Innodata (INOD) 2026年第三季度业绩电话会：营收增长53%，上调业绩指引

核心要点

• Innodata公布2026年第三季度营收为9560万美元，同比增长53%，环比增长4%。这标志着该公司已连续13个季度实现同比增长。
• 调整后毛利率达到公司40%的目标水平。调整后EBITDA为1770万美元，占营收的19%；净利润增长24%，达到1030万美元。
• 在新增项目和客户拓展的支撑下，管理层将2026年营收增长指引从40%上调至至少42%。
• Innodata历史上第一大客户于10月中旬终止了一项合作项目，预计将导致第四季度营收减少约1200万美元。管理层预计将在第一季度前通过新项目填补这一缺口，但具体时间尚未最终确定。
• 重点增长领域包括具身智能（Physical AI）、强化学习环境、信任与安全、端到端模型训练数据、企业级AI以及联邦政府项目。
• 截至第三季度末，Innodata拥有1.64亿美元现金及现金等价物，且无负债，富国银行提供的5000万美元信贷额度仍未动用。

核心财务业绩

业务与运营表现

客户拓展仍是主要的增长动力。此前被列为Innodata第二大客户的公司在第三季度跃升为其第一大客户。Innodata在该季度与该客户签署了一项价值2900万美元的预训练数据项目，并在第四季度迄今追加签署了一项价值约1800万美元的新项目。

在另一家大型超大规模云服务商处，Innodata赢得了五个新项目。过去12个月内该客户贡献的营收约为310万美元，管理层预测其在2027年有望贡献超过2000万美元。此外，公司在第三季度新增了5家新客户，并于近期签约了一家专注于企业级基础模型的公司。

具身智能（Physical AI）

Innodata正通过第一人称视角视频、UMI夹爪数据和动作捕捉，扩大机器人领域的数据采集与标注业务。2026年迄今，该公司已交付超过62万个机器人可执行的标注任务。

管理层表示，一家新签约的人形机器人客户在2027年可能带来数百万美元的业务机会。此外，与一家前沿AI实验室开展的试点项目若成功推进并实现规模化，在2027年有望创造高达700万美元的年化营收。

强化学习与AI安全

公司正在为个人智能体和知识工作智能体构建强化学习环境。这些沙箱环境允许模型在不访问实际生产系统的情况下演练多步骤任务。

在信任与安全领域，Innodata正在敲定一份工作说明书，涉及CBRNE（化生放核爆）风险评估和智能体安全，潜在营收约为600万美元。另一项已达成口头承诺的项目有望在2027年产生约250万美元的营收。其他试点项目还涵盖医疗健康、网络安全和机器人安全等领域。

训练数据与企业级AI

Innodata表示，公司正积极推进覆盖专用预训练数据、人工评估、智能体环境和模型测试的端到端项目。潜在机遇包括一个来自前沿实验室、月度营收规模达数百万美元的项目，以及一个价值约200万美元的高端影视级视频数据项目。

该公司还与一家全球法律与合规解决方案提供商签订了价值约120万美元的合同。Innodata的法律专家将为专用法律AI模型提供人工验证和反馈。

上一季度，企业级AI落地项目新增了约500万美元的年化营收运行率。公司还将与一家现有社交媒体客户的合作规模扩大至每年约250万美元。其企业战略涵盖与Anthropic、Databricks、Snowflake以及谷歌云（Google Cloud）的合作伙伴关系。

联邦政府项目

Innodata的联邦政府业务目前有两个正在执行的项目，并通过总承包商落实了第三个项目。管理层还透露已口头赢得一项为期五年、年价值500万美元的合同，预计将于第四季度末完成签署。该中标项目仍有待最终合同流程的完成。

管理层业绩指引

管理层将2026年营收增长指引从40%上调至至少42%。

公司继续将调整后毛利率目标设定为40%。管理层指出，季度毛利率可能会因项目结构以及为构建核心能力或支持未来增长而在低毛利项目上的投入而出现波动。

Innodata历史上第一大客户终止某项合作项目，预计将导致第四季度营收减少约1200万美元。管理层预计将在第一季度前通过新项目填补这一营收缺口，但表示无法提供确切时间，且目前对若干业务机会仍处于后期洽谈阶段。

