NASDAQ: INOD

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.

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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.

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.

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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

Our AI-enabled industry platforms address specific market requirements where we believe we can deliver differentiated value through domain expertise, proprietary data models, and applied AI.

Our Synodex® platform transforms medical records into structured digital data for insurance and healthcare workflows. Our Agility PR SolutionsTM platform provides media intelligence and public relations workflow software enhanced with AI-driven monitoring, analytics, and content capabilities. We continue to invest in these platforms to incorporate advances in AI while emphasizing reliability, transparency, and user trust. Agility is now ranked by software review site G2 Crowd as meeting the requirements of customers better than its two largest competitors that have combined revenues of over $1 billion.

The Company’s operations are presently classified and reported in three reporting segments: Digital Data Solutions (DDS), Synodex and Agility.

Market Growth

We believe the market opportunity for Innodata is driven by sustained growth in global AI spending and the increasing emphasis on trustworthy deployment. Industry analysts forecast significant increases in AI-related spending across infrastructure, applications, and services. For example, in August 2024 the International Data Corporation (IDC) published an article: Worldwide AI and Generative AI Spending Guide which forecasted worldwide spending on AI to reach $632 billion by 2028. Separately, according to Generative AI 2025 Outlook, published in November 2024 by Bloomberg Intelligence, generative artificial intelligence technologies are forecast to represent a total addressable revenue opportunity of approximately $1.6 trillion globally by 2032.

As AI investment scales, we believe the need for high-quality data and the ability to validate model behavior at scale expands in parallel. By way of illustration, according to Data Collection and Labeling Market (2025–2030) published by Grand View Research, Inc. in November 2024, the global data collection and labeling market was valued at approximately $3.8 billion in 2024 and is projected to reach about $17.1 billion by 2030, representing a compound annual growth rate of approximately 28.4% from 2025 through 2030. We believe these estimates understate the longer-term opportunity associated with next-generation evaluation, safety, and assurance services required for agentic systems and regulated AI deployments, which are increasingly viewed as necessary components of AI commercialization.

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In 2025, we launched a dedicated Federal Practice to address accelerating demand from U.S. government agencies for secure, high-quality data engineering and AI enablement services. This practice is focused on mission-critical use cases across defense, intelligence, civilian, and regulatory environments, where requirements for data provenance, model evaluation, safety, security, and compliance are especially stringent. We intend to apply the same research-driven methodologies developed through our work with leading technology companies building advanced AI systems and with enterprises deploying AI in production to federal programs operating under heightened operational, security, and regulatory requirements. We believe sustained federal investment in artificial intelligence, digital modernization, and data-driven operations meaningfully expands our total addressable market, as agencies increasingly require specialized capabilities spanning data preparation, evaluation, and ongoing model performance oversight. As federal AI initiatives progress from experimentation to scaled, multi-year programs, we expect our Federal Practice to represent an increasing share of our addressable market and to support long-term revenue growth through larger, longer-duration engagements.

We believe AI-enabled workflow automation and human augmentation represent a significant and growing market opportunity, particularly for workflows whose effectiveness increases as artificial intelligence capabilities advance. Through our work with leading technology companies building frontier AI models, we gain early exposure to emerging model capabilities, deployment patterns, and technical constraints as they are introduced into production environments. We apply these insights to inform the design and evolution of our industry-specific AI platforms, integrating new AI techniques into core workflows in ways that reflect where the market is moving. Through this approach, we seek to deliver differentiated outcomes by combining domain expertise, proprietary data assets, and applied AI. Agility operates in the global media intelligence and public relations software market, which according to Media Intelligence and PR Software Market Size and Forecast report published in March 2024 by Verified Market Research, the global media intelligence and PR software market was valued at approximately $10.6 billion in 2023 and is projected to reach approximately $27.5 billion by 2030, representing a compound annual growth rate of approximately 14.6% from 2024 through 2030. In January 2026, Fortune Business Insights published a report: Artificial Intelligence in Healthcare Market that valued the global AI healthcare market at approximately $39.3 billion in 2025 and projected to reach approximately $1,033.27 billion by 2034, representing an expected compound annual growth rate of approximately 44% between 2026 and 2034.

