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    INOD
    Earnings call· Jun 2026(Q2 FY26)

    INNODATA Q2 FY26 earnings call INOD

    Aug 6, 2026 Source

    Executive summary

    Innodata Q2 FY26 — Record Revenue, Profit, and Cash with Strong AI-Driven Growth

    Innodata delivered a record Q2 FY26, driven by strong demand for its AI-focused data engineering and evaluation services, leading to significant revenue, profit, and cash generation. The company is strategically expanding its customer base and service offerings, particularly in agentic AI and federal markets, while undergoing a planned leadership transition to capitalize on future growth opportunities. Management reiterated its full-year revenue growth guidance of 40% or more.

    Highlights

    5

    • Revenue reached $92.1 million, up 58% year-over-year, exceeding analyst consensus by $5.8 million (7%).

    • Adjusted gross margin was 49%, a 2-point sequential increase and 9 points above the 40% target.

    • Adjusted EBITDA grew 92% year-over-year to $25.4 million, surpassing analyst consensus by $8.5 million (50%).

    • Fully diluted earnings per share was $0.41, nearly double the analyst consensus of $0.21.

    • Cash and short-term investments (net of customer prepayments) increased by $37 million sequentially to $134 million.

    Concerns

    1

    • Revenue contribution from the largest customer decreased to 37% of total revenue in Q2, down from 56% in Q1, due to changes in program structure and service mix.

    Guidance & targets

    1
    CategoryTargetConfidence
    Full-year revenue growth
    40% or more year-over-year
    high materiality
    High

    Operational metrics

    15
    Revenue
    $92.1M58% YoY, 2% sequentially
    Q2 FY26

    12th consecutive quarter of year-over-year growth.

    Adjusted gross profit
    $45.4M
    Q2 FY26
    Adjusted gross margin
    49%2 points sequentially
    Q2 FY26
    Adjusted EBITDA
    $25.4M92% YoY
    Q2 FY26
    Net income
    $14.4Mdouble Q2 last year ($7.2M)
    Q2 FY26
    Fully diluted EPS
    $0.41
    Q2 FY26
    Effective tax rate
    18%
    Q2 FY26
    Cash and short-term investments (total)
    $250.4M
    Q2 FY26

    Ended the quarter with this balance.

    Cash and short-term investments (net of customer prepayments)
    $134Mup $37M sequentially
    Q2 FY26

    Excluding customer prepayments, which are a pass-through.

    Largest customer revenue contribution
    37%down from 56% in Q1
    Q2 FY26

    Expected to grow year-over-year for the full year.

    Second-largest customer revenue contribution
    34%scaled from 17% of revenue in Q1
    Q2 FY26

    This customer generated essentially no revenue a year ago.

    Hyperscaler capital spending
    $700Bnearly double last year
    CY26
    Drone and small object detection benchmark improvement
    6.45%exceeds prior state-of-the-art benchmarks
    Q2 FY26

    In a field where progress is often measured in fractions of a percentage point, a 6.45% improvement is a material advance.

    Consecutive quarters of year-over-year growth
    12
    Q2 FY26
    Adjusted EBITDA growth vs. Revenue growth
    1.6x
    Q2 FY26

    Illustrates operating leverage, where each incremental program builds on the same core operating infrastructure.

    Product announcements

    1
    ProductTypeDetails
    AI Cyber Training Suitelaunch

    Deals & partnerships

    2
    one of the fastest-scaling frontier labsNew customer acquisition

    Innodata landed an important new customer in the quarter, identified as one of the fastest-scaling frontier labs.

    leading universityResearch agreements

    Signed two research agreements with a leading university as part of building the data and measurement layer for embodied intelligence.

    Capital programs

    1
    Motion capture labcommitted

    Benefit: collecting sub-millimeter precision data for training robots and physical AI foundation models

    Committed to a motion capture lab that is expected to come online in the next few months, capable of collecting sub-millimeter precision data for training robots and physical AI foundation models.

    Risks & headwinds

    2
    Customer concentrationQ2 FY26

    Largest customer represented 37% of revenue, down from 56% in Q1

    Mitigation: Management expects the largest customer to grow year-over-year for the full year; the customer base continues to broaden with new customers and programs.

    Potential for lower gross margin projectsFuture

    Company bids on work with lower gross margins than current levels, which could be large projects.

