Innodata Inc (INOD) 2025 Q4 法說會逐字稿

Good afternoon, ladies and gentlemen, and welcome to the Inno Data2 report 4th quarter and fiscal year 2025 results conference call.

女士們、先生們，下午好，歡迎參加 Inno Data2 2025 財年第四季業績報告電話會議。

At this time, all lines are in listen-only mode. Following the presentation, we will conduct a question-and-answer session. If at any time during this call you require immediate assistance, please press 0 for the operator. This call is being recorded on Thursday, February 26, 2026. I would now like to turn the conference over to Amy Agress, general counsel. Please go ahead.

目前所有線路均處於只聽模式。演講結束後，我們將進行問答環節。通話期間，如果您需要立即協助，請按 0 聯絡接線員。本次通話於2026年2月26日星期四進行錄音。現在我將把會議交給總法律顧問艾米·阿格雷斯。請繼續。

Thank you, operator. Good afternoon, everyone.

謝謝接線生。大家下午好。

Thank you for joining us today. Our speakers today are Jack Appelhoff, Chairman and CEO of Innodata, and Maris Espinelli, interim CFO. Also on the call today is Anish Pentakhar, senior Vice President. Finance and corporate development. Rahul Singhal, President and Chief Revenue Officer, is unable to be here today but looks forward to joining us on our next call. We'll hear from Jack first, who will provide perspective about the business, and then Maris will provide a review of our results for the 4th quarter and fiscal year 2025. We'll then take questions from analysts.

感謝您今天蒞臨。我們今天的演講嘉賓是 Innodata 董事長兼執行長 Jack Appelhoff 和臨時財務長 Maris Espinelli。今天參加電話會議的還有資深副總裁 Anish Pentakhar。金融與企業發展。總裁兼首席營收長 Rahul Singhal 今天無法到場，但他期待參加我們下次的電話會議。首先，我們將聽聽傑克對公司業務的看法，然後瑪麗斯將回顧我們第四季和 2025 財年的業績。接下來我們將回答分析師提出的問題。

Before we get started, I'd like to remind everyone that during this call we will be making forward-looking statements which are predictions, projections, and other statements about future events. These statements are based on current expectations, assumptions, and estimates and are subject to risks and uncertainties. Actual. Results could differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release in the risk factors section of our Form 10K, Form 10Q, and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward-lookinging information. In addition, during this call, we may discuss certain non-GAAP financial measures in our earnings release files with the SEC today as well as in our other SEC. Filings which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliation of these measures with comparable GAAP measures.

在正式開始之前，我想提醒大家，在本次電話會議中，我們將發表一些前瞻性聲明，這些聲明是對未來事件的預測、展望和其他陳述。這些陳述是基於當前的預期、假設和估計，並存在風險和不確定性。實際的。實際結果可能與這些前瞻性陳述所預期的結果有重大差異。可能導致這些結果與預期存在重大差異的因素已在今天的盈利新聞稿中列出，並在我們提交給美國證券交易委員會的 10-K 表格、10-Q 表格和其他報告和文件中列明了風險因素部分。我們不承擔更新前瞻性資訊的義務。此外，在本次電話會議中，我們可能會討論今天提交給美國證券交易委員會 (SEC) 的盈利發布文件中的某些非公認會計準則 (non-GAAP) 財務指標，以及我們提交給 SEC 的其他文件中的某些非公認會計準則 (non-GAAP) 財務指標。在我們的網站上發布的文件中，您可以找到有關這些非GAAP財務指標的更多披露信息，包括這些指標與可比GAAP指標的調節表。

Thank you. I will now turn the call over to Jack.

謝謝。現在我將把通話轉給傑克。

Thank you, Amy, and good afternoon, everyone.

謝謝你，艾米，大家下午好。

Q4 was another strong quarter for Innodata.

Innodata第四季表現依然強勁。

We generated $72.4 million in revenue, reflecting 22% year‑over‑year growth. This brought our full‑year revenue to $251.7 million, representing 48% year‑over‑year growth for 2025.

我們創造了 7,240 萬美元的收入，年增 22%。這使得我們的全年營收達到 2.517 億美元，比 2025 年成長了 48%。

Our Q4 consolidated adjusted gross margin was 42%, exceeding our externally communicated target of 40%.

我們第四季綜合調整後的毛利率為 42%，超過了我們對外公佈的 40% 的目標。

Our adjusted EBITDA totaled $15.7 million, or 22% of revenue, also exceeding analyst consensus by $1.2 million.

我們調整後的 EBITDA 總計 1,570 萬美元，佔營收的 22%，也比分析師的普遍預期高出 120 萬美元。

In fact, our results exceed the analysts' consensus across the range of key metrics including revenue, adjusted EBITDA, net income, and EPS.

事實上，我們的業績在包括營收、調整後 EBITDA、淨收入和每股盈餘在內的各項關鍵指標上都超過了分析師的普遍預期。

We ended the year with $82.2 million in cash, subsequently up by approximately $8.4 million.

年底我們持有現金 8,220 萬美元，比前一年增加了約 840 萬美元。

We achieved these results while making meaningful growth‑oriented investments in both COGS and SG&A.

我們在實現這些成果的同時，對銷售成本和銷售、管理及行政費用進行了有意義的、以成長為導向的投資。

In COGS, we carried capacity ahead of revenue ramp, which consistently proved to be the right move. And in SG&A we invested in engineers, data scientists, and customer‑facing account leadership, which investments also proved prudent, yielding innovation that has expanded our opportunities.

在銷售成本方面，我們提前儲備了產能以因應收入成長，事實證明這是一個正確的舉措。在銷售、一般及行政費用方面，我們投資了工程師、資料科學家和客戶導向的客戶經理，事實證明這些投資也是明智的，帶來了創新，從而擴大了我們的機會。

We believe our business momentum to be at an all‑time high.