Our Global Delivery Platform and Operating Scale

Innodata operates what we believe to be one of the largest and most geographically diverse global delivery platforms in the AI data and eval ecosystem. We employ and engage 12,200 professionals across more than 70 countries spanning North America, Europe, and Asia-Pacific. Our global footprint enables multilingual training and evaluation, culturally and jurisdictionally informed safety assessments, follow-the-sun delivery supporting rapid iteration cycles, and operational resilience for mission-critical programs.

Our delivery operations are organized around centers of excellence focused on training and post-training data, alignment and preference optimization, large-scale eval operations, red teaming and adversarial research, and domain-specific safety programs. We believe this structure aligns with the operational needs of frontier AI developers running continuous training, post-training, and evaluation cycles at scale.

We are continuously building and refining technology infrastructure to support our service capabilities and products. Our technology investments are designed to enable scalable, secure, and repeatable delivery across the AI lifecycle, including data engineering, post-training, evaluation, and ongoing quality assurance. We develop and operate a combination of proprietary platforms, internal tools, and integrated third-party technologies that support automation, workflow orchestration, quality control, and human-in-the-loop operations. This infrastructure is designed to evolve as AI architectures, training techniques, and evaluation methodologies change, allowing us to incorporate new approaches without disrupting customer programs. We believe that continued investment in adaptable technology infrastructure is critical to maintaining service quality at scale and to supporting increasingly complex and mission-critical AI use cases.

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Our infrastructure supports a range of strategies to suit our customers’ requirements for data security, compliance, scalability and reliability. Our user endpoints are secured with cloud-managed Endpoint detection and response (EDR) security solutions consisting of firewall, IDS/IPS, vulnerability scanning and patch management engines. We host data and applications in our own data centers at our operations centers, in our customers’ data centers, and on third-party cloud services including Amazon Web Services (“AWS”), Microsoft Azure (“Azure”), Oracle Cloud Infrastructure (“OCI”), and Google Cloud Platform (“GCP”) that provide the benefit of “infinite scalability” of information technology resources. Our data operations are linked by multiple redundant network connections. Our Wide Area Network – along with our Local Area Networks, Storage Area Networks, Network Attached Storage and data centers – are configured with industry standard redundancy, often with more than one backup to establish 24x7 availability. In 2025, our Wide Area Network had 99.97% uptime excluding scheduled maintenance. We encrypt all sensitive information, both at rest and in transit, to the Advanced Encryption Standard (AES) 256 or similar standard, and we employ a range of security features, including industry-leading managed firewalls, intrusion detection and prevention services. (See “Information Security”, below.)

Growth Strategy

We believe we are living in a period of significant technological transition, as artificial intelligence increasingly becomes embedded in software, enterprise workflows, and mission-critical systems. Unlike traditional software, AI systems learn behavior from data, making data quality, training, evaluation, and ongoing oversight central to system performance and reliability. We believe a data-centric and evaluation-centric approach to AI development and deployment will increasingly differentiate successful AI programs from unsuccessful ones.

Our growth strategy is designed to leverage our more than 35 years of experience creating high-quality data and operating complex information workflows at scale. We intend to align our business with large, dynamic, and rapidly growing markets related to the development, evaluation, and commercialization of advanced AI systems, as well as the deployment of AI across enterprise and public-sector environments. Our solutions and platforms leverage our technology infrastructure, global delivery capabilities, and culture of discipline around data quality and process rigor, together with our ongoing investment in applied AI and machine learning research and development.

Key elements of our growth strategy include the following:

Expanding Relationships with Existing Customers

We seek to expand the scope and scale of our relationships with existing customers as their AI initiatives mature. Initial engagements often focus on specific datasets, use cases, or evaluation programs. As customers experience the benefits of our delivery quality, operational reliability, and domain expertise, engagements frequently expand to additional use cases, data modalities, post-training activities, or evaluation and deployment support. We believe our ability to support multiple stages of the AI lifecycle positions us well to grow organically within customer accounts over time.