    Mitigation: While such projects could reduce weighted gross margin, they are expected to offer compelling cash flow. Strategically, innovation and IP retention are expected to trend revenue quality upward over time.

    What to watch in Q3 FY26

    5

    Revenue contribution from largest customer

    Full-year FY26
    Current37% of total revenue
    TargetYear-over-year growth for the full year

    Why it matters

    Indicates diversification progress and stability of key customer relationships, crucial for long-term growth.

    While our largest customer contributed less revenue in Q2 than in Q1 as a result of a change in the quarter to program structure and service mix, we continue to expect it to grow year-over-year for the full year.

    Q&A highlights

    6

    Can management provide more detail on the significant opportunities not yet factored into guidance and if updates will be more regular?

    Jack Abuhoff confirmed significant opportunities across government, enterprise, and frontier models, particularly in agentic AI deployment and training. He reiterated the company's discipline of only factoring wins into forecasts once they are 100% secured and timing of revenue recognition is clear, promising more updates in the second half of the year.

    “We have some large new potential engagements in our pipeline with both existing and new customers that we believe are likely wins, but we have not yet factored them at all into our forecast at this point. As a matter of prudence, we will only factor them into our forecast when we know they're 100% won, and we can forecast the timing of revenue recognition.”

    asked by George Sutton · answered by Jack Abuhoff

    2 min read7 chapters

    Detailed Narrative

    01

    Leadership Transition and Strengthened Management

    Jack Abuhoff will transition to Executive Chairman, and Rahul Singhal will become President and CEO of Innodata, effective September 30. This planned transition aims to leverage Jack's focus on building long-term differentiating capabilities in enterprise and federal markets. Jayant Chauhan joined as Chief Financial Officer in July, strengthening the financial leadership team and focusing on capital allocation, capital markets, M&A, and investor communications.

    02

    AI Innovation as a Growth Engine

    Research and innovation are increasingly pivotal, driving differentiation and expanding partnerships across the full model training life cycle, from pretraining and posttraining to model evaluation and benchmarking. This innovation is producing intellectual property and differentiation that generates demand, positioning Innodata as a strategic partner to leading AI builders.

    03

    Agentic AI and Enterprise Opportunity

    Innodata has established an early position in agentic reinforcement learning, winning significant new programs covering personalization of long-horizon agents and reinforcement learning environments for desktop computer-use agentic tasks. The company aims to be the 'AI deployment assurance layer' for enterprises struggling to deploy AI agents with confidence, opening substantial opportunities in insurance and banking, with delivery already deepened with one big tech customer and started with another.

    04

    Benchmarking and Advanced Data Generation

    The company released two public benchmarks designed to test frontier models on multi-turn, long-context, and multi-model interactions, surfacing failure modes that standard leaderboards miss. These benchmarking engagements lead to data strategy recommendations and scaled data generation to improve models, extending reasoning capabilities across five frontier labs and five domains.

    05

    Physical AI and Multimodal Data Collection

    Innodata is building the required data and measurement layer for embodied intelligence, signing two research agreements with a leading university and committing to a motion capture lab expected to come online in the next few months⏳. This lab will collect sub-millimeter precision data for training robots and physical AI foundation models. Successful egocentric data collection pilots with robotics companies are leading to scoping enterprise-scale multimodal programs, including a multilingual speech program and a large egocentric program.

    06

    AI Cyber Training Suite for Secure Code

    The first stage of Innodata's AI Cyber Training Suite was released, comprising 12 datasets and evaluation systems designed to train AI coding agents to write secure code and repair software vulnerabilities. Testing showed that fine-tuning open-weight models on a portion of this data more than doubled their repair rate for verified flaws, addressing a critical blocker for enterprise AI adoption related to trust in AI-generated code.

    07

    Structural Economics and Market Positioning

    Innodata's business model demonstrates strong operating leverage, with adjusted EBITDA growing 1.6x faster than revenue. Margin quality is driven by a mix shift towards higher-value pretraining programs and off-the-shelf datasets where the company retains IP. Management believes Innodata is well-positioned to benefit from broader market dynamics, including debates around AI CapEx, commoditization of inference, and security incidents, as these trends all increase the need for data evaluation and assurance services.

    AI-generated summary of the company’s earnings call. Not investment advice.