Innodata Inc.
- Open
- 56.68
- Day high
- 57.05
- Day low
- 55.51
- Prev close
- 56.59
- Volume
- 738K
- Mkt cap
- $1.9B
- P/E (TTM)
- 39.5
- EPS (TTM)
- $1.43
- P/B
- 12.2
- P/S
- 6.1
- Yield
- —
- Per share
- —
- ▼Insiders net selling -$49.4M over the last 3 months (0 open-market buys, 29 sales)
- 🏛Institutions accumulating (13F)
Innodata Inc. (INOD) is a Technology company listed on NASDAQ. The stock is up 50% over the past year. Over the trailing 3 months, insiders filed 0 open-market buys and 29 sales (SEC Form 4). Drillr has 1 published research article covering INOD.
Innodata Inc. (INOD) financials & analyst ratings
Fundamentals (TTM)
Analyst consensus · 2 analysts
Source: exchange market data + company filings. Figures are trailing-twelve-month or as most recently reported. For informational purposes only — not investment advice.
INOD earnings date, history & EPS estimates
| Report date | EPS est | EPS actual | Surprise | Revenue | Rev. surprise |
|---|---|---|---|---|---|
| Aug 6, 2026 | $0.21 | $0.41 | +92.5% | $92M | +6.8% |
| May 7, 2026 | $0.13 | $0.42 | +223.1% | $90M | +17.8% |
| Nov 6, 2025 | $0.14 | $0.24 | +71.4% | $63M | -10.0% |
| Jul 31, 2025 | $0.11 | $0.20 | +81.8% | $58M | -2.3% |
| May 8, 2025 | $0.17 | $0.22 | +29.4% | $58M | +3.5% |
| Feb 20, 2025 | $0.11 | $0.31 | +181.8% | $59M | +11.6% |
| Nov 7, 2024 | $0.07 | $0.35 | +400.0% | $52M | -1.4% |
| Aug 8, 2024 | $-0.00 | $-0.00 | -100.0% | $33M | +18.0% |
| Feb 22, 2024 | — | $0.05 | — | $26M | — |
| Nov 2, 2023 | — | $0.01 | — | $22M | — |
| Aug 10, 2023 | — | $-0.03 | — | $20M | — |
| May 11, 2023 | — | $-0.08 | — | $19M | — |
INOD insider trading activity (SEC Form 4)
| Date | Insider | Type | Shares | Price |
|---|---|---|---|---|
| Jul 7, 2026 | Chauhan Jayantofficer: EVP and CFO | Grant | 8,889 | — |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 1,300 | $108.50 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 12,010 | $103.49 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 23,592 | $112.39 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 25,653 | $105.81 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 15,942 | $104.64 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 16,869 | $107.31 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 1,562 | $113.16 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Option | 94,059 | $3.41 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 6,240 | $107.35 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 54,411 | $109.55 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 41,593 | $110.42 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Option | 65,941 | $3.41 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Option | 134,059 | $1.24 |
| Jun 16, 2026 | ABUHOFF JACKdirector, officer: CEO | Sell | 42,870 | $111.50 |
Source: INOD SEC Form 4 filings, latest Jul 7, 2026. For informational purposes only — not investment advice.
See the full INOD insider & 13F page →Innodata Inc. company profile
Overview
Innodata Inc. (NASDAQ:INOD) is a global data engineering company founded in 1988 and headquartered in Ridgefield Park, New Jersey. Originally incorporated as Innodata Isogen, Inc., the company changed its name to Innodata Inc. in June 2012 and went public in 1993. Over its 35-year history, Innodata has evolved from a traditional data processing company into a specialized provider of artificial intelligence training data and AI-enabled solutions. The company has experienced remarkable growth in recent years, particularly since the generative AI boom, transforming from a struggling business with negative EBITDA in 2022 to a profitable enterprise generating over $170 million in annual revenue by 2024.
Business
Innodata operates as a data engineering company that specializes in creating, processing, and managing data for artificial intelligence and machine learning applications. The company has positioned itself at the intersection of traditional data services and cutting-edge AI development, serving as a critical partner for technology companies building generative AI models and enterprises adopting AI solutions. The company operates through three main business segments. The Digital Data Solutions (DDS) segment represents the core of Innodata's business, generating the majority of revenue by providing AI-enabled software platforms and managed services to companies requiring high-quality data for training AI and machine learning algorithms. This segment offers comprehensive data engineering services including data annotation (the process of labeling data to teach AI models), data transformation, curation, hygiene, consolidation, compliance, and master data management. The DDS segment has become particularly valuable in the generative AI era, as it provides the specialized training data that large language models and other AI systems require to function effectively. The Synodex segment operates an industry-specific platform that transforms medical records into usable digital data using proprietary or client-specific data models. This healthcare-focused division addresses the critical need for structured medical data in an industry where information is often fragmented across different systems and formats. The Agility segment provides a specialized platform for marketing communications and public relations professionals, enabling them to target and distribute content to journalists and social media influencers while monitoring and analyzing global news channels including print, web, radio, television, and social media. While exact revenue breakdowns vary by quarter, the DDS segment dominates the company's revenue profile, particularly driven by contracts with major technology companies for generative AI training data services. The Agility segment, while smaller, has shown consistent growth and crossed the $5 million annual revenue mark, while Synodex serves as a specialized healthcare data platform with strong growth potential.