管理层计划在公布第四季度业绩时提供2027年展望。在确定的六大增长领域的投资预计将通过经营性现金流提供资金。内生性投资仍是资本配置的首要任务，同时在有助于加速推进公司战略的情况下，也会审慎考虑并购机会。

风险与关注重点

• 项目的启动与终止可能导致季度营收出现重大波动，正如某客户项目终止预计在第四季度产生1200万美元的负面影响所表明的那样。
• 能否在第一季度前填补营收缺口，取决于Innodata能否成功锁定并启动目前仍处于后期洽谈阶段的项目。
• 当项目结束时，人员并不总能立即重新调配，因为新项目可能需要不同的专业技能。
• 毛利率可能会随项目结构以及公司在构建战略能力时对较低毛利率的容忍度而发生波动。
• 机器人、安全及联邦政府领域的多个机会仍处于试点、口头承诺或分阶段授约阶段，其实际营收取决于执行成效、业务规模化以及合同的最终签署。
• 客户集中度仍是一个值得关注的问题，不过管理层表示，新大客户的拓展正在改善客户多元化结构。

分析师问答要点

管理层解释称，项目结束后员工的重新调配取决于当前可用工作是否需要类似技能。预计于第一季度前启动的项目属于独立的业务机会，而非已终止项目的直接延续。

在具身智能（Physical AI）方面，管理层表示可能会出现持续性需求，因为机器人数据无法直接从现有的互联网级语料库中获取。随着机器人逐步迈向现实场景部署，模型开发者必须持续采集、生成并标注物理交互数据。

公司表示其指引制定方法依然保持审慎。指引通常仅涵盖正在执行的项目、已中标业务以及营收可见度高的小型项目，而在中标结果和营收确认时间最终确定之前，不会将储备管线中的大型机会纳入其中。

管理层重申，40%的调整后毛利率目标综合反映了各项目不同的经济效益以及对核心能力的持续投资。管理层拒绝透露预计来自历史上第一大客户的替代项目在具体项目层面的毛利率。

财报电话会议完整实录

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完整财报电话会议逐字稿

管理层陈述

Operator

Hello, everyone. Thank you for your patience, and thank you for joining us. Welcome to the Innodata Q3 2026 Earnings Call. [Operator Instructions]

I will now hand the conference over to Amy Agress, General Counsel. Amy, please go ahead.

Amy Agress

Thank you, operator. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Rahul Singhal, President and Chief Executive Officer of Innodata; and Jayant Chauhan, Chief Financial Officer. Also on the call today is Aneesh Pendharkar, Senior Vice President, Finance and Corporate Development. We'll hear from Rahul first, who will provide perspective about the business, and then Jayant will provide a review of our results for the third quarter. We'll then take questions from analysts.

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 reflect management's views as of today and are based on current expectations, assumptions and estimates as of today, and as such, the statements are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements. These statements include, but are not limited to, statements regarding our future financial and operational performance, business strategies, growth initiatives, expectations, projections and guidance.

Factors that could cause these results to differ materially are set forth in more detail in today's earnings press release and in our most recent annual report on Form 10-K, including the sections entitled Cautionary Note Regarding Forward-Looking Statements and Risk Factors, as well as our subsequent quarterly reports on Form 10-Q and other reports and filings with the Securities and Exchange Commission. These documents are available through the commission's website and our website. We undertake no obligation to publicly update or revise any forward-looking information.

In addition, during this call, we may discuss certain non-GAAP financial measures. In our earnings release filed 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 reconciliations of these measures with comparable GAAP measures.

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

Rahul Singhal

Thank you, Amy, and good afternoon, everyone. Before I turn to the quarter, I want to take a moment to acknowledge the occasion. This is my first earnings call as Innodata's CEO. I'm honored by the confidence that Jack and the Board have placed in me, and I'm mindful of what I have inherited, a company with extraordinary momentum, an extraordinary team and an extraordinary opportunity in front of it. I could not be more excited to discuss our results and strategy.

I also want to acknowledge Jack. As Executive Chairman, he remains deeply engaged, partnering with me and the research team to build differentiated capabilities and to bring them to the federal government and the enterprise. Our strategy is shared, and the research-led engine that has been driving our results is the one I intend to build on.

