UiPath Inc (PATH) 2027 Q1 法說會逐字稿

Good day, everyone. My name is Megan, and I will be your conference operator today. At this time, I would like to welcome you to the UiPath first-quarter 2027 earnings conference call. (Operator Instructions)

大家好，祝各位有個美好的一天。我叫 Megan，今天將擔任本次電話會議的接線員。此刻，我謹代表主辦方歡迎各位參加 UiPath 2027 會計年度第一季財報電話會議。（接線員指示）

At this time, I would like to turn the call over to Allise Furlani, Vice President of Investor Relations.

此刻，我想將電話會議交給投資人關係副總裁 Allise Furlani。

Good afternoon, and thank you for joining us today to review UiPath's first-quarter fiscal 2027 financial results, which we announced in our earnings press release issued after the close of the market today.

各位下午好，感謝各位今天與我們一同回顧 UiPath 2027 會計年度第一季財務結果；我們已於今日收盤後發布的財報新聞稿中對外公告。

On the call with me are Daniel Dines, Founder and Chief Executive Officer; and Ashim Gupta, Chief Operating and Financial Officer, to deliver our prepared comments and answer questions.

與我一同出席本次電話會議的有：創辦人暨執行長 Daniel Dines，以及營運長暨財務長 Ashim Gupta；我們將發表事先準備的談話並回答提問。

Our earnings press release and financial supplemental materials are posted on the UiPath Investor Relations website. These materials include GAAP to non-GAAP reconciliations. We will be discussing non-GAAP metrics on today's call.

我們的財報新聞稿與財務補充資料已發布於 UiPath 投資人關係網站。這些資料包含 GAAP 與非 GAAP 的調節表。我們將在今天的電話會議中討論非 GAAP 指標。

This afternoon's call includes forward-looking statements regarding our financial guidance for the second-quarter and full-year fiscal 2027 and our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings, and market opportunity. Actual results may differ materially from those expressed in the forward-looking statements due to many factors, and therefore, investors should not place undue reliance on these statements.

今天下午的電話會議包含前瞻性陳述，內容涉及我們對 2027 會計年度第二季與全年之財務指引，以及我們推動並加速未來成長與營運效率、並擴展我們的平台、產品供給與市場機會的能力。由於多項因素，實際結果可能與前瞻性陳述所表達者存在重大差異，因此投資人不應過度依賴這些陳述。

For a discussion of the material risks and uncertainties that could affect our actual results, please refer to our annual report on Form 10-K for the year ended January 31, 2026, and our subsequent reports filed with the SEC.

關於可能影響我們實際結果之重大風險與不確定性的討論，請參閱我們截至 2026 年 1 月 31 日止年度的 Form 10-K 年報，以及其後向美國證券交易委員會（SEC）提交的報告。

Forward-looking statements made on this call reflect our views as of today. We undertake no obligation to update them. I would like to highlight that this webcast is being accompanied by slides. We will post the slides and a copy of our prepared remarks to our Investor Relations website immediately following the conclusion of this call. In addition, please note that all comparisons are year over year, unless otherwise indicated.

本次電話會議中的前瞻性陳述反映我們截至今日的觀點。我們不承擔更新這些陳述的任何義務。我想特別說明，本次網路直播將搭配投影片。我們將在本次電話會議結束後，立即於投資人關係網站發布投影片與事先準備的講稿副本。此外，除非另有說明，所有比較皆為年對年（YoY）。

Now, I would like to hand the call over to Daniel.

現在，我想把電話會議交給 Daniel。

Thank you, Allise. Good afternoon, everyone. Thanks for joining us. We deliver a strong start to fiscal 2027, once again exceeding our guidance across all key financial metrics.

謝謝你，Allise。各位下午好。感謝各位加入我們。我們在 2027 會計年度取得強勁開局，再次在所有關鍵財務指標上超越我們的指引。

Before I dive into the results, I want to take a moment to reflect on our progress over the last year. In May of last year, we launched our agentic and business process orchestration products into general availability. One year in, adoption has moved from early experimentation to production deployment. We are seeing this play out across three areas in particular: install base expansion, process orchestration adoption, and vertical AI workflows.

在我深入說明本季結果之前，我想先花點時間回顧我們過去一年的進展。去年 5 月，我們將代理式（agentic）與業務流程協同編排（business process orchestration）產品正式推向全面可用（GA）。一年之後，採用情況已從早期試驗轉向正式上線部署。我們特別看到這一點在三個面向展現：既有裝機客戶基礎擴張、流程協同編排採用提升，以及垂直領域 AI 工作流程。

A great example is one of the largest healthcare distribution companies in the US. One end-to-end workflow combining UiPath agents and deterministic automations is expected to drive multimillion-dollar annual savings, which led to a seven-figure expansion in the quarter.

一個很好的例子是美國最大的醫療保健分銷公司之一。一個端到端工作流程結合 UiPath 代理與確定性自動化（deterministic automations），預期可帶來每年數百萬美元的節省，並在本季促成七位數美元規模的擴張。

One of the world's largest construction companies adopted our purchase-to-pay vertical solution and told us they chose UiPath not as a software vendor, but as a strategic co-development partner for their enterprise AI transformation.

全球最大的建築公司之一採用了我們的採購到付款（purchase-to-pay）垂直解決方案，並告訴我們，他們選擇 UiPath 並非把我們視為軟體供應商，而是作為其企業 AI 轉型的策略性共同開發夥伴。

And the Fortune 500 energy company placed UiPath at the center of a $70 million cost-reduction initiative made possible by our ability to bring deterministic agentic and process orchestration together as a single platform.

此外，一家《財富》500 強能源公司將 UiPath 置於一項 7,000 萬美元的成本削減計畫核心；該計畫得以實現，源於我們能將確定性代理式能力與流程協同編排整合為單一平台。

Turning to the quarter, first-quarter ARR reached $1.901 billion, up 12% year over year, driven by $49 million of net new ARR and revenue of $418 million, up 17% year over year. We grew first-quarter non-GAAP operating income to $92 million, a 22% margin, driven by improved operational efficiency and disciplined execution across the business, and we delivered first-quarter gap profitability for the first time in company history. This quarter's performance is built on the strength of our enterprise automation install base. Thousands of customers with deep platform adoption, proven ROI, and a track record of expanding with us over time.

回到本季，第一季 ARR 達到 19.01 億美元，年增 12%，主要由 4,900 萬美元的 ARR 淨新增所帶動；營收為 4.18 億美元，年增 17%。我們第一季非 GAAP 營業利益成長至 9,200 萬美元，營業利益率為 22%，受惠於營運效率提升與全公司嚴謹的執行力；同時，我們也在公司歷史上首次於第一季實現 GAAP 獲利。本季表現建立在我們企業自動化裝機客戶基礎的強勁之上。我們擁有數千家客戶深度採用平台、投資報酬率（ROI）已獲驗證，並且長期持續擴大與我們的合作。

And it reflects continued momentum with our AI products. In the quarter,16 out of top 20 deals included AI and expansion deals that included AI were six times larger than those that did not. The drivers behind these results are the same core differentiators we outlined last quarter, our platform that brings together deterministic and agentic automation with enterprise-grade process orchestration. Our install-based flywheel, our governance foundation, and our ability to combine a horizontal automation platform with deep vertical solutions.

這也反映出我們 AI 產品的持續動能。本季前 20 大交易中有 16 筆包含 AI；且包含 AI 的擴張交易規模是未包含 AI 者的六倍。推動這些成果的因素，與我們上季所概述的核心差異化相同：我們的平台將確定性與代理式自動化結合，並搭配企業級流程協同編排。我們以裝機客戶基礎驅動的飛輪效應、我們的治理基礎，以及我們將水平式自動化平台與深度垂直解決方案相結合的能力。

I saw that momentum firsthand across our global events, including in India at our annual Fusion event and DevCon developer conference. Across customers, developers, and partners, the message was consistent: enterprises increasingly need a platform that can govern and orchestrate humans, agents, workflows, automations, and systems, an area where UiPath has a structural advantage.

我在我們的全球活動中親眼見證了這股動能，包括在印度舉辦的年度 Fusion 活動與 DevCon 開發者大會。在客戶、開發者與合作夥伴之間，訊息一致：企業日益需要一個能治理並協同編排人員、代理、工作流程、自動化與系統的平台，而這正是 UiPath 具備結構性優勢的領域。

At DevCon, we launched UiPath for coding agents, enabling developers to connect their coding agent of choice to create, test, deploy, and manage automations across the full life cycle on the UiPath platform with enterprise-grade governance and reliability built in.

在 DevCon 上，我們推出了 UiPath for coding agents，讓開發者能將其選擇的程式碼代理連接至 UiPath 平台，在內建企業級治理與可靠性的前提下，於完整生命週期中建立、測試、部署並管理自動化。

This matters because nearly every customer conversation surfaces the same constraint, an automation backlog that outpaces their capacity to build and maintain. Implementation is often the hardest part, particularly in complex enterprise environments where upstream system changes can drive maintenance costs over time.

這很重要，因為幾乎每一次與客戶的對話都會浮現同一個限制：自動化待辦清單（backlog）的增長速度超過其建置與維護的能力。導入往往是最困難的部分，尤其在複雜的企業環境中，上游系統變更會隨時間推升維護成本。

By combining coding agents with the governance, orchestration, and self-healing capabilities built into our platform, we can dramatically reduce that operational burden and compress deployment timeless from quarters to weeks. We expect this to accelerate time to value for our customers, drive deeper adoption, and strengthen long-term retention across our customer base.

