your hiring mix to be able to offer GAIN platform to your customers?

你提到像是 Forward Deployed Engineers。你們能訓練現有員工成為 FTE 嗎？還是你們必須改變招募的人才組合，才能向客戶提供 GAIN 平台？

I think, let me try to unpack some of your questions. It's a lot than one. But let's go backwards, probably a little bit easier. So let's start with engineering talent and Forward Deployed Engineers.

我想，讓我試著拆解一下你的一些問題。不只一個，內容很多。不過我們倒著回答，可能會比較容易一些。那就先從工程人才與 Forward Deployed Engineers 談起。

Majority of the people who we deploy, obviously, are internally trained. We have a large number, substantial large number of very technically educated people who we internally build our services and promotions and train them in the models. And it's led by our R&D organization, so you see Eugene is going to give you some more comments, which combining with retraining the delivery organization brings the talent. Obviously, when we bring the talent from the market, it still needs to be structured so they're going to be able to adapt Grid Dynamics GAIN platforms approach.

我們派駐的大多數人員，顯然都是內部培訓出來的。我們有相當大量、非常具技術教育背景的人才；我們在內部打造服務與晉升體系，並在模型方面訓練他們。這由我們的研發組織主導，所以你會看到 Eugene 會再補充一些看法；把交付組織的再培訓結合起來，就能帶來所需的人才。當然，當我們從市場引進人才時，也仍需要有結構化安排，讓他們能適應 Grid Dynamics 的 GAIN 平台方法。

The GAIN platforms approach is really what makes us different. So rather than talking about a very specific model for each individual customers, let me explain a little bit in the words what these new platforms means for the contracts.

GAIN 平台的方法，才是真正讓我們與眾不同的地方。所以與其針對每一位客戶談一個非常特定的模型，我用文字稍微解釋一下，這些新平台對合約意味著什麼。

So basically, we developed a lot of tools over time. And even in the last Board meeting, we introduced lots and lots of different names. And now we're maturing to the point that we can offer a suite of solutions to the client where we actually define a kind of a combination of Grid Dynamics IP and open available sources into the total solution. And the total solutions which we offer are driven by adoption of the engineers and agents in the form of the guidance, where we expect the return on investment for the client.

基本上，我們隨著時間推進開發了很多工具。甚至在上一次董事會會議上，我們介紹了非常非常多不同的名稱。而現在我們成熟到一個程度，可以向客戶提供一套解決方案：我們會把 Grid Dynamics 的智慧財產（IP）與公開可用的來源加以組合，形成完整解決方案。我們提供的整體解決方案，是由工程師與代理（agents）的採用所驅動，並以 guidance 的形式推動，我們也期待客戶能獲得投資報酬（ROI）。

So answering your question, the number of non-T&M projects -- and because there is a lot, there is a tokenization, there is offering of the fixed bid, there is a performance related. They are significantly increased and they continue to increase. And you will actually see that as we continue to answer your questions today because that model itself requires not only training the FD engineers, but adapting the internal processes and the program management and delivery team to actually control a proper engagement in a different venue. So answering your question, definitely, there is a big shift toward non-T&Ms.

所以回答你的問題，非 T&M（非按工時與材料計費）專案的數量——因為其中包含很多元素：token 化、固定總價（fixed bid）的提供、以及與績效相關的模式。它們已經大幅增加，而且還在持續增加。而且你今天在我們持續回答問題時會看到這一點，因為這種模式本身不僅需要訓練 FD 工程師，還需要調整內部流程、專案管理與交付團隊，才能在不同的場域中有效控管並正確執行合作案。所以回答你的問題，確實正大幅轉向非 T&M。

The training and rollout of our engineering force is going very successfully. You haven't seen right now from the absolute number of employees, how the dynamics of the headcount has changed yet because number looks flat. But if you again unpack that number, you will see a significantly higher contribution on the engineering workforce because some of them require an additional training and reclassification before we deploy them to the clients. But the good news is, overall, we have a very strong vector where we are building our position with adopting our clients, new models related to the GAIN platforms.

我們工程團隊的訓練與推廣進展非常順利。目前從員工總數的絕對數字來看，你還看不出人力結構的變化，因為總數看起來持平。但如果你把這個數字拆開看，你會看到工程人力的貢獻顯著提高，因為其中一些人在派駐到客戶之前，需要額外訓練與重新分類。但好消息是，整體而言，我們有非常強的發展向量：透過客戶採用與 GAIN 平台相關的新模型，我們正在建立更有利的位置。

Got it. No, it's a big change. And so it seems like you're already doing a lot of hard work that's involved.

了解。不，這是很大的改變。而且看起來你們已經在做很多必要的艱苦工作了。

Let me ask Anil. So the guidance, like the full year on top line, so it does imply like a mid single digit growth even in the lower half, mid single digit average sequential growth in second half to hit the lower half of the guidance. So what drives the confidence or the visibility on achievement of this guidance for the full year?

我想問 Anil。關於全年營收（top line）的指引，這確實意味著即使在指引區間的下緣，下半年也需要達到中個位數成長，且為了達到下緣，第二季下半年需要中個位數的平均季增。那麼，對於達成全年指引的信心或能見度，是由什麼驅動的？

So there are two or three factors here. Leonard, do you want to talk about pipeline, then I can take it.

這裡有兩到三個因素。Leonard，你要先談談 pipeline（商機管線）嗎？然後我再接著說。

Well, I will answer the easy part. And then Anil will dive you a little bit of the numbers. There are two parts of the confidence level we have. The number one, the demand has grown substantially. So we have the record number of demand. And I'm avoiding the word number of engineering demand because, again, we're talking about the teams, the platforms, the offering, but overall demand, the vector is very steep right now. That's a subjective factor because, again, this could happen, it may not happen or whatever, but it's a good news. It's a record high.

好，我先回答比較容易的部分。然後 Anil 會再帶你深入看一些數字。我們的信心來源有兩個部分。第一，需求已大幅成長。所以我們有創紀錄的需求量。我刻意避免用「工程需求量」這個詞，因為我們談的是團隊、平台、產品提供（offering），但整體需求的成長向量目前非常陡峭。這是較主觀的因素，因為它可能發生、也可能不發生等等，但這是好消息。它處於歷史新高。

The more interesting factor is, and Anil will dive into the financial estimates, we are facing a larger, as I mentioned in the previous comment to you, number of non-T&M projects. This work force is defined by a different estimate, how do we qualify the revenue based on this project in which point. So when we unpack the number, we are a bit more conservative, which we're going to guide this particular quarter or the next quarter because now it becomes a little bit more of a financial exercise.

更有意思的因素是——Anil 會深入說明財務估算——我們正面臨更大量的非 T&M 專案，如同我在先前的回覆中提到的。這類工作需要用不同的估算方式來界定：在專案的哪個節點、如何認列（qualify）收入。因此當我們拆解這些數字時，我們會更保守一些，這也會反映在我們對本季或下季的指引上，因為現在它更像是一個財務上的演算與判斷。

The work has been signed. The work is going on, but Anil probably give you a little bit better feedback. But the summary for you, the takeaway for me, two parts, significantly higher number of the pipeline and a very large number of the non-T&M project, which require a little bit more financial attention, how we guide the numbers for the near future for the next couple of months.