Driving New Customer Acquisition in Existing and Emerging Markets

We believe we remain in the early stages of penetrating our addressable markets and intend to pursue new long-term customer relationships, particularly with organizations making significant investments in AI innovation. In addition to expanding within our existing markets, we are pursuing opportunities in emerging areas, including sovereign AI initiatives and public-sector programs.

We have established and are expanding a Federal Practice focused on supporting U.S. government agencies and contractors, where AI programs often require heightened standards for data governance, security, documentation, and evaluation. We believe our experience operating in regulated environments and our global delivery infrastructure position us to compete effectively in these markets.

To support new customer acquisition, we continue to invest in sales and account management talent. We believe our current sales organization is operating effectively and supports our near-term growth objectives.

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Developing New Capabilities and Product Innovation

We intend to continue developing new capabilities in response to evolving customer needs and advances in AI technologies. As AI systems become more autonomous and agentic, customers increasingly require solutions that support trust, safety, and control across development and deployment. We are investing in capabilities related to agentic trust and safety, including evaluation frameworks designed to assess agent behavior, tool use, and adherence to constraints across multi-step workflows.

We also pursue charter customer relationships to co-develop new solutions and platforms, enabling us to align innovation efforts closely with real-world requirements while scaling capabilities across our broader customer base.

Continuing Innovation and Long-Term Perspective

We believe our ability to innovate will remain an important contributor to growth and market traction. We maintain defined roadmaps for our services and platforms and work closely with customers to identify opportunities for enhancement and expansion. Our innovation strategy emphasizes translating emerging research and customer requirements into scalable, repeatable operational systems.

We have also sought to establish external advisory relationships to support our innovation initiatives and to inform our thinking on responsible and ethical uses of artificial intelligence. In mid-2022, we initiated the formation of an advisory board intended to provide strategic guidance on innovation, AI governance, and the future evolution of AI technologies. We actively recruit qualified individuals and may add advisory board members from time to time as appropriate.

We expect to fund investments associated with our growth strategy primarily through internal resources, while maintaining flexibility to access capital through debt or equity financing as appropriate. We believe AI adoption will continue to unfold over many years and that sustained growth will favor companies focused on foundational capabilities - high-quality data, rigorous evaluation, and trustworthy deployment - rather than any single application or model architecture.

Our Customers

Our customers include leading global organizations across multiple industry verticals, including banking, insurance, financial services, technology, digital retailing, and information and media. A substantial portion of our revenue is derived from customers that are among the largest technology companies and AI innovators globally, many of which are making significant, long-term investments - often measured in the hundreds of millions of dollars - in artificial intelligence research, development, and deployment. Our customer base includes five of the companies commonly referred to as the “Magnificent Seven,” as well as several leading artificial intelligence research labs and model developers in the United States and internationally.

For the fiscal year ended December 31, 2025, one customer in our Digital Data Solutions (“DDS”) segment accounted for approximately 58% of the Company’s total revenues. For the fiscal year ended December 31, 2024, one customer in the DDS segment accounted for approximately 48% of the Company’s total revenues. No other customer accounted for 10% or more of total revenues in either of these periods. Revenues from customers located outside the United States represented approximately 16% and 21% of the Company’s total revenues for the years ended December 31, 2025 and 2024, respectively.

We maintain long-standing relationships with many of our customers, several of which have engaged us across multiple projects, use cases, or business units over time. Our ability to deliver services at scale with consistency, quality, and operational reliability has supported recurring engagements and the expansion of relationships following initial customer engagements.

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Our services for our customers 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.

Sales and Marketing

We market and sell our solutions and platforms primarily through a direct sales model supported by professional staff, senior management, and dedicated sales personnel operating from locations in the United States, Canada, the United Kingdom, and Europe. In addition, we are selectively developing and expanding strategic partnerships and channel relationships to support customer acquisition and the expansion of existing customer relationships.

