INNODATA INC (INOD)

Item 2.

MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL

CONDITION AND RESULTS OF OPERATIONS

Cautionary Note Regarding Forward-Looking Statements

Disclosures in this Quarterly Report on Form 10-Q (this “Report”) contain certain forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended. These forward-looking statements include, without limitation, statements concerning our operations, economic performance, financial condition, developmental program expansion and position in the generative AI services market. Words such as “project,” “believe,” “expect,” “can,” “continue,” “could,” “intend,” “may,” “should,” “will,” “anticipate,” “indicate,” “predict,” “likely,” “estimate,” “plan,” “potential,” “possible,” or the negatives thereof, and other similar expressions generally identify forward-looking statements.

These forward-looking statements are based on management’s current expectations, assumptions and estimates and are subject to a number of risks and uncertainties, including, without limitation, impacts resulting from ongoing geopolitical conflicts; anticipated and actual use cases and outcomes; investments in large language models; that contracts may be terminated by customers; projected or committed volumes of work may not materialize; pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work; the likelihood of continued development of the AI markets, particularly new and emerging markets, that our services support; the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services; continuing reliance on project-based work and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay or cancel projects; potential inability to replace projects that are completed, canceled or reduced; revenue concentration among a limited number of customers; our dependency on third-party providers and partners; our ability to achieve revenue and growth targets; difficulty in integrating and deriving synergies from acquisitions, joint ventures and strategic investments; potential undiscovered liabilities of companies and businesses that we may acquire; potential impairment of the carrying value of goodwill and other acquired intangible assets of companies and businesses that we acquire; a continued downturn in or depressed market conditions; changes in external market factors; the potential effects of U.S. global trade and monetary policy, including the interest rate policies of the Federal Reserve; changes in our business or growth strategy; the emergence of new, or growth in existing competitors; various other competitive and technological factors; our use of and reliance on information technology systems, including potential security breaches, cyber-attacks, privacy breaches or data breaches that result in the unauthorized disclosure of consumer, customer, employee or company information, or service interruptions; and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission (“SEC”).

Our actual results could differ materially from the results referred to in any forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, the risks discussed in Part I, Item 1A. “Risk Factors,” Part II, Item 7. “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and other parts of our Annual Report on Form 10-K, filed with the SEC on February 26, 2026 and in our other filings that we may make with the SEC.

In light of these risks and uncertainties, there can be no assurance that the results referred to in the forward-looking statements will occur, and you should not place undue reliance on these forward-looking statements. These forward-looking statements speak only as of the date hereof.

We undertake no obligation to update or review any guidance or other forward-looking statements, whether as a result of new information, future developments or otherwise, except as may be required by the U.S. federal securities laws.

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The following Management’s Discussion and Analysis of Financial Condition and Results of Operations (“MD&A”) is intended to help the reader understand the results of operations and financial condition of Innodata Inc. and its subsidiaries and should be read in conjunction with our unaudited condensed consolidated financial statements and the accompanying notes to condensed consolidated financial statements contained in Part I, Item 1. “Financial Statements” of this Report.

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 and 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.

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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.

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. We believe this represents a durable and expanding market opportunity distinct from, but complementary to, data preparation and model training.

From Output Scoring to Behavioral and Agentic Evals

Early AI evaluation focused primarily on output correctness. In contrast, today’s frontier systems - particularly agentic and tool-using systems - require behavioral and agentic evals that assess how models plan, reason, and act over time. These evals may examine reasoning coherence, tool selection and invocation, multi-step task execution, adherence to system instructions, and robustness under adversarial or ambiguous inputs.

This shift toward agentic evaluation materially increases the importance of structured human judgment, domain expertise, and scalable evaluation operations. We believe that the ability to measure not only what a model outputs, but how it arrives at those outputs, is increasingly central to deployment readiness and long-term safety.

Human-in-the-Loop Evals and Evidence for Trust

As AI systems are deployed into regulated or high-stakes environments, customers increasingly require evidence that systems have been evaluated, documented, and monitored. This has driven demand for human-in-the-loop eval frameworks that combine expert judgment with automation to produce results that are interpretable, repeatable, and auditable.

Innodata’s evaluation programs emphasize rubricized scoring for consistency, subject-matter experts for high-risk domains, hybrid human-plus-automated evaluation pipelines, and longitudinal measurement to track regressions and improvements over time. We believe these capabilities position us to support emerging governance and

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Item 7. 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:

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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.

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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).

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Adjusted EBITDA

We define Adjusted EBITDA as net income (loss) attributable to Innodata Inc. and its subsidiaries in accordance with GAAP before interest expense, income taxes, depreciation and amortization of intangible assets (which derives EBITDA), plus additional adjustments for loss on impairment of intangible assets and goodwill, stock-based compensation, income (loss) attributable to non-controlling interests, non-recurring severance, and other one-time costs. We use Adjusted EBITDA to evaluate core 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 GAAP net income (loss) attributable to Innodata Inc. and its subsidiaries to Adjusted EBITDA for the years ended December 31, 2025 and 2024 (in thousands).

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