Business Description

Business Overview Innodata Inc. (Nasdaq: INOD) (together with its subsidiaries, the “Company”, “Innodata”, “we”, “us” or “our”) is a global data engineering and AI systems services company that supports the development, training, post-training, evaluation, and deployment of advanced artificial intelligence systems. We partner with leading technology companies, frontier AI laboratories, and enterprises to help enable AI systems that perform reliably, align with intended objectives, and operate safely in real-world environments. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We believe that AI will increasingly function as a foundational layer of the digital economy - embedded across consumer products, enterprise workflows, and mission-critical systems. As AI systems grow more capable and autonomous, we believe the quality of training data, the effectiveness of post-training alignment, and the rigor of ongoing evaluation will be decisive factors in determining whether AI systems are adopted, regulated, and scaled responsibly. Innodata was founded more than 35 years ago on the principle that high-quality, well-structured data is essential to leading information-retrieval systems. In 2016-2017, we began building proprietary AI language models based on then-emerging research and frameworks and integrating them into our data production workflows. Through this work, we developed and refined techniques for generating, curating, and validating human-created data used to train probabilistic, learning-based AI systems, and recognized that data quality and structure were critical determinants of model performance. This insight led us to invest in the development of an integrated set of AI lifecycle data solutions, addressing a growing market need for specialized data engineering, evaluation, and refinement capabilities across the full lifecycle of AI systems. Today, leading AI innovation labs and Big Tech companies (including five of the so-called “Magnificent Seven”) building frontier generative AI models and leading enterprises engage us to provide (i) training and post-training data development; (ii) alignment and preference optimization; (iii) capabilities, alignment, and safety evaluation; and (iv) AI enablement and operationalization, including support for agentic and tool-using systems. We believe Innodata is differentiated by: (i) our ability to operate across the AI lifecycle in alignment with AI developers’ internal development and deployment pipelines; (ii) our scale of specialized human expertise; (iii) purpose-built platforms and processes that combine automation with rigorous human oversight; (iv) a research-driven approach to measurement, safety, and operational reliability, which is particularly relevant for frontier model developers and enterprises deploying AI in high-stakes environments; and (v) our dual role supporting leading technology companies building advanced AI systems and enterprises deploying those systems in production, which we believe creates a reinforcing feedback loop that strengthens our capabilities across both contexts and differentiates us from competitors focused on only one side of the market. Market Opportunities AI Training and Post-Training Data Modern AI systems are trained using large volumes of data rather than explicit, rule-based programming. Foundation models - such as large language models (“LLMs”) and multimodal models - learn statistical representations of language, images, code, and other modalities from vast training corpora. As model architectures have matured, leading developers have increasingly emphasized the importance of training data quality, data provenance, supervised fine-tuning, and post-training alignment techniques. We believe that as model scale increases, marginal improvements in data quality and post-training signals can have an outsized impact on performance, reliability, and usability - often exceeding the impact of further parameter scaling alone. 4 Table of Contents 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.…