透過將程式碼代理與我們平台內建的治理、協同編排與自我修復能力結合，我們可以大幅降低營運負擔，並將部署時程從以季為單位壓縮到以週為單位。我們預期這將加速客戶的價值實現時間（time to value）、推動更深度的採用，並強化整體客戶基礎的長期留存。

Our internal teams and customers are also seeing great results with coding agents, including one of the world's largest consumer electronics companies, which reduced a four-week project built to three hours, and one of the world's largest chip manufacturers reduced a two-month project built to a few days. What stood out most this quarter is how clearly customer priorities have evolved with the focus consistently centered on process orchestration.

我們的內部團隊與客戶也在程式碼代理上看到很好的成果；例如，全球最大的消費性電子公司之一，將原本需要四週完成的專案縮短到三小時；而全球最大的晶片製造商之一，則將原本需要兩個月完成的專案縮短到幾天。本季最突出的，是客戶優先事項的演進非常清晰，焦點持續集中在流程協同編排上。

As one customer put it during DevCon, models are easy, orchestration is not. That directly reflects what we hear across our customer base. Customers are no longer asking us simply to deploy more agents or generate more code. They are asking us to transform how entire business functions operate through end-to-end workflows that span departments, connect systems, and deliver measurable operational outcomes. And delivering that kind of transformation requires more than individual AI agents. It requires a platform that can orchestrate agents, automations, API, systems, and people together within secure governed enterprise workflows.

正如一位客戶在 DevCon 期間所說：模型很容易，協同編排不容易。這也直接反映了我們在整體客戶群中聽到的聲音。客戶不再只是要求我們部署更多代理或產生更多程式碼。他們要求我們透過跨部門、連接系統、並交付可衡量營運成果的端到端工作流程，來改造整個業務職能的運作方式。而要交付這種層級的轉型，僅靠單一 AI 代理是不夠的。它需要一個平台，能在安全且受治理的企業工作流程中，將代理、自動化、API、系統與人員一同協同編排。

A great example is one of the world's largest telecommunications company. With nearly 2,000 processes already automated and over $30 million in annual cost savings, they are now expanding their deterministic base further and moving into agentic workflows, building a pipeline of more than 200 additional deterministic automations and over 20 agentic use cases.

一個很好的例子是全球最大的電信公司之一。在已自動化近 2,000 個流程並每年節省超過 3,000 萬美元成本之後，他們正進一步擴大其確定性基礎，並邁向代理式工作流程，建立超過 200 個額外確定性自動化與 20 多個代理式使用案例的管線。

That same process orchestration capability also drove a competitive displacement with the Fortune Global 500 electronics manufacturer where we work, the only platform that could take them from task-based automation to enterprise-wide business process orchestration.

同樣的流程協調能力，也促成了我們在所服務的一家《財富》全球500大電子製造商客戶中的競爭性替換；我們是唯一能將其從以任務為主的自動化，提升到企業級端到端業務流程協調的平台。

Building on a strong deterministic foundation, they are now expanding across manufacturing and supply chain workflows using Maestro to coordinate automations, agents, systems, and human decisioning globally.

在堅實的確定性基礎之上，他們目前正使用 Maestro 在製造與供應鏈工作流程中擴大導入，於全球範圍協調自動化、代理（agents）、系統以及人工決策。

Maestro already excels at structured workflows like invoice approvals and deployment pipelines, where the process itself is clearly defined. But increasingly, enterprise work is non-linear, and it's dynamic, exception-driven, and centered around decisions that move across teams and systems. This is why at DevCon, we launch Maestro case into public preview, extending Maestro beyond traditional process orchestration into the orchestration of unstructured enterprise work.

Maestro 已經在發票核准與部署管線等結構化工作流程上表現出色，因為流程本身定義清楚。但企業工作正日益呈現非線性特徵，具備動態性、由例外情況驅動，並以跨團隊與跨系統流轉的決策為核心。因此在 DevCon，我們將 Maestro Case 推出公開預覽，將 Maestro 從傳統的流程協調延伸到非結構化企業工作的協調。

The breadth is what makes UiPath the most complete process orchestration and automation platform in the market and it's already driving broader customer adoption, including Sonic Automotive, an early adopter of our agentic products. They initially deployed UiPath to automate vehicle stocking and sales lead follow-up.

這樣的廣度使 UiPath 成為市場上最完整的流程協調與自動化平台，並已推動更廣泛的客戶採用，其中包括我們代理式（agentic）產品的早期採用者 Sonic Automotive。他們最初部署 UiPath 以自動化車輛入庫配置與銷售線索跟進。

They are now standardizing their genetic automation strategy on the UiPath platform under a broader C-suite initiative and expanding into workflows such mouth and close and employee onboarding. The key driver of the expansion was Maestro Case's ability to orchestrate complex multi-stage workflows across agents, automations, and people.

目前他們在更廣泛的高階主管（C-suite）倡議下，正將其代理式自動化策略標準化於 UiPath 平台上，並擴展到如月結與關帳以及員工入職等工作流程。此次擴展的關鍵驅動因素，是 Maestro Case 能夠在代理、自動化與人員之間協調複雜的多階段工作流程。

Beyond process orchestration, documents remain one of the biggest sources of friction in enterprise work. And customers are increasingly turning to UiPath IXP to automate document-intensive workflows at enterprise scale.

除了流程協調之外，文件仍是企業工作中最大的摩擦來源之一。客戶也日益轉向 UiPath IXP，以在企業規模下自動化文件密集型工作流程。

In May, we were named a leader in The Forrester Wave: Document Mining and Analytics Platforms Q2 2026. We're seeing that momentum translate directly into largest enterprise deployments and competitive wins.

在 5 月，我們在《Forrester Wave：文件探勘與分析平台（Document Mining and Analytics Platforms）2026 年第二季》中被評為領導者。我們看到這股動能正直接轉化為最大型的企業部署與競爭性勝出。

A great example is the leading medical technology company that is standardizing on UiPath IXP to automate high-volume unstructured documents like invoices and purchase orders. The customer is already realizing approximately $5 million in annual savings and expects that to grow to $10 million as they scale.

一個很好的例子是某家領先的醫療科技公司，正以 UiPath IXP 作為標準平台，自動化發票與採購單等高量的非結構化文件。該客戶目前已實現約 500 萬美元的年度節省，並預期隨著規模擴大可成長至 1,000 萬美元。

Demand for industry-specific governed workflows continues to grow as enterprises increasingly adopt purpose-built AI solutions tailored to their business. What differentiates UiPath is our ability to combine those deep domain-specific solutions with the same process orchestration, automations, and governance platform. This quarter, we expanded our portfolio across financial services, retail and manufacturing, and the office of the CFO.

隨著企業日益採用為其業務量身打造的專用 AI 解決方案，對具備治理的產業特定工作流程需求持續成長。UiPath 的差異化在於，我們能將這些深度的領域專屬解決方案，與同一套流程協調、自動化與治理平台相結合。本季度，我們在金融服務、零售與製造，以及 CFO 辦公室等領域擴充了產品組合。

We are already seeing momentum in healthcare in a seven-figure new logo win. A leading Latin American healthcare provider selected our vertical solutions to support revenue cycle management, medical record summarization and claim denial management and expect $12 million in cumulative benefits.

我們已在醫療保健領域看到動能，並取得一筆七位數的新客戶（new logo）勝出。一家領先的拉丁美洲醫療保健服務提供者選擇了我們的垂直解決方案，以支援營收循環管理、病歷摘要以及理賠拒付管理，並預期可帶來累計 1,200 萬美元的效益。

Customers are also realizing meaningful operational benefits from these vertical solutions. A leading healthcare technology company reduced clinical summary review times by 90% using our medical record summarization solution.

客戶也正從這些垂直解決方案中獲得顯著的營運效益。一家領先的醫療保健科技公司使用我們的病歷摘要解決方案，將臨床摘要審閱時間縮短了 90%。

We are seeing similar momentum in financial services. Original bank is now automating 61% of sanctions hit reviews with our transaction screening alert review solution, processing roughly 14,000 alerts per month.

我們在金融服務領域也看到類似動能。Original Bank 目前透過我們的交易篩檢警示審閱解決方案，已將 61% 的制裁命中（sanctions hit）審查流程自動化，每月處理約 14,000 則警示。

AI is accelerating software creation, but is also accelerating the need to validate it. As code volume grows, so does the testing board. Independent research firms have consistently recognized UiPath as a leader in this space, and we believe that validation reflects a real and growing structural advantage.

AI 正在加速軟體開發，但也同時加速了對其進行驗證的需求。隨著程式碼量增加，測試負擔也隨之上升。獨立研究機構一再認可 UiPath 在此領域的領導地位，我們相信這項驗證反映了真實且持續擴大的結構性優勢。

Test Cloud is at the center of that, helping customers move testing from a downstream bottleneck to a continuous intelligent function embedded across the delivery lifecycle.

Test Cloud 是其中的核心，協助客戶將測試從下游瓶頸，轉變為嵌入交付生命週期各階段的持續、智慧化功能。

One example this quarter is a leading US utility provider that adopted UiPath Test Cloud for agentic testing to streamline customer platform support launch. The solution is expected to significantly reduce manual testing while generating nearly $3 million in savings.

本季度的一個例子是，美國一家領先的公用事業供應商採用 UiPath Test Cloud 進行代理式測試，以簡化其客戶平台支援的上線。預期該解決方案將大幅降低人工測試，並帶來近 300 萬美元的節省。

During the quarter, we continue to deepen our partnerships across both go-to-market and technical integrations. This included our expanded collaborations with Deloitte, embedding UiPath Test Cloud into their extended delivery platform, bringing agentic testing capabilities to Deloitte's global client base.

