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. 27 Table of Contents 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. 28 Table of Contents 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 regulatory expectations related to transparency, accountability, and risk management in AI systems. 29 Table of Contents Red Teaming, Adversarial Evals, and Safety Research AI safety has expanded to include misuse, exploitability, and unintended system behaviors - particularly as models are connected to retrieval systems, code execution environments, autonomous agents, and enterprise tools. Innodata conducts structured red teaming and adversarial evaluations to surface failure modes that are not observable through standard benchmarks. These efforts include probing prompt-injection and jailbreak vulnerabilities, testing misuse scenarios involving retrieval-augmented generation, agent workflows, and tool use, identifying degradation under distribution shift, and supporting mitigation through targeted post-training datasets. In parallel, we have expanded our cybersecurity capabilities as applied to LLMs and AI agents, including threat modeling for agent-based systems, assessment of data exfiltration and privilege-escalation risks, evaluation of secure tool invocation and sandboxing controls, and testing of monitoring and guardrail mechanisms designed to reduce exposure to adversarial attacks and enterprise security breaches. We believe red teaming and adversarial evals are increasingly viewed as prerequisites for deployment rather than optional safeguards. High-Risk Domains and Societal Safety As frontier AI capabilities advance, developers and governments have raised concerns about misuse in high-impact domains, including non-proliferation, chemical and biological risk, and large-scale misinformation. Innodata supports mitigation efforts through domain-specific safety evals, targeted mitigation datasets, collaboration with academic and government-adjacent experts, and evaluation frameworks designed to preserve performance on legitimate use cases while reducing the risk of harmful behaviors or misuse. AI Model Deployment and Integration We believe that over the next decade, AI will be embedded across nearly all industries. Innodata supports customers in operationalizing AI systems, including model customization, workflow integration, context engineering, and continuous quality assurance. Our platforms and services are designed to accommodate rapid innovation in model architectures and techniques, enabling customers to adopt new approaches without re-architecting their AI operations. AI-Enabled Industry Platforms We offer a range of AI-enabled platforms and solutions that support data transformation, analytics, and workflow automation across industries, including solutions that convert medical records into structured digital data for use in insurance and healthcare applications, as well as media intelligence and public relations workflow software enhanced with AI-driven monitoring, analytics, and content capabilities. These offerings operate within a unified technology and service delivery model and leverage shared data, infrastructure, and artificial intelligence capabilities. We continue to invest in these solutions to incorporate advances in artificial intelligence, with a focus on reliability, transparency, and user trust. We operate under one single segment. Prevailing Economic Conditions and Seasonality Prevailing Economic Conditions With the current level of demand for our services, we believe we have existing cash and cash equivalents that provide sufficient sources of liquidity to satisfy our financial needs for at least the next 12 months from the date of the filing of this Report (refer to Item 2. “Management’s Discussion and Analysis of Financial Condition and Results of Operations – Liquidity and Capital Resources” for additional information). In the event we experience a significant or prolonged reduction in revenues, we would seek to manage our liquidity by utilizing the Revolving Credit Facility, reducing capital expenditures, deferring investment activities, and reducing operating costs. 