INNODATA INC (INOD)

Business overview

Business. Business Overview Innodata Inc. (Nasdaq: INOD) (together with its subsidiaries, the “Company”, “Innodata”, “we”, “us” or “our”) is a global data engineering and AI systems services company that supports the development, training, post-training, evaluation, and deployment of advanced artificial intelligence systems. We partner with leading technology companies, frontier AI laboratories, and enterprises to help enable AI systems that perform reliably, align with intended objectives, and operate safely in real-world environments. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We believe that AI will increasingly function as a foundational layer of the digital economy - embedded across consumer products, enterprise workflows, and mission-critical systems.

As AI systems grow more capable and autonomous, we believe the quality of training data, the effectiveness of post-training alignment, and the rigor of ongoing evaluation will be decisive factors in determining whether AI systems are adopted, regulated, and scaled responsibly. Innodata was founded more than 35 years ago on the principle that high-quality, well-structured data is essential to leading information-retrieval systems. In 2016-2017, we began building proprietary AI language models based on then-emerging research and frameworks and integrating them into our data production workflows. Through this work, we developed and refined techniques for generating, curating, and validating human-created data used to train probabilistic, learning-based AI systems, and recognized that data quality and structure were critical determinants of model performance.

This insight led us to invest in the development of an integrated set of AI lifecycle data solutions, addressing a growing market need for specialized data engineering, evaluation, and refinement capabilities across the full lifecycle of AI systems. Today, leading AI innovation labs and Big Tech companies (including five of the so-called “Magnificent Seven”) building frontier generative AI models and leading enterprises engage us to provide (i) training and post-training data development; (ii) alignment and preference optimization; (iii) capabilities, alignment, and safety evaluation; and (iv) AI enablement and operationalization, including support for agentic and tool-using systems.

We believe Innodata is differentiated by: (i) our ability to operate across the AI lifecycle in alignment with AI developers’ internal development and deployment pipelines; (ii) our scale of specialized human expertise; (iii) purpose-built platforms and processes that combine automation with rigorous human oversight; (iv) a research-driven approach to measurement, safety, and operational reliability, which is particularly relevant for frontier model developers and enterprises deploying AI in high-stakes environments; and (v) our dual role supporting leading technology companies building advanced AI systems and enterprises deploying those systems in production, which we believe creates a reinforcing feedback loop that strengthens our capabilities across both contexts and differentiates us from competitors focused on only one side of the market.

Market Opportunities AI Training and Post-Training Data Modern AI systems are trained using large volumes of data rather than explicit, rule-based programming. Foundation models - such as large language models (“LLMs”) and multimodal models - learn statistical representations of language, images, code, and other modalities from vast training corpora. As model architectures have matured, leading developers have increasingly emphasized the importance of training data quality, data provenance, supervised fine-tuning, and post-training alignment techniques. We believe that as model scale increases, marginal improvements in data quality and post-training signals can have an outsized impact on performance, reliability, and usability - often exceeding the impact of further parameter scaling alone. 4 Organizations developing AI systems therefore require partners that can design, execute, and continuously refine data pipelines capable of supporting large-scale training and post-training cycles while maintaining quality, consistency, and auditability.

We believe Innodata is well positioned to meet these requirements. Model Evaluation (“Evals”), Alignment, and Safety We believe that evaluation of model capabilities and safety (“evals”) are emerging as foundational layers of the AI technology stack, analogous to testing, security, and reliability engineering in traditional software systems. Unlike deterministic software, generative AI systems are probabilistic and context dependent. Their behavior may vary across prompts, tasks, and deployment environments, and may change over time as models are updated or integrated with tools and new data sources. As a result, organizations increasingly require continuous evals to understand, measure, and manage model behavior throughout development and deployment. These evals typically include: (i) capabilities evals that assess reasoning, knowledge, and task competence; (ii) alignment and safety evals that measure harmful behavior, misuse risk, and adherence to constraints; and (iii) regression evals designed to detect drift or degradation across model versions. […]

Management discussion and analysis

Management’s Discussion and Analysis of Financial Condition and Results of Operations. The following discussion should be read in conjunction with our consolidated financial statements and the related notes thereto included elsewhere in this Report. In addition to historical information, this discussion includes forward-looking information that involves risks and assumptions based upon management’s current expectations. Our actual results could differ materially from the results referred to in any forward-looking statement. See “Cautionary Note Regarding Forward-Looking Statements” included elsewhere in this Report. Executive Overview We are a global data engineering company.

We operate in three reporting segments: Digital Data Solutions (DDS), Synodex and Agility. The following table sets forth certain financial data for the years ended December 31, 2025 and 2024: ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ (Dollars in millions) ​ ​ Years Ended December 31, ​ ​ ​ ​ 2025 ​ ​ ​ % of revenue ​ ​ ​ 2024 ​ ​ ​ % of revenue Revenues ​ $ 251. 7 100. 0 % $ 170. 5 100. 0 % Direct operating costs ​ 152.

2 60. 5 % 103. 4 60. 7 % Gross Profit ​ $ 99. 5 39. 5 % $ 67.

1 39. 4 % Selling and administrative expenses ​ ​ 59. 6 ​ 23. 7 % ​ 42. 7 ​ 25. 0 % Income from operations ​ 39.

9 15. 8 % 24. 4 14. 3 % Interest income, net ​ (1. 6) ​ ​ (0. 1) ​ ​ Income before provision for income taxes ​ 41.