本季度，我們持續在市場推進（go-to-market）與技術整合兩方面深化合作夥伴關係。其中包括與 Deloitte 擴大合作，將 UiPath Test Cloud 嵌入其延伸交付平台，為 Deloitte 的全球客戶群帶來代理式測試能力。

We are seeing similar momentum with Accenture. A life sciences customer we highlighted last quarter worked with Accenture to deploy a global agentic sales entry solution and has now scaled this across 70 countries. Building on that success, they signed a seven-figure expansion and are now partnering with us to design an office of the CIO intake solution built on our process orchestration platform.

我們也在 Accenture 方面看到類似動能。我們在上季度提到的一家生命科學客戶與 Accenture 合作部署全球代理式銷售輸入解決方案，現已擴展至 70 個國家。在此成功基礎上，他們簽署了一筆七位數的擴充合約，並正與我們合作設計一套建立在我們流程協調平台之上的 CIO 辦公室需求受理（intake）解決方案。

On the technical side, we continue to broaden our reach across key enterprise ecosystems. With Microsoft, we integrated UiPath with their security test suite to help automate threat detection and response. With Salesforce, we launched a new agent exchange offering that extends Maestro process orchestration across Salesforce and back office systems.

在技術層面，我們持續擴大在關鍵企業生態系中的覆蓋範圍。與 Microsoft 合作方面，我們將 UiPath 與其安全測試套件整合，以協助自動化威脅偵測與回應。與 Salesforce 合作方面，我們推出新的 agent exchange 產品，將 Maestro 的流程協調延伸至 Salesforce 與後台系統。

With Google Cloud. We brought our IXP solution to their marketplace. And with Databricks, we connected their data intelligence platform directly with UiPath process orchestration to help enterprises move from data insights to automated action within governed workflows.

與 Google Cloud 合作方面。我們將 IXP 解決方案上架至其 Marketplace。並且與 Databricks 合作，我們將其資料智慧平台直接連接至 UiPath 流程協調，協助企業在具治理的工作流程中，將資料洞察轉化為自動化行動。

In summary, this quarter reflected discipline execution across the business, continued AI adoption, and growing momentum across our platform. No other vendor can bring together deterministic automation, agentic AI, document intelligence, and business process orchestration on a single platform. And that completeness is what customers are standardizing on. We believe we are uniquely positioned for this next phase of enterprise AI adoption and our strong start to fiscal 2027 reinforces both the durability of our business and the scale of the opportunity ahead.

總結而言，本季度展現了我們在全公司範圍內的紀律性執行、持續的 AI 採用，以及平台動能的增長。沒有任何其他供應商能在單一平台上同時整合確定性自動化、代理式 AI、文件智慧與業務流程協調。而這種完整性正是客戶用來作為標準化平台的原因。我們相信自己在企業 AI 採用的下一階段具備獨特優勢，而 2027 財年的強勁開局也再次印證了我們業務的韌性與前方機會的規模。

Before I turn it over to Ashim, I want to take a moment to acknowledge the loss of our dear friend and Board member, Soma Somasegar.

在我把電話交給 Ashim 之前，我想花點時間悼念我們摯友與董事會成員 Soma Somasegar 的離世。

Soma was a longtime investor in UiPath and rejoined our Board just eight months ago. His impact on UiPath was immediate and profound. He was a mentor, trusted advisor, and someone I deeply admire both professionally and personally.

Soma 長期投資 UiPath，並在八個月前重新加入我們的董事會。他對 UiPath 的影響立刻且深遠。他是導師、值得信賴的顧問，也是我在職業與個人層面都深深敬重的人。

I will miss him greatly, and I know our entire Board and leadership team share that feeling. Our hearts are with his family.

我會非常想念他，我也知道我們整個董事會與領導團隊都有同樣的感受。我們的心與他的家人同在。

With that, I'll turn the call over to Ashim.

接下來，我把電話交給 Ashim。

Thank you, Daniel, and good afternoon, everyone.

謝謝你，Daniel，各位下午好。

Before turning to the financials, I'd like to provide a quick operational update. We continue to make meaningful progress across the key priorities we outlined last year. Our partner ecosystem is becoming more deeply integrated with both our go-to-market motion and customer adoption efforts, helping us scale larger enterprise deployments across industries.

在進入財務數字之前，我想先提供一個簡短的營運更新。我們在去年所提出的關鍵優先事項上持續取得實質進展。我們的合作夥伴生態系正與我們的市場推進模式與客戶採用工作更深度整合，協助我們在各產業擴大更大型的企業部署。

As Daniel mentioned, partners like Deloitte and Accenture are increasingly instrumental not just in selling but in helping customers operationalize and scale AI-driven workflows. And we are seeing that play out across financial services, healthcare, and other key verticals.

如 Daniel 所提到，Deloitte 與 Accenture 等合作夥伴正日益扮演關鍵角色，不僅在銷售上，也在協助客戶將 AI 驅動的工作流程落地營運並擴大規模。我們也看到這一點正在金融服務、醫療保健及其他關鍵垂直領域中逐步展開。

At the same time, our internal focus on customer adoption remains a central operating priority. We continue to invest in our services organization and industry expertise to help customers accelerate deployment and expand platform usage.

同時，我們內部對客戶採用的聚焦仍是核心營運優先事項。我們持續投資於服務組織與產業專業能力，以協助客戶加速部署並擴大平台使用。

A key part of that effort is our forward deployed engineering program, which we launched six months ago. FDEs are proving to be an effective bridge between product innovation and customer deployment, shaping vertical workflows directly in customer environments and accelerating time to value.

該項努力的一個關鍵部分是我們的前線部署工程（Forward Deployed Engineering，FDE）計畫，我們在六個月前啟動。FDE 正證明是連結產品創新與客戶部署的有效橋樑，能在客戶環境中直接塑造垂直領域工作流程，並加速實現價值的時間。

In addition to adoption, our go-to-market teams are executing with discipline and customer centricity. AI is now part of virtually every strategic customer conversation, and those discussions are increasingly expanded into platform, orchestration, and vertical solutions. The deal data Daniel mentioned reflects that. AI was included in 16 of our top 20 deals, and expansion deals that include AI were six times larger than those that did not.

除了採用之外，我們的上市（go-to-market）團隊也以紀律與以客戶為中心的方式執行。AI 現在幾乎已成為每一場策略性客戶對話的一部分，而這些討論也日益延伸到平台、編排（orchestration）與垂直解決方案。Daniel 提到的交易數據也反映了這一點。在我們前 20 大交易中，有 16 筆包含 AI，而包含 AI 的擴張交易規模是未包含者的六倍。

Finally, on operational efficiency, AI is changing how we run the business internally. We are seeing increased operating leverage across the organization while continuing to invest deliberately in R&D, vertical solutions, and customer-facing functions.

最後，在營運效率方面，AI 正在改變我們內部經營業務的方式。我們在持續有計畫地投資研發、垂直解決方案與面向客戶的職能之同時，也看到整個組織的營運槓桿提升。

Turning to the quarter, unless otherwise indicated, I will be discussing results on a non-GAAP basis and all growth rates are year over year. I also want to note that since we price and sell in local currency, fluctuations in FX rates impact results.

接著談本季表現，除非另有說明，我將以非 GAAP 基礎討論結果，且所有成長率皆為年對年。我也要指出，由於我們以當地貨幣定價與銷售，外匯匯率波動會影響結果。

Since the time of our last earnings call through the end of the first quarter, rates remained largely stable and resulted in an incremental tailwind to our first-quarter ARR and revenue results of less than $1 million.

自我們上次財報電話會議至第一季末，匯率大致維持穩定，對第一季 ARR 與營收結果帶來不到 100 萬美元的額外順風。

First-quarter revenue grew to $418 million, an increase of 17%. Normalizing for the year-over-year FX tailwind of approximately $7 million, revenue grew 15%. ARR totaled $1.901 billion, an increase of 11%. This included a $9 million year-over-year FX tailwind.

第一季營收成長至 4.18 億美元，增加 17%。若將約 700 萬美元的年對年外匯順風影響正常化，營收成長為 15%。ARR 總計 19.01 億美元，增加 11%。其中包含 900 萬美元的年對年外匯順風。

Net new ARR was $49 million. Normalized for foreign exchange and the impact of M&A, net new ARR improved on a year-over-year basis.

淨新增 ARR 為 4,900 萬美元。若就外匯與併購（M&A）影響進行正常化，淨新增 ARR 較去年同期有所改善。

Our dollar-based gross retention rates remain best-in-class at 97% and our dollar-based net retention rate was 109%, underscoring the durability of our customer base as they embrace our agentic automation solutions. Adjusting for FX, dollar-based net retention rate was 108%, demonstrating stabilization across our business. We ended the quarter with approximately 10,550 customers.

我們以美元計價的總留存率（gross retention rate）仍維持同業最佳的 97%，以美元計價的淨留存率（net retention rate）為 109%，凸顯客戶基礎的韌性，因為他們正採用我們的代理式（agentic）自動化解決方案。若調整外匯影響，以美元計價的淨留存率為 108%，顯示我們的業務正趨於穩定。本季末我們約有 10,550 名客戶。

Attrition continues to be concentrated amongst our smallest customers, while customers generating more than $30,000 in ARR grew 7% year over year. That dynamic is also reflected in our cohort performance. Customers with $100,000 or more in ARR increased 11% to 2,624 and customers with $1 million or more in ARR increased 18% to 374.