Our sales efforts are supported by cross-functional teams that include solutions architects, technical specialists, and consultants who assist in developing new customer engagements and expanding existing ones. These resources work within structured teams - both permanent and engagement-specific - to support customer needs throughout the sales and delivery lifecycle.

Our sales and marketing organizations operate in close coordination. Marketing initiatives are designed to generate awareness, articulate our value proposition, and produce qualified leads, while our sales professionals identify and qualify prospects, establish relationships with decision makers, and facilitate interactions between customer stakeholders and our delivery teams. For each prospective engagement, we assemble a multidisciplinary team of senior employees who follow a formalized process to understand customer objectives, assess technical and operational requirements, and collaboratively design solutions.

Our marketing organization is responsible for increasing the visibility of our brand and service offerings, supporting thought leadership initiatives, and providing sales enablement tools. As part of this strategy, we engage with media organizations, trade associations, industry publications, conference producers, and consulting organizations to build awareness and credibility, particularly within enterprise and technology-driven markets.

Our marketing outreach activities include content marketing, event marketing (including participation in and sponsorship of industry conferences, summits, and seminars), direct and database marketing, public and media relations (including speaking engagements), and digital marketing initiatives such as integrated campaigns, search engine optimization, and website development. During 2025, we sponsored and hosted an AI Summit in San Francisco, California which convened technology leaders, enterprise customers, and industry experts to discuss advances in generative and agentic AI, trust and safety, and enterprise deployment considerations. We believe events of this nature support our positioning as a partner to organizations building and deploying advanced AI systems.

Sales activities include lead generation and nurturing, engagement with prospective customers to understand requirements, demonstration of our capabilities, solution design, response to requests for proposals, and ongoing management of customer relationships. Our solutions analysis, engineering services, and customer services teams closely support these activities by assisting with technical assessments, demonstrations, prototypes, pricing estimates, and implementation planning. In addition, account managers provide ongoing project-level support to customers following contract execution, helping to maintain continuity between sales and delivery.

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Competition

We operate in a highly competitive and rapidly evolving market. Major competitors across industry verticals include providers of AI data engineering, training, and evaluation services such as Appen, CloudFactory, Surge AI, Invisible Technologies, Turing, Mercor, Define.a, TELUS Digital, and Scale AI, several of which are large firms with established customer bases and global delivery capabilities. We also compete with broader technology and business process services providers, including Accenture, Cognizant Technology Solutions, EXLService Holdings, Inc., Genpact Limited, Infosys Limited, PwC, QuantumBlack, and Tata Consultancy Services, which may offer overlapping services as part of broader consulting or outsourcing engagements.

We compete primarily on the basis of service quality, technical depth, scalability, information security, and the ability to support complex, mission-critical AI programs over time. Our competitive position is supported by our proprietary platforms and workflows, global delivery infrastructure, deep pool of domain experts, and ability to operate across multiple stages of the AI lifecycle, including data engineering, post-training, evaluation, and deployment support. These capabilities are particularly relevant for customers undertaking large-scale or high-stakes AI initiatives, including those subject to regulatory, security, or reliability requirements.

In addition, we believe our increasing emphasis on model evaluation, trust and safety, and agentic AI workflows differentiates us from providers that focus primarily on data labeling or staffing-based services. Customers may also choose to perform certain functions internally or pursue alternative technical approaches, which represents an ongoing source of competition.

Intellectual Property

We depend in part on our proprietary technologies (including platforms, applications, data models, evaluation frameworks, and workflows) and methodologies, as well as operational know-how developed in the course of supporting the development, evaluation, and deployment of artificial intelligence systems. Our intellectual property includes a patent, trademarks, copyrights, trade secrets, and unpatented proprietary processes and methods, including techniques for data preparation, model evaluation, quality assurance, and safety-related assessments. We hold a patent and believe the remaining term of approximately two years of this patent is adequate relative to the expected commercial life of the covered application. Consistent with industry practice, we rely primarily on trade secret protections, licensing arrangements, confidentiality and nondisclosure agreements, and applicable copyright and trademark laws - rather than patents alone - to protect our proprietary methodologies and operational innovations.