30 Table of Contents Seasonality Our quarterly operating results are subject to certain fluctuations. We experience fluctuations in our revenue and earnings as we replace and begin new projects, which may have some normal start-up delays, or we may be unable to replace a project entirely. These and other factors may contribute to fluctuations in our operating results from quarter to quarter. In addition, as some of our Asian facilities are closed during holidays in the fourth quarter, we typically incur higher wages, due to overtime, that reduce our margins. For further information, refer to the risk factor titled “Quarterly fluctuations in our revenues and results of operations could make financial forecasting difficult and could negatively affect our stock price.” in Part I, Item 1A. “Risk Factors” of our Annual Report on Form 10-K for the year ended December 31, 2025. Changes in Segment Reporting Effective as of the quarter ended March 31, 2026, the Company revised its segment reporting structure. Previously, we reported three segments; following a change in the CODM’s approach, we now report as a single segment. This change was made to align segment reporting with the CODM’s resource allocation and performance assessment process. For additional information, refer to Note 13 to the financial statements. 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 with greater transparency by providing 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 Quarterly Report on Form 10-Q 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 U.S. GAAP, plus depreciation and amortization of intangible assets, stock-based compensation and other one-time costs included within direct operating cost. We define Adjusted Gross Margin by dividing Adjusted Gross Profit over total U.S. 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. 31 Table of Contents The following table contains a reconciliation of Gross Profit and Gross Margin in accordance with the U.S. GAAP attributable to Innodata Inc. and its subsidiaries to Adjusted Gross Profit and Adjusted Gross Margin for the three- and six-month periods ended June 30, 2026 and 2025 (in thousands). ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ For the Three Months Ended June 30, For the Six Months Ended June 30, ​ ​ ​ ​ ​ 2026 ​ ​ ​ 2025 ​ ​ ​ 2026 ​ ​ ​ 2025 ​ Gross Profit attributable to Innodata Inc. and Subsidiaries ​ $ 42,460 $ 23,023 ​ $ 82,252 ​ $ 46,275 ​ Depreciation and amortization ​ 2,242 ​ 1,583 ​ ​ 4,361 ​ ​ 3,127 ​ Stock-based compensation ​ 681 ​ 441 ​ ​ 1,345 ​ ​ 868 ​ Adjusted Gross Profit ​ $ 45,383 $ 25,047 ​ $ 87,958 ​ $ 50,270 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Gross Margin ​ 46 % ​ 39 % ​ 45 % ​ 40 % Adjusted Gross Margin ​ ​ 49 % ​ 43 % ​ 48 % ​ 43 % ​ Adjusted EBITDA We define Adjusted EBITDA as net income attributable to Innodata Inc. and its subsidiaries in accordance with U.S. GAAP before net interest expense (income), 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 attributable to non-controlling interests 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 U.S. GAAP net income attributable to Innodata Inc. and its subsidiaries to Adjusted EBITDA for the three- and six-month periods ended June 30, 2026 and 2025 (in thousands). ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ For the Three Months Ended June 30, ​ ​ ​ For the Six Months Ended June 30, ​ ​ ​ ​ 2026 ​ ​ ​ 2025 ​ ​ ​ 2026 ​ ​ ​ 2025 Net income attributable to Innodata Inc. and Subsidiaries ​ $ 14,412 ​ $ 7,219 ​ $ 29,310 ​ $ 15,006 Provision for income taxes ​ ​ 3,143 ​ ​ 2,269 ​ 5,587 ​ 2,881 Interest income, net ​ ​ (1,695) ​ ​ (577) ​ (2,137) ​ (704) Depreciation and amortization ​ ​ 2,299 ​ ​ 1,602 ​ ​ 4,475 ​ ​ 3,164 Stock-based compensation ​ ​ 7,196 ​ ​ 2,721 ​ ​ 13,104 ​ ​ 5,602 Adjusted EBITDA ​ $ 25,355 ​ $ 13,234 ​ $ 50,339 ​ $ 25,949 ​ Results of Operations The amounts in the MD&A below have been rounded. All percentages have been calculated using rounded amounts. Three Months Ended June 30, 2026 and 2025 Revenues Total revenues were $92.1 million and $58.4 million for the three months ended June 30, 2026 and 2025, respectively, an increase of $33.7 million or approximately 58%. Revenue increased primarily due to higher volume for AI data engineering services from existing customer programs. One customer generated approximately 37% and 58% of the Company’s total revenues for the three months ended June 30, 2026 and 2025, respectively. Another customer generated approximately 34% of the Company’s total revenues for the three months ended June 30, 2026. No other customer accounted for 10% or more of total revenues during these periods. Revenues from non-U.S. customers accounted for 13% and 17% of the Company’s total revenues for the three months ended June 30, 2026 and 2025, respectively. 