流失仍主要集中在我們最小型的客戶群，而 ARR 超過 30,000 美元的客戶數年對年成長 7%。這一動態也反映在我們的同批客戶（cohort）表現上。ARR 達 100,000 美元以上的客戶增加 11% 至 2,624 家，而 ARR 達 100 萬美元以上的客戶增加 18% 至 374 家。

Our customer strategy has continued to focus on deepening our presence within the world's most complex enterprises, where we see the greatest opportunity for long-term expansion.

我們的客戶策略持續聚焦於深化我們在全球最複雜企業中的布局，因為我們在那裡看到長期擴張的最大機會。

Consistent with that strategy, we continue to add new enterprise customers with significant long-term expansion potential, including new logos like Candela Medical, Tire Rack, Shoprite Holdings, and a global semiconductor company who is replacing a legacy RPA vendor with UiPath as their strategic automation buff.

與該策略一致，我們持續新增具備顯著長期擴張潛力的新企業客戶，包括 Candela Medical、Tire Rack、Shoprite Holdings 等新客戶（new logos），以及一家全球半導體公司，正以 UiPath 取代其既有的傳統 RPA 供應商，將 UiPath 作為其策略性自動化平台。

Our cross-system integration and end-to-end process orchestration capabilities give them a scalable foundation they need to migrate their existing automation program beyond task-based automation into broader agentic workflows. Remaining performance obligations increased to $1.413 billion, up 15%. Normalizing for the FX headwind, which was approximately $9 million, RPO grew 16%. Current RPO increased to $908 million, up 17%.

我們的跨系統整合與端到端流程編排能力，為他們提供可擴展的基礎，使其能將既有自動化計畫從以任務為主的自動化，遷移到更廣泛的代理式工作流程。剩餘履約義務（RPO）增加至 14.13 億美元，成長 15%。若將約 900 萬美元的外匯逆風影響正常化，RPO 成長 16%。當期 RPO 增加至 9.08 億美元，成長 17%。

Turning to expenses, we delivered first-quarter overall gross margin of 83% and software gross margin was 90%. First-quarter operating expenses were $256 million. For the first time in company history, we delivered a GAAP profitable first quarter with GAAP operating income of $28 million up from the prior-year GAAP operating loss of $16 million. GAAP operating income included $53 million of stock-based compensation expense.

接著談費用，我們第一季整體毛利率為 83%，軟體毛利率為 90%。第一季營業費用為 2.56 億美元。公司史上首次，我們第一季在 GAAP 基礎下實現獲利，GAAP 營業利益為 2,800 萬美元，較去年同期 GAAP 營業損失 1,600 萬美元改善。GAAP 營業利益包含 5,300 萬美元的股份基礎給付費用。

First quarter non-GAAP operating income was $92 million, representing a 22% margin, up over 250 basis points year over year and driven by our continued focus on operational efficiency. First-quarter non-GAAP adjusted free cash flow was $130 million.

第一季非 GAAP 營業利益為 9,200 萬美元，營業利益率為 22%，年對年提升超過 250 個基點，主要受惠於我們持續聚焦營運效率。第一季非 GAAP 調整後自由現金流為 1.30 億美元。

We ended the quarter with a healthy balance sheet of $1.4 billion in cash equivalents and marketable securities and no debt.

本季末我們的資產負債表維持健康，現金約當物與有價證券合計 14 億美元，且無負債。

During the first quarter, we repurchased 20 million shares at an average price of $11.47. Since April 30, our 10b5-1 plan, we have repurchased an additional 2 million shares at an average price of $9.63 through May 27, 2026.

第一季期間，我們以平均價格 11.47 美元回購 2,000 萬股。自 4 月 30 日起，依據我們的 10b5-1 計畫，截至 2026 年 5 月 27 日，我們又以平均價格 9.63 美元額外回購 200 萬股。

Now turning to guidance. We are pleased with the team's execution and what continues to be a variable macroeconomic environment. We continue to maintain a prudent outlook and guide to what we see in front of us.

接著談財測指引。我們對團隊的執行表現感到滿意，儘管宏觀經濟環境仍具變動性。我們仍維持審慎展望，並依據我們眼前所見提供指引。

Since we provided guidance on our last call, the euro has remained largely stable while other currencies such as INR and Romanian Lei have experienced volatility. As a result, for the second quarter and full year, we expect a nominal incremental FX headwind to ARR and revenue. Despite the incremental FX headwind, we are raising guidance for the progress we've made on our operating priorities.

自上次電話會議提供指引以來，歐元大致維持穩定，而其他貨幣如印度盧比（INR）與羅馬尼亞列伊（Romanian Lei）則出現波動。因此，對第二季與全年而言，我們預期外匯將對 ARR 與營收帶來名目上的小幅額外逆風。儘管有額外外匯逆風，我們仍因在營運優先事項上的進展而上調指引。

Turning to the specifics of our guide. For the second fiscal quarter 2027, we expect revenue in the range of $395 million to $400 million, ARR in the range of $1.929 billion to $1.934 billion, non-GAAP operating income of approximately $75 million, and we expect second-quarter basic share count to be approximately 518 million shares.

接著說明指引細節。2027 會計年度第二季，我們預期營收介於 3.95 億至 4.00 億美元，ARR 介於 19.29 億至 19.34 億美元，非 GAAP 營業利益約 7,500 萬美元，且預期第二季基本股數約為 5.18 億股。

For the fiscal full year 2027, we expect. Revenue in the range of $1.776 billion to $1.781 billion, ARR in the range of $2.058 billion to $2.063 billion, non-GAAP operating income of approximately $430 million, and finally, we continue to expect fiscal year 2027 non-GAAP adjusted free cash flow of approximately $425 million and non-GAAP gross margin of approximately 84%.

2027 會計年度全年，我們預期：營收介於 17.76 億至 17.81 億美元，ARR 介於 20.58 億至 20.63 億美元，非 GAAP 營業利益約 4.30 億美元；最後，我們仍預期 2027 會計年度非 GAAP 調整後自由現金流約 4.25 億美元，非 GAAP 毛利率約 84%。

Thank you for joining us today and we look forward to speaking with many of you during the quarter.

感謝各位今天加入我們，我們也期待在本季期間與各位中的許多人交流。

With that, I will now turn the call over to the operator. Operator, please pull for questions.

接下來，我將把電話會議交給接線員。接線員，請開始提問。

(Operator instructions) Bryan Bergin, TD Cowen.

（接線員指示）Bryan Bergin，TD Cowen。

Hey, guys. Good afternoon. Thank you. Maybe just to start on the overall demand environment, any interesting changes in the underlying demand trends and pipeline conversion, anything as it relates to deal timing, sales cycles, things like that, just as this conflict has been extended?

嗨，各位。下午好。謝謝。或許先從整體需求環境開始，隨著這場衝突延長，底層需求趨勢與管線轉換率是否有任何值得注意的變化？例如交易時點、銷售週期之類的情況？

No, we actually feel like the environment has stayed relatively stable versus what we saw in the first quarter. Sorry, when we guided to the first quarter earlier this year.

沒有，我們其實覺得環境相較於第一季所見仍相對穩定。抱歉，我是指我們今年稍早對第一季提供指引時所看到的情況。

Bryan, I think we've actually felt very positive about the momentum in the business or, the health of our pipeline and the conversion rates and the predictability. The customer conversations are going really well. A lot of the pilots are beginning to now start, starting to convert, which we feel really positive about.

Bryan，我認為我們對業務動能、或說我們管線的健康度、轉換率以及可預測性，其實感到非常正面。客戶對話進展得很順利。許多試點專案現在開始啟動，也開始轉換，我們對此感到非常樂觀。

So overall, we're actually very positive overall on our pipeline, and the environment remains variable as it has been. It feels like a new normal is the way we've got to think about it.

總體而言，我們對我們的銷售管線其實非常正面，而外部環境仍如以往一樣充滿變動。感覺我們必須把這視為一種新的常態來思考。

Okay, and then on AI product [IRR] levels, any sizing you can update us there and how the pricing conversation across those solutions is evolving?

好的，那在 AI 產品 [IRR] 的層級方面，有沒有任何規模上的更新？以及這些解決方案的定價討論目前是如何演進的？

Yeah, we'll disclose the product IRR periodically here. We feel really good about the momentum. I think we pointed to it in terms of 16 of the top 20 deals for the quarter involved AI. I think agentic and our AI products in general just have really good strong momentum. And our vertical solutions are also starting to really get traction both from customer interest and pipeline, particularly in healthcare and financial services.

是的，我們會在這裡定期揭露產品 IRR。我們對這股動能感覺非常好。我想我們也提到，本季前 20 大交易中有 16 筆涉及 AI。我認為 agentic（代理式）以及我們的 AI 產品整體都展現出非常強勁的動能。而我們的垂直解決方案也開始真正取得進展，無論是客戶興趣或是管線，特別是在醫療保健與金融服務領域。

And then lastly, I think Test, which is our agentic testing solutions, that has really good traction as well. We look forward to update the numbers here in the coming periods. But right now, we feel really good momentum. And I think the deal traction kind of speaks to the overall trajectory for the AI products.

最後，我想提到 Test（我們的代理式測試解決方案）也有非常好的牽引力。我們期待在接下來幾期更新相關數字。但就目前而言，我們對動能感覺非常好。我認為交易的牽引力某種程度上也反映了 AI 產品的整體發展軌跡。

Scott Berg, Needham.

Scott Berg，Needham。

Hi, everyone. Nice quarter. Thanks for taking my questions. Daniel, you spoke extensively about orchestration, and it's a key topic that comes up in our work on the space consistently over the last probably year -- when you think about Maestro and the deals that you have out there, is there any reason why Maestro is a part of basically every deal that has AI? Or is there some combination that would suggest that, that's not going to be a part of every deal going forward.