我們相信，我們的業務發展勢頭正處於歷史最高水準。

We are seeing robust demand across the entire generative AI life cycle, spanning development, evaluation, and ongoing model optimization. And we believe we are gaining traction with a broad and diversified number of large customers.

我們看到，在生成式人工智慧的整個生命週期中，包括開發、評估和持續的模型最佳化，都存在著強勁的需求。我們相信，我們正在贏得許多大型客戶的廣泛認可。

As a result of market demand and growing traction, we anticipate another year of potentially extraordinary growth in 2026.

由於市場需求和不斷增長的市場認可度，我們預計 2026 年將迎來另一個可能具有非凡成長潛力的年份。

We currently estimate our 2026 year‑over‑year growth to potentially be approximately 35% or more.

我們目前預計 2026 年的年增長率可能達到 35% 或更高。

This estimate reflects active programs, recently awarded wins, latest evaluations, and opportunities where we have a clear line of sight.

該估算反映了正在進行的項目、最近獲得的獎項、最新的評估以及我們能夠清晰預見的機會。

Because we are early in the year and because LLM initiatives spin up quickly, we believe there may potentially be significant upside to this range.

由於現在還處於年初，而且LLM計畫啟動速度很快，我們認為這個區間可能還有很大的上漲空間。

However, we prefer to guide conservatively and adjust upward as visibility increases.

但是，我們傾向於採取保守的策略，並隨著能見度的提高而向上調整。

At the same time, given the scale and complexity of the programs we support, timing variability in customer ramp schedules, budget approvals, or shifts in research priorities could influence the pace at which revenue materializes.

同時，鑑於我們所支援專案的規模和複雜性，客戶啟動計劃、預算審批或研究重點的轉變的時間變化可能會影響收入實現的速度。

Embedded in our outlook is the expectation that spend from our largest customer will increase somewhat in the year and that the remaining customer base in the aggregate will grow at a faster rate.

我們的展望中包含這樣的預期：我們最大的客戶的支出將在今年略有增加，而其餘客戶群的整體成長速度將更快。

We expect this other customer growth to come from a mix of the MAG 7, domestic AI innovation labs, sovereign AI initiatives, and leading enterprises. We believe this will meaningfully contribute to customer diversification.

我們預計其他客戶成長將來自 MAG 7、國內人工智慧創新實驗室、主權人工智慧計畫和領先企業等多個方面。我們相信這將對客戶多元化做出重要貢獻。

Our customers are moving fast, driving shorter development cycles and responding faster to research breakthroughs. In 2025, we succeeded in this environment in no small part because we followed the research, anticipated customer needs, and pivoted where required.

我們的客戶行動迅速，推動開發週期縮短，並更快回應研究突破。2025年，我們在這種環境下取得成功，很大程度上是因為我們遵循了研究，預測了客戶需求，並在必要時進行了調整。

To illustrate, in the first quarter of this year for our largest customer, we deprecated a meaningful number of post‑training workflows which represented in the aggregate approximately $20 million of annualized revenue run rate, but replaced them with a combination of new post‑training workflows and scaled pre‑training programs, an area of recent focus and investment.

舉例來說，今年第一季度，我們為最大的客戶棄用了大量的培訓後工作流程，這些工作流程合計約佔年化收入的 2000 萬美元，但我們用新的培訓後工作流程和規模化的培訓前計劃（這是我們最近關注和投資的領域）的組合來取代它們。

From a revenue run rate perspective, the net effects turned out positive.

從營收成長率的角度來看，淨效應是正面的。

Indeed, we believe continuous innovation is critical to achieving our ambitious plans for 2026 and beyond.

事實上，我們相信持續創新對於實現我們2026年及以後的宏偉計劃至關重要。

The truly exciting news is we believe we are entering a golden age of innovation in Innodata as a result of investments we have made and intend to make in the future.

真正令人興奮的消息是，我們相信，由於我們已經進行和計劃在未來進行的投資，Innodata 正在進入創新的黃金時代。

I'm now going to share some of our recent innovation initiatives. For competitive reasons, we'll be appropriately circumspect, but what we share will give you a meaningful window into how we're thinking, where we're investing, successes we're having, and how we intend to capitalize on the opportunity ahead.

接下來，我將分享我們最近的一些創新舉措。出於競爭原因，我們將保持謹慎，但我們分享的內容將讓您深入了解我們的想法、投資方向、取得的成功以及我們打算如何掌握未來的機會。

I'll briefly walk through our recent innovation in 3 areas: generative AI model training, agentic AI, and physical AI.

我將簡要介紹我們最近在三個領域的創新：生成式人工智慧模型訓練、智能體人工智慧和物理人工智慧。

Before I do, I want to underscore a unifying theme.

在此之前，我想強調一個貫穿始終的主題。

Every innovation I am about to discuss is fundamentally a data innovation.

我接下來要討論的每一項創新，從根本上來說都是數據創新。

Whether the goal is more capable LLMs, more reliable autonomous agents, or more intelligent physical AI systems, data quality, data composition, data validation, and data engineering at scale are at the heart of the matter.

無論是更強大的LLM、更可靠的自主代理，或是更智慧的實體AI系統，大規模的資料品質、資料組成、資料驗證和資料工程都是問題的核心。

These are our core competencies.

這些都是我們的核心競爭力。

We'll start with generative AI training.

我們將從生成式人工智慧訓練開始。

Historically, customers told us the kind of training data they wanted.

過去，客戶會告訴我們他們想要什麼樣的訓練資料。

Increasingly, however, they're asking us to diagnose model performance, design the right training data sets, and demonstrate that those data sets will materially improve outcomes.

然而，他們越來越要求我們診斷模型效能，設計正確的訓練資料集，並證明這些資料集將實質地改善結果。

Here's how that works. We begin by identifying performance gaps using our evaluation frameworks. We then engineer targeted data sets and validate their efficacy by fine‑tuning either the customer's model or a structurally similar proxy model.