32 Table of Contents Direct Operating Costs Direct operating costs consist of direct and indirect labor costs, occupancy costs, data center hosting fees, cloud services, AI technology subscriptions, content acquisition costs, depreciation and amortization, travel, telecommunications, computer services and supplies, realized (gain) loss on forward contracts, foreign currency revaluation (gain) loss, recruitment costs and other direct expenses that are incurred in providing services to our customers. Direct operating costs were $49.7 million and $35.4 million for the three months ended June 30, 2026 and 2025, respectively, an increase of $14.3 million or approximately 40%. The cost increase was primarily attributable to an increase in headcount to support higher volumes of AI data engineering services from existing customer programs. The $14.3 million increase in direct operating costs includes $10.6 million from direct and indirect labor-related costs, primarily driven by new hires, salary increases, stock-based compensation expense, and third-party delivery resources. Additional increases primarily included approximately $1.4 million in cloud services and AI technology-related subscription costs driven by increased cloud usage and data processing requirements to support higher revenue and expanded customer engagements, shipping and related costs of $0.9 million, depreciation and amortization of capitalized developed software of $0.7 million, content-related costs of $0.3 million, occupancy-related costs of $0.2 million, and travel and entertainment costs of $0.2 million. Direct operating costs as a percentage of total revenues were 54% and 61% for the three months ended June 30, 2026 and 2025, respectively. The decrease in direct operating costs as a percentage of total revenues was primarily attributable to higher revenues, offset in part by increased direct operating costs. Gross Profit and Gross Margin Gross profit is derived by revenues less direct operating costs, while the Gross margin percentage is derived by dividing gross profit over revenues. Gross profit was $42.4 million and $23.0 million for the three months ended June 30, 2026 and 2025, respectively. The $19.4 million increase in gross profit was primarily due to higher revenues, offset in part by higher direct operating costs. Gross margin was 46% and 39% for the three months ended June 30, 2026 and 2025, respectively. The increase in gross margin was primarily due to higher revenues, offset in part by higher direct operating costs. Selling and Administrative Expenses Selling and administrative expenses consist of payroll and related costs including commissions, bonuses, and stock-based compensation; marketing, advertising, trade conferences and related expenses; new services research and related software development expenses; software subscriptions; AI technology subscriptions; professional and consultant fees; provision for credit losses; and other administrative overhead expenses. Selling and administrative expenses were approximately $26.6 million and $14.1 million for the three months ended June 30, 2026 and 2025, respectively, an increase of $12.5 million or approximately 89%. The increase in selling and administrative expenses was primarily due to continued investments in growth-oriented and capability-building functions. In addition, labor costs increased as we continue to invest in sales, account management, and marketing resources to support new customer acquisition, expand relationships with existing customers, and strengthen our market presence through solution design, go-to-market execution, and thought leadership initiatives. 33 Table of Contents The $12.5 million increase in selling and administrative expenses was primarily attributable to increased selling, marketing, and administrative payroll and related expenses of $11.0 million, driven by new hires, salary increases, stock-based compensation expense, incentives and bonuses. Additional increases included higher business software and AI technology subscriptions of $1.3 million, marketing-related expenses of $0.4 million, travel and entertainment costs of $0.3 million, unused line of credit facilitation fees of $0.2 million, an unfavorable impact of foreign exchange rate fluctuations of $0.2 million and an increase in other selling and administrative expenses of $0.3 million, offset in part by credit loss recoveries of $0.8 million and lower professional and recruitment fees of $0.4 million. Selling and administrative expenses as a percentage of total revenues were approximately 29% and 24% for the three months ended June 30, 2026 and 2025, respectively. The increase in selling and administrative expenses as a percentage of total revenues was primarily attributable to increased selling and administrative expenses offset by higher revenues. Income Taxes We recorded an income tax provision of approximately $3.1 million and $2.3 million for the three months ended June 30, 2026 and 2025, respectively. The effective tax rate was 17.9% and 23.9% for the three months ended June 30, 2026 and 2025, respectively. The effective tax rate was favorably impacted by net windfalls related to stock-based compensation, partially offset by IRS section 162(m) adjustments and the U.S tax implications of Global Intangible Low