大家好。本季表現不錯。謝謝讓我提問。Daniel，你談了很多關於編排（orchestration）的內容，而這也是我們在這個領域過去大概一年持續討論的關鍵主題——當你想到 Maestro 以及你們目前在外的交易時，是否有任何原因使得 Maestro 幾乎出現在所有含有 AI 的交易中？或者是否存在某種組合，意味著未來它不會出現在每一筆交易裡？

I don't think Maestro can be part of every deal. The way we are looking at our business, we have an entire platform that can address the whole spectrum of task and process orchestration.

我不認為 Maestro 會是每一筆交易的一部分。我們看待業務的方式是：我們有一個完整的平台，可以涵蓋任務與流程編排的整個光譜。

Maestro is a solution that comes into play when customers are doing process orchestration and automation, end-to-end process orchestration and automation. But we have customers out there that are happy to start with the task automation product. And task automation can also be deterministic and cognitive. I would say that RPA and API automation place into deterministic task automation while we have agents that can be applied to task level.

當客戶在做流程編排與自動化、端到端的流程編排與自動化時，Maestro 這個解決方案就會派上用場。但我們也有一些客戶很樂意先從任務自動化產品開始。而任務自動化也可以是確定性（deterministic）與認知式（cognitive）的。我會說，RPA 與 API 自動化屬於確定性的任務自動化；同時我們也有可應用在任務層級的代理（agents）。

Maestro comes into place when you need more complex orchestration of work that involves humans, task automations, enterprise workflows, systems, and agents. So it's naturally more for our more involved customers. Maestro helps us landing bigger deals makes our install base stickier to the customers, but I cannot say it can be deployed in every single deal.

當你需要更複雜的工作編排，且涉及人員、任務自動化、企業工作流程、系統以及代理時，Maestro 就會介入。因此它自然更適用於需求更深入的客戶。Maestro 幫助我們拿下更大的交易，讓我們的安裝基礎對客戶更具黏著度，但我不能說它能部署在每一筆交易中。

Got it, helpful. And then Ashim, a follow-up to the last questions that were out there. I think what we're all trying to understand is the impact of obviously some of your AI modules on the business and the bookings and what the general trajectory is. I understand that you don't want to necessarily report that AI metric every quarter, but if I ask a question a slightly different way is, if I think about those 16 deals in the top 20 that had an AI component of them, how significant of those transactions is coming from some of the AI functionality? I think we're all trying to understand, is it still traditional RPA heavy in those transactions or if we're seeing a bigger impact from some of the AI function?

了解，很有幫助。接著 Ashim，我想延伸前面大家的問題。我想我們都在試著理解，你們一些 AI 模組對業務與訂單（bookings）的影響，以及整體發展軌跡。我理解你們不一定想每季都揭露那個 AI 指標，但如果我換個方式問：在本季前 20 大交易中有 16 筆包含 AI 元件，這些交易中有多大一部分的金額是來自某些 AI 功能？我們都想了解：這些交易仍然以傳統 RPA 為主嗎？還是我們看到 AI 功能帶來更大的影響？

No, we're seeing a bigger impact. I think the way I look at it is, I kind of would divide it into three areas, like our top customers and our top deals, the majority of our transactions have a significant AI, if not a majority AI component, Scott, that's driving it. They're not piecemeal where it's kind of like one or two SKUs that get moved in or small quantities. They are materially what we are selling to our customers.

不，我們確實看到更大的影響。我看待這件事的方式是，我大致會分成三個區塊：像是我們的頂級客戶與最大型的交易，其中大多數交易都有顯著的 AI 成分，甚至是以 AI 成分為主在驅動，Scott。它們不是那種零碎的、像是只加一兩個 SKU 或少量帶入。AI 在實質上就是我們賣給客戶的主要內容。

I think there is a mid-tier of customers where you see actually a continued demand. In traditional RPA and deterministic automation. And those are companies that, are not that either are have embraced agentic and AI in a major way. And they are actually pulling forward more deterministic automations, as they weigh both the cost and the trust and governance that agents versus deterministic automations give you.

我認為還有一個中間層級的客戶，你會看到對傳統 RPA 與確定性自動化仍有持續需求。（同上）這些公司並不是那種已經以很大的規模擁抱代理式與 AI 的客戶。在他們權衡代理相較於確定性自動化所帶來的成本、信任以及治理時，他們其實會把更多確定性自動化的需求提前拉動。

And then really, the kind of some of the drag that we talked about is really from the low end of the market, smaller customers and personal productivity. That's kind of the way I would divide up the quarter. So we are actually really pleased with the pull that we're getting on the agentic side and its contribution to our growth.

然後，我們提到的一些拖累，主要來自市場的低端：較小型的客戶以及個人生產力（personal productivity）。這大概就是我對本季的切分方式。因此我們其實對代理式這一端帶來的拉動力，以及它對我們成長的貢獻感到非常滿意。

Sanjit Singh, Morgan Stanley.

Sanjit Singh，Morgan Stanley。

This is Abhishek Murli on for Sanjit Singh. Thanks for taking the question. I would love to hear a little more on the beat and kind of dig into giving Q1 revenue upside was strong, but then the beat was largely given by license revenue and then ARR was relatively in line. So can you kind of help us understand the quality of that revenue beat? Is there anything unusual in license timing or customer behavior that we should be aware of?

我是代 Sanjit Singh 提問的 Abhishek Murli。謝謝讓我提問。我想更深入了解這次超預期（beat）的情況：Q1 營收上修幅度很強，但實際超預期主要是由授權（license）營收帶動，而 ARR 相對上大致符合預期。你們能否協助我們理解這次營收超預期的品質？在授權認列時點或客戶行為上，有沒有任何不尋常之處是我們需要留意的？

And then how should we think about the relationship between license performance and ARR trajectory for the rest of the year?

另外，我們應該如何看待今年剩餘期間授權表現與 ARR 走勢之間的關係？

Yeah, I mean, I would say two things. One is we feel really good about the quality of that revenue, both in terms of the products as well as the deal quality and structures. I would say our quarters have been very clean, and we feel very good about the overall deal quality and construction.

是的，我想我會說兩點。第一，我們對那個營收的品質感覺非常好，無論是就產品本身，或是交易品質與結構而言。我會說我們的季度表現一直都很乾淨，我們對整體交易品質與架構都非常有信心。

Remember, the revenue is a quarterly performance metric when you're looking at the growth rates. And we are on ASC 606 versus ARR, which is a 12-month metric, right?

請記得，當你看成長率時，營收是一個季度性的績效指標。而我們是依 ASC 606 認列，對比 ARR 則是一個 12 個月的指標，對吧？

So if I break down the question, you look at revenue growth at 17%, when you look at a trailing 12-month period, the revenue growth rate is 15%. So actually, which makes me feel very good about 15% growth on a trailing 12-month basis. And it's relatively in line with the ARR growth at 12%.

所以如果我把問題拆開來看：你看到營收成長 17%；但若看過去 12 個月（trailing 12-month）的期間，營收成長率是 15%。因此，過去 12 個月基礎的 15% 成長其實讓我感覺非常好。而這也與 ARR 12% 的成長相對一致。

In terms of ARR beat versus revenue beat, it's really just a mix of deals with 606 timing. And the license revenue being a factor in that is a sign actually a really good quality revenue overall.

至於 ARR 超預期相較於營收超預期的差異，主要只是交易組合在 606 認列時點上的不同。而授權營收在其中扮演因素，實際上也代表整體營收品質非常好。

Thank you. And then as a follow-up, anything you can share in terms of the mix between consumption-based revenue proceed?

謝謝。接著追問一下，你們能否分享一下按用量計費（consumption-based）營收的組合占比趨勢？

We don't -- consumption-based revenue is a very small part of what we do. We still have subscription really dominates our pricing model and perceived pricing as well. That is not the majority of what we do. We are really selling executions as well as kind of our typical server-based pricing that we have for unattended robots in particular.

我們不——按用量計費的營收在我們業務中占比非常小。我們的定價模式以及所謂的「感知定價」仍然以訂閱制為主。那並不是我們業務的主要部分。我們主要銷售的是執行次數（executions），以及我們典型的伺服器式定價，特別是針對無人值守機器人（unattended robots）。

I just really emphasize again, personal productivity is, a very small part of our portfolio, simple task-based automation. So what we sell is the larger complex use cases now, and that really mixes higher towards both server-based and subscription-based pricing.

我再強調一次，個人生產力其實只佔我們產品組合中很小的一部分，也就是簡單、以任務為基礎的自動化。因此，我們現在銷售的是更大型、更複雜的使用案例，而這也確實更偏向於伺服器型與訂閱型的定價模式。

Sanika Merchant, RBC.

Sanika Merchant，RBC。

Hey, guys. This is Sanika Merchant on for Matt Hedberg from RBC. Thank you so much for taking the question.

嗨，各位。我是RBC的Sanika Merchant，代替Matt Hedberg提問。非常感謝讓我提問。

Could you talk a little bit about the broader competitive environment for orchestration and any changes or trends you're seeing?

你們能否談談編排（orchestration）領域更廣泛的競爭環境，以及你們看到的任何變化或趨勢？

And there have also been a lot of developments around frontier model capabilities. Could you talk through how you see these developments impacting the broader competitive landscape and the company specifically? Thanks.

另外，前沿模型能力也有很多進展。你們能否說明你們如何看待這些發展對整體競爭格局以及對公司本身的影響？謝謝。

Yeah, sure. I would like to start by saying that we have a really unique platform in the market, and it's based on three major pillars.