具體操作方法如下。我們首先利用評估框架找出績效差距。然後，我們設計有針對性的資料集，並透過微調客戶模型或結構相似的代理模型來驗證其有效性。

Only after we measure and demonstrate performance impact do we scale.

只有在衡量並證明績效影響之後，我們才會擴大規模。

This shifts the discussion from how much is the data to how effective is the data.

這使得討論的重點從資料量有多大轉移到資料的有效性有多高。

We believe this shift is being driven by two forces: the accelerating pace of AI research and the cost and time incurred to train ever larger models.

我們認為這種轉變是由兩種力量驅動的：人工智慧研究步伐的加快以及訓練越來越大的模型所產生的成本和時間。

And conversations about data efficacy play directly to our strengths.

關於數據有效性的討論正好發揮了我們的優勢。

We're also advancing methods for creating data sets that improve long‑context reasoning and AI model's ability to absorb and reason over very large amounts of information at once.

我們也在推進創建資料集的方法，以提高長上下文推理能力和人工智慧模型一次性吸收和推理大量資訊的能力。

This remains one of the industry's most important technical challenges.

這仍然是該行業最重要的技術挑戰之一。

Solving it requires not just architectural improvements, but advances in the creation at scale of very specific types of structured training data.

解決這個問題不僅需要架構上的改進，還需要大規模創建非常特定類型的結構化訓練資料。

Creating training data that improves long‑context reasoning is a non‑trivial problem, but we have made and are continuing to make meaningful progress on it.

創建能夠提高長上下文推理能力的訓練資料並非易事，但我們已經取得了並將繼續取得有意義的進展。

The second area of innovation is around evaluating systems of autonomous agents and improving them through targeted dataset creation.

第二個創新領域是評估自主代理系統，並透過創建有針對性的資料集來改進這些系統。

We believe that autonomous agents may represent the most significant business innovation opportunity since the advent of electricity, but companies quickly discovered that many AI agents that performed impressively in controlled laboratory settings degrade in real‑world production.

我們認為，自主代理可能是自電力出現以來最重要的商業創新機會，但企業很快就發現，許多在受控實驗室環境中表現令人印象深刻的人工智慧代理在現實世界的生產中表現下降。

The real world is chaotic. It's shaped by edge cases, conflicting constraints, unpredictable user behavior, and adversarial conditions. Addressing this is fundamentally a data challenge. Agents must be continuously trained and rigorously stress‑tested with data sets that are realistic, diverse, and complex.

現實世界是混亂的。它受極端情況、相互衝突的約束、不可預測的使用者行為和對抗性條件的影響。解決這個問題從根本上來說是一個數據挑戰。必須不斷訓練智能體，並使用真實、多樣化和複雜的資料集對其進行嚴格的壓力測試。

For this, we have developed a set of 3 highly complementary hybrid solutions.

為此，我們開發了 3 種高度互補的混合解決方案。

The first is an agent evaluation and observability platform.

第一個是代理評估和可觀測性平台。

Data scientists can use our platform during development to visualize and annotate agent trace data, to build LLM‑as‑a‑judge evaluators, to create business‑aligned evaluation rubrics, to generate golden data sets for regression testing, and to generate test data at scale.

資料科學家可以在開發過程中使用我們的平台來視覺化和註釋代理追蹤數據，建立 LLM 作為評估器，創建與業務一致的評估標準，產生用於回歸測試的黃金資料集，以及大規模生成測試資料。

Then once the agent is deployed, our platform can be used to continuously monitor its performance, perform root cause analysis of performance issues, and obtain mitigation data sets.

代理部署完成後，我們的平台可用於持續監控其效能，對效能問題進行根本原因分析，並取得緩解資料集。

We're pleased to share that we anticipate soon kicking off a managed services engagement with a hyperscaler in which we will use our platform to create test data at scale, perform automated evaluations, and identify critical model vulnerabilities in order to improve performance of its customer‑facing intelligent virtual assistant.

我們很高興地宣布，我們預計很快將與超大規模資料中心營運商開展託管服務合作，我們將利用我們的平台大規模創建測試資料、執行自動化評估並識別關鍵模型漏洞，以提高其面向客戶的智慧虛擬助理的效能。

The second innovation is a managed agent optimization pipeline designed to systematically train for and therefore neutralize the chaos of real‑world deployment at scale.

第二項創新是管理代理商優化管道，旨在系統地訓練並因此消除大規模現實世界部署的混亂。

The pipeline generates realistic test scenarios, automates evaluation, rigorously measures constraint satisfaction, and produces reinforcement‑learning data sets.

此管道產生逼真的測試場景，自動評估，嚴格衡量約束滿足情況，並產生強化學習資料集。

Using this system, we have demonstrated improvements of up to 25 points in constraint satisfaction.

利用該系統，我們已經證明約束滿足度提高了 25 個百分點。

Importantly, agents trained using conventional techniques tend to degrade significantly as task complexity increases. By contrast, agents trained through our pipeline sustain their performance under escalating real‑world difficulty.

重要的是，使用傳統技術訓練的智能體，隨著任務複雜性的增加，其表現往往會顯著下降。相較之下，透過我們的流程訓練的智能體能夠在不斷升級的現實世界難度下保持其性能。

In the most demanding scenarios, the performance gap between standard approaches and our system widens to more than 31 points. We currently have multiple AI innovation labs and enterprise customers actively exploring the system.

在最苛刻的情況下，標準方法與我們的系統之間的效能差距擴大到 31 個百分點以上。我們目前有多個人工智慧創新實驗室和企業客戶正在積極探索該系統。

The third solution we've designed to support enterprise agentic AI is an adversarial simulation system that generates high‑quality, semantically diverse and scalable adversarial attacks to stress‑test agents.