Taxed Income (GILTI) in the current quarter. In each quarter, we update the estimated annual effective tax rate and make a year-to-date adjustment to the provision. The estimated annual effective tax rate is subject to significant volatility due to several factors, including our ability to accurately predict the proportion of income (loss) before provision for income taxes in multiple jurisdictions, the effects of tax law changes, and the U.S. tax implications related to Global Intangible Low-Taxed Income. (Refer to Note 6 of Notes to Condensed Consolidated Financial Statements for the components of the income tax provision for the three-month periods ended June 30, 2026 and 2025). Net Income Net income was $14.4 million and $7.2 million for the three months ended June 30, 2026 and 2025, respectively. The $7.2 million increase was a result of higher revenue, offset in part by higher direct operating costs and higher selling and administrative expenses in the current quarter. Earnings per share For the three months ended June 30, 2026, basic and diluted earnings per share were $0.43 and $0.41, respectively, compared to $0.23 and $0.20 for the prior year period. This represents a per share increase of $0.20 for basic EPS and $0.21 for diluted EPS. Earnings per share increased for the quarter due to continued improvement in profitability and operating leverage, reflecting higher revenues and cost efficiencies across the business. Adjusted Gross Profit and Margin Adjusted Gross Profit and Adjusted Gross Margin are non-GAAP financial measures. For a reconciliation of Adjusted Gross Profit and Adjusted Gross Margin to the most directly comparable GAAP measure, please see the description of “Non-GAAP Financial Measures – Adjusted Gross Profit and Adjusted Gross Margin” above. Adjusted gross profit was $45.4 million and $25.0 million for the three months ended June 30, 2026 and 2025, respectively. The $20.4 million increase in adjusted gross profit was primarily due to higher revenues, offset in part by higher direct operating costs. Adjusted gross margin was 49% and 43% for the three months ended June 30, 2026 and 2025, respectively. The increase in adjusted gross margin was primarily due to higher revenues, offset in part by higher direct operating costs. 34 Table of Contents Adjusted EBITDA Adjusted EBITDA is a non-GAAP financial measure. For a reconciliation of Adjusted EBITDA to the most directly comparable GAAP measure, please see the description of “Non-GAAP Financial Measures – Adjusted EBITDA” above. Adjusted EBITDA was $25.4 million and $13.2 million for the three months ended June 30, 2026 and 2025, respectively. The $12.2 million increase in Adjusted EBITDA was due to higher net income, higher stock-based compensation and depreciation and amortization, offset in part by higher interest income in the current quarter. Six Months Ended June 30, 2026 and 2025 Revenues Total revenues were $182.2 million and $116.7 million for the six months ended June 30, 2026 and 2025, respectively, an increase of $65.5 million or approximately 56%. Revenue increased primarily due to higher volume for AI data engineering services from existing customer programs. One customer generated approximately 46% and 59% of the Company’s total revenues for the six months ended June 30, 2026 and 2025, respectively. Another customer generated approximately 26% of the Company’s total revenues for the six months ended June 30, 2026. No other customer accounted for 10% or more of total revenues during these periods. Revenues from non-U.S. customers accounted for 13% and 17% of the Company’s total revenues for the six months ended June 30, 2026 and 2025, respectively. Direct Operating Costs Direct operating costs consist of direct and indirect labor costs, occupancy costs, data center hosting fees, cloud services, AI technology subscriptions, content acquisition costs, depreciation and amortization, travel, telecommunications, computer services and supplies, realized (gain) loss on forward contracts, foreign currency revaluation (gain) loss, recruitment costs and other direct expenses that are incurred in providing services to our customers. Direct operating costs were $100.0 million and $70.5 million for the six months ended June 30, 2026 and 2025, respectively, an increase of $29.5 million or approximately 42%. The cost increase was primarily attributable to an increase in headcount to support higher volumes of AI data engineering services from existing customer programs. The $29.5 million increase in direct operating costs includes $24.6 million from direct and indirect labor-related costs, primarily driven by new hires, salary increases, stock-based compensation expense, severance and third-party delivery resources. Additional increases