工作已經簽約。工作也正在進行中，但 Anil 可能會給你更好的回饋。不過給你的總結、我的重點是兩點：商機管線顯著增加，以及大量的非 T&M 專案；這需要更多財務面的關注，來決定我們在未來幾個月、近期對數字的指引方式。

Yeah, no, , look, I mean, Leonard, you pretty much hit it. Let me kind of build upon that. Leonard and the team in our prepared remarks talked about a fundamental transformation on how we're moving. And the word you will see again and again is a platform. Now the historical approach we all know is that you take the engineer, you have a certain T&M rate, you multiply it by hours, days; and the formula, as you know, is very linear. We're transitioning. We're seeing that. Rahul is leading the way from a partnership and Eugene is leading the way, obviously, on the CTO.

是的，沒錯，我的意思是，Leonard 你基本上都說到了。我再在此基礎上補充一下。Leonard 和團隊在我們事先準備的發言中談到，我們正在進行一個根本性的轉型，關於我們如何前進。你會一再看到的關鍵字是「平台」。我們都知道歷史上的做法是：以工程師為單位，有一個 T&M 費率，乘上工時、天數；如你所知，這個公式非常線性。我們正在轉型。我們也正在看到這個變化。Rahul 在合作夥伴關係方面帶頭推進，而 Eugene 顯然在 CTO 方面帶頭推進。

We've introduced all these new products and platforms, and we're working on monetization. Now there are stages of monetization. There's upfront, that will get start off small. There's greater stickiness with these engineers. And as our clients become comfortable with both our products as well as our engineers in this new model, that's when we start seeing a lot more monetization there.

我們推出了所有這些新產品與平台，並且正在推動變現（monetization）。而變現是分階段的。一開始會有前期（upfront）的部分，起步會比較小。這些工程師會帶來更高的黏著度。當我們的客戶對我們的產品以及在這種新模式下的工程師都更為熟悉時，我們就會開始看到更多的變現空間。

So when we started looking at these numbers, the obviously, revenue recognition is a key component to it, right? And we're taking, think of it as baby steps right now. We see the pipeline. I look at year-to-date from January 1 through now, compare that with last year, really good. I look at some of these initiatives we're working on, on AI, really good. But the question will be, how do we time it? Is it a linear timing or nonlinear timing? So from that context, for the full year, we're keeping it.

所以當我們開始檢視這些數字時，很明顯地，收入認列是其中的關鍵組成部分，對吧？而我們目前是採取循序漸進的方式。我們看到案源管線。我看今年截至目前（從 1 月 1 日到現在）的表現，跟去年相比，真的很好。我看我們正在推動的一些 AI 相關計畫，也非常好。但問題會是：我們要如何掌握時點？是線性的時點安排，還是非線性的時點安排？因此在這個脈絡下，就全年而言，我們維持原先的看法。

Now let's see the couple of quarters. Does it turn out much stronger because we have some of the recognitions or not. So we're still experimenting with this. We're working through it. So the optics of it looks slightly different from what you can see underneath from a business point of view.

現在我們來看接下來幾個季度。是否會因為有部分認列而呈現更強勁的結果，或是不會。所以我們仍在就此進行試驗。我們正在推進並逐步釐清。因此從外觀上看起來，可能會跟你從業務底層所看到的情況略有不同。

Let me add one more factor, because it could be a bit missed from the first point of view. We also guide substantially better margins. So if you look at the delta between Q1 and Q2, you may ask a question, how can you grow such a steep increase of profitability on relatively modest increase of revenue? So this gives you a little bit more a story that we look at the new projects we've been awarded to us -- as Rahul was mentioning in his statement -- at a different margin profile than the current business. We just don't want to run ahead of the time and do all the financial qualification of that until we see the results. But we are very confident in the progress we're about to make.

我再補充一個因素，因為從第一個角度看可能會有點被忽略。我們也給出了顯著更好的毛利率／利潤率指引。所以如果你看 Q1 和 Q2 之間的差異，你可能會問：在營收相對溫和成長的情況下，怎麼可能讓獲利能力大幅躍升？這會讓你更理解我們的敘事：我們看待新近得標的專案——正如 Rahul 在他的發言中提到的——其毛利結構與現有業務不同。在看到結果之前，我們不想操之過急、提前把所有財務上的資格認定都做完。但我們對即將取得的進展非常有信心。

Got it. So it seems like you are at the cusp of that monetization and that drives the confidence. I appreciate it. Thanks for the color.

了解。所以看起來你們正處在變現的臨界點，而這也帶來信心。我很感謝這些說明。謝謝補充的細節。

Thank you.

謝謝。

Thank you.

謝謝。

Maggie Nolan of William Blair.

William Blair 的 Maggie Nolan。

Hi. Thank you. I wanted to ask about your partner revenue that crossed 19% of revenue. So where do you anticipate that going? And to what extent do you expect that to be a positive margin driver for the company?

嗨。謝謝。我想問一下你們的合作夥伴收入已經超過營收的 19%。那你們預期它會往哪個方向發展？以及你們預期它在多大程度上會成為公司正向的利潤率驅動因素？

I think the best way to start is with the person who is responding to that. I think, Rahul, you have a perfect opportunity to tell how you build the business continue to grow. So please go ahead.

我認為最好的開始方式，是先由最適合回答的人來回應。Rahul，我想你有很好的機會說明你如何打造並持續成長這塊業務。所以請開始。

Thanks for that question, Maggie. Like you have seen, partnerships have become one of our key go-to-market channels, and it will continue to be. We have a long-term goal to get to about 25% to 30% of our revenues being influenced by partnerships.

謝謝這個問題，Maggie。如你所見，合作夥伴關係已成為我們關鍵的市場進入（go-to-market）通路之一，而且未來也會持續如此。我們的長期目標是讓大約 25% 到 30% 的營收受到合作夥伴關係的影響。

And we are well on our path to achieve that. In fact, I would say we are tracking slightly ahead when we look at our internal goals to achieve that. And with GAIN platforms being deployed on the hyperscaler marketplaces, we'll probably see acceleration of that partner inference revenues in the future quarters.

而我們正穩步朝這個目標前進。事實上，如果看我們內部為達成該目標所設定的指標，我會說我們的進度略微超前。隨著 GAIN 平台在超大規模雲服務商（hyperscaler）的市集上部署，我們可能會在未來幾個季度看到這類合作夥伴影響收入加速成長。

Let me just add one more color maybe on this. Rahul, a bit kind of mentioned in his prepared remarks, but it's important because, again, it's new. So we talked with Puneet about the new model of the business. Now we talk a little bit different model of engagement with our partners.

我再補充一點背景說明。Rahul 在事先準備的講稿中有稍微提到，但這點很重要，因為這同樣是新的。我們先前和 Puneet 討論了新的商業模式。現在我們再談談與合作夥伴互動的另一種不同模式。

In the past, we've basically been talking about hyperscalers. And that was a very consistent is, frankly, the influence revenue generated with these partnerships. Now we start adding, especially with the physical AI, some interesting new level of partnerships. And monetization is a little bit lower yet, but we see a substantial growth because now we're adding into with the heavy hitters in the industry because it adds more addressable market.

過去，我們基本上談的是超大規模雲服務商（hyperscalers）。坦白說，透過這些合作夥伴所帶來的影響收入一直都相當穩定。現在我們開始加入——特別是在實體 AI（physical AI）方面——一些很有意思、層級更高的新合作夥伴關係。目前變現程度還稍低一些，但我們看到顯著的成長，因為我們正在與產業中的重量級玩家合作，這會擴大可服務市場（TAM）。

The other element, which is kind of getting also related to our GAIN platforms, it's a consultancy part. So now we're also getting partnerships with some of the business organizations which are asking us to become the lead technology implementation partner, which is adding a little bit more of the flavor from transition from the business conceptual idea to implementation related to specific AI platforms.