我們為支援企業智慧體人工智慧而設計的第三個解決方案是一個對抗性模擬系統，它可以產生高品質、語義多樣化和可擴展的對抗性攻擊，以對智慧體進行壓力測試。

The system generates a full spectrum of attack types: direct jailbreaks, indirect prompt injection via RAG pipelines, multi‑turn social‑engineering stenographic payloads, and compound attacks that combine injection techniques with domain‑specific knowledge.

該系統產生全方位的攻擊類型：直接越獄、透過 RAG 管道的間接提示注入、多輪社會工程速記有效載荷，以及將注入技術與特定領域知識相結合的複合攻擊。

Once vulnerabilities are identified, it generates highly targeted mitigation data sets to strengthen guardrails.

一旦發現漏洞，它就會產生高度針對性的緩解資料集，以加強防護措施。

We believe our system generates realistic adversarial attacks at scale in a meaningful way that exceeds existing alternatives.

我們相信，我們的系統能夠以有意義的方式大規模地生成逼真的對抗性攻擊，其效果超過了現有的替代方案。

Many tools on the market produce simplistic or templated hostile content that lacks the nuance and sophistication of real‑world threat actors, fails to scale across diverse scenarios, or relies on generic tactics that models quickly learn to anticipate and overfit to.

市面上許多工具產生的敵對內容過於簡單或模板化，缺乏現實世界威脅行為者的細微差別和複雜性，無法擴展到各種不同的場景，或依賴通用策略，而這些策略很容易被模型預測並過度擬合。

By contrast, our framework is designed to simulate adaptive, multi‑step, and strategically coherent attack patterns, including highly sophisticated model extraction, cybersecurity, cybercrime, and sobering threat scenarios that better reflect how advanced adversaries operate and allow our partners to stay ahead of emerging threats.

相較之下，我們的框架旨在模擬適應性強、多步驟且具有戰略連貫性的攻擊模式，包括高度複雜的模型提取、網路安全、網路犯罪和令人警醒的威脅場景，這些場景更好地反映了高級對手的運作方式，並使我們的合作夥伴能夠領先於新興威脅。

The result is adversarial training data that is both scalable and durable, forcing models to generalize rather than memorize and enabling more robust real‑world resilience.

結果是產生了可擴展且持久的對抗訓練數據，迫使模型進行泛化而不是記憶，從而實現了更強大的現實世界適應能力。

Our work is garnering interest from CISOs and security leaders at some of the world's premier AI and cybersecurity companies, as well as relevant experts in government, and has led to early‑stage engagements with several of them.

我們的工作引起了世界頂級人工智慧和網路安全公司首席資訊安全長和安全領導者以及政府相關專家的興趣，並已與其中幾家公司建立了早期合作關係。

At a time when the cyber industry is experiencing significant disruption, these capabilities bolster our position in the emerging field of AI trust and safety. An area where we are meaningfully deepening work with several hyperscalers, we believe Innodata is well positioned to emerge as a leader in prompt‑layer security, protecting AI systems at the point of interaction rather than relying solely on traditional perimeter or endpoint defenses.

在網路安全產業正經歷重大變革之際，這些能力鞏固了我們在人工智慧信任和安全這一新興領域的地位。我們正在與多家超大規模資料中心營運商深入合作，我們相信 Innodata 已做好充分準備，成為即時層安全領域的領導者，在交互點保護人工智慧系統，而不是僅依賴傳統的邊界或端點防禦。

Taken together, we believe these solutions position us not just as a data supplier but as a life cycle partner in agent reliability.

我們相信，這些解決方案不僅使我們成為數據供應商，而且使我們成為代理可靠性生命週期合作夥伴。

We believe 2026 will also mark the acceleration of physical AI, intelligent systems that perceive and interact with the physical world.

我們相信，2026 年也將標誌著實體人工智慧（能夠感知物理世界並與之互動的智慧系統）的加速發展。

While robotics provides the mechanical framework, physical AI provides the intelligence.

機器人技術提供機械框架，而物理人工智慧提供智慧。

The primary bottleneck in this domain is dataset quality and scale.

該領域的主要瓶頸是資料集的品質和規模。

Manual annotation and static QA sampling simply do not scale to billion‑sample corpora and continuously evolving environments.

人工標註和靜態 QA 抽樣根本無法擴展到數十億個樣本的語料庫和不斷變化的環境。

We have developed a large‑scale data engineering system that incorporates structural validation, distribution monitoring, temporal consistency checks, and model‑in‑the‑loop instrumentation.

我們開發了一個大規模資料工程系統，該系統包含結構驗證、分佈監控、時間一致性檢查和模型在環檢測。

This enables us to identify and correct defects in data sets before they propagate into performance failures. We're already using components of this system in the high‑visibility engagements we recently announced with Volunteer.

這使我們能夠在資料集中的缺陷擴散到效能故障之前，識別並修正這些缺陷。我們最近宣布與 Volunteer 開展高知名度合作專案時，已經使用了該系統的部分元件。

We recently secured a significant engagement to create foundational data sets for next‑generation robotic systems, including egocentric data.

我們最近獲得了一項重要的合作項目，為下一代機器人系統創建基礎資料集，包括以自我為中心的資料。

Egocentric data captures the world from the robot's point of view — what it sees and experiences in motion.

以自我為中心的數據從機器人的角度捕捉世界——它在運動中看到和體驗到的一切。

We're also working with a leading robotics lab to create affordance data sets at scale.

我們還與一家領先的機器人實驗室合作，大規模創建可供性資料集。

Affordance data teaches the system what actions are possible in a given setting — not just identifying objects, but understanding how they can be used.

可供性資料教會系統在給定的環境中可以執行哪些操作——不僅僅是識別物體，而是理解如何使用它們。

Egocentric data and affordance data taken together form the cognitive scaffolding that allows machines to act intelligently in dynamic environments.

自我中心資料和可供性資料共同構成了認知支架，使機器能夠在動態環境中智慧地行動。

This work also positions us to support the development of so‑called world models — internal simulations that allow AI systems to anticipate outcomes, reason about cause and effect, and plan several steps ahead.