In the course of providing services, we may develop tools, workflows, annotations, evaluations, or other work product that incorporate our pre-existing intellectual property or general methods and expertise. Unless otherwise agreed by contract, our customers generally retain ownership of their underlying data, models, and customer-specific deliverables, while Innodata retains ownership of its pre-existing intellectual property, methodologies, platforms, and any improvements, enhancements, or derivative works thereof that are of general applicability and not specific to a particular customer. Our agreements typically grant customers appropriate rights to use service outputs for their intended purposes, while preserving our ability to reuse our proprietary methods, workflows, and know-how across engagements.

We require employees, contractors, and certain third parties to enter into confidentiality and intellectual property assignment agreements, and we maintain policies and controls designed to limit access to and distribution of proprietary information, including information belonging to our customers. Despite these measures, we cannot assure that such protections will prevent unauthorized disclosure, use, or misappropriation of our intellectual property, or that we will be able to detect or enforce violations of our intellectual property rights in a timely or cost-effective manner. Our reliance on trade secrets and contractual protections may provide less protection than patent rights in certain circumstances, and any unauthorized use of our intellectual property could adversely affect our competitive position and business results.

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Information Security

We maintain an enterprise information security program designed to protect the confidentiality, integrity, and availability of our information assets and those of our customers, including sensitive training, evaluation, and model-related data used in the development and deployment of artificial intelligence systems. Our operations facilities in Asia and our New Jersey, United States facility are certified to the ISO/IEC 27001:2022 information security management standard. Our security program employs a multi-layered, defense-in-depth approach and includes administrative, technical, and physical safeguards intended to address evolving cybersecurity risks associated with large-scale data processing and AI workflows.

Our security controls include, among others, multi-factor authentication to enhance access security; role-based access controls and segregation of environments to limit exposure of sensitive datasets; centralized patch and vulnerability management processes designed to support timely remediation of software and system updates; full-disk encryption for mobile devices and sensitive endpoints; endpoint protection incorporating anti-malware, firewall, and intrusion detection and prevention capabilities; and network-level security controls, including Web Application firewalls (WAF) and next-generation network firewalls with intrusion detection and prevention systems, to monitor and protect network traffic. These controls are designed to support secure handling of high-volume, high-sensitivity datasets and to reduce the risk of unauthorized access or misuse.

For engagements involving personally identifiable information or protected health information subject to HIPAA and similar regulatory requirements, as well as other customer data subject to heightened contractual or regulatory constraints, we utilize U.S.-based, co-located data centers or HIPAA-compliant cloud infrastructure. Such environments are designed to employ industry-standard encryption, including AES-256 or equivalent, to protect data at rest and in transit, along with monitoring and logging controls consistent with applicable legal and contractual obligations. Our information security program and related controls are subject to periodic internal assessments and annual independent audits. However, no security program can eliminate all risks, and we cannot provide assurance that our controls will prevent all cybersecurity incidents or unauthorized access.

Government Regulation

Our business is subject to a broad and evolving set of U.S. federal, state, and foreign laws and regulations governing data protection, privacy, cybersecurity, and the use of data in technology-enabled services, including the development, evaluation, and deployment of artificial intelligence systems. These regulatory requirements address, among other things, the collection, processing, storage, security, transfer, and use of data, and may differ materially across jurisdictions in which we operate or support customer programs.

We are subject to, and seek to comply with, applicable data protection and privacy laws, including the United States Health Insurance Portability and Accountability Act of 1996, as amended by the Health Information Technology for Economic and Clinical Health Act (HIPAA and HITECH), the European Union General Data Protection Regulation (EU GDPR), the United Kingdom General Data Protection Regulation as tailored by the Data Protection Act 2018, and other national, state, and local laws governing privacy, data security, and cross-border data transfers, as applicable. Certain customer engagements, including those involving government agencies, regulated enterprises, or sensitive training, evaluation, or model-related data, may also be subject to additional contractual, sector-specific, or jurisdiction-specific regulatory requirements.