4 ​ ​ 24. 5 ​ ​ Provision for income taxes ​ 9. 2 ​ ​ ​ (4. 2) ​ ​ Net Income ​ $ 32. 2 ​ ​ $ 28. 7 ​ ​ ​ For a summary of our Significant Accounting Estimates and Policies, please refer to Note 1 of the Notes to our Consolidated Financial Statements, which are included elsewhere in this Report.

Non-GAAP Financial Measures In addition to the financial information prepared in conformity with U. S. GAAP (“GAAP”), we provide certain non-GAAP financial information. We believe that these non-GAAP financial measures assist investors in making comparisons of period-to-period operating results. In some respects, management believes non-GAAP financial measures are more indicative of our ongoing core operating performance than their GAAP equivalents by making adjustments that management believes are reflective of the ongoing performance of the business. We believe that the presentation of this non-GAAP financial information provides investors a more complete understanding of our financial performance, competitive position, and prospects for the future, particularly by providing the same information that management and our Board of Directors use to evaluate our performance and manage the business.

However, the non-GAAP financial measures presented in this Annual Report on Form 10-K have certain limitations in that they do not reflect all of the costs associated with the operations of our business as determined in accordance with GAAP. Therefore, investors should consider non-GAAP financial measures in addition to, and not as a substitute for, or as superior to, measures of financial performance prepared in accordance with GAAP. Further, the non-GAAP financial measures that we present may differ from similar non-GAAP financial measures used by other companies. Adjusted Gross Profit and Adjusted Gross Margin We define Adjusted Gross Profit as revenues less direct operating costs attributable to Innodata Inc. and its subsidiaries in accordance with GAAP, plus depreciation and amortization of intangible assets, stock-based compensation, non-recurring severance and other one-time costs.

32 We define Adjusted Gross Margin by dividing Adjusted Gross Profit over total GAAP revenues. We use Adjusted Gross Profit and Adjusted Gross Margin to evaluate results of operations and trends between fiscal periods and believe that these measures are important components of our internal performance measurement process. The following table contains a reconciliation of Gross Profit and Gross Margin in accordance with the GAAP attributable to Innodata Inc. and its subsidiaries to Adjusted Gross Profit and Adjusted Gross Margin for the years ended December 31, 2025 and 2024 (in thousands). ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Year Ended December 31, ​ Consolidated ​ ​ ​ 2025 ​ ​ ​ 2024 ​ ​ ​ ​ ​ ​ ​ ​ ​ Gross Profit attributable to Innodata Inc. […]

Key risk disclosures

Risk Factors. The risk factors set forth below describe what the Company believes to be the material factors, risks, and uncertainties related to our business, financial condition, and results of operations. The risks and uncertainties set forth below, as well as other factors described elsewhere in this Form 10-K or in other filings by the Company with the SEC, could adversely affect the Company’s business, financial condition and results of operations. Additional risks and uncertainties that are not currently known to the Company or that are not currently believed by the Company to be material may also harm the Company’s business, financial condition and results of operations. Risks Related to Our Business and Operations We have historically relied on a limited number of customers that have accounted for a significant portion of our revenues, and our results of operations could be adversely affected if we were to lose one or more of these significant customers.

We have historically relied on a limited number of customers that have accounted for a significant portion of our revenues. One customer in the DDS segment generated approximately 58% and 48% of the Company’s total revenues in the fiscal year ended December 31, 2025 and 2024. No other customer accounted for 10% or more of total revenue during these periods. Further, in the years ended December 31, 2025 and 2024, revenues from non-U. S. customers accounted for 16%, and 21%, respectively, of the Company’s revenues.

We may lose one or more of these customers, or our other major customers, as a result of our failure to meet or satisfy our customer’s requirements, the completion or termination of a project or engagement, or the customer’s selection of another service provider. In addition, the volume of work performed for our major customers may vary from year to year, and services they require from us may change from year to year. They may also request that we modify certain key terms of our agreements with them as a condition of continuing to do business with us. If the volume of work performed for our major customers vary, if the services they require from us change, or if they require price concessions, our revenues and results of operations could be adversely affected.

Our services are typically provided under master service agreements which establish general terms and conditions, with individual project-based statements of work, service orders, or purchase orders governing the scope, pricing, and duration of specific engagements. These contractual arrangements are negotiated periodically and generally do not obligate customers to purchase services in future periods. Our customer agreements are generally terminable by our customer upon 30 to 90 days’ notice. A substantial portion of the services we provide is performed on a project or program basis and is subject to customer requirements, including scope, timing, and continuation of funding, and may be terminable with shorter notice periods. The loss of these customers or a significant variation in the volume of work performed for these customers may have a material adverse effect on our business, financial condition and results of operations.

A portion of our services is provided on a non-recurring basis for specific projects, and our inability to replace large projects when they are completed or otherwise terminated has adversely affected, and could in the future adversely affect, our revenues and results of operations. We provide a portion of our services for specific projects that generate revenues that terminate on completion of a defined task. While we seek, whenever possible, on completion or termination of large projects, to counterbalance periodic declines in revenues with new arrangements to provide services to the same customer or others, our inability to obtain sufficient new projects to counterbalance any decreases in such work may adversely affect our future revenues and results of operations.

16 New acquisitions, joint ventures or strategic investments or partnerships could harm our operating results. We may pursue acquisitions, joint ventures or engage in strategic investments or partnerships to grow and enhance our capabilities. There can be no assurance that we will successfully consummate any acquisitions or joint ventures, or realize profit from strategic investments, or achieve desired financial and operating results. Further, such activities involve a number of risks and challenges, including proper evaluation, diversion of management’s attention and proper integration with our current business. Accordingly, we might fail to realize the expected benefits or strategic objectives of any such venture we undertake. […]

Source and methodology

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