I said last quarter that I would repay the confidence placed in me with results, and I'm pleased to be starting with a quarter that lets me do exactly that. Q3 was another record revenue quarter for Innodata. Revenue was $95.6 million, up 53% year-over-year, exceeding analyst consensus of $83.3 million by approximately $12.3 million or 15%. This was our 13th consecutive quarter of year-over-year growth.

We exceeded analyst consensus on other key metrics as well. Adjusted gross margin was 40%, in line with our publicly stated 40% target. Adjusted EBITDA was $17.7 million or 19% of revenue, exceeding consensus by approximately $2.3 million. Net income was $10.3 million, up 24% year-over-year and fully diluted earnings per share were $0.29 compared with consensus of $0.17.

Much as we hoped, Q3 and Q4 to date have been strong in new business wins. The customer we referred to last quarter as our second largest customer became our largest customer in Q3 and continues to expand with us. In the quarter, we signed a new program with this customer for pretraining data with a contract value of $29 million. So far in Q4, we have signed an additional program with this customer valued at approximately $18 million, and we have a robust pipeline with them going into 2027.

We also have won 5 new programs at another customer, a large hyperscaler. Just to give you a sense for the momentum and success we have already achieved with this customer, in the trailing 12 months, we did approximately $3.1 million of revenue with this customer. But based on these new programs, we are now forecasting this customer may contribute more than $20 million in revenue in 2027. And beyond that, there's significant additional pipeline in discussion.

We also added 5 new logos in Q3, including model builders and an enterprise data platform. And in the last few weeks, we signed an enterprise-focused foundation model company. Beyond that, we are in promising conversations with the leading frontier model developer for a major expansion, and we just opened discussions with a leading social media company. The business we are winning reflects how well we continue to align with the needs of frontier model builders even as those needs grow more sophisticated across virtually every dimension.

In just the last 18 months, model training emphasis has shifted from human-based evaluations to agentic traces within sandbox replicas of real-world environment, commonly called RL gyms, and from synthetic data to a renewed appetite for real-world data. Each of these shifts has expanded both what is possible and what is required, and each has created new opportunities for us.

Our significant new wins are also enabling us to meaningfully drive customer diversification. One dynamic worth noting, several times, researchers we have worked with at the leading labs have moved to other labs or to well-funded start-ups. And when they land, they call us. They knew our capabilities and our people, so they brought the relationship with them. This has helped us, and we believe it will continue to help us grow our pipeline at a rate that outpaces SG&A growth. Today, we are raising our guidance from 40% to 42% for 2026.

Given the magnitude of the business we are winning, our raise could have been more substantial but for the fact that in mid-October, our traditionally largest customer decided to end one of its programs with us. The good news is that we are expecting this revenue to be replaced by new programs at this customer by Q1, but we expect it to cost us about $12 million in Q4 revenue.

I'd like to emphasize again, as we have in the past, that within our large customers at any point in time, there are programs ending, other programs starting and still other programs in discussion. This reflects the dynamic nature of our business and represents our business running as designed.

I'd now like to take a few minutes to drill down on 6 specific growth drivers that we believe will propel our growth over the next 12 months. I'll start with driver #1, physical AI. Capital is pouring into physical AI faster than into any category since the LLM boom began. Physical AI is sometimes referred to as embodied intelligence or simply AI robotics. In the first half of 2026, global venture funding in the space totaled $47.4 billion across 521 deals. This exceeds the amount invested in physical AI from 2022 through 2024 combined. The bets are concentrated in robot foundation models and companies looking to build consumer and industrial robotics.

Recent research confirms that robotics foundation models improve with the data the same way LLMs do. But because no Internet scale corpus of physical interaction exists, the data must be manufactured through teleoperation, egocentric human video and simulation, all work we've begun doing. And as models move from lab demos to deployment in factories, warehouses and homes, we anticipate demand expanding from one-off pretraining corpora to continuous task-specific data for fine-tuning and evaluation.