這項工作也使我們能夠支持所謂的「世界模型」的開發——內部模擬，使人工智慧系統能夠預測結果、推理因果關係並提前規劃幾個步驟。

World models require richly structured data sets that capture interactions over time and the consequences of actions — precisely the type of data we are now engineering.

世界模型需要結構豐富的資料集來捕捉隨時間推移的互動以及行動的後果——這正是我們現在正在建構的資料類型。

Finally, we recently developed an AI model for drone and other small‑object detection that exceeds prior state‑of‑the‑art benchmarks by 6.45%. In a field where progress is often measured in fractions of a percentage point, a 6.45% improvement is a material advance.

最後，我們最近開發了一種用於無人機和其他小型物體偵測的 AI 模型，該模型比之前最先進的基準高出 6.45%。在進步往往以百分之幾來衡量的領域，6.45% 的改進是一項實質的進步。

The model improves detection fidelity under real‑world conditions where small size, speed, cluttered backgrounds, and environmental noise make reliable perception extraordinarily difficult.

該模型提高了真實世界條件下的檢測保真度，在真實世界中，小尺寸、速度、雜亂的背景和環境噪音使得可靠的感知變得異常困難。

We believe this advancement has compelling dual‑use implications that we are now actively exploring with potential customers.

我們認為這項技術進步具有引人注目的兩用價值，我們目前正積極與潛在客戶探討這項價值。

I'd like to underscore one of the important points I just made.

我想強調一下我剛才提到的一個重要觀點。

For decades, Innodata has specialized in creating high‑quality complex data sets.

幾十年來，Innodata 一直專注於創建高品質的複雜資料集。

Today, these capabilities are central to unlocking the next generation of AI systems.

如今，這些能力對於解鎖下一代人工智慧系統至關重要。

Advanced LLM reasoning, agent reliability in chaotic environments, and robotics perception in the physical world all depend on engineered data ecosystems. And this is precisely where we operate.

高階 LLM 推理、混亂環境中的智能體可靠性以及物理世界中的機器人感知都依賴工程化的資料生態系統。而這正是我們的業務所在地。

Our innovations in LLM training, agentic AI, and physical AI are not separate initiatives. Rather, they are extensions of a single strategic advantage — our ability to engineer data that measurably improves model performance in real‑world conditions.

我們在LLM訓練、智能體AI和物理AI方面的創新並不是相互獨立的舉措。相反，它們是單一戰略優勢的延伸——我們有能力設計數據，從而在真實世界條件下顯著提高模型效能。

We believe our innovation pipeline will be margin enhancing as well as revenue enhancing.

我們相信，我們的創新產品線將提高利潤率和收入。

We expect early 2026 adjusted gross margins to be in the 35 to 40% range as we ramp up new programs, with normalization toward our target 40% or better adjusted gross margins as new programs ramp up and as innovation‑driven workflows scale. Automation, synthetic systems, and evaluation platforms all structurally increase our operating leverage.

我們預計，隨著新專案的推進，2026 年初的調整後毛利率將在 35% 至 40% 的範圍內，隨著新專案的推進和創新驅動型工作流程的規模化，調整後毛利率將趨於正常化，達到我們 40% 或更高的目標。自動化、合成系統和評估平台從結構上提高了我們的營運槓桿。

I'll now turn the call over to Maris, who will go through the numbers.

現在我將把電話交給瑪麗斯，她將逐一檢查數字。

Thank you, Jack, and good afternoon, everyone. Revenue for Q4 2025 reached $72.4 million, up 22% year over year. Sequentially, revenue increased 15.7% from Q3's $62.6 million. Adjusted gross profit for Q4 2025 was $30.1 million, an increase of 6% year over year and 9% sequentially, with an adjusted gross margin of 42%. Adjusted EBITDA was $15.7 million, or 22% of revenue, and net income for the quarter was $8.8 million. To reiterate, this is net of significantly expanded data science and engineering efforts that are yielding the types of innovations Jack just spoke about. We ended the quarter with $82.2 million in cash, up from $73.9 million at the end of the prior quarter, and $46.9 million at the end of 2024, and we did not draw down on our $30 million Wells Fargo credit facility. As Jack mentioned, based on our current momentum, we presently forecast 35% or more year‑over‑year revenue growth in 2026.

謝謝你，傑克，大家下午好。2025 年第四季營收達 7,240 萬美元，年增 22%。與第三季相比，營收成長了 15.7%，達到 6,260 萬美元。2025 年第四季調整後毛利為 3,010 萬美元，較去年同期成長 6%，較上季成長 9%，調整後毛利率為 42%。經調整後的 EBITDA 為 1,570 萬美元，佔營收的 22%，該季淨收入為 880 萬美元。重申一下，這還不包括大幅擴展的數據科學和工程工作，這些工作正在產生傑克剛才提到的那種創新。本季末，我們持有現金 8,220 萬美元，高於上一季末的 7,390 萬美元，預計到 2024 年底將達到 4,690 萬美元，而且我們沒有動用 3,000 萬美元的富國銀行信貸額度。正如傑克所提到的，根據我們目前的勢頭，我們目前預測 2026 年的年收入成長率將達到 35% 或更高。

Thank you everyone for joining us today. Operator, please open the line for questions.

感謝各位今天蒞臨。接線員，請開通提問線。

(Operator instruction)

（操作說明）

George Sutton of Craig Hallam.

來自克雷格·哈勒姆的喬治·薩頓。

Thank you, Jack. I feel like I just sat through an advanced AI data science class, so thanks for that. I wanted to, step back a little bit because I think.

謝謝你，傑克。感覺就像剛上完一堂高階人工智慧資料科學課一樣，非常感謝。我想稍微退後一步，因為我覺得。

People have the assumption that some of what's working for you is somewhat temporary, and I think you've done an interesting job of kind of walking us through in past quarters from post-training as a start and then pre-training and now there are dramatic other use cases including things like robotics and autonomous agents. Can you just talk about the breadth of the things you're seeing and sort of where you see us in this continuum of data science opportunity for you?