Revenue model
Innodata generates revenue primarily through service contracts and platform subscriptions across its three business segments. The company's business model has evolved significantly, with the most substantial revenue now coming from data engineering services provided to major technology companies developing generative AI models. These contracts typically involve creating high-quality training data through human annotation, data curation, and specialized data processing services. The company charges for these services based on project scope, data volume, and complexity, with contracts ranging from pilot projects to multi-year agreements worth tens of millions of dollars. The company's largest revenue driver comes from its relationships with Big Tech customers, particularly companies developing foundation models for generative AI. Innodata has secured contracts with seven major technology companies, including five of the "Magnificent Seven" tech giants, with its largest customer representing a $135 million annualized run rate. These customers pay for specialized data annotation services, where Innodata's workforce of linguists, domain experts, and trained annotators create the high-quality datasets necessary for training large language models and other AI systems. The Agility platform operates on a subscription-based model, serving marketing and PR professionals who pay for access to media monitoring, content distribution, and analytics tools. The Synodex healthcare platform generates revenue through data transformation services and platform access fees from healthcare organizations needing to digitize and structure medical records. Several factors significantly impact Innodata's margins and profitability. Positive margin drivers include the company's ability to scale operations efficiently, the high-value nature of specialized AI training data, and the premium pricing commanded by quality data engineering services in a supply-constrained market. The company's global workforce, particularly in lower-cost regions like the Philippines and India, provides cost advantages while maintaining quality standards. Negative margin pressures include intense competition in the AI data services market, the need for continuous investment in recruiting and training specialized talent, and the cyclical nature of large technology contracts. Additionally, the company faces margin pressure from the significant upfront costs of scaling operations to meet large contract demands, as evidenced by the $3.6 million recruiting investment in 2024 that temporarily compressed margins.
Competitive moat
Innodata's competitive moat is moderately strong but not insurmountable, built primarily on operational excellence, specialized expertise, and established customer relationships rather than proprietary technology or network effects. The company's primary competitive advantages center on its ability to deliver high-quality, domain-specific training data at scale, which requires significant expertise in linguistics, data annotation, and quality control processes that are difficult to replicate quickly. The company's strongest moat elements include its established relationships with major technology companies, particularly the trust built through successful execution of complex, high-stakes AI training data projects. Innodata has demonstrated the ability to manage large-scale data annotation projects across multiple languages and specialized domains, from mathematics and chemistry to multilingual content creation. The company's global workforce of trained linguists and domain experts, combined with proprietary quality control processes, creates operational barriers that competitors cannot easily overcome in the short term. However, Innodata faces significant competitive threats that limit the durability of its moat. The company operates in a market with at least 17 identified competitors, and the barriers to entry, while substantial, are not prohibitive for well-funded competitors. Large technology companies could potentially develop in-house data annotation capabilities, and emerging AI technologies like synthetic data generation could reduce demand for human-annotated training data. Additionally, the company's success is heavily dependent on the continued growth of the generative AI market and the specific approaches currently used for model training, both of which could evolve rapidly. The competitive landscape is particularly challenging because Innodata's services, while specialized, are not based on proprietary technology that cannot be replicated. The company's moat is more operational than technological, relying on execution excellence, quality control, and customer relationships rather than unique intellectual property or network effects that would create sustainable competitive advantages.
Risks & safety
Innodata demonstrates a strong margin of safety with robust financial health and conservative valuation metrics relative to its growth trajectory. • Liquidity and Solvency: Excellent cash position with $56.6 million in cash and short-term investments as of Q1 2025, supported by strong free cash flow generation of $8.5 million in the quarter. Current ratio of 2.36 indicates strong short-term liquidity. Debt-to-equity ratio of 0.17 shows minimal leverage risk. • Operational Cash Generation: Strong cash flow from operations of $10.9 million in Q1 2025, demonstrating the business's ability to convert earnings into cash. The company has moved from negative free cash flow in 2022 to consistently positive generation. • Valuation Metrics: While P/E ratio of 36.2 appears elevated, it reflects rapid earnings growth from a low base. EV/EBITDA of 27.6 is reasonable given 120% revenue growth rates. Price-to-book ratio of 15.0 is high but justified by strong ROE of 10.3%. • Other Considerations: Revenue concentration risk with largest customer representing significant portion of business, though diversification efforts are underway with seven Big Tech customers. Market position in rapidly evolving AI sector provides both opportunity and risk from technological disruption.
Recent development
Innodata has undergone a dramatic strategic transformation over the past few years, pivoting from a traditional data processing company to become a specialized provider of training data for generative artificial intelligence models. This transformation began in earnest around 2022-2023 when the company recognized the massive opportunity in the emerging generative AI market and repositioned itself to serve the data needs of companies developing large language models and other AI systems. The company's most significant strategic move was securing contracts with major technology companies, expanding from working with four of the five largest tech companies in 2022 to contracting with seven Big Tech customers by 2024, including five of the "Magnificent Seven" technology giants. The company's largest customer relationship grew from a initial engagement to a $135 million annualized run rate contract, demonstrating Innodata's ability to expand within existing accounts while adding new major customers. Product and service development has focused heavily on generative AI capabilities, including expert domain data collection in specialized fields like mathematics and chemistry, multilingual training data creation, complex reasoning model training, and pre-training data collection at scale. The company has also developed capabilities in emerging areas such as agentic AI solutions, generative AI trust and safety evaluations, and enterprise AI integration services. Additionally, Innodata is developing a proprietary AI test and evaluation platform to further differentiate its offerings. Operational scaling has been a major focus, with the company investing heavily in recruiting and training specialized talent. In 2024, the company spent $3.6 million on recruiting efforts to build its workforce of linguists, domain experts, and data annotation specialists. The company has also expanded its global footprint, leveraging talent in the Philippines, India, and the United States to provide cost-effective, high-quality services. The development of internal recruiting capabilities has helped reduce external recruiting costs while maintaining growth momentum.
INOD company profile · for informational purposes only — not investment advice.
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