primarily included approximately $2.4 million in cloud services and AI technology-related subscription costs driven by increased cloud usage and data processing requirements to support higher revenue and expanded customer engagements, shipping and related costs of $1.6 million, depreciation and amortization of capitalized developed software of $1.2 million, content-related costs of $0.8 million, occupancy-related costs of $0.4 million, and travel and entertainment costs of $0.2 million, offset in part by a reduction in recruitment fees of $1.0 million and a favorable impact of foreign exchange rate fluctuations of $0.7 million. Direct operating costs as a percentage of total revenues were 55% and 60% for the six months ended June 30, 2026 and 2025, respectively. The decrease in direct operating costs as a percentage of total revenues was primarily attributable to higher revenues, offset in part by increased direct operating costs. Gross Profit and Gross Margin Gross profit is derived by revenues less direct operating costs, while the Gross margin percentage is derived by dividing gross profit over revenues. Gross profit was $82.2 million and $46.2 million for the six months ended June 30, 2026 and 2025, respectively. The $36.0 million increase in gross profit was primarily due to higher revenues, offset in part by higher direct operating costs. Gross margin was 45% and 40% for the six months ended June 30, 2026 and 2025, respectively. The increase in gross margin was primarily due to higher revenues, offset in part by higher direct operating costs. 35 Table of Contents Selling and Administrative Expenses Selling and administrative expenses consist of payroll and related costs including commissions, bonuses, and stock-based compensation; marketing, advertising, trade conferences and related expenses; new services research and related software development expenses; software subscriptions; AI technology subscriptions; professional and consultant fees; provision for credit losses; and other administrative overhead expenses. Selling and administrative expenses were approximately $49.5 million and $29.1 million for the six months ended June 30, 2026 and 2025, respectively, an increase of $20.4 million or approximately 70%. The increase in selling and administrative expenses was primarily due to continued investments in growth-oriented and capability-building functions. In addition, labor costs increased as we continue to invest in sales, account management, and marketing resources to support new customer acquisition, expand relationships with existing customers, and strengthen our market presence through solution design, go-to-market execution, and thought leadership initiatives. The $20.4 million increase in selling and administrative expenses was primarily attributable to increased selling, marketing, and administrative payroll and related expenses of $19.4 million, driven by new hires, salary increases, stock-based compensation expense, incentives and bonuses. Additional increases included higher business software and AI technology subscriptions of $1.8 million, travel and entertainment costs of $0.4 million, marketing-related expenses of $0.3 million, unused line of credit facilitation fees of $0.3 million, higher insurance cost of $0.2 million, an increase in indirect taxes of $0.2 million, an increase in proxy filing expenses of $0.1 million and an increase in other selling and administrative expenses of $0.5 million, offset in part by lower professional and recruitment fees of $2.0 million, and credit loss recoveries of $0.8 million. Selling and administrative expenses as a percentage of total revenues were approximately 27% and 25% for the six months ended June 30, 2026 and 2025, respectively. The increase in selling and administrative expenses as a percentage of total revenues was primarily attributable to increased selling and administrative expenses offset by higher revenues. Income Taxes We recorded an income tax provision of approximately $5.6 million and $2.9 million for the six months ended June 30, 2026 and 2025, respectively. The effective tax rate was 16.0% and 16.1% for the six months ended June 30, 2026 and 2025, respectively. The effective tax rate was favorably impacted by net windfalls related to stock-based compensation, partially offset by IRS section 162(m) adjustments, the U.S tax implications of Global Intangible Low Taxed Income (GILTI) and the impact of foreign exchange fluctuations in the current period. In each quarter, we update the estimated annual effective tax rate and make a year-to-date adjustment to the provision. The estimated annual effective tax rate is subject to significant volatility due to several factors, including our ability to accurately predict the