人們認為你所取得的一些成就只是暫時的，我認為你在過去幾個季度裡做得很有趣，你從訓練後開始，然後是訓練前，現在又出現了其他一些引人注目的應用案例，包括機器人和自主代理等。您能否談談您所看到的各種事物的廣度，以及您認為我們在數據科學機會的這個發展歷程中處於什麼位置？

Sure, thank you, George.

當然，謝謝你，喬治。

Thank you for the question.

謝謝你的提問。

So, as we look out, near term 2026, we see ourselves as being, incredibly well set up by the innovations that we invested in 2025 and we see that innovation, output as a flywheel.

因此，展望2026年，我們認為，由於我們在2025年投資的創新，我們已經做好了非常充分的準備，我們認為這種創新產出就像飛輪一樣。

We're getting better, we're getting stronger, we're creating solutions that are solving problems that are the actual impediments that enterprises have when they're looking to integrate AI into their operations. So when you look across the spectrum of current capabilities in AI and.

我們正在變得更好，變得更強大，我們正在創造解決方案，以解決企業在將人工智慧融入其營運時遇到的實際障礙。所以，當你縱觀人工智慧目前的各項能力時，你會發現…

Future capabilities in things like egentic systems, physical AI robotics.

未來在基因係統、實體人工智慧機器人等領域的能力。

All of this boils down to Challenges in terms of of data engineering, of course they're going to be continuous improvements in architectures. It'll be, bigger models. There'll be narrower models for the domain specific, challenges, but at the heart of it in terms of making systems reliable, making them safe at an enterprise level, it's going to be, about innovations such as the ones we're announcing today.

這一切歸根究底都是資料工程的挑戰，當然，架構方面也會不斷改進。將會是更大的型號。針對特定領域的挑戰，會有更具體的模型，但就使系統可靠、在企業層面安全而言，其核心在於創新，例如我們今天宣布的這些創新。

In in in in in in data sets that are used for evaluation, data sets that are used, for training and improving safety and reliability of models, so we think that we're at the very beginning and that our relevance is by no means diminishing but only increasing. It's increasing not just at the level of, foundation model builders but it's clearly extending. Through the enterprise, we're super excited about where we are right now and about the uptake that the innovations that we're creating are having and are going to be having over the next couple of years.

在用於評估的資料集中，在用於訓練和提高模型安全性和可靠性的資料集中，我們認為我們才剛起步，而且我們的相關性不僅沒有降低，反而還在增加。這種現像不僅在基礎模型建構者的層面上增加，而且顯然正在向外擴展。透過企業的發展，我們對目前所處的位置以及我們正在創造的創新在未來幾年內所取得和將要取得的成就感到非常興奮。

That's great. And just one other question, having lived through, the last couple of years where you started the years with an expectation and you, then ended up meaningfully exceeding those initial expectations, is anything set up differently going into 2026 relative to what you see in your sites relative to what you're committing to today?

那太棒了。還有一個問題，經歷了過去幾年，你們年初都抱有期望，最終卻遠遠超出了這些期望，那麼相對於你們目前所承諾的，你們在展望 2026 年時，會有什麼不同的安排嗎？

No, not at all. We're following exactly that same methodology, we're really limiting our, or we're taking a conservative approach to forecasting growth based on opportunities where we have a very clear line of sight, but where we can't predict a close rate, where we can't, feel, pretty confident in something happening, we're just not baking that into our guidance.

不，完全不是。我們遵循的正是同樣的方法，我們確實限制了我們的成長預測，或者說，我們採取了一種保守的方法，基於我們能夠非常清楚地看到的機會來預測增長，但是對於我們無法預測成交率，或者我們無法對某件事的發生感到非常有信心的事情，我們只是沒有將其納入我們的指導方針中。

You our aspiration is to surprise and to beat expectations. When I look at this year, I think it will likely be another year of of of doing exactly that, we're seeing enormous opportunity with a much larger set of customers.

我們的目標是帶給您驚喜，超越您的期望。展望今年，我認為很可能又是我們繼續這樣做的一年，我們看到了面對更大規模客戶群的巨大機會。

We think that that's going to result in growth. I think it's likely that we'll be increasing guidance as we move through the year, and I think it's going to be a year where we accomplish very meaningful customer diversification. On top of that, as we already discussed, I think it's going to be a year where, we're starting to see increasingly hybrid human slash technologically driven solutions.

我們認為這將帶來成長。我認為隨著今年的推進，我們可能會提高業績預期，而且我認為今年我們將實現非常有意義的客戶多元化。除此之外，正如我們之前討論過的，我認為今年我們將開始看到越來越多由人類和技術驅動的混合解決方案。

That spells or presents the promise, I believe, for, increased recurring revenue. I think it promises greater margins over time, greater stickiness, a whole lot of things that will, over time be, I believe, consistently improving revenue quality as well on top of everything else. In terms of the work we do with foundation model builders, we're seeing tons of traction, not just in our largest customer, but in others as well. We're very much aligned with what they're looking to accomplish and things like long context reasoning improvements. We have innovations that are contributing to that. So we're tremendously excited about where we are right now.

我認為，這預示著經常性收入將會增加。我認為它有望帶來更高的利潤率、更強的客戶黏著度，以及許多其他方面，我相信這些優勢最終會持續提高收入品質。就我們與地基模型建構者合作而言，我們看到了巨大的成效，不僅在我們最大的客戶中，而且在其他客戶中也是如此。我們與他們想要實現的目標以及諸如改進長上下文推理等目標非常一致。我們有一些創新正在為此做出貢獻。所以，我們對目前所處的階段感到無比興奮。

All right, good stuff. Thanks, Jeff.

好的，不錯。謝謝你，傑夫。

Thank you.