另一個要素，也與我們的 GAIN 平台相關，是顧問諮詢（consultancy）這一塊。因此我們也開始與一些商業組織建立合作夥伴關係，對方希望我們成為主要的技術導入夥伴，這讓我們在從商業概念到落地實作的轉換過程中，增加了更多元素，並且與特定 AI 平台的導入更直接相關。

As you know, business leaders are a little bit more cautious about spending the budget because you can spend a lot of money on experimentation. So they would like to seek some clarity where they would have a confidence that the investment is not going to be not just risky, but send them to wrong direction. And Grid Dynamics is becoming the partner of that, their consultancy work. So I think it's another really important difference from the past.

如你所知，企業領導者在預算支出上會更謹慎，因為在試驗上可能花很多錢。所以他們希望獲得更清楚的方向，讓他們有信心這筆投資不僅不會太冒險，也不會把他們帶往錯誤的方向。而 Grid Dynamics 正在成為他們在這方面的合作夥伴，提供諮詢工作。所以我認為這是相較過去另一個非常重要的差異。

Got it. Thank you. And then on the TMT growth, do you think that's durable into the back half of the year? To what extent was that driven by concentration with particular clients? And what's the visibility into those clients that drove that?

了解。謝謝。另外關於 TMT 的成長，你認為在今年下半年仍具持續性嗎？其中有多大程度是由特定客戶的集中度所驅動？對於帶動成長的那些客戶，目前的能見度如何？

Yeah, Maggie, that's clearly a highlight, and it's super exciting. Not only the TMT, but if you look at some of our financial clients there, we have seen many of these customers consolidating. And the other thing is that in some of them, we have now become a preferred vendor. We were always there, but now as they were consolidating, we reached the preferred vendor status.

是的，Maggie，這確實是一個亮點，也非常令人振奮。不只是 TMT，如果你看我們的一些金融客戶，我們看到許多客戶正在進行供應商整併。另外，在其中一些客戶那裡，我們現在已成為優先供應商（preferred vendor）。我們一直都在，但在他們整併的過程中，我們取得了優先供應商的地位。

With the TMT, there are two nuances to the movement. There's obviously our work with them, what we're doing. They know what AI is, and they appreciate us. It's a very interesting thing. The smartest technology customers are the one who are seeking our AI capabilities and more, which is a little counterintuitive, right? But the other interesting thing that is going on with these customers is that there's a hyperscaler relationship too. So on both fronts, we are seeing a lot of activity.

在 TMT 方面，這個變化有兩個細微之處。一方面當然是我們與他們的合作、我們正在做的事情。他們了解 AI 是什麼，也很認可我們。這很有意思。最聰明的科技類客戶反而更在尋求我們的 AI 能力以及更多服務，這有點違反直覺，對吧？但這些客戶身上另一個有趣的點是：他們也同時存在與 hyperscaler 的關係。所以在這兩個面向上，我們都看到很多活動。

Now every quarter, there might be some negatives moving there, but the trajectory is very strong as we get consolidated as we're one of the few vendors, as we've got a clean sheet with many of these new stakeholders and we augment that with some of the hyperscaler growth that is going on.

當然每一季可能都會有一些不利因素波動，但整體趨勢非常強勁：隨著客戶整併、我們成為少數供應商之一；我們在許多新利害關係人面前有「乾淨的起點」；再加上 hyperscaler 的成長動能，我們看到的軌跡非常正向。

Yeah, but I think the important color, very specific color for you, Maggie, is that Anil mentioned about selection being a preferred vendor. We're not talking about generic preferred niche vendor anymore. The AI proliferation equalize the supply base.

是的，但我想給你一個很重要、也很具體的補充，Maggie：Anil 提到我們被選為優先供應商。我們現在談的已不再是一般性的、利基型的優先供應商。AI 的普及正在讓供應基礎趨於同質化。

In other words, there is -- the size does not provide advantage to some of the largest vendors. The capability of deploying AI solution at scale has been determined as a vital part. And being a smaller company and being able to transition faster remember, again, the very first question from Puneet -- how quickly we can train people. It's amount of quality work with those specialized teams, which determine our awards on the business side. And with the TMT, it's definitely the number 1 followed right now with the financial clients. We'll talk a little bit more about others as time comes. But the top 5, top 6 clients, we are in the driver seat for AI deployments.

換句話說，規模不再為某些最大型供應商帶來優勢。能夠以規模化方式部署 AI 解決方案的能力，已被認定為至關重要的一環。而作為一家較小的公司、能更快完成轉型——再回到 Puneet 的第一個問題：我們能多快訓練人員——決定我們在業務端得標的，是那些專業團隊所交付的高品質工作量。在 TMT 方面，目前絕對是第一，其次是金融客戶。其他領域我們會在適當時機再多談一些。但就前 5、前 6 大客戶而言，我們在 AI 部署上處於主導位置。

Surinder Thind, Jefferies.

Surinder Thind，Jefferies。

Thank you, guys. When we think about the non-time and materials model, how do we think about the incremental risk that you're taking on? Obviously, over the past decade, two decades, we moved in that direction because projects got bigger, they got more complex. There is maybe greater uncertainty about scope or changes in scope. How does that work in the new model? Because if you're looking at an outcome-based or fixed price token usage, like where is the risk in the model for you guys? Or how are you guys addressing that?

謝謝，各位。當我們思考非按工時與材料（non-time and materials）的模式時，我們該如何看待你們所承擔的增量風險？顯然，在過去十年、二十年，我們朝那個方向移動，是因為專案變得更大、更複雜。在範疇或範疇變更方面，可能存在更大的不確定性。在新模式下這是如何運作的？因為如果你們看的是以成果為基礎或固定價格的代幣使用，那麼對你們而言，這個模式的風險在哪裡？或者你們是如何因應的？

So, Surinder, I will actually have Eugene Steinberg, our CTO, to start talking because she is a bit of an architect of the system. And uncertainty has 2 prongs. One of them is a risk level, the second one is a reward level. And I will let Eugene talk about the coexist on both and how we handle it. Please, Eugene.

Surinder，我會請我們的 CTO Eugene Steinberg 先來談，因為她算是這個系統的架構設計者之一。而不確定性有兩個面向。其中一個是風險層級，第二個是報酬層級。我會讓 Eugene 談談兩者如何並存，以及我們如何處理。請，Eugene。

Yes. Of course, when you are taking a fixed price project, you always have to balance risk versus reward. So on the risk standpoint, the main risks in the fixed price projects are coming from uncertainty. Uncertainty is coming usually from understanding of the requirements and finding gaps in the requirements of the project. We are using very actively our AI agents and our specific game, Rosetta framework, to uncover all the uncertainties in the requirements and clarify with our sources ahead of time during the presale phase, and that builds us a very strong confidence in the understanding of what needs to be done.

是的。當然，當你承接固定價格專案時，你總是必須在風險與報酬之間取得平衡。就風險面而言，固定價格專案的主要風險來自不確定性。不確定性通常來自對需求的理解，以及在專案需求中找出缺口。我們非常積極地使用 AI 代理（agents）以及我們特定的遊戲化方法 Rosetta framework，來揭露需求中的所有不確定性，並在售前階段提前與資訊來源釐清，這讓我們對需要完成的事項有非常強的理解信心。

During implementation, we are very actively using always AI coding assistance and our GAIN Rosetta framework, helping to accelerate the delivery of a project and building the buffer for any unknown unknowns, which usually happen in those projects.