好的，當然。我想先說，我們在市場上有一個非常獨特的平台，而它建立在三大支柱之上。

We have a very modern process orchestration technology that is built on a very innovative workflow engine capabilities. We have a proven of 10 years deployment of scales of automations in a secured and governed environments with some of the largest companies in the world. And we have a unique ability to connect to both modern API based systems and legacy systems.

我們擁有非常現代化的流程編排技術，建立在非常創新的工作流程引擎能力之上。我們在安全且受治理的環境中，已經與全球一些最大的企業一起，證明了可在規模化自動化部署上累積10年的實績。同時，我們也具備獨特能力，能連接現代的API系統與傳統舊系統。

These three pillars make our platform quite unique in the market.

這三大支柱讓我們的平台在市場上相當獨特。

And in terms of the new developments that we are seeing, I think we all recognize the huge impact of the coding agents of the entire ecosystem.

至於我們看到的新發展，我想大家都認知到，程式碼代理（coding agents）對整個生態系帶來了巨大的影響。

And I want to point you to an interesting phenomenon that it's something that we spot with our customers and within our own UiPath operations. It's becoming increasingly easier to build deterministic automations.

我想指出一個有趣的現象，這是我們在客戶端以及UiPath自身營運中觀察到的。建置確定性（deterministic）的自動化正變得越來越容易。

You are using coding agents to build deterministic automations and deploy them at scale. It's becoming really easier to address the long tail of opportunities of work. And it was not economically feasible before coding agents to get to this level of automations. And building automations, it's really creating the substrate for deploying agentic AI later on.

你可以使用程式碼代理來建置確定性自動化，並以規模化方式部署。處理工作機會的長尾（long tail）也變得更容易。在程式碼代理出現之前，要達到這種程度的自動化在經濟上並不可行。而建置自動化，其實是在為日後部署代理式AI（agentic AI）打造基礎底層（substrate）。

I would point to why coding agents are so successful nowadays, because they really combine models, the strength of the models, with the strength of deterministic automations. Claude code, it's so good because there is this deterministic harness around it. So Claude generates code, but then it uses a compiler, which is a deterministic piece of technology to compile the code. And then it's using testing, which are another deterministic piece of code to validate the code that is generated. I think it's becoming more clear to everyone that the combination of deterministic automations and models are what makes the real deployments in production.

我想說明為什麼程式碼代理如今如此成功，因為它們真正把模型的優勢與確定性自動化的優勢結合起來。Claude code之所以這麼好，是因為外圍有一個確定性的「框架／約束」（deterministic harness）。Claude會生成程式碼，但接著會使用編譯器——這是一個確定性的技術——來編譯程式碼。然後它還會使用測試——也是另一段確定性的程式碼——來驗證生成的程式碼。我認為大家越來越清楚：確定性自動化與模型的結合，才是能在生產環境真正落地部署的關鍵。

And I would say that in this regard, we do have tremendous advantage. Our platform is already enabled for coding agents, and we showed that our DevCon in India. We show that we can reduce significantly the implementation times. Think for a second, weeks to hours. That really means a lot when you go and deploy automations to the long day of possible work.

在這方面，我們確實有巨大的優勢。我們的平台已經支援程式碼代理，我們也在印度的DevCon上展示過。我們展示了可以大幅縮短導入時間。想一下，從數週縮短到數小時。當你要把自動化部署到可能工作的長尾時，這真的意義重大。

Thanks, guys. I appreciate the color there. And as a quick follow-up, so you've talked a lot about sort of profitability and last quarter you also updated your long-term non-GAAP operating margin target to 30%. And keeping in mind the fact that growth remains a priority for the company, what are some keys to margin expansion in fiscal year '27? And is there any seasonality you would point out on that? Thanks.

謝謝各位。我很感謝你們提供的補充說明。再快速追問一下：你們談了很多關於獲利能力，而上季你們也把長期non-GAAP營業利益率目標更新到30%。考量到成長仍是公司的優先事項，2027會計年度（FY '27）擴大利潤率的一些關鍵是什麼？另外，這方面你們會指出任何季節性因素嗎？謝謝。

Look, I think from a cost seasonality, nothing except for, obviously our later parts of the year, we have sales compensation, which is just normal SaaS seasonality from an expense standpoint. Otherwise, I think we're pretty, there's no real seasonality to mention.

我想就成本的季節性而言，除了很明顯地在年度後段會有銷售薪酬（sales compensation）之外，沒有其他因素；從費用角度來看，這就是典型SaaS的季節性。除此之外，我認為我們相當……沒有什麼真正值得一提的季節性。

From my standpoint, I think, we're looking as growth as our first priority. So we are investing in FDEs, we are investing in test, we are investing in vertical solutions. We are investing in coding agents as evidenced by the speed of the launch by which we're moving through things. And so from our standpoint, that investment is our first priority.

就我這邊的觀點，我們把成長視為第一優先。因此我們投資於FDE（前線部署工程師），投資於測試，投資於垂直解決方案。我們也投資於程式碼代理，從我們推進與發布的速度就能看出來。所以就我們而言，這些投資是第一優先。

At the same time, we updated our long-term models because we are able to find increasing levels of efficiency, both through continued discipline and scrutiny, and then also from implementing both our platform as well as broader AI tools within the company. And so I would say we're an invest first mindset and a waste nothing mindset. And that combination, I think, gives us the ability to both grow and drive the strategic initiatives while extending operating margins.

同時，我們更新了長期模型，因為我們能找到越來越高的效率水準：一方面來自持續的紀律與審視，另一方面也來自在公司內部導入我們的平台以及更廣泛的AI工具。因此我會說，我們是「先投資」的心態，以及「不浪費任何資源」的心態。我認為這樣的組合，讓我們能在推動成長與策略性計畫的同時，擴大營業利益率。

Pat McIlwee, William Blair.

Pat McIlwee，William Blair。

Hi, Daniel and Ashim, thanks for taking my questions. My first question, I thought the AI summit you put on earlier this year was very helpful in envisioning how customers can evolve from your traditional RPA workflows towards more agentic enabled workflows and specifically how they can choose their own autonomy level and then kind of use a feedback loop to evolve the level of automation in that process over time. So I know it's early on, but for your existing customers, where are they in that autonomy evolution right now? Are a lot of them content with the value they're getting from current RPA workflows and leveraging AI within newer workflows? Or are they really racing towards these agentic solutions to maximize the ROI they're getting from the platform, both existing and new workflows alike?

嗨，Daniel和Ashim，謝謝回答我的問題。我的第一個問題是：我覺得你們今年稍早舉辦的AI峰會非常有幫助，讓人能想像客戶如何從傳統RPA工作流程演進到更具代理能力（agentic enabled）的工作流程，特別是他們如何選擇自己的自主程度，並透過回饋迴路，隨時間推進逐步提升該流程的自動化程度。我知道現在還算早期，但就你們既有客戶而言，他們目前在這個自主性演進上處於什麼階段？是否很多客戶已經滿足於目前RPA工作流程帶來的價值，並在較新的工作流程中運用AI？或者他們是否真的在加速奔向這些代理式解決方案，以最大化他們從平台（包含既有與新工作流程）所獲得的ROI？

Yeah, I would like to point out that despite the technology being very new, it is hailed by our customers with a lot of enthusiasm. Even when we were in closed preview, we got a lot of requirements from the customers. Some of the customers even went to find online some of the skills that we publish and use them with the coding agents.

是的，我想指出，儘管這項技術非常新，但客戶對它抱持高度熱情。即使在封閉預覽（closed preview）期間，我們也收到很多客戶需求。有些客戶甚至會到線上去找我們發布的一些技能（skills），並把它們與程式碼代理一起使用。

And also I would like to point out to the fact that basically coding agents saw two of the biggest hurdles in deployment of automations.

另外我也想指出，基本上程式碼代理解決了自動化部署中的兩個最大障礙。

Number one was always the implementation. Leading time until an enterprise would get value from automation. So that's been already proven internally by our own forward deployed engineers and externally by a few advanced customers that can be shrink in many cases from weeks to hours, which is very significant.

第一個一直都是導入（implementation）。也就是企業從開始到能從自動化獲得價值的前置時間（lead time）。這點已經在內部由我們自己的前線部署工程師（forward deployed engineers）證明，也在外部由少數先進客戶證明：在許多情況下可以從數週縮短到數小時，這非常顯著。

The second way that coding agents. Unlocks a bottleneck of automation is in maintenance. One of the apparent flaws of automations was always the fact that they are fragile and they break. If there is an upstream modification in an enterprise system that automations are not aware they might break and that will require human intervention and many days of reviewing and understanding.

第二個方面是，程式碼代理在維護上解開了自動化的瓶頸。自動化一直有個明顯缺陷：它們很脆弱、容易壞。如果企業系統上游有修改，而自動化並不知情，就可能會中斷，並需要人工介入，花上許多天去檢視與理解。

Now, we offer both a healing agent and the diagnose agent. So the healing agent can do a lot of the work during runtime, during execution. And in many cases, the healing agent can fix the execution in itself and the processes run unaffected.

現在，我們同時提供修復代理（healing agent）與診斷代理（diagnose agent）。因此修復代理可以在執行期間、在運行時做很多工作。而在許多情況下，修復代理可以自行修復執行問題，讓流程不受影響地繼續運行。

When there is an exception, we help tremendously developers with these diagnose agents to gather all the context around an automation and they can publish a fix much faster than before.

當發生例外狀況時，我們透過這些診斷代理（diagnose agents）大幅協助開發人員蒐集自動化周邊的所有情境資訊，讓他們能比以往更快發布修復。

So yes, I would conclude that for us, this is a really big unlock. And we see the potential for a huge acceleration of customer adoption.

