Slide 1: Q4 Investor Presentation
Q4 Investor Presentation February 2026 Nasdaq: INOD
Slide 2: Disclaimers
Disclaimers Forward-Looking Statements Our actual results could differ materially from the results referred to in any forward-looking statements. Factors This presentation contains forward-looking statements within the meaning of Section 21E of the Securities that could cause or contribute to such d
Slide 3: Innodata is a Global Data Engineering Company
Q4 2025 Investor Presentation Innodata is a Global Data Engineering Company. Data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to bu
Slide 4: End-to-End AI Lifecycle Solutions
About Innodata End-to-End AI Lifecycle Solutions 36+ History with a rich legacy as a trusted partner to leading companies. YEARS Strategically positioned as a "picks and shovels" beneficiary of the AI and GenAI revolution. AI Solutions Gen AI Implementation Contracts with five of the “Magnificent Se
Slide 5: Why Invest in Innodata
About Innodata Why Invest The technology partner-of-choice for data quality, scale, and Enormous total addressable market (TAM) opportunity for Supporting Big Tech builders and enterprise adopters of large in Innodata. agility. generative AI of $1.6 trillion by 2032.1 language models (LLMs)2, agenti
Slide 6: Positioned at the Data Layer of Generative AI
Well-Positioned in Growing Market Positioned at the Data Layer of Generative AI. 2022 – 2024 2017 Onwards 2021 – 2022 Inflection Point Applied AI and Classical AI for Generative AI for 2025 – Beyond Machine Learning Leading Tech Leading Tech (ML) Companies Companies Advancing Frontier AI Capabilitie
Slide 7: Innodata Supports GenAI Builders and Adopters
About Innodata Innodata Supports GenAI Builders and Adopters • Data Curation • Consulting • AI Stack Services (Foundation Models, • Data Transformation • AI Services (AIOps, Tooling, Data Architecture, CoPilots, Model Trust, Training Data Preparation, • LLM Instruction Datasets / Safety and Evaluati
Slide 8: Our Services + Platforms Supported by Subject Matter Experts Across the Globe
About Innodata Our Services + Platforms Supported by Subject Matter Experts Across the Globe. SME Domain Specialization Supporting customers across all time zones. Delivery Locations • Data Science • Law + Regulation • Engineering • Computational • Medicine + • Business + Linguistics Allied Health F
Slide 9: Our Platforms Support Both Big Tech + Enterprise AI Use Cases
About Innodata Our Platforms Support Both Big Tech + Enterprise AI Use Cases. Foundational Goldengate Platform1 Regulatory Change Management LLM Annotation and Red teaming Platform Proprietary, State-of-the-Art Platform Integrated workflow application enabling legal Customized platforms for accelera
Slide 10: We Believe We Have Opportunities for Continued Growth + Expansion
Well-positioned in Growing Market We Believe We Have Opportunities for Continued Growth + Expansion. Magnificent Seven Enterprise AI AI/LLM-Enabled Federal Practice Sovereign AI Global Tech Services and Vertical Platforms Growing our federal AI Strategic Companies & Other Platforms Successfully busi
Slide 11: Generative AI is Anticipated to be the Next-Generation Computation Platform
Well-positioned in Growing Market Generative AI is Anticipated to be the Enormous generative AI addressable Next-Generation Computation Platform. market opportunity4 $ in billions We believe the generative AI market opportunity will rival the internet and mobile in significance. • “Magnificent Seven
Slide 12: Strategy to be the Leading Global AI Technology Company
Well-positioned in Growing Market Strategy to be the Leading Global AI Technology Company Serving Both AI Builders + AI Adopters. New Customers + Engagement with New Capabilities + Operations + Grow Revenue Existing Customers Platforms Platforms Drive growth in targeted Listen to the needs of our Ex
Slide 13: LLM Training — Improving AI Outcomes Through Targeted Data
Recent Innovation Areas LLM Training — Improving AI Outcomes Through Targeted Data The Industry Shift Innodata Approach Strategic Impact As AI models grow more complex • Diagnose model performance Positions Innodata as a partner and costly to train, the focus is gaps using structured in improving AI
Slide 14: Agentic AI — The Next Phase of Enterprise AI
Recent Innovation Areas Agentic AI — The Next Phase of Enterprise AI Agentic AI systems operate with greater autonomy, adaptability, and contextual reasoning. These systems increasingly: As autonomy increases, so do: Interact with Risk, safety, Make Execute complex Data Evaluation and trust decision
Slide 15: What Innodata Does in Agentic AI Today
Recent Innovation Areas What Innodata Does in Agentic AI Today High-quality training data and expert Agentic AI evaluation to build and refine agentic systems Development and workflows. Enterprise Agent Integration and orchestration of agents within Implementation enterprise workflows. Agentic Innod