在實作期間，我們也一貫非常積極地使用 AI 程式碼輔助，以及我們的 GAIN Rosetta framework，協助加速專案交付，並為任何未知的未知（unknown unknowns）建立緩衝，而這些通常會在這類專案中發生。

So let me just add one thing to what Eugene just said. So Surinder, you know you've been in the IT industry, and this is a risk not unique to Grid. It's a universal risk. All I'll add is a couple of additions to what Eugene said. The first thing is that when you scope out projects, if you don't have a deep understanding of the project or as Eugene says, the risk, it's a problem.

我再補充 Eugene 剛才說的一點。Surinder，你也在 IT 產業待過，你知道這種風險並非 Grid 獨有。這是普遍存在的風險。我只是在 Eugene 的說法上再加兩點。第一，當你界定專案範疇時，如果你對專案沒有深入理解，或如 Eugene 所說，沒有掌握風險，那就會出問題。

Now when I look back at the history over the last five years, historically, we were a T&M shop. We moved towards fixed price. And actually, during those first year or two of our fixed price, we learned a lot. We have committed mistakes in the past. This is the pre-AI era, and we worked. As a matter of fact, there were times when our fixed price project margins were comparable with our T&M, and I always went back to the team what's going on. So we learned. Now when you look at our fixed price margins pre-AI, they're higher than our T&M. And those learnings are now moving into our AI.

回顧過去五年的歷史，過去我們主要是按工時與材料（T&M）的模式。後來我們轉向固定價格。而且在固定價格的頭一、兩年，我們學到了很多。我們過去確實犯過錯。那是 AI 出現之前的年代，我們一路摸索。事實上，有些時候我們固定價格專案的利潤率與 T&M 相當，我總會回頭問團隊到底怎麼回事。所以我們學到了。現在你看我們在 AI 之前的固定價格利潤率，是高於 T&M 的。而這些學習成果現在也正在導入到我們的 AI。

So we really know what we're doing. I think what we've learned is that if you don't understand the problem that you're dealing with and you don't have a technological know-how, you're absolutely right, there is a heightened level of risk. We'll always have that risk. But as Leonard pointed out, there's a reward component too with that.

所以我們很清楚自己在做什麼。我想我們學到的是：如果你不了解你正在處理的問題，且沒有技術上的專業能力，你說得完全對，風險水準會顯著提高。我們永遠都會有那個風險。但如 Leonard 指出的，這也有報酬的面向。

Yeah, and I just want to close on that with one simple statement. In my prepared remarks, I mentioned clearly that Grid Dynamics is not a system integrator. We are a product-centric engineering company. And that actually gives us the higher level of confidence that we take on the projects, we have a higher probability of success.

是的，我想用一句話做個收尾。在我事先準備的發言中，我清楚提到 Grid Dynamics 不是系統整合商。我們是一家以產品為中心（product-centric）的工程公司。而這其實讓我們在承接專案時更有信心，成功的機率也更高。

So Eugene was mentioning Rosetta, another methodology we're using. It's all part of the GAIN platforms. Now the outcomes on a greater scale, Surinder, will be seen as we will propagate more and more results of this work. So it's not about how much money we generate in the project, but how much rate of growth we're going to see in this project going forward. Right now, at the size that we have and the scale of the tasks, we are training not only the models, but our customers, how to react on gradual, I would say, continuation of the development and approaching the goals.

所以 Eugene 提到 Rosetta，這是我們正在使用的另一套方法論。這些都是 GAIN 平台的一部分。Surinder，更大規模的成果會在我們持續擴散、產出更多這項工作的結果時逐步顯現。所以重點不在於我們在單一專案中賺了多少錢，而在於未來這個專案將帶來多大的成長率。以我們目前的規模與任務的尺度來看，我們不只是在訓練模型，也在訓練我們的客戶，讓他們知道如何因應我會說是漸進式的、持續的開發，以及如何朝目標推進。

So it's very, very important for the fixed bid for us to make sure we have intermediary goals because the approximation of the work and deliver results have to be iterative process. And that's very important. So we're improving not only our technology capability.

因此，對我們而言，在固定投標（fixed bid）下，確保有中間里程碑非常、非常重要，因為工作量的估算與交付成果必須是迭代式的流程。這非常重要。所以我們不只是在提升我們的技術能力。

And then maybe just a quick related follow-on. Any color or commentary on the delta between kind of the fixed price margins that you're able to achieve currently and what you're achieving on the time and materials side?

接著再追問一個相關的問題。能否談談你們目前能達到的固定價格利潤率，與按工時與材料那邊的利潤率之間的差異（delta）？

Sure. So when I look at -- now it varies quite a bit, right? So I'll throw a number out and somewhere in the ZIP code. I have seen the contribution margins when we get to some of our AI work somewhere in the 60-plus range too. Now I mean, not every project is a 60%. Otherwise, we would have been a 60% gross margin, but this is a contribution margin and then obviously, you have to offset by some of the overhead.

當然。我現在看——這其實差異很大，對吧？所以我先丟一個大概的數字，落在某個區間。我看過當我們做到某些 AI 工作時，貢獻毛利（contribution margin）也會落在 60% 以上的範圍。當然，我的意思不是每個專案都是 60%。不然我們的毛利率就會是 60% 了；但這裡講的是貢獻毛利，然後顯然你還需要用部分管理費用（overhead）去抵銷。

In general, if you look at most of our AI work, it is higher margins. If you look at the deltas between our T&M business and non-T&M business, there is a delta. So we see non-T&M in general being higher. And then when you look at AI business portions of the business, we do see some outliers, very positive outliers.

整體而言，如果你看我們大多數的 AI 工作，利潤率是比較高的。如果你看我們 T&M 業務與非 T&M 業務之間的差異，確實存在差距。所以我們看到非 T&M 整體上更高。而當你再看 AI 業務在整體業務中的部分時，我們確實看到一些離群值，而且是非常正向的離群值。

Got it. And then ultimately, what does this mean from a gross margin perspective? There's obviously the near term that you're able to handle from both managing headcount. But can you talk about where utilization is relative to your headcount goals and how we should think about the evolution over not just next quarter, but the next 12 to 24 months? Because it sounds like there's a big opportunity here, and I just want to make sure I understand the component that you control through managing headcount and utilization versus the component that's ultimately going to roll out as a result of just the revenue mix itself.

了解。那最終，從毛利率（gross margin）的角度來看，這代表什麼？顯然短期內你們可以透過管理人力規模來因應。但你能否談談目前的稼動率（utilization）相對於你們的人力目標處於什麼位置，以及我們應該如何看待未來不只是下一季、而是未來 12 到 24 個月的演變？因為聽起來這裡有很大的機會，我只是想確保我理解：你們透過管理人力與稼動率所能控制的部分，與最終會因為營收組合本身而逐步展開的部分，各自是什麼。

Very good question. So the way I look at, Surinder, your question is there is what I call the near to intermediate areas of focus, which is part of our 300 bps margin expansion, right, Q4 to Q4, and you're already seeing that, right?

非常好的問題。Surinder，我看你的問題方式是：有一些我稱之為短期到中期的聚焦領域，這是我們 300 個基點（bps）利潤率擴張的一部分，對吧，從 Q4 到 Q4，而你已經看到這點了，對吧？

Then there's a more fundamental question that you're asking is what is this pricing model and what is the margin model. So that is a more evolutionary thing that will not happen overnight, that has a more longer term. And that is what we are all working on as we work on these AI platforms.