所以是的，我的結論是，對我們而言，這是一個非常重大的突破。而我們也看到客戶採用速度可能大幅加速的潛力。

Right, okay. Thanks, Daniel, for the thoughts there. And to kind of continue on, it sounds like AI agents are largely extending, not replacing deterministic automation within your platform. But as we think about that, I wanted to ask, is there any sort of dynamic where you're seeing customers leverage agentic AI to somewhat cannibalize some of the traditional bot monetization? Or is it largely building incremental automation and therefore incremental monetization on top of those workflows?

好的，了解。謝謝你，Daniel，分享你的看法。接著延伸一下，聽起來 AI 代理主要是在你們的平台上延伸、而不是取代確定性（deterministic）的自動化。但在思考這點時，我想問的是，你們是否看到某種情況：客戶利用代理式 AI（agentic AI）在某種程度上侵蝕（cannibalize）傳統機器人（bot）的變現？還是說它主要是在既有工作流程之上建立增量自動化，因而帶來增量變現？

Yeah, I would like to say that perhaps this is one of the biggest confusion that AI brings into the table. The idea that non-deterministic, probabilistic technology can replace deterministic automations. This is not true. It's not true from the capability perspective and it's not true from an economical standpoint. And let me elaborate a bit on both.

是的，我想說這可能是 AI 帶來的最大混淆之一。也就是非確定性、機率式（probabilistic）技術可以取代確定性自動化的想法。這不是真的。不論從能力角度或從經濟性角度來看都不成立。我稍微就這兩點多說一些。

A probabilistic technology is not architecturally meant to follow a dozen of steps and sometimes hundreds of steps in the same order, in the same sequence. Every step will have a probability when you multiply these probabilities you will end up with something that is not reliable end of the day, and there are many regulated industries that cannot tolerate anything that is not 100% reliable. They will prefer an automation to fail as an exception rather than produce an unexpected result. So what deterministic bots cannot be replaced by non-deterministic AI agents.

機率式技術在架構上並不是為了以相同順序、相同序列去遵循十幾個步驟、甚至有時上百個步驟而設計的。每一步都有一個機率，當你把這些機率相乘，最後得到的結果在一天結束時就會變得不可靠；而許多受監管產業無法容忍任何不是 100% 可靠的東西。他們寧可讓自動化以例外狀況的方式失敗，也不願產生非預期的結果。因此，確定性機器人無法被非確定性的 AI 代理取代。

Again, this is the architecture that is proven over and over again by all the AI agents that are out there. The most sophisticated agents like Claude codes or OpenAI codex are built on the foundation of deterministic tools that they call. So it's a harness around the model and deterministic tools.

同樣地，這個架構已被市面上各種 AI 代理一再驗證。最先進的代理，例如 Claude Code 或 OpenAI Codex，都是建立在它們所呼叫的確定性工具之基礎上。所以它是一個包覆在模型外的框架（harness），加上確定性工具。

This is exactly how they work. This is exactly what we are proposing to our customers. Yes, reuse your investment in your existing deterministic automation and surround it with process orchestration, which is also deterministic, that can orchestrate models, agents in the context of determinism. That's really the only way to deploy it effectively into an enterprise context.

它們就是這樣運作的。這也正是我們向客戶提出的做法。是的，重用你們既有確定性自動化的投資，並在其外圍加上流程編排（process orchestration）——這同樣是確定性的——用以在確定性的脈絡下編排模型與代理。這真的是在企業情境中有效部署的唯一方式。

And now to the second point about the economical aspect. Even if in certain cases an agent can replicate some steps that are deterministic, why would you do something that is costly and is going to consume tokens at every step in a process rather than generate a script that works and it costs nothing in order to run.

接著談第二點：經濟性面向。即便在某些情況下，代理可以複製一些確定性的步驟，你為什麼要做一件成本更高、而且在流程的每一步都要消耗 token 的事，而不是產生一段可運作的腳本，執行成本幾乎為零？

So to my previous answer, this is the best combination between AI and deterministic. AI creates automation, sometimes maybe even on the fly. AI will run those automations. It's very cheap to run, very deterministic, reliable, auditable. And only when these scripts break, you can invoke again AI to fix the scripts. But that's basically the right model to run agentic AI and automation into enterprise context.

所以回到我前面的回答，這就是 AI 與確定性之間的最佳組合。AI 會建立自動化，有時甚至可以即時生成。AI 也會執行那些自動化。它的執行成本非常低、非常確定、可靠、可稽核。而只有當這些腳本壞掉時，你才再次呼叫 AI 來修復腳本。但這基本上就是在企業情境中運行代理式 AI 與自動化的正確模型。

Raimo Lenschow, Barclays.

Raimo Lenschow，巴克萊（Barclays）。

Perfect. Thank you. Thanks for squeezing me in. Daniel, could you stay on that subject? Because that's obviously where a lot of the investor questions are coming around. So how does the world work then going forward? If you do the deterministic part and you have all the experience in the world, so you should do that, who is doing then the probabilistic part? What are you seeing there in terms of work, customers thinking and how they think about you in that context?

太好了。謝謝。謝謝你把我安排進來。Daniel，你可以延續這個主題嗎？因為顯然很多投資人的問題都圍繞在這裡。那麼未來這個世界會怎麼運作？如果你們做確定性的部分，而且你們在這方面有全世界最豐富的經驗，所以你們應該做這個，那機率式的部分是由誰來做？你們在工作分工、客戶的想法，以及他們在這個脈絡下如何看待你們方面，看到了什麼？

And then I had one follow-up for Ashim.

另外我還有一個給 Ashim 的追問。

Well, I think the answer varies. We are model agnostic in terms of how we see the world.

嗯，我認為答案會因情況而異。就我們看待世界的方式而言，我們對模型是不可知（model agnostic）的。

We provide deterministic orchestration and we can infuse that deterministic auto-orchestration at any steps with agentic AI. That agentic AI use behind the scene, frontier lab models can use open weight models. We have a bring your own model policy. So we will accommodate every spectrum of requirements from our customers.

我們提供確定性的編排能力，並且可以在任何步驟把代理式 AI 注入到這個確定性的自動編排之中。這些代理式 AI 在幕後可以使用前沿實驗室（frontier lab）的模型，也可以使用開放權重（open weight）模型。我們有「自帶模型」（bring your own model）政策。因此，我們會滿足客戶在各種需求光譜上的要求。

But again, I think it's important to note that even on the frontier lab models, the offering, it's a combination between deterministic and the model itself, which is purely cognitive. We extend in a way that model into the enterprise work itself.

但同樣地，我認為需要注意的是，即便是前沿實驗室模型的產品供給，本身也是確定性與模型（純粹認知）的組合。我們以某種方式把該模型延伸到企業工作的實際運作之中。

And when you go and I think very important distinction to understand the enterprise work is to think of. Who initiates an agent or process automation. It's a big difference if it's initiated by a person and the agent work on a person desktop versus an automation is triggered by an event or by an enterprise workforce where you will need to have different degree of auditability and reliability.

而當你進一步看——我認為理解企業工作的非常重要區別在於思考：是誰啟動一個代理或流程自動化。如果是由人啟動、代理在個人桌面上工作，與由事件觸發或由企業工作流程觸發的自動化相比，差異非常大；後者需要不同程度的可稽核性與可靠性。

And again, this is where we really shine. We have this 10 years of experience in running a large scale unattended automations that work on event triggers. And we are involving agentic AI and models into these workflows that can run unattended.

同樣地，這正是我們真正發光的地方。我們有 10 年在大規模、無人值守（unattended）、以事件觸發運行自動化的經驗。而我們正在把代理式 AI 與模型納入這些可無人值守運行的工作流程之中。

Yeah, okay. Makes sense. That's very clear. Thank you, Daniel.

好的，了解。有道理。非常清楚。謝謝你，Daniel。

And then Ashim, the one other question I get from investors a lot at the moment is you're doing really well on the revenue side. ARR is very steady, but at some point, they need to start lining up. So revenue at the moment keeps growing faster than ARR. How do we need to think about that dynamic? Because in theory, you would think that they should line up, I would think.

接著 Ashim，我目前從投資人那裡常被問到的另一個問題是：你們在營收端表現非常好。ARR 很穩定，但在某個時間點，兩者需要開始對齊。因為目前營收成長速度持續快於 ARR。我們應該如何理解這個動態？因為理論上，你會覺得它們應該要對齊，我是這麼想的。

Yeah, I think the first piece is, again, like when you look at it on a trailing 12-month basis, the revenue growth rate is 15% versus the ARR growth rate of 12% that you see.

是的，我認為第一點是：同樣地，當你用過去 12 個月（trailing 12-month）的基礎來看，營收成長率是 15%，而你看到的 ARR 成長率是 12%。

The second piece is within the revenue growth rate, there's obviously the license revenue growth rate, and then there's, services, et cetera. You can see we actually had good services revenue as FDEs, et cetera, are in demand from our customers. So that's a second piece that is there.

第二點是，在營收成長率之中，顯然包含授權（license）營收成長率，另外還有服務等。你可以看到我們的服務營收其實表現不錯，因為 FDE 等人力服務受到客戶需求帶動。所以這是第二個因素。

Over time, this has moved in different directions. There's been times where with 606, revenue has trailed as certain duration and mix has moved the growth rate and where it succeeded. But when you look at it on like an overall average over a longer period of time, it's together.