As a data provider, we become recurring infrastructure. We've been working hard to capture the opportunity and have made significant strides across robotic data collection, including egocentric data, UMI gripper data and motion capture data, the creation of large-scale AI-assisted action annotations for VLM training and robot safety testing and evaluation.

Our physical AI data business is scaling materially. We operate large egocentric data collection programs, stereo camera headset capture in home environments across the United States and Latin America and 6-camera headset capture of complex industrial tasks across hundreds of businesses. Our customers are leading humanoid and frontier robotic labs with big ambitions. We are seeing just one of our recently acquired humanoid customers representing a potentially multimillion-dollar opportunity for us next year.

In September, we launched our state-of-the-art Vicon motion capture lab to capture high-fidelity 3D human motion for world model training and humanoid robot training, and we have demonstrated an end-to-end real-to-SIM-to-real pipeline, bringing captured human motion into Isaac Sim, retargeting it to humanoid kinematics and executing our own Unitree G1. Beyond collection, we are quickly becoming a trusted annotation partner to physical AI labs, delivering more than 620,000 robot executable labeling jobs this year so far. This is a new focus area for us, which I expect to become a big growth driver for 2027 as robots move from labs to production.

We are launching an annotation pilot with a leading robotics lab, which if scaled, could be a sizable opportunity. We also won a pilot with a leading frontier lab. If the pilot is successful, we anticipate it could yield up to $7 million of run rate revenue in 2027. We're also working with a major technology company on safety red teaming data for robot foundation models.

I'll next talk about driver #2, RL environment for personal and knowledge work agents. I mentioned earlier the shift towards training in RL gyms. The reason for this shift is simple. An agent learns by acting and seeing the consequences, and labs can't let agents learn on live systems for safety and privacy reasons and because many actions can't be undone. So the agent trains inside a sandbox replica where it can explore, make mistakes and reset thousands of times.

Innodata is now producing at scale the kind of high-quality RL environments that we believe will dominate model training for the foreseeable future. We anticipate this movement, and we have built strong research team with deep expertise in environment design and reinforcement learning. We are particularly focused on 2 use cases, personal agents and knowledge work agents, and we intend to lead in these areas.

On the personal agent side, we've been working for some time with one of our largest customers on personalization for its consumer agents. If you follow the consumer agent launches this year, you saw what personalization at that level looks like in the hands of hundreds of millions of users. We are a part of the work behind that. Our data, our environment and our methodologies are embedded in how agents of that kind learn what a person actually wants. We believe personal agents are the next competitive battleground, and the winners will be the companies whose agents truly know you and can tailor the experiences to you.

The sophisticated RL gyms that capture that kind of depth are a key differentiator in that race. And our customers have told us the methodologies and tooling we have developed for extracting the data behind the personal agents are unlike anything they have seen. We expect to be building more of these personalization agent gyms over the next several quarters.

On the enterprise side, the battleground among model builders has shifted from coding agents to agents that perform everyday knowledge work across desktop and browser-based workflows that span multiple systems, start from unstructured inputs and require judgment at every step. Models are now being asked to do the expert work that rules-based automation never could. We are building sophisticated RL gyms to train these models, and we are building what goes inside them as well. We are in the early innings of this work, and we believe our team and the technology position us well, and we intend to lead in training these new generation of agents.

I'll next talk about driver #3, trust and safety. And I'll start with alignment, by which we mean the work of making AI systems behave safely and in accordance with human intent and policy. We believe the need for this work is increasing for a couple of very straightforward reasons. First, as AI moves from one-shot answers to multistep reasoning and from chatbot assistance to autonomous agents, the cost of alignment failure increases and evaluating safety becomes more demanding.

Secondly, models are being deployed in sensitive domains such as health and finance, where failure carry real consequences. As we have all seen in recent months, as models become more capable, the attendant risk become greater. The labs are responding by shifting more efforts towards alignment post training and specifically humans in the loop data. This presents a significant opportunity for Innodata.

Our alignment work today spans several high-stakes areas. For example, we are finalizing a new statement of work valued at approximately $6 million of potential revenue to perform chemical, biological, radiological, nuclear and explosive, or CBRNE, risk evaluation agent safety work. This is with a large hyperscaler that we announced on our first quarter call has selected us as a global trust and safety partner.