接著你問的是一個更根本的問題：這個定價模式是什麼、利潤模式是什麼。所以那會是一個更具演進性的事情，不會一夕之間發生，時間軸更長。而這正是我們在打造這些 AI 平台時，大家正在共同努力的方向。

The whole GAIN -- as a finance guy, if you really look at what I tell Rahul from a GAIN platform and Eugene, who's always excited about technology is, what does it do to the margins and what does it do to the stickiness and what does it do to the growth? I mean, that's what it really boils down to, right? And our long-term model is to embed GAIN platforms with our customers -- that is just not human capital, but it's agents and actually IP -- create more stickiness, move towards a more fixed price model, which should result in a higher margin structure. Now what is that finally going to end up being? It's work in progress.

整個 GAIN——作為一個財務人，如果你真的去看我從 GAIN 平台的角度跟 Rahul 說的，以及一向對科技很興奮的 Eugene，我關注的是：它對毛利率有什麼影響？它對黏著度有什麼影響？它對成長有什麼影響？我的意思是，最後其實就歸結到這些，對吧？而我們的長期模型，是把 GAIN 平台嵌入到客戶之中——不只是人力資本，還包括代理（agents）以及實際上的 IP——以創造更高的黏著度，朝向更固定價格的模式移動，這應該會帶來更高的利潤率結構。那最終會變成什麼樣？這還在進行中。

Yeah, so I think Anil gave you a lot of financial guidance. Let me break it down to a couple of key elements, which I gauge the business.

是的，所以我想 Anil 已經給了你很多財務指引。我把它拆解成幾個我用來衡量這門生意的關鍵要素。

So there are three elements, obviously, adoption of AI in terms of the efficiency of the business, the marginality of the business. But there's a third factor, which you guys use quite often, which is not totally irrelevant. I think it's quite appropriate. It's the revenue per person. So utilization of the test becomes more driven by the revenue per person increase. And there are two parts of it.

所以顯然有三個要素：AI 的採用對業務效率的影響、對業務邊際（marginality）的影響。但還有第三個因素，你們其實很常用，而且並非完全不相關。我覺得它相當恰當。那就是人均營收（revenue per person）。所以測試的利用率（utilization）會更受到人均營收提升所驅動。而它又分成兩個部分。

On an overall EBITDA margin on a net margin, this is the fourth pillar of the platform, how internally we utilize it. But that doesn't help with the growth of the business. With the growth of the business, it comes actually with the idea that we are going to have repeatable and kind of reusable IP intelligence of our platforms. So the utilization part comes with the utilization of humans and IP capital. So it's a new formula, which is really -- will be gauged in my opinion, which I'm going to drive the company -- is increased revenue per person.

就整體 EBITDA 利潤率或淨利率而言，這是平台的第四支柱：我們內部如何使用它。但那對業務成長沒有幫助。業務成長其實來自於：我們的平台將擁有可重複、可再利用的 IP 智能。因此，利用率這一塊，會同時來自人力與 IP 資本的利用。所以這是一個新公式；在我看來、也是我將用來推動公司的衡量方式，就是提升人均營收。

Now saying that, there's another factor, right? It's Europe versus India versus US local consultancy. Different categories of different regions create a different ratio between revenue and the margin. And I'm telling my team, it's irrelevant. The revenue per person as a guidance for utilization has to grow everywhere. The new ability to create game-based platforms Forward Deployed Engineers and the models should drive the efficiency as we already see in the early adoption regardless of the regions and the traditional T&M models, which are not going to be as much used as we go forward.

話說回來，還有另一個因素，對吧？歐洲對比印度對比美國本地顧問。不同地區的不同類別，會造成營收與利潤率之間不同的比例。但我跟團隊說，這不重要。作為利用率的指引，人均營收必須在各地都成長。打造以 GAIN 為基礎的平台、Forward Deployed Engineers 以及各種模型的新能力，應該要推動效率——我們在早期採用中已經看到——不論地區，也不論傳統的 T&M（按時按料）模式；隨著我們往前走，T&M 將不會再被那麼多使用。

Bryan Bergin, TD Cowen.

Bryan Bergin，TD Cowen。

Hey, guys. Thanks for taking the question. Good afternoon. Maybe just at a high level to start on client sentiment. Just given the war in Iran, anything you can comment on how the conversation with enterprises has progressed over the last 2 months here? And just more recently as well, anything in recent weeks that's different?

嗨，各位。謝謝讓我提問。下午好。也許先從客戶情緒的高層面談起。考量伊朗戰爭，你們能否評論一下，過去兩個月與企業客戶的對話有什麼進展？以及更近期，最近幾週是否有任何不同？

Yeah, I can do that. Thanks for that question, Bryan. So there are clear trends, Bryan, that we are seeing with our clients. Number one is whereas last year, there was clearly clients who were looking at AI projects as POCs and trying to progress them into projects. Clearly, this year, there are production projects being invested in clients across the industries, very consistent.

可以，我來回答。謝謝你的問題，Bryan。Bryan，我們確實看到客戶端有一些明確趨勢。第一，去年很明顯有些客戶把 AI 專案當作 POC（概念驗證），並嘗試把它們推進成正式專案。而今年很明顯，各產業的客戶都在投資於上線（production）的專案，而且非常一致。

Second trend we are seeing is with AI, it is driving more projects and programs even for application modernization and data platforms. So we are seeing our pipeline grow in those 2 areas as well.

第二個趨勢是，AI 正在帶動更多專案與計畫，甚至包括應用程式現代化與資料平台。所以我們也看到在這兩個領域的管線在成長。

Third, very clearly we are saying -- whereas the last year, they were the early adopters of AI, now we are seeing a wave of fast followers. That is increasing really our pipeline as well as, in some ways, our total addressable market.

第三，很明顯我們看到——去年他們是 AI 的早期採用者，現在我們看到一波快速跟進者（fast followers）。這也確實提升了我們的管線，並在某種程度上擴大了我們的總可服務市場（TAM）。

Bryan, coming to your point, the Iran war, to me, at least when I look at the business, it's a non-event at this stage, right, in the third place.

Bryan，回到你提到的伊朗戰爭，至少就我看這門生意而言，目前這件事並沒有造成影響（non-event），對吧，至少在第三點上。

Yeah, I would say I would not really comment right now because the situation is very fluid there. We don't conduct the business in an area of the direct impact. So it's very hard to say that. The secondary impact on the business, again, it's negligible. I think that we had a huge impact continuing to the impact of the Russian invasion to Ukraine, right? That's much more dear to us. I don't think we're affected as much. But the global world has changed more with the conflict of Middle East and obviously conflict between Russia and Ukraine. And there are various factors.

是的，我會說我現在不太方便評論，因為那邊情勢非常多變。我們並沒有在直接受影響的區域開展業務。所以很難下定論。至於對業務的次級影響，同樣可以忽略不計。我認為俄羅斯入侵烏克蘭所帶來的影響一直都很大，對吧？那對我們更切身。我不覺得我們會受到那麼大的影響。但全球局勢確實因中東衝突，以及顯然的俄烏衝突而改變了更多。而其中有各種因素。

I mean, look, ultimately, the peace and resolution is the benefit for everyone. But how the peace is going to be achieved is very important. Right now, we're just plugging alone. And in our business model and our customer relationship, there is no detriment. There are some positive movements related to their retooling, especially in the manufacturing space because there are obviously more demand for manufacturing of certain type of products.