隨著時間推移，這個關係曾朝不同方向變動。有些時候在 606（ASC 606）之下，因合約期間與組合（mix）的變化影響成長率，營收會落後；也有些時候則會超前。但如果你用更長期間的整體平均來看，它們是一起走的。

I don't really see any major disconnect at this moment that is driven by a business specific area. It's really just a mix of 606 impacts on the business. And again, I would emphasize to look it on a trailing 12-month basis versus looking at it where it is just in a particular quarter because ARR is obviously an annual metric.

我目前不認為存在任何由特定業務領域所驅動的重大脫鉤。這真的只是 606 對業務造成影響的組合結果。同樣地，我會強調用過去 12 個月的基礎來看，而不是只看某一個特定季度，因為 ARR 顯然是一個年度指標。

Michael Turrin, Wells Fargo.

Michael Turrin，富國銀行。

Hey, great. Thanks very much. I appreciate you taking the questions. I'll just ask the two upfront. And you can take them on whatever sequence you like.

嗨，很好。非常感謝。感謝你們願意回答問題。我先把兩個問題一起問。你們可以依照你們想要的順序回答。

I guess the first is just in terms of public sector. As we roll into midyear, maybe just remind us how you're thinking about public sector this year, any updates in terms of progress or deal progression from that vertical specifically?

我想第一個是關於公部門。隨著我們進入年中，能否請你們提醒一下今年你們對公部門的看法，特別是該垂直領域在進展或交易推進方面是否有任何更新？

And then maybe, Ashim, just on the net retention rate, just what you're seeing currently in the uptick there and how to think about the trend line. Obviously without guidance, which is taking through the drivers there. Thanks very much.

然後也許，Ashim，關於淨留存率，你們目前看到那邊回升的情況如何，以及該如何看待趨勢線。顯然在不提供指引的前提下，請談談背後的驅動因素。非常感謝。

Yeah, I can answer both questions. I'll start with the net dollar retention rate. I'm actually super excited with the net dollar retention rate and the progress we've made on it.

好的，我可以回答這兩個問題。我先從淨美元留存率談起。其實我對淨美元留存率以及我們在這方面取得的進展感到非常振奮。

As you can see, we have a 2-point increase quarter over quarter. That's one of the first times we've had an increase as we've stabilized net ARR and beginning to point the trajectory up towards that re-acceleration mark. When you normalize for foreign exchange and the impact of M&A. That is one point, but it's still a re-acceleration of a net dollar retention rate that we're actually very encouraged by. And as I said, as we start to stabilize net new ARR, the next step is re-acceleration, so we're moving into that territory. And I think that's really great progress by the teams and speaks to the strategy and the operational execution that you see.

如你所見，我們季度對季度提升了2個百分點。這是我們在淨ARR趨於穩定後，少數幾次出現提升之一，也開始把軌跡往再加速的目標方向推進。若將匯率與併購（M&A）的影響正規化後，提升是1個百分點，但這仍然是淨美元留存率的再加速，我們對此其實非常受到鼓舞。如我所說，當我們開始穩定淨新增ARR後，下一步就是再加速，所以我們正在進入那個區間。我認為這是團隊非常好的進展，也反映出你們所看到的策略與營運執行力。

In terms of public sector, I actually was at the public sector, a fusion event that we had, the energy was very strong. I think public sector in terms of disruption of budgets, et cetera, we feel like there is good stability. Obviously, as funding moves with different defense initiatives and wars, et cetera, that are there, we stay on top of it. But within many agencies, we actually have a very good presence, strong relationships with really good use cases, whether that is audit compliance within the government, which we have a very strong set of solutions and partners that we're working with, or other transactional areas, we actually feel like our relationships are very good.

至於公部門，我其實剛參加了我們舉辦的一場公部門Fusion活動，現場氣氛與能量非常強。我認為就預算擾動等因素而言，我們覺得公部門具備良好的穩定性。當然，隨著不同國防倡議、戰爭等帶動資金流向變化，我們會持續密切掌握。但在許多機構中，我們其實有很好的布局、強健的關係與很好的使用案例；不論是政府內部的稽核合規（audit compliance）——我們在這方面有很強的解決方案組合與合作夥伴——或是其他交易型領域，我們都覺得彼此關係非常良好。

In terms of what's in for, as we talk about that, we continue to guide what's in front of us there, which is, we're pretty measured and prudent, we know what projects are generally funded. And we're looking to execute against that.

至於前方可見的情況，當我們談到這一點時，我們會持續以眼前所見來做指引；也就是說，我們相當審慎且保守，我們知道哪些專案通常已獲得資金支持。我們將致力於把這些專案執行到位。

Radi Sultan, UBS.

Radi Sultan，瑞銀。

Awesome. Yeah, thanks for taking the question. First for Daniel, just on the UiPath for coding agents, you mentioned this would be targeted at the full software development life cycle. But I guess, are there like one or two areas where you see the biggest pain points where you can add the most value? And then you also mentioned this could strengthen retention, maybe just how you imagine monetizing or bundling these agents into the broader suite?

太好了。謝謝讓我提問。先問Daniel，關於用於編碼代理（coding agents）的UiPath，你提到這會鎖定整個軟體開發生命週期。但我想問，是否有一兩個你們看到痛點最大、能創造最多價值的領域？另外你也提到這可能強化留存率，能否談談你們如何想像把這些代理變現，或是如何把它們打包進更廣泛的產品套件？

So we plan to bring agents across the entire development lifestyle. We are starting with an agent that helps with planning for an automation. So you can have a business analyst that can, with the help of the agent, interview different subject matter experts, gather all the information, create a process documentation document.

所以我們計畫在整個開發生命週期導入代理。我們會先從一個協助自動化規劃的代理開始。也就是說，商業分析師可以在代理的協助下，訪談不同的領域專家，蒐集所有資訊，並建立流程文件（process documentation document）。

And then we will have like a solution architect agent that will take this design document and convert it into code. And we will have different agents for different types of code. We have an agent that can create enterprise user interface. We have an agent that will create RPA, another agent that can create API, of course, an agent that will create process orchestration based on Maestro.

接著我們會有一個類似解決方案架構師（solution architect）的代理，將這份設計文件轉換成程式碼。而且我們會針對不同類型的程式碼提供不同代理。我們有能建立企業級使用者介面（enterprise user interface）的代理。我們有能建立RPA的代理，還有能建立API的代理；當然，也有能基於Maestro建立流程編排（process orchestration）的代理。

This can be deployed and tested again it's fully agentic once they are in production we have agents that monitor the entire execution and can fix proactively the errors that are coming. And once there is an exception, we have also agents that help our developers to diagnose faster the exception and fix them faster so the entire life cycle, there is no single point in the life cycle that is not touched by agents.

這些可以被部署並再次測試；而且一旦進入正式環境，它就是完全代理化（fully agentic）的。我們有代理會監控整體執行，並能主動修復出現的錯誤。一旦發生例外狀況，我們也有代理協助開發者更快診斷例外並更快修復，因此在整個生命週期中，沒有任何一個環節不會被代理觸及。

In fact, we believe that the entire authoring surface of our platform is basically agentic first. Humans, we think, are mostly going to do validation. They will inject goals to the agents and they will do the validation and supervision of the work, but most of the work itself is going to be created by agents.

事實上，我們相信我們平台的整個創作介面（authoring surface）基本上會以「代理優先」（agentic first）為核心。我們認為人類主要會做驗證。人們會把目標注入代理，並對工作進行驗證與監督，但大部分實際工作本身將由代理來完成。

Got it. Maybe just one follow-up for Ashim. Last quarter, we talked about core RPA still growing and becoming increasingly strategic to the AI product offerings. Can you just talk through how you think about how pricing should evolve for the RPA, deterministic automation side of the business, given that it is becoming increasingly more strategic to customer AI initiatives to kind of capture that incremental value?

了解。我想再追問Ashim一題。上季我們談到核心RPA仍在成長，且對AI產品供應變得愈來愈具策略性。鑑於它正變得愈來愈關鍵、用以支撐客戶的AI計畫並捕捉額外價值，你能否談談你們如何看待RPA、確定性自動化（deterministic automation）這一側的定價應該如何演進？

Yeah. Look, I think that there's a lot of discussions around outcome based pricing that are real and active more than they ever have been before. So I think that is one tier of pricing to our top customers that I think is it's a real evolution. We see real line of path and we see people, especially with their fears of about getting ROI with AI, really looking for that.

好的。我認為，關於以成果為基礎的定價（outcome based pricing），目前有很多討論，而且比以往任何時候都更真實、也更活躍。所以我認為，對我們的頂級客戶而言，這是一個定價層級，我覺得這確實是一種演進。我們看到很清楚的路徑；尤其在大家擔心AI能否帶來ROI的情況下，客戶真的在尋找這種模式。

The second piece I would say is I think where we're looking through is we also see like use case or process-based pricing where people are looking for restricted use cases so they can solve problems and be able to use different parts of the platform that enable them to do so.

第二點我想說的是，我們也看到像是以使用案例或流程為基礎的定價（use case or process-based pricing）；也就是人們希望針對受限的使用案例來解決問題，並能使用平台中不同的部分來達成。

Those are two evolutions that are there in terms of where we are with the deterministic side and overall.

就我們在確定性那一側以及整體而言，這兩者都是正在發生的定價演進方向。

This now concludes the Q&A session. I'd like to turn the call back over to management for closing remarks.

問答環節到此結束。我想把電話交回管理層做結語。

Thank you so much for the questions. And as usual, we would like to speak directly with many of you over the next few days. Thank you so much.

非常感謝大家的提問。一如往常，我們希望在接下來幾天能與各位中的許多人直接交流。非常感謝。