Slide 16: Physical AI — Building the Intelligence Layer for Real-World Systems
Recent Innovation Areas Physical AI — Building the Intelligence Layer for Real-World Systems The Opportunity Innodata’s Advantage Early Traction AI is moving into Physical AI • Purpose-built and in-the- • Deployed within recently which includes robotics and wild data collection announced customer em
Slide 17: Q4 2025 Highlights + Financial Overview
Q4 2025 Investor Presentation Q4 2025 Highlights + Financial Overview. 17
Slide 18: Q4 2025 Key Highlights
Financial Overview Q4 2025 Key Highlights $72.4M 22% ~92% / 8% 2 Q4 2025 Revenue Q4 YoY Quarterly Organic Revenue Growth Services / SaaS 42% 22% $82.2M 3 Adjusted Gross Margin1 Adjusted EBITDA as a % of revenue1 Cash on Balance Sheet 1 Non-GAAP measure. Please see “Reconciliation of Non-GAAP Measure
Slide 19: 2025 Full Year Key Highlights
Financial Overview 2025 Full Year Key Highlights. $251.7M 48% ~91% / 9% 2 FY2025 Revenue YoY Growth Services / SaaS 43% 23% $82.2M 3 Adjusted Gross Margin1 Adjusted EBITDA as a % of revenue1 Cash on Balance Sheet 1 Non-GAAP measure. Please see “Reconciliation of Non-GAAP Measures” on Slide 24. 2 Saa
Slide 20: Revenue Growth
Financial Overview Revenue Growth. In Q4 2025, we delivered 22% year-over-year (YoY) revenue growth, bringing our full year revenue growth rate to 48%. In millions US$ $72.4 $62.6 $59.2 $58.3 $58.4 $52.2 $32.6 $26.1 $26.5 $22.2 $18.8 $19.7 Q1'23 Q2'23 Q3'23 Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25
Slide 21: Q4 2025 Revenue and Adjusted EBITDA
Financial Overview Q4 2025. In millions US$ $72.4M 22% $15.7M 22% Q4 YoY Quarterly Organic Q4 2025 Adjusted Q4 2025 Adjusted Q4 2025 Revenue Revenue Growth EBITDA1 EBITDA as a % of revenue1 In millions US$ 73.0 Historical Q1 2023 through Q4 2025 27% 24% 26% 30% 63.0 22% 23% 22% 53.0 14% 16% 14% 20%
Slide 22: FY26 Revenue Guidance of 35% or More
FY 2026 Revenue Guidance FY26 Revenue Guidance of 35% or More. In millions US$ $340.0 $251.7 2025 2026 22
Slide 23: We Have a Healthy Balance Sheet with High Net Cash
Q4 2025 Highlights and Financial Overview We Have a Healthy Balance Sheet with High Net Cash. Cash and short-term Balance sheet Available credit facility investments of cash and short-term balance sheet with no external line of credit with Wells Fargo, no investments as of Dec’25 debt/borrowings dra
Slide 24: Reconciliation of Non-GAAP Measures
Q4 2025 Highlights and Financial Overview Reconciliation of Non-GAAP Measures. $ in Millions FY’23 FY’24 FY’25 Q4’24 Q4’25 $ in Millions FY’23 FY’24 FY’25 Q4’24 Q4’25 Net income (loss) Gross profit attributable attributable to Innodata Inc. ($0.9) $28.7 $32.2 $10.3 $8.8 to Innodata Inc. and $31.3 $6
Slide 25: Appendix
Q4 2025 Investor Presentation Appendix. 25
Slide 26: Data Engineering Services are at the Core of Generative AI and LLMs
What We Do Data Engineering Services are at the Core of Generative AI and LLMs. Pretraining Supervised Fine-Tuning Reward Modeling Reinforcement Learning Evaluation Raw Internet Demonstrations Comparisons Prompts Evaluation + Red Trillions of words (low-quality / Ideal LLM prompts & (Highest-quality
Slide 27: Capture and Enrich Application Event Data for AI-Powered Automation
Case Study 1: Global Software Company | Agentic AI We’re Helping Capture and Enrich Application Event Data for AI-Powered Automation. The Challenge What We Provide A US-based global software company • Capture of digital interactions using the customer's recording agent, such as that develops robotic
Slide 28: Helping Safeguard Multimodal LLMs
Case Study 2: Leading Cloud Provider | Multimodal Red Teaming Services We’re Helping Safeguard Multimodal LLMs. The Challenge What We Provide A major cloud provider preparing to launch a • Expert Red Teaming with thousands of multimodal prompts created and suite of multi-modal LLMs required: tested
Slide 29: Improve Model Performance Through Complex RLHF
Case Study 3: Leading Tech Company | Reward Modeling for Chatbot Summarization We’re Helping Improve Model Performance Through Complex RLHF. The Challenge What We Provide A leading technology customer wanted to • Expert team assembly for analyzing requests (e.g., requests for information, improve th
Slide 30: Reimagine Regulatory Change Management
Case Study 4: Top-3 Global Bank | Generative AI-Enabled Industry Platforms We’re Helping Reimagine Regulatory Change Management. The Challenge What We Provide One of the most critical analytical functions in financial services institutions Innodata automated the monitoring and aggregation of laws, (
Slide 31: Innodata (Nasdaq: INOD) is a global data engineering company
Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to b