我的意思是，最終，和平與解決方案對所有人都有利。但如何達成和平非常重要。眼下，我們就是繼續往前推進。在我們的商業模式與客戶關係上，沒有任何不利影響。在某些方面，確實有一些正向動能，與他們的再工具化（retooling）有關，特別是在製造業領域，因為某些類型產品的製造需求顯然更高。

If we talk about our digital twin approach and about our physical AI approach, we're gaining momentum. But I would hate to say that it's really driven specifically by the individual event. But we definitely see the shift of manufacturing to the much higher retooling and scaling the production. And one of them is related to the traditional manufacturing. One of them is related to more semiconductor manufacturing.

如果我們談到數位孿生（digital twin）方法，以及我們的實體 AI（physical AI）方法，我們正在獲得動能。但我不想說這完全是由某個單一事件所特別驅動。不過我們確實看到製造業正轉向更高程度的再工具化，以及擴大產能（scaling the production）。其中一部分與傳統製造有關。另一部分則與更多的半導體製造有關。

Okay, I appreciate all that detail. But second qquestion here, just as it relates to kind of the AI productivity conversation, just coming out of a lot of the larger traditional SIs, the conversation around productivity, pricing compression for them became more pronounced here in recent weeks. I fully understanding you're not competing in many of the places that they are. But just how are the enterprise conversations for you in engagements that are not transitioning under the game framework as far as that type of a dynamic? Okay.

好的，我很感謝這些細節。但第二個問題是，關於 AI 生產力的討論：從許多大型傳統 SI（系統整合商）那邊聽到的說法是，最近幾週他們對生產力、以及價格被壓縮（pricing compression）的討論變得更明顯。我完全理解你們在很多他們所在的領域並不直接競爭。但對你們而言，在那些尚未轉換到 GAIN 框架下的合作案中，企業端的對話在這類動態上是怎麼發展的？好的。

So how the conversations are going in the framework -- so in this case, very often, we still enjoy significant productivity improvements from AI. I can give you some examples. So we just completed a project with one of the wealth management client of ours. And this is where we deployed AI agent across the CA pipelines in one of their large business units.

所以在這個框架下，對話進展如何——在這種情況下，我們仍然經常能從 AI 中獲得顯著的生產力提升。我可以舉一些例子。我們剛完成一個專案，客戶是我們的一家財富管理客戶。在那裡，我們在他們某個大型事業單位的 CA pipelines（持續自動化管線）中部署了 AI 代理（AI agent）。

So there, we saw 3 times to 6 times productivity improvements in the creation of the test coverage. And that allowed us to go wide in this customer and increase our stickiness and increase our reach to all business units of these customers going forward. That proved that we can do more with less resources and this differentiates us across other vendor base of this customer.

因此，我們在建立測試覆蓋率（test coverage）方面看到了 3 倍到 6 倍的生產力提升。這讓我們能在該客戶中橫向擴展（go wide），提升我們的黏著度，並在未來擴大觸及該客戶的所有事業單位。這證明我們可以用更少的資源做更多的事，並使我們在該客戶的其他供應商群中形成差異化。

Yeah, So let me add a couple of statements to what Eugene just said. So the question is really how is the pricing environment right now beyond the AI. So AI obviously has its own dynamics, and I will put that aside.

是的，讓我在 Eugene 剛才說的基礎上補充幾點。所以問題其實是：撇開 AI 之外，目前的定價環境如何。AI 當然有它自己的動態，我先把那部分放一邊。

When I look at the business, I look at a couple of very interesting things. One is that I do not see clients coming and asking that now that same engineer give me a big discount now. I'm not seeing that. Now we can argue whether I'm seeing a premium or more premium, that's second question. But we're not seeing any pricing pressures.

當我審視這門業務時，我會看幾個非常有意思的面向。第一是，我沒有看到客戶跑來要求：既然現在是同一位工程師，就要我給一個很大的折扣。我沒有看到這種情況。至於我看到的是溢價還是更高的溢價，那是第二個問題。但我們沒有看到任何定價壓力。

Number two is that in our case, tied to Leonard's opening comments, we've seen a lot of vendor consolidation over the last 18 months. Very interesting thing about vendor consolidation, it's good news and not so good news. The good news is that they go from hundreds to dozens. The bad news is that, okay, they say that you're one of the chosen one, give me a little bit of a discount for the next year or so, something like that, right? So we've gone through that.

第二點是，就我們而言，呼應 Leonard 開場的評論，我們在過去 18 個月看到大量的供應商整併。供應商整併很有意思：有好消息，也有不那麼好的消息。好消息是，他們從數百家縮減到數十家。壞消息是，他們會說：好，你是被選中的其中一家，那接下來一年左右給我一點折扣之類的，對吧？所以我們也經歷過這種情況。

So I would say maybe that would be the closest thing I could come to. But the team does a very good job when it comes to new customers, new logos. They're very particular. We have a very strong discipline in terms of ensuring that the margins come in. It's with our well-established customers. And there, we're seeing some of these trends. I don't know, Ran.

所以我會說，這可能是我能想到最接近的例子。但團隊在爭取新客戶、新標誌（new logos）方面做得非常好。他們非常挑剔。我們在確保毛利率達標方面有非常強的紀律。至於我們那些合作已久的客戶，在那裡我們確實看到一些這類趨勢。我不知道，Ran。

Yeah, go ahead. It's important. You have a very clear example now.

對，你說吧。這很重要。你現在有一個非常清楚的例子。

Yeah, no, I just want to add a couple of points there, Bryan. Number one, productivity improvement in the industry is still being shown at individual developer level. When you translate that into projects, especially brownfield projects where majority of our business is, where you are integrating into legacy systems, that productivity at a project level actually falls down to significantly lower numbers, right? So from that perspective, there is less pressure because you are executing projects and programs and not providing individual engineers.

對，我只是想補充幾點，Bryan。第一，產業的生產力提升目前仍主要體現在單一開發者層級。當你把它轉換到專案層級，特別是棕地（brownfield）專案——也就是我們大部分業務所在、需要整合既有系統（legacy systems）的情境——專案層級的生產力其實會下降到顯著更低的數字，對吧？所以從這個角度來看，壓力較小，因為你是在執行專案與計畫，而不是提供單一工程師。

At the same time, when we have examples of consistently showing productivity improvements, we are able to go back to our customers and grab more business. So it becomes expansion of a business strategy rather than play on the margin or the rate.

同時，當我們能持續展現生產力提升的案例時，我們就能回到客戶那裡拿到更多業務。因此這會變成一種業務擴張策略，而不是在毛利或費率上做文章。

I think let me just conclude. In a good environment people talk about their side cases and I kind of summarize from the global business positioning. So what I see, and this is quite promising because when I personally meet with the leaders or clients and usually, when you go to the top, the conversations on the overall spendings, and the priorities and budgets come quite clearly as a critical path, especially when those leaders coming from technology organizations, which depend to show concrete results to their business leaders. They are much more focused on productivity in terms of the overall return to the clients.