Regulatory frameworks applicable to artificial intelligence, automated decision-making, and the use of large-scale datasets are rapidly evolving in the United States and internationally. Emerging and proposed regulations and guidance addressing AI governance, model transparency, accountability, safety, data provenance, and human oversight - particularly in connection with high-impact or regulated use cases - may impose additional compliance obligations, increase costs, limit certain business practices, or affect the scope or timing of customer programs. Compliance with these requirements requires ongoing investment in policies, technical controls, and operational processes, and there can be no assurance that future regulatory developments will not materially affect our business, operating results, or ability to serve customers across jurisdictions.

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Research and Development

Our research and development activities are designed to support the evolution and competitiveness of our service offerings and platforms over both the near and long term. We focus our research and development investments on capabilities that improve the scalability, reliability, and repeatability of AI-enabled solutions, while enabling us to respond to rapidly evolving customer requirements and advances in artificial intelligence technologies. Customer engagement and feedback play an important role in shaping our applied research and development priorities, helping ensure alignment with real-world use cases and emerging market needs. The successful translation of these efforts into commercially viable solutions depends on technological feasibility, market adoption, and execution capabilities.

We believe our culture of innovation supports our ability to attract and retain highly skilled AI practitioners, engineers, and technologists. Our research and development activities are conducted through a combination of Innodata Labs, our Technology Practices, and our platform engineering organizations. These activities are carried out across multiple geographic locations, including centers in North America and the Asia-Pacific region.

Innodata Labs

Innodata Labs is the Company’s long-term innovation and research function, focused on applied research and the exploration, prototyping, and validation of emerging concepts in artificial intelligence. Innodata Labs is intentionally designed to operate ahead of near-term customer demand and market readiness, with a typical research horizon of two to three years. Its work emphasizes experimentation across next-generation AI capabilities, including agentic systems, training and post-training paradigms, data/compute tradeoffs, and evaluation methodologies, as well as the implications of these developments for safety, governance, and regulatory oversight. These activities are exploratory in nature and are primarily expensed as incurred.

Innodata Labs engages with academic researchers, frontier model developers, and emerging technologies to assess future customer needs, industry practices, and regulatory expectations. While Innodata Labs is not operated with immediate revenue objectives, it is intended to inform and seed future service offerings, platforms, methodologies, and intellectual property. We believe this long-horizon research capability supports our ability to adapt as AI architectures, development practices, and deployment environments continue to evolve.

Technology Practices

Innodata’s Technology Practices are responsible for translating research insights, engineering techniques, and operational learnings into scalable, commercially deployable solutions aligned with current and near-term customer needs. These practices integrate applied research with delivery execution, enabling repeatable implementation across customers, industries, and use cases.

Our Technology Practices focus on areas including agentic system evaluation and trust and safety; model fine-tuning, alignment, and human-in-the-loop evaluation; custom data collection across multiple modalities; robotics and computer vision data and model support; reusable and licensed datasets; and enterprise AI implementations such as retrieval-augmented generation, agentic workflows, and production-scale AI platforms. We believe this structure allows us to accelerate time-to-value for customers while maintaining consistency, quality, and governance across large-scale AI programs.

These practices represent internal capability groupings rather than separate operating segments or standalone businesses.

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Platform Engineering

Our platform engineering teams conduct ongoing research and development activities aimed at enhancing the functionality, performance, and scalability of existing and proposed AI engineering and industry platforms. These efforts include developing new platforms and enhancing capabilities of existing platforms to support current and emerging customer use cases.