proportion of income (loss) before provision for income taxes in multiple jurisdictions, the effects of tax law changes, and the U.S. tax implications related to Global Intangible Low-Taxed Income. (Refer to Note 6 of Notes to Condensed Consolidated Financial Statements for the components of the income tax provision for the six-month periods ended June 30, 2026 and 2025). Net Income Net income was $29.3 million and $15.0 million for the six months ended June 30, 2026 and 2025, respectively. The $14.3 million increase was a result of higher revenue, offset in part by higher direct operating costs and higher selling and administrative expenses in the current period. Earnings per share For the six months ended June 30, 2026, basic and diluted earnings per share were $0.89 and $0.86, respectively, compared to $0.47 and $0.43 for the prior year period. This represents a per share increase of $0.42 for basic EPS and $0.43 for diluted EPS. Earnings per share increased for the period due to continued improvement in profitability and operating leverage, reflecting higher revenues and cost efficiencies across the business. 36 Table of Contents Adjusted Gross Profit and Margin Adjusted Gross Profit and Adjusted Gross Margin are non-GAAP financial measures. For a reconciliation of Adjusted Gross Profit and Adjusted Gross Margin to the most directly comparable GAAP measure, please see the description of “Non-GAAP Financial Measures – Adjusted Gross Profit and Adjusted Gross Margin” above. Adjusted gross profit was $88.0 million and $50.3 million for the six months ended June 30, 2026 and 2025, respectively. The $37.7 million increase in adjusted gross profit was primarily due to higher revenues, offset in part by higher direct operating costs. Adjusted gross margin was 48% and 43% for the six months ended June 30, 2026 and 2025, respectively. The increase in adjusted gross margin was primarily due to higher revenues, offset in part by higher direct operating costs. Adjusted EBITDA Adjusted EBITDA is a non-GAAP financial measure. For a reconciliation of Adjusted EBITDA to the most directly comparable GAAP measure, please see the description of “Non-GAAP Financial Measures – Adjusted EBITDA” above. Adjusted EBITDA was $50.3 million and $25.9 million for the six months ended June 30, 2026 and 2025, respectively. The $24.4 million increase in Adjusted EBITDA was due to higher net income, higher stock-based compensation and depreciation and amortization, offset in part by higher interest income in the current period. Liquidity and Capital Resources Selected measures of liquidity and capital resources, expressed in thousands, were as follows: ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ June 30, ​ ​ ​ December 31, ​ ​ 2026 ​ 2025 Cash and cash equivalents ​ $ 240,278 ​ $ 82,216 Short term investments ​ ​ 10,093 ​ ​ 14 Working capital ​ 135,161 ​ 84,862 ​ As of June 30, 2026, $31.3 million of our cash and cash equivalent balance was held by our foreign subsidiaries, and $209.0 million was held in the United States. As of June 30, 2026, cash and cash equivalents were $240.3 million, including amounts received in advance in connection with ongoing customer programs. At June 30, 2026, $67.0 million remained recorded within advances from customers, while $61.8 million of related project costs that had been incurred but remained unpaid was included in accounts payable and accrued expenses. The cash is not required to be segregated. Management considers both advances from customers and the related unpaid project obligations when assessing the Company’s liquidity requirements. As of June 30, 2026, our short-term investments consisted of $10.1 million of held-to-maturity U.S. Treasury Notes. We have used, and plan to use, our cash and cash equivalents for (i) capital investments; (ii) the expansion of our operations; (iii) technology innovation; (iv) product management and strategic marketing; (v) general corporate purposes, including working capital; and (vi) possible business acquisitions. As of June 30, 2026, we had working capital of approximately $135.2 million, as compared to working capital of approximately $84.9 million as of December 31, 2025. Working capital grew primarily due to strong revenue-driven cash inflows, which were partially offset by increased payroll and other operating expenses to support expanding AI and AI-related services and customer engagements. We did not have any material commitments for capital expenditures as of June 30, 2026. 