In addition, the customer has verbally committed to a program with us under which we will test agentic safety at some of their platforms. We believe this program has the potential to yield approximately $2.5 million of revenue next year. We are also in pilots on several mission-critical safety programs in health, cybersecurity and robotics. We believe these pilots will be successful and that the programs will scale significantly. Several of these pilots use what we call temptation gyms, environments built to reveal whether the model will pursue a legitimate goal by illegitimate means or cut corners to get there. I'll say more about these in a moment.

Across trust and safety, as across our agentic and RL work, we are gaining ground because of how our customers see us as a trusted adviser, a serious applied research organization and a reliable delivery partner. That combination makes us sticky within these accounts, and it lets us help shape demand, not just respond to it.

I'll now turn to driver #4, end-to-end training pipelines that span pretraining to agentic AI. Here is the context. The open web is running dry. Epoch AI estimates that labs will exhaust the supply of public human written text sometime between 2026 and 2032. Every stage of model development now depends on data that must be deliberately created, such as specialized pretraining corpora and RL environments and evaluations. I believe Innodata is one of the few companies in the world doing this work end-to-end and bringing deep domain expertise to all of it.

On the pretraining side, we are going beyond standard web-based sourcing, instead securing unique differentiated data sets. We see demand for this in several areas. Our largest opportunity is a potential program we are discussing with a leading frontier lab. It is intended to span a wide variety of unique data types at a run rate in the millions of dollars per month. Video is a good example of data that can't simply be scraped at the quality and with the rights that labs need. We are in conversation with one customer on a high-end cinematic video opportunity valued at approximately $2 million.

Charts are another area we are focused on. Our teams are building complex charts and graph data sets to strengthen visual reasoning in models, and we have secured several new customers for these data sets.

We are also expanding our real-world business data licensing practice. Frontier labs want raw real-world data as well as agentic trajectories with verifiable outcomes required to train their models. Because we work so closely with our customers, engineering teams, we have an advanced understanding of which data sets will deliver the performance lift they're looking for. And that same knowledge makes us a natural partner for the owners of that data who rely on us to connect it to the problems it can solve.

Human evaluation continues to progress from generalist crowds to expert pools. We recently won a new contract value at approximately $1.2 million with a leading global provider of legal and regulatory solutions. Under this agreement, our legal experts will provide human-in-the-loop validation and the expert feedback used to fine-tune the customers' specialized legal AI models. The win highlights our strength in high-stakes workflows and establishes a scalable blueprint for high-margin multiyear growth in enterprise AI.

We're applying the same expert model to agentic AI using knowledge workers to build agentic gyms, environments where agents perform real computer use and knowledge work tasks against realistic data. As agents take on more autonomous work, labs need to know not only whether an agent can complete a task, but whether it completes it honestly. Our temptation gyms are exactly built for that. These environments are deliberately designed with opportunities to cut corners, gaining a reward, fabricating a result, misrepresenting what was actually done, and they are instrumental so labs can see whether an agent takes it.

That lets lab catch behavior that in real deployment would amount to a misrepresentation of fraud. This kind of integrity testing is becoming essential and requires both domain expertise and environment building capability. Temptation gyms are a new category, and we believe being early gives us capability that will differentiate us.

Next, I'll address driver #5, enterprise AI. As we have said previously, implementation of generative AI models in the enterprise is a very large opportunity for us. We have been building a practice for the last 3 quarters, and we continue to gain momentum. Just last quarter, we added approximately $5 million of revenue run rate of agent implementation for a large customer. We also initiated 2 new enterprise relationships with a global technology hardware company and a major financial services firm. And we expanded our work with the social media customer we have served since last year to about $2.5 million a year.

We believe these accomplishments give us a strong foundation to accelerate from in 2027. We have built a 3-tier partnership strategy that enables us to help our enterprise customers at the foundation model layer, the data layer and the infrastructure layer. Our partnership with Anthropic, a leading foundation model provider, helps us drive enterprise agentic solutions into production. Our partnerships with Databricks and Snowflake, leading data platforms help us build the AI-ready data foundations that every successful deployment depends on. And our partnership with Google Cloud enables us to deliver and scale solutions on enterprise-grade cloud infrastructure. Moreover, these channel partnerships gives us additional growth paths and joint opportunities.