Given the pace of innovation in AI models, tools, and infrastructure, our platform development emphasizes modularity, interoperability, and model-agnostic design. We regularly release updates and new versions of our platforms to incorporate new capabilities, support evolving workflows, and maintain competitive positioning. These activities are primarily performed by internal engineering teams, with third-party providers used selectively under the Company’s oversight, and certain development activities may be eligible for capitalization in accordance with applicable accounting standards. We believe the timely development and deployment of platform functionality is important to supporting efficient services delivery as well as seizing new market opportunities and promoting product/service differentiation.

Environmental, Social, and Governance

We have built a robust corporate ESG program focused on social responsibility; improving how we perform as a steward of the environment; and sustainability.

Social Responsibility

We are inspired by the vision of fostering a future of broadly distributed sustainable prosperity that can result from ethical AI and broad access to the benefits of AI. We launched our i-Hope Program in 2016 to help children in marginalized or economically disadvantaged communities face the challenges of an increasingly AI-driven world. Our goal of delivering the gift of computer literacy to 25,000 children by 2025 was achieved ahead of schedule in the third quarter of 2023. As a part of this initiative, one of our operating subsidiaries handed over a smart classroom, an ideation room, and an open library (with over 100,000 books) to a publicly-funded higher education institution in the Philippines.

Since 2016 we have contributed over 4,100 person-days to this and other Corporate Social Responsibility (CSR) programs. We have built and made operational 20 fully functional computer labs and 66 smart classrooms across India, Sri Lanka, and the Philippines. As a result, approximately 52,300 children have become more technology-proficient and better equipped to seize opportunities in the AI era.

Our contributions have been well-recognized. Examples of award recognition received in 2025 through our operating subsidiaries include: Diversity Company of the Year; the Best Environmental Responsibility Initiative; Innovation in CSR Practices; Women Empowerment Initiative from National CSR Leadership Congress & Awards; and Sustainable Diversity, Equality, and Inclusion award from the World Sustainability Congress.

Environmental Stewardship

We are also committed to conducting our business in an environmentally responsible manner that supports global efforts to mitigate climate change. By implementing practices that minimize our carbon footprint, conserve resources, and promote sustainability, we aim to be a positive force for the environment.

We monitor and target reductions in greenhouse gas emissions, energy consumption, and water usage for our production facilities. This data-driven approach has enabled us to improve our sustainability initiatives and share emissions data (Scopes 1, 2, and 3) with our customers.

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Across all our global operations, we recycle e-waste and paper. In our Mandaue, Philippines facility, we installed a 300 KVA solar panel equivalent to a CO2 reduction of approximately 299 metric tons annually. In India, the Philippines, and Sri Lanka, we actively support grass-roots environmental conservation initiatives in the communities in which we operate. In 2025, we planted over 5,200 saplings and seedlings in nature reserves, bringing our cumulative total to over 17,000 since 2018. This initiative includes follow-up practices to ensure the saplings receive proper care during the critical early growth phases, improving the saplings’ long-term survival rates.

Sustainability

Our sustainability program is based on the following core elements: health and safety, business continuity management, information security, labor standards, anti-bribery and corruption, management engagement and social impact. Our sustainability program is backed by ISO 27001:2022 (information security) certification, policies, and employee training for these core areas.

Employees

As of December 31, 2025, we employed 10,107 employees, 10,020 of which were full-time. Many of our employees hold advanced degrees in specialized fields such as law, business, technology, medicine, and social sciences. No employees are currently represented by a labor union, and we believe that our relations with our employees are satisfactory.

Corporate Offices

Our principal executive offices are located at 55 Challenger Road, Ridgefield Park, New Jersey 07660, just outside New York City, and our telephone number is (201) 371-8000. We were founded in 1988.

Our website is www.innodata.com; information contained on our website is not included as a part of, or incorporated by reference into, this Annual Report on Form 10-K. There we make available, free of charge, our Annual Report on Form 10-K, Quarterly Reports on Form 10-Q, Current Reports on Form 8-K, and any amendments to those reports, as soon as reasonably practicable after we electronically file that material with, or furnish it to, the Securities and Exchange Commission (“SEC”). Our SEC reports can be obtained through the Investor Relations section of our website or from the SEC at www.sec.gov.

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