37 Table of Contents We believe that our existing cash and cash equivalents and internally generated funds will provide sufficient sources of liquidity to satisfy our financial needs for at least the next 12 months from the date of this Report. We maintain a revolving line of credit facility. See Note 16, Line of Credit, of the Notes to the Condensed Consolidated Financial Statements included in this Quarterly Report on Form 10-Q, which is incorporated by reference herein. On August 8, 2024, we filed a Registration Statement on Form S-3 (Registration No. 333-281379) (the “Form S-3”), as amended on September 16, 2024, and declared effective on October 10, 2024, with the SEC, which includes a base prospectus that allows us to offer and sell, from time to time, in one or more offerings, common stock, preferred stock, debt securities, warrants or units up to an aggregate public offering price of $50.0 million. The Form S-3 is intended to preserve our flexibility to raise capital from time to time, if and when needed. Cash Flows Net Cash Provided by Operating Activities Cash provided by our operating activities for the six months ended June 30, 2026 was $164.4 million resulting from net income of $29.3 million, adjusted for non-cash expenses of $17.2 million and an increase in working capital of $117.9 million. Refer to the Condensed Consolidated Statements of Cash Flows for further details. Cash provided by our operating activities for the six months ended June 30, 2025 was $15.0 million resulting from net income of $15.0 million, adjusted for non-cash expenses of $10.8 million and a decrease in working capital of $10.8 million. Refer to the Condensed Consolidated Statements of Cash Flows for further details. Net Cash Used in Investing Activities Cash used in our investing activities was $15.4 million and $4.1 million for the six-month periods ended June 30, 2026 and 2025, respectively. This increase in cash usage was primarily driven by purchases of short-term investments of $10.1 million, reflecting our strategy to optimize yields on surplus cash generated from operations and capital expenditures of $5.3 million principally for the purchase of technology equipment including servers, network infrastructure and workstations, and expenditures for capitalized developed software. During the next 12 months, it is anticipated that capital expenditures for capitalized developed software and ongoing technology, equipment and infrastructure upgrades will approximate to $13.1 million, a portion of which we may finance. Net Cash Provided by Financing Activities Cash provided by financing activities for the six months ended June 30, 2026 was $10.0 million primarily from proceeds of stock option exercises of $10.9 million, offset in part by payment for long-term obligations of $0.9 million. Cash provided by financing activities for the six months ended June 30, 2025 was $1.4 million primarily from proceeds of stock option exercises of $1.5 million, offset in part by payment of long-term obligations of $0.1 million. 38 Table of Contents Critical Accounting Policies and Estimates Our discussion and analysis of our results of operations, liquidity and capital resources is based on our condensed consolidated financial statements, which have been prepared in conformity with U.S. GAAP. The preparation of the condensed consolidated financial statements requires us to make estimates and judgments that affect the reported amounts of assets, liabilities, revenues and expenses, and disclosure of contingent assets and liabilities. On an ongoing basis, we evaluate our estimates and judgments, including those related to revenue recognition, allowance for credit losses and billing adjustments, long-lived assets, intangible assets, goodwill, valuation of deferred tax assets and income tax provision, value of securities underlying stock-based compensation, litigation accruals, pension benefits, valuation of derivative instruments and estimated accruals for various tax exposures. We base our estimates on historical and anticipated results and trends and on various other assumptions that we believe are reasonable under the circumstances, including assumptions as to future events. These estimates form the basis for making judgments about the carrying values of assets and liabilities that are not readily apparent from other sources. By their nature, estimates are subject to an inherent degree of uncertainty. Actual results may differ from our estimates and could have a significant adverse effect on our condensed consolidated results of operations and financial position. The significant accounting policies used in preparing our condensed consolidated financial statements contained in this Report are the same as those described in the Company’s Annual Report on Form 10-K, unless otherwise noted, and we believe those critical accounting policies affect our more significant estimates and judgments in the preparation of our condensed consolidated financial statements. Off-Balance Sheet Arrangements None. ​