We believe our experience in training frontier models puts us in a unique position to understand how to create golden data sets, benchmark models, build custom rubrics and deploy end-to-end data engineering at scale to create robust production-ready solutions for enterprises and gives us a unique advantage compared to traditional systems integrators.

Lastly, I'll talk about our sixth driver, our federal practice. We launched our federal practice about a year ago. Since then, it has built a position in government AI programs that compound each award. In its first year that produced 2 programs and a third committed through our prime partners. The opportunity we described as a finalist in May, an AI development environment for the Department of War, is in Phase 1 delivery, with a production decision expected in the coming months. We were awarded advanced data labeling on our U.S. Navy autonomy program, the first phase of a competitive down-select, selected on the computer vision capability we demonstrated to the government this year.

And we have been identified for a subcontract role on the second multiyear department award program. Each of these is staged, with a larger award decided after the current phase performs, which is where we have chosen to compete. We have a verbal win for one contract of $5 million per year for 5 years with signing expected by end of the fourth quarter. We hope to continue momentum across all programs.

We are ending the year with real momentum, a clear strategy and tailwinds from each of the 6 growth drivers I've just walked you through. I've never been more confident about where Innodata is positioned. The opportunities in front of us are both large and they are close at hand. We are investing to capture them, and we expect those investments to pay back quickly and to build durable advantage. The next 12 to 18 months will be about execution, and that is where Jayant and I are focused.

Let me turn over the call to Jayant, our CFO, to share his perspectives about the business and our Q3 financial results.

Jayant Chauhan

Thank you, Rahul, and good afternoon, everyone. Let me walk through our third quarter results and also share our thinking on investments and capital allocation. Revenue for Q3 2026 was $95.6 million, up 53% year-over-year and 4% sequentially, our 13th consecutive quarter of year-over-year growth. This exceeded analyst consensus by $12.3 million or 15%.

Adjusted gross profit was $38.3 million, representing adjusted gross margin of 40%. This is in line with our 40% target. In any quarter, our gross margin is driven by program mix. Adjusted EBITDA was $17.7 million or 19% of revenue, up 9% year-over-year. This was ahead of consensus of $15.4 million by approximately 15%. Net income was $10.3 million, up 24% from the $8.3 million we reported in Q3 last year. Fully diluted earnings per share was $0.29 compared with consensus of $0.17, a beat of approximately 70%. Our year-to-date effective tax rate was approximately 12% compared to our long-term target range of 23% to 25%. The lower tax rate was driven by tax benefits recognized this quarter.

Turning to the balance sheet. We ended the quarter with approximately $164 million in cash and cash equivalents. Excluding approximately $10.3 million of customer prepayments, which are a pass-through, our cash was $153.7 million. This is up $19.3 million from $134.4 million on the same basis at the end of Q2. We have no debt. Our $50 million Wells Fargo credit facility remains undrawn. We did not use our ATM program during the quarter.

With the full quarter behind me, let me share some perspectives on investments and capital allocation. Given the opportunity set Rahul described, we will continue to invest in areas we believe will become meaningful growth drivers in 2027 and beyond. We intend to fund these investments from operating cash flow, and we expect them to pay back quickly. On capital allocation, with approximately $154 million of cash, net of customer prepayments and no debt, we have real flexibility. Our first priority is organic investment in the growth drivers. Beyond that, we will be selective about M&A that we believe can accelerate our strategy, and we intend to make these investments while maintaining a strong balance sheet.

To sum up, Q3 delivered record revenue, adjusted EBITDA ahead of expectations at our 40% gross margin target and $19.3 million of underlying cash generation, while we continue to invest in the drivers we believe will enable our next phase of growth.

With that, I'll turn it back to the operator. Operator, we are ready for questions.

Operator

[Operator Instructions] Your first question comes from the line of George Sutton with Craig-Hallum.

分析师问答

George Sutton

Can you explain the process of your labor pool and how that works? So you have a program that ends and this happens all the time. I think typically, you would like to deploy those folks into other jobs immediately. You mentioned Q1 could be a similar kind of a deployment. Do these require different skill sets? Do you then rehire from that pool? Can you just give us a picture there?