我想我做個總結。在好的環境下，人們會談各自的個案，而我會從全球業務定位的角度來做歸納。我所看到的是——這相當令人振奮——當我親自與領導者或客戶會面時，通常你一旦到高層，關於整體支出、優先事項與預算的對話就會非常清楚地成為關鍵路徑（critical path），尤其是那些來自科技組織的領導者，他們需要向業務端領導者交付具體成果。他們更聚焦於生產力，著眼於客戶整體回報（return）。

Remember, we talked about this in the past. So you agree with business people on ROI on a total budget versus outcome and then you go to the VMO, and VMO breaks it down by the rate per person. We are getting right now in a budget discussion overall projects, where the budgets are driven by the fixed bid by the deliverables. And that model, that productivity conversation usually goes on a deployment of the measurable results before somebody starts looking at productivity, because when are you going to ask productivity if it's a total budget being agreed between both sides.

記得我們過去談過這點。你會先和業務端就「總預算相對於成果」的 ROI 達成共識，然後你去到 VMO（供應商管理辦公室），VMO 會把它拆解成每人費率。我們現在在做的是整體專案的預算討論，預算是由固定價格（fixed bid）與交付物（deliverables）所驅動。在那種模式下，關於生產力的對話通常會先聚焦在可衡量成果的落地，之後才會有人開始看生產力；因為如果雙方已就總預算達成一致，你什麼時候會去問生產力呢？

So this environment a little bit better. But before when Surinder was talking about, he acknowledged, obviously, the question of the risk of the model. But that risk is not related directly to productivity anymore at those new adapted businesses.

所以這個環境稍微好一些。但先前 Surinder 提到時，他也承認，顯然存在這種模式的風險問題。不過在那些新採用的業務中，這個風險已不再直接與生產力相關。

Very good. Thank you for all that color. I've got one last one for Rahul here since he's on the call. Just Rahul beyond the major hyperscalers, as you think ahead, what other types of partner ecosystems are you focused on?

非常好。謝謝你提供這麼多背景說明。我這裡還有最後一個問題要問 Rahul，既然他在線上。Rahul，除了主要的超大規模雲端服務商（hyperscalers）之外，往前看，你還聚焦在哪些類型的夥伴生態系？

So I think there are going to be at least three categories. I already spoke about NVIDIA. I do expect that partnership to take off from here.

我認為至少會有三大類。我已經談過 NVIDIA。我確實預期那個合作夥伴關係會從現在開始起飛。

The second category would be specialized partners. I talked about on the AI consulting area. But I do expect as technology evolves, there are more specialized AI firms that we will start to partner with, potentially even the likes of your LLM providers, right, as their strategies evolve.

第二類會是專業化夥伴。我談過 AI 顧問諮詢領域。但我預期隨著技術演進，會出現更多專精的 AI 公司，我們會開始與其合作，甚至可能包括你們所說的 LLM 供應商之類的，對吧？隨著他們策略的演進。

The third category is what Leonard had talked about. We are starting to see interest from large consulting business consulting companies who are looking for technology partners to enable capabilities that they want their clients to have, right? And that's the third very interesting partnership area that I see us progressing with.

第三類是 Leonard 談到的。我們開始看到大型顧問／管理諮詢公司對我們有興趣，他們正在尋找技術合作夥伴，以提供他們希望客戶具備的能力，對吧？這是我看到我們正在推進的第三個非常有意思的合作領域。

This is immediate. This is we're developing right now.

這是立即的。我們現在正在開發。

This is we're developing right now, yes.

對，我們現在正在開發。

Very good. Thanks.

非常好。謝謝。

Thank you, Brian.

謝謝你，Brian。

Mayank Tandon, Needham.

Mayank Tandon，Needham。

I don't know if there's much to ask. But I'll go ahead anyway, give it a shot.

我不知道還有沒有太多可以問的。不過我還是試試看。

Mayank, we expect you to be the best questions.

Mayank，我們期待你問出最好的問題。

I'm sorry, I'm running out of questions here. But I guess just very quickly, just to keep the call on schedule. The question I had was around your visibility. I think you talked about that earlier, Anil.

抱歉，我這邊問題快問完了。不過我想很快問一下，為了讓電話會議按時進行。我想問的是關於你們的可見度（visibility）。我想你之前提過這點，Anil。

In terms of the revenue, how much of the business would you say is sold versus you have to still go out and win? So what is sort of potentially at risk versus what you already have in the bag in terms of your guidance?

就營收而言，你會說有多少業務是已經賣出去（sold）的，還是你們仍需要出去爭取（win）？也就是說，就你們的財測指引（guidance）而言，哪些可能有風險，哪些是你們已經「放進袋子裡」的？

Yeah, so you recall, Mayank, we have had a very traditional model or a well-established model about 85%, 10% and 5%, right, where 85% of our revenue in any given year comes from customers who have been with us 2 years and beyond, 10% comes from over the last 12 months and 5% comes from new. That framework more or less continues to be intact. There might be some variations, especially as we ramp some of these new customers. So the way -- I look at it through this lens.

對，Mayank，你還記得，我們一直有一個非常傳統或已相當成熟的模型：大約 85%、10% 和 5%，對吧？也就是說，任何一年我們 85% 的營收來自與我們合作滿 2 年以上的客戶，10% 來自過去 12 個月內的客戶，5% 來自新客戶。這個框架大致上仍維持不變。當然可能會有一些變動，尤其是當我們加速導入（ramp）某些新客戶時。所以我會用這個視角來看。

Now when you look at our whole guidance philosophy and when you look at our whole outlook philosophy, what we know well is potentially where we have some of these downside risks, right? I mean, we're dealing with these customers and these are big customers, and we have some sense of what we do.

當你看我們整體的指引哲學，以及我們整體的展望哲學時，我們很清楚的是：哪些地方可能存在一些下行風險（downside risks），對吧？我的意思是，我們正在與這些客戶合作，而且這些都是大客戶，我們對自己在做什麼有一定程度的掌握。

So when we give our guidance, for example, at least in the short term, we're taking that into account. When I switch from my short-term guidance to my long-term guidance, I basically switch from a bottoms up to a top down a little bit, right, where I look at the overall pipeline, I look at the forecast, I look at our customer engagements and come up with this.

因此當我們給出指引時，例如至少在短期內，我們會把這些因素納入考量。當我從短期指引切換到長期指引時，我基本上會從由下而上（bottoms up）轉為稍微由上而下（top down），對吧？我會看整體管線（pipeline）、看預測（forecast）、看我們的客戶互動情況，然後得出這個結論。

Now if you were to ask me whether I have a number that I believe is at risk, I mean, it's a whole probabilistic distribution, right, on how I look at it. I would say when I look at the business today versus three months ago versus four months ago, things are improving. So qualitatively, I would say that things are improving.

現在如果你問我，是否有一個我認為存在風險的數字，我的意思是，這其實是一整個機率分布，對吧，取決於我怎麼看。我會說，當我看今天的業務相較於三個月前、四個月前，情況是在改善的。所以從質性的角度來看，我會說情況正在好轉。

Now there's always that risk that we have with any one particular customer due to circumstances or as someone asked a question on the Iran war, there's a macro issue, consumer-sensitive industries are impacted. That's always there. But as we see right now, we feel good about where we see the overall business.

當然，對任何單一特定客戶，我們總是會有那樣的風險，可能因為各種情況；或者像有人問到伊朗戰爭的問題，宏觀層面的議題會影響到對消費者敏感的產業。這種風險一直都存在。但就我們目前所看到的，我們對整體業務的狀況感覺良好。

Right, So let me just give you, as always, direct pointers. After listening to Anil we need some guidance on his guidance. There are two areas which I think are very important to understand. Number one, the retail business, which traditionally was the most volatile has been derisked and continues to be derisking because it's a smaller contribution. It's not little, but it's small.