謝謝。

(Operator instruction)

（操作說明）

Hamid Koran of BWS Financial.

BWS Financial 的 Hamid Koran。

Hi, so just the first question is you were talking earlier about, scaling your operations as, revenue ramps.

您好，第一個問題是，您之前提到過，隨著收入的成長，如何擴大營運規模。

Do you have enough employees now? Do you see the need to add more employees? What's your timeline as far as expecting gross margin to move up from here?

你們現在人手夠嗎？您認為有必要增加員工嗎？您預計毛利率從目前水準開始成長的時間表是怎樣的？

Thank you.

謝謝。

Sure, thanks, Hamed. So I think it really depends on what we're seeing. I think if we begin to project.

當然，謝謝你，哈米德。所以我覺得這真的取決於我們看到的是什麼。我認為如果我們開始進行預測。

And internally, growth rates that are, very significant, we're going to be making investments in order to ensure that we capture those growth rates. I do think that as a result of digesting some of those, people investments that we're making in COGS, as a result of the innovations that we're.

內部成長率非常顯著，我們將進行投資以確保我們能夠抓住這些成長機會。我確實認為，由於消化了我們在銷售成本方面的一些人員投資，由於我們正在進行的創新，情況會有所改善。

Discussing, different things like that. I do think that we're going to be seeing movement, back toward our target gross margins over time.

討論諸如此類的事情。我認為隨著時間的推移，我們會看到毛利率逐漸恢復到目標水準。

Okay, and then is there a timing as far as you know this pipeline of, deals that you're talking about, the with the other customers other than your largest, customer?

好的，那麼就您所知，您提到的這一系列交易，除了您最大的客戶之外，與其他客戶的交易，是否有時間安排呢？

So.

所以。

There are pipelines, but we're the deals that I'm referring to are largely deals that we're closing or have closed, so you know we're not depending on, we're not speculating about what will be happening. These are are are things that are actively underway.

雖然有專案儲備，但我所指的交易主要是我們正在完成或已經完成的交易，所以你知道，我們並不依賴未來會發生什麼，我們也不會去猜測未來會發生什麼。這些都是正在積極進行中的事情。

Okay, thank you.

好的，謝謝。

(Operator instruction)

（操作說明）

Alan Klee of Maxim Group. Please go ahead.

Maxim集團的Alan Klee。請繼續。

Yes, hi, for 2025, I think your adjust need with margins around 23%, and I know it's important for you to reinvest back into the business for the health of the company. My question is there any reason to think that you would target a higher or lower adjusted EBITDA margin than what you did in 2025?

是的，您好，對於 2025 年，我認為您需要將利潤率調整到 23% 左右，我知道為了公司的健康發展，將利潤再投資於業務對您來說非常重要。我的問題是，是否有任何理由認為您會設定比 2025 年更高或更低的調整後 EBITDA 利潤率目標？

So we're very much focused on seizing opportunity right now. We believe that we can do that and stay profitable, but we also believe that it's more important to seize opportunity and to do some of the things that we're describing and and prove out those innovations than it is to track.

所以我們現在正全力以赴抓住機會。我們相信我們可以做到這一點並保持盈利，但我們也相信，抓住機遇，去做我們正在描述的一些事情，並驗證這些創新，比跟踪更重要。

Adjusted gross margin percentages and TRY to maintain a certain percentage so you know we're we're going to be actively reinvesting in the business.

調整後的毛利率百分比，並努力保持一定的百分比，這樣您就知道我們將積極地對業務進行再投資。

The more opportunities we see to some extent, the more we'll be reinvesting. We do believe though that that maintaining profitability is something that we can do while we drive very aggressive growth and while we Become more progressively more critical to a larger and widening set of customers.

我們看到的機會越多，我們就會進行越多的再投資。不過我們相信，在實現高速成長的同時，在不斷擴大客戶群並使其變得越來越重要的情況下，我們仍然可以保持獲利能力。

Okay, one of the, bullet points you had on the, innovation was the structural foundation for margin expansion through automation, synthetic data generation, and evaluation platforms. Can you explain a little what you mean of.

好的，你提到的創新要點之一是透過自動化、合成資料產生和評估平台來建立擴大利潤率的結構基礎。能稍微解釋一下你的意思嗎？

Which margin expansion are are you referring to?

您指的是哪些利潤率擴張？

Thank you.

謝謝。

Yeah, so we're referring to overtime gross margin expansion.

是的，我們指的是加班期間毛利率的擴張。

So a lot of the innovations that we're working on now and that we're bringing into the market are hybridizations of Software and human teams and I think that over time we're going to be seeing the gross margins associated with those capabilities to be perhaps, well in excess of the gross margins that we target today.

因此，我們現在正在研發並推向市場的許多創新都是軟體團隊和人類團隊的混合體，我認為隨著時間的推移，這些能力帶來的毛利率可能會遠遠超過我們今天設定的毛利率目標。

Okay, got it. That makes a lot of sense. And the last question I had was, just for first quarter 26, is there anything you'd want to point out in terms of That that might that that might stand out just in terms of.

好的，明白了。這很有道理。我最後一個問題是，就 2026 年第一季而言，您有什麼想指出的，有哪些方面可能比較突出？

I don't know revenues or expense spend.

我不知道收入或支出狀況。

Well, I'm not going to say it's next quarter necessarily, but I think, very soon we're going to be seeing quarters that from a revenue perspective are beating what our revenue was for an entire year, 3 years ago, so that's pretty good news right there.

我不會說一定是下個季度，但我認為，很快我們就會看到，從收入角度來看，我們幾個季度的收入將超過三年前全年的收入，這絕對是個好消息。

As we move through the year, I think you're going to be seeing, more proof points and more evidence and more engagement that we have with some, very interesting companies around the innovations that we're describing. I think that we'll start to demonstrate that we're somewhat migrating from, a vendor to like a foundational layer within AI ecosystems, becoming a. Someone that is able to unlock the promise of AI, within enterprise engagements, a company that's able to help.