Rahul Singhal

George, thank you for your question. So yes, I think the way the program start and stop is dependent on the types of tasks that these frontier model builders are working on. Sometimes the skills that they're working on could be redeployed. And sometimes those skills cannot be redeployed.

The other thing to note is when these different frontier model builders are in different cycles of their training, and they are in different training -- training the different tasks. So I think it becomes a matter of what's available, what's usable, reusable at that time from a labor pool perspective. And I think when we talk about Q1, I think it's different programs that we are expecting us to start.

George Sutton

Understand. And you made an interesting comment when you were talking about the physical AI, I think specifically the robotics work that you look to become recurring infrastructure, which is quite different than the normal programs that you've traditionally worked on. Can you just explain that a bit further?

Rahul Singhal

Yes. Absolutely. I think we are very excited about physical AI. So if you think about the investment that is going in physical AI, it is, I believe, going to outpace the investment that happened in LLM. Now one of the most interesting things about physical AI or embodied intelligence is the fact that you can't scrape data from the web as the LLM started to. So the pretraining data or the pretraining corpora doesn't exist. The way these models are getting trained is it starts by doing something called an egocentric data, where you are using cameras to collect data for different tasks.

So a task could be you're collecting data for loading a dishwasher or you could be collecting the data for folding your laundry, right? Those are egocentric data. Then you have something called as UMI data sets, and then you have something called as motion capture data sets. Now these are different modalities, different ways, different technologies and techniques that are being used. And each one progressively becomes a lot more interesting, a lot more complex and the models start to become better. This is where the world model start to play in.

Because there is not a single layer of physical corpora that people can use, you have to continuously be collecting, creating, annotating the data so that these robots can actually execute around that. So I think it's a very interesting and exciting opportunity for us.

George Sutton

Super. And then just one for Jayant. So relative to the guide for Q4, historically, what you've done is your guide includes only what you have as booked business. So if you get additional business during the quarter, that creates some upside potential. Can you just talk to the thought process with the Q4 guide? And then also reference the pipeline of opportunities and kind of speed of deal cycles that could result in more revenues in Q4?

Jayant Chauhan

George, thank you for the question. In terms of how we think about guidance, that approach is still consistent with the past -- what in the past, what we have done. We continue to be prudent about it. And you're right, we generally factor in only revenue from active programs, new awards or smaller pipeline opportunities where we actually have a clear line of sight. We do not factor large pipeline opportunities until they are awarded and until we know the timing of that revenue recognition. So continue to be consistent with that approach as of now and going forward as well.

And on to your second question on -- of course, on your second question of what we are seeing in the pipeline, I believe Rahul talked about a lot of those opportunities which we are seeing today in terms of guiding for 2027, as you're aware, we'll announce that along -- or rather we intend to announce that along with our Q4 results. So we'll hear more about that over the next quarter.

Operator

Your next question comes from the line of Allen Klee with Maxim Group LLC.

Allen Klee

Yes. You spoke about how your adjusted gross margin hit your target of 40% plus for the quarter. In the prior 2 quarters, it kind of overearned. It was above. And could you just give us a little more color about what happened with the mix? And is there a reason why you think it might go in a certain direction going forward?

Rahul Singhal

Allen, so I think as we have consistently said that our target gross margin is 40%. And in any quarter, the program mix, the margin mix on those programs change. And we also tend to do a lot of programs where we believe that they could be big growth drivers for us in following quarters or they are building capabilities, which we tend to take at lower margin. So we have consistently guided to 40% gross margin, and we will continue to guide to 40% gross margin because I think this business requires us to ensure that we are investing in different programs, building our capabilities and investing going ahead in a way that we can continue to lead the market.

Allen Klee

Okay. And then for the project that was shut down in October, and then you -- I think you said that's going to have a $12 million impact in the fourth quarter, but that the same former largest customer is going to add that amount in the first quarter. Is those -- the margins of the business shut down, is there a way to think -- is that materially different than the margins of the new business?

Jayant Chauhan

Let me take that, Rahul. Well, look, on the new build, as you know, we don't really guide or we don't talk about any project level margin. So I would just point to the fact that we'll talk about 2027 and what that looks like during our Q4 results.