好的，那我就一如既往，給你一些直接的重點。聽完 Anil 之後，我們需要對他的指引再多一些指引。我認為有兩個領域非常重要，需要理解。第一，零售業務，傳統上波動最大，已經在去風險化，並且仍在持續去風險化，因為它的貢獻占比更小了。不是很小，但確實是小。

So that's area where the variance of uncertainty you are talking about. But the second risk is actually growing as we're going to grow the business is how the AI deployments will actually convert into the measurable profits and gain, not Grid Dynamics GAIN platform, but the client gain, right? And that business is growing very fast.

所以那就是你所談到的不確定性變動幅度所在的領域。但第二個風險其實正在增加，因為隨著我們要擴大業務，AI 部署究竟會如何轉化為可衡量的利潤與收益——不是 Grid Dynamics 的 GAIN 平台，而是客戶的收益，對吧？而且那塊業務成長非常快。

So we're very happy that we can actually forecast a better deployment of these projects. But again, when we talk about fixed bids, we're talking about outcome-based, we're talking about criterion, which before was not that clear, exactly it's how do you measure that ROI. So this criterion becomes a system of criteria, which is growing more and more of our business.

因此我們非常高興，我們其實能夠更好地預測這些專案的部署情況。但同樣地，當我們談固定報價時，我們談的是以成果為基礎，我們談的是衡量標準（criterion），而過去這點並不那麼清楚——也就是你要如何衡量那個投資報酬率（ROI）。所以這個衡量標準會變成一套標準體系，而這正逐漸成為我們業務中越來越大的一部分。

So I would say that the business we project is very certain that we're substantially derisking with retail. However, I see as we grow macro going forward, we need to make sure we bet on the right partners. And that's when actually the ecosystem of the partners also evolves. Remember, Bryan's question, who is going to be the next level of partners besides hyperscalers. And then Rahul mentioned two parts, of course, consulting is very clear gain. But then which of the other elements of the LLMs on other substantial guys who will provide us data centers, who provide us the material traffic of these deployments, the cost of these models is going to play a much bigger role.

所以我會說，我們所預期的業務是相當確定的：我們在零售方面正在大幅去風險化。然而，我看到隨著未來宏觀層面成長，我們需要確保押注在正確的合作夥伴上。而這也正是合作夥伴生態系會演進的時候。記得 Bryan 的問題嗎：除了超大規模雲端服務商（hyperscalers）之外，下一層級的合作夥伴會是誰。然後 Rahul 提到兩個部分，當然，顧問服務帶來的收益很明確。但接下來，在其他 LLM 的要素上，還有哪些重要玩家會提供資料中心、會提供這些部署所需的實質流量；模型成本將扮演更大的角色。

We are tuned to the system. We're selected to be preferred in many cases. We're confident. But the whole dynamics of AI deployed deliverable value, it's still something we have to prove on a major scale for everyone.

我們已經與這個系統保持同步。在許多情況下，我們被選為優先合作方。我們有信心。但 AI 部署所交付的價值這整套動態，仍然是我們必須在更大規模上為所有人證明的事情。

Got it. And then just to close out, Anil, you mentioned that M&A is still a priority for you. So just wanted to get some context in terms of what you might be looking for. And then, have private companies maybe sort of recognize that valuations have come down a lot and maybe are more inclined to sell versus resisting a potential sale to a company like Grid?

了解。那最後收尾一下，Anil，你提到併購（M&A）對你們仍然是優先事項。所以想請你提供一些背景脈絡，談談你們可能在尋找什麼。另外，私營公司是否可能已經某種程度上意識到估值大幅下修，因此更傾向出售，而不是抗拒把公司賣給像 Grid 這樣的公司？

Yeah, so as you rightly pointed out, yes, we're very focused, fingers crossed. We hope to close some deals -- and most of them are tuck-ins. What we're looking at right now are tuck-ins from a capability point of view. So obviously, technology has elevated to be very important, data, AI and certain end markets tied to our strategy.

是的，正如你所指出的，沒錯，我們非常專注，也希望一切順利。我們希望能完成一些交易——而且大多數都是補強型收購（tuck-ins）。我們目前看的，是從能力角度出發的補強型收購。所以很明顯，技術的重要性已被提升到非常高的程度，包括資料、AI，以及與我們策略相連結的特定終端市場。

So now when it comes to the valuation, you will always have to pay a premium for good companies. For good, capable companies, you will always have to pay some level of premium. But overall, you're right, they have come in. And things are looking better from a valuation point of view. But at the end of the day, if someone has some true differentiation, you do have to pay.

至於估值，你永遠都得為好公司支付溢價。對於優質且有能力的公司，你總是需要支付某種程度的溢價。但整體而言，你說得對，估值已經下來了。從估值角度看，情況正在變得更好。但歸根結底，如果有人具備真正的差異化，你確實就得付出代價。

The bottom line is, the accretiveness of these acquisitions have been the vital point, and we're very close to prove to the market we can still come back and do our M&As because, again, you're right, the appetite for them has been a little bit more modest, but it's not as critical as our broader net, which we threw around the world related to the two elements, really two elements:

重點是，這些收購是否具備增厚效益（accretiveness）一直是關鍵點；而我們也非常接近要向市場證明，我們仍然可以回來做併購，因為同樣地，你說得對，市場對併購的胃納確實比較保守一些，但這並不像我們更廣泛的網那麼關鍵——我們在全球撒下的那張網，主要圍繞兩個要素，確實是兩個要素：

AI-related technologies, especially the cutting-edge technologies, we can benefit more as a congruent business than the particular company on themselves. And the second part is looking for the partnership outside of the traditional path, which we're enhancing. So stay tuned. We're in good shape with that.

第一是 AI 相關技術，尤其是最前沿的技術；相較於那些公司單獨運作，我們作為一個協同一致的業務體，能從中獲得更大的效益。第二部分是尋找傳統路徑之外的合作夥伴關係，而我們正在強化這一點。敬請期待。我們在這方面進展良好。

Great. Thank you, guys. Appreciate it.

很好。謝謝各位。感謝。

Thank you.

謝謝。

Ladies and gentlemen, this concludes the Q&A portion of our call. I will now turn it over to Leonard for closing (technical difficulty).

各位女士、先生，問答環節到此結束。我現在把時間交給 Leonard 做結語（技術問題）。

Is proof that our AI transformation is working. Our revenue reached 29.3% of total revenue. GAIN has matured from a framework to platforms with Forward Deployed Engineers. Agentic AI solutions are now in production across a range of industry verticals and are generating measurable ROI at commercial scale.

這證明我們的 AI 轉型正在奏效。我們的 AI 相關營收已達總營收的 29.3%。GAIN 已從一個框架成熟為具備前線部署工程師（Forward Deployed Engineers）的平台。Agentic AI 解決方案目前已在多個產業垂直領域投入生產，並在商業規模上產生可衡量的投資報酬率（ROI）。

The pipeline entering Q2 is the strongest it has ever been. AI consulting and hyperscale partnerships are expanding. We're executing on our strategic road map, including AI-native delivery, productized GAIN platforms, consulting and internal automation.

進入第二季（Q2）的案源管線是我們歷來最強的一次。AI 顧問服務與超大規模合作夥伴關係正在擴張。我們正按既定策略路線圖執行，包括 AI 原生交付、產品化的 GAIN 平台、顧問服務以及內部自動化。

We look forward to updating you next quarter. Thank you.

我們期待在下個季度向各位更新。謝謝。