隨著今年的推進，我認為你會看到更多證據、更多證明，以及我們與一些非常有趣的公司就我們所描述的創新進行的更多互動。我認為我們將開始證明，我們正在從供應商轉型為人工智慧生態系統中的基礎層，成為一個…能夠釋放人工智慧在企業應用中潛力的人，以及能夠提供幫助的公司。

Enterprises embrace, complex agents that plan, call tools, execute complex workflows, and create a lot of value, so I think we'll be seeing that. I think we'll see evidence of that in the 1st quarter. I think we'll continue to see evidence of that through the year.

企業樂於接受能夠規劃、調用工具、執行複雜工作流程並創造大量價值的複雜代理，所以我認為我們將會看到這種情況。我認為我們會在第一季看到這方面的證據。我認為今年我們會繼續看到這方面的證據。

Maybe one last quick one, when you were talking about your largest customer, I don't know if I fully understand, but you said you mentioned something about $20 million that maybe it's going to be replaced with more than that, or could you just explain what.

最後一個問題，您剛才談到您最大的客戶時，我不太明白，您提到過 2000 萬美元，也許會被更多人取代，您能解釋一下嗎？

That, yeah, I think the point that we were making there is how important innovation is to our company today and how it's becoming increasingly important. There are things that We complete and we're starting new things and by following the path of innovation by, what did Wayne Gretzky used to say by skating to where the puck is going, we're able to deprecate things that the companies no longer require but be there for them for the things that they're that that are the emerging requirements again, we're seeing the emerging requirements to be more interesting from a business perspective.

是的，我認為我們當時想表達的重點是，創新對我們公司如今的重要性，以及它變得越來越重要。有些事情我們完成了，有些事情我們開始了新的嘗試，透過遵循創新之路，就像韋恩·格雷茨基常說的那樣，滑向冰球將要去的地方，我們能夠淘汰公司不再需要的東西，但同時也能滿足他們正在湧現的新需求。從商業角度來看，我們看到這些新需求更有趣。

And a revenue quality perspective and a differentiation perspective than the things that came before.

從收入品質和差異化角度來看，都比以前的做法更具優勢。

So the investments are proving out, they're enabling us to scale and increase the breadth of engagements. They're enabling us to win new engagements and new customers that, some of which we think are going to be very substantial. They're going to really flower this year.

因此，這些投資正在取得成效，它們使我們能夠擴大規模並擴大業務範圍。它們幫助我們贏得了新的合作項目和新客戶，我們認為其中一些項目將會非常重要。今年它們一定會開出很多花。

That's going to address the diversification issue. So you know we're, when you look at 2026, we see a huge growth here we believe that we're going to be increasing likely our our guidance from what we're starting the year at.

這樣就能解決多元化問題了。所以你知道，展望 2026 年，我們看到了巨大的成長，我們相信我們可能會提高年初的預期。

We think that the solutions and how we're embedded in workflows is going to be progressively more interesting and margin and revenue enhancing.

我們認為，我們的解決方案以及我們融入工作流程的方式將會越來越有趣，並能提高利潤率和收入。

And it promises to be a tremendous year on all of those fronts.

在所有這些方面，今年都有望取得巨大的成功。

That's great. Congratulations.

那太棒了。恭喜。

Thank you.

謝謝。

Thank you.

謝謝。

There are no further questions at this time. I will now turn the call back over to Jack Abuhoff. Please continue.

目前沒有其他問題了。現在我將把電話轉回給傑克阿布霍夫。請繼續。

Thank you, operator. So, yeah, to wrap up, 2025 was a great year and 2026 holds the promise of being even better. In 2025, we delivered strong topline growth. We exceeded expectations across major financial metrics. We expanded margins, we strengthened our balance sheet, we invested successfully ahead of demand.

謝謝接線生。總之，2025 年是精彩的一年，而 2026 年有望會更加美好。2025年，我們實現了強勁的營收成長。我們在各項主要財務指標上均超出了預期。我們擴大了利潤率，增強了資產負債表，並在需求出現之前成功進行了投資。

And those investments proved wildly, successful and set us up well for 2026. I believe the 2026 is likely to be an incredible year. We, we've guided to approximately 35% growth based on visibility today, but I believe there may be very considerable upside to that. We'll update you through the course of the year, much like we have done in the last couple of years.

事實證明，這些投資非常成功，為我們2026年的發展奠定了良好的基礎。我認為2026年很可能是令人難以置信的一年。根據目前的市場狀況，我們預計成長幅度約為 35%，但我認為成長空間可能非常大。我們會像過去幾年一樣，在今年內定期向您報告最新情況。

I also want to underscore, our belief that this year we will potentially diversify our revenue streams significantly.

我還想強調，我們相信今年我們的收入來源將實現顯著多元化。

And we believe expertly engineered data ecosystems are going to be every bit as important as bigger models and new architectures will be in terms of advancing, language models, media models, autonomous agents, robots, world models, and other kinds of AI that hasn't even been conceived of yet.

我們相信，精心設計的資料生態系統對於推進語言模型、媒體模型、自主代理、機器人、世界模型以及其他尚未被構想出來的人工智慧類型而言，其重要性將與更大的模型和新的架構不相上下。

So we're very excited about what lies ahead. We're very confident in our positioning. We're very committed to building one of the most important and we think most capable AI enablement companies in the industry. It's going to be an exciting year. So thank you all for being on the journey with us. Look forward to next time.

所以我們對未來充滿期待。我們對自己目前的市場定位非常有信心。我們致力於打造業界最重要、我們認為最有能力的AI賦能公司之一。這將是令人興奮的一年。所以，感謝大家一路陪伴我們。期待下次見面。

Ladies and gentlemen, that concludes today's conference call.

女士們、先生們，今天的電話會議到此結束。

Thank you for your participation. You may now disconnect.

感謝您的參與。您現在可以斷開連線了。