Allen Klee

Okay. And then the last thing about capital allocation where you were talking about organic growth. Were you trying to imply that the amount of money you might spend on organic growth to fund that will increase like I guess relative to where it had been? Or what were you trying to get across to us?

Jayant Chauhan

Yes, I'll take that, Rahul. Allen, yes, as we've been talking to you guys in the past and to our investors as well, there are a lot of questions on how are we thinking about capital allocation given the balance sheet strength. So just basically clarifying that organic investments continue to be our priority, and that's where we'll focus as we invest behind the various growth vectors and opportunities Rahul laid out. So that continues to be the priority.

And if there is opportunistically, we see M&A opportunities, which we can -- which can accelerate our growth strategy and create value for our shareholders, we will continue to look at that as well, but priority is organic investments.

Operator

Your next question comes from the line of Hamed Khorsand with BWS Financial.

Hamed Khorsand

Just one question is about this large customer moving out one project to another. Is that other project won by you? Or do you have a confirmation that's happening in Q1?

Rahul Singhal

Yes. So Hamed, we don't comment on exact timing. We have -- we anticipate winning more work, and we are in late stages of several programs that we can -- we anticipate will help us fill the gap. So I think the way to think about it is program start and stop. New programs are starting. We are working with multiple stakeholders, multiple business units within our largest customer, and this business moves really, really fast. So we anticipate filling the gap.

Hamed Khorsand

And then it sounds like you have a new large customer. What's the percentage of revenue?

Jayant Chauhan

Yes. So you're talking about the third large customer, Rahul talked in his comments earlier?

Hamed Khorsand

Yes.

Jayant Chauhan

Yes. So at this point in time, we are expecting to do about $20 million in revenue from that customer over the next year.

Hamed Khorsand

Okay. And then as far as what you're seeing from your largest customer, has that changed at all? Or has that been pretty consistent as far as revenue is concerned?

Rahul Singhal

Yes. I think the -- what we're seeing from our largest customer is this market is moving significantly faster and the investments go in a variety of different directions. The customers tend to invest in things like VLMs, which is vision language models. They're investing in robotics. They're investing in LLMs. They're investing in agents, right? So the business is moving fast and you have to -- and we are working with a variety of different business units across the gamut within the largest customers in trying to address their needs and work with them.

We are spending time with them on reinforcement learning gyms where we are creating a cutting-edge gyms that allow the agents to be trained and giving them our expertise and our thought leadership in helping them trying to dominate that market. So I think different places is where we are -- we continue to work with them in a variety of different programs.

Operator

There are no further questions at this time. I will now turn the call back to Rahul Singhal for closing remarks.

Rahul Singhal

Thank you, operator. To wrap up, Q3 2026 was another record revenue quarter for Innodata and another across-the-board beat. We delivered 53% revenue growth, our 13th consecutive quarter of year-over-year growth, adjusted gross margin of 40%, adjusted EBITDA ahead of consensus and strong net income, all while continuing to invest in the research and innovation that is driving the business.

Our diversification continued. A customer that represented less than 5% of our revenue a year ago is now our largest customer, giving us 2 customers of comparable scale. Another large hyperscaler customer with which we did only $3.1 million in revenue in the trailing 12 months is on track to be a $20 million-plus customer next year. We also have won notable new customers across frontier labs, enterprise data platforms and robotics. We are raising our guidance to 42% or more growth for 2026. Given our wins, the raise would have been a lot more, but for an air gap in Q4 revenue from our historically largest customer that we forecast getting filled by Q1.

Most importantly, we believe we are positioned at the epicenter of the most important transition in AI right now, the move to the next S-curve of agents, reinforcement learning and embodied intelligence, and at the center of the trust, safety and alignment work that will determine whether that transition succeeds. These are primarily data challenges and they're precisely in our wheelhouse. I believe 2026 will finish as a strong year for Innodata and 2027 will prove equally or even more exciting.

It is a privilege to be leading this company. I thank our team and customers around the world and all of you for continuing to be on this journey with us. We look forward to speaking with you next quarter. Thank you.

Operator

This concludes today's call. Thank you for attending. You may now disconnect.