Innodata NASDAQ: INOD reported what management described as a “record-setting” first quarter of 2026, posting new highs in revenue, adjusted gross profit, adjusted EBITDA, and cash while raising its full-year growth outlook. Executives also detailed new and expanding engagements across large technology customers, frontier AI model builders, and government-aligned work, alongside early traction for a new agent observability platform.
Record Q1 results and raised 2026 growth outlook
Chairman and CEO Jack Abuhoff said the company delivered “step change results” in the quarter, with revenue of $90.1 million, up 54% year-over-year. Interim CFO Marissa Espineli added that revenue increased 24% sequentially from $72.4 million in Q4 2025 and exceeded analyst consensus of $76.5 million by about $13.6 million.
Adjusted gross profit was $42.6 million, representing an adjusted gross margin of 47%, which Espineli said was six percentage points higher than Q4 and seven points above Innodata’s “externally communicated 40% target.” Adjusted EBITDA was $25.0 million, or 28% of revenue, compared to analyst consensus of $10.4 million, according to management. Net income was $14.9 million and fully diluted EPS was $0.42, versus consensus of $0.08.
Abuhoff said Innodata ended the quarter with $117.4 million in cash, up $35.1 million sequentially, and noted the company had no debt drawn on its credit facility. Espineli said the cash increase reflected “continued strong profitability, disciplined working capital management, and customer prepayments related to our pre-training programs.” She also said Innodata renewed and expanded its Wells Fargo credit facility during the quarter from $30 million to $50 million on a three-year term, and remained fully undrawn.
On guidance, Abuhoff said that “with one quarter behind us and progressively increasing visibility,” the company is raising full-year 2026 revenue growth guidance to “approximately 40% or more,” up from “35% or more” provided on the prior call. Both Abuhoff and Espineli characterized the guidance as “prudent,” with Abuhoff noting there are “several potentially large programs” not included in the forecast and that guidance could be adjusted as timing and scope are finalized.
New big tech engagements and a shifting customer mix
Abuhoff highlighted what he called a new set of engagements with “one of the world’s leading big tech companies,” which management believes “could potentially generate approximately $51 million of revenue this year.” He said that 12 months ago the company had zero revenue from that customer and now expects it to become Innodata’s second-largest customer in 2026. In Q&A, Abuhoff said the work spans “pre-training, mid-training, post-training activities, as well as evaluation,” and that there are “active conversations” with the customer about additional work not included in the $51 million figure.
Management also emphasized improving customer diversification. Abuhoff said Innodata expects its largest customer to represent a decreasing percentage of total revenue in 2026 “even as our absolute dollar revenue with that customer expands.” He added that in Q1, revenue from the company’s other big tech customers in the aggregate grew 453% year-over-year.
Strategy focused on frontier AI, trust and safety, and “physical AI”
President and Chief Revenue Officer Rahul Singhal framed the company’s opportunity around what he called “AI innovation labs and frontier model builders,” which he defined as roughly 20 organizations globally developing advanced foundation models. He said Innodata is positioning its offerings for the shift “from text to multimodal,” “to multi-step reasoning,” and toward “autonomous agents” and ultimately “embodied intelligence and robotics.”
Singhal said Innodata has “deliberately moved up the stack towards high-quality pre-training data, expert-graded reasoning data, agent trajectories, evaluation infrastructure, and trust and safety services.” Regarding the newly announced big tech engagements, he said Innodata is producing “high-quality text-based pre-training data at scale,” including STEM datasets across disciplines such as physics, mathematics, chemistry, engineering, and biology, and is also working on post-training datasets “for advanced reasoning, creative writing, and agent improvement.”
Singhal also described other customer activity:
- He said “a large hyperscaler” selected Innodata as its “global trust and safety partner for evaluating models before they're released into production,” adding that the company anticipates the initial statement of work could yield “approximately $3 billion of potential annual run rate revenue,” with possible expansion.
- At “one of the world's largest cloud and commerce companies,” he said Innodata has moved from “execution partner to strategic partner,” with “line of sight on approximately $7 million of total contract value” across trust and safety and responsible AI programs, and “more than $8 million” across additional areas including “agent safety,” “game data generation,” “global responsible AI testing,” and “physical AI.”
Abuhoff later provided additional color on offerings, describing pre-training, mid-training, post-training, and evaluation work as “classifications of data that's required in order to train and fine-tune large language models.” He also described trust and safety services as evaluating and testing models, identifying underperformance, and “prescribing the data mixes” required to improve performance.
Federal practice and government-aligned work
Singhal said Innodata launched its federal practice in September and that it is gaining traction. He cited an engagement with Palantir that is generating “strong customer feedback in computer vision,” initiation of work with a “major federal systems integrator,” and that the company was “selected as a finalist for potentially significant award.” He also said the company was awarded a prime contract position under the Missile Defense Agency’s SHIELD program, which he said is part of the broader “Golden Dome strategy,” positioning Innodata to compete for future task orders.
Platforms, margin discussion, and segment reporting change
Management tied margin performance and future leverage to offerings that reduce dependence on linear headcount growth. Abuhoff pointed to “off-the-shelf datasets” where Innodata retains IP rights and can resell the same dataset to multiple customers. He said Q1 margins benefited from that model and that Q2 margins are expected to benefit as well.
He also discussed proprietary platforms, including what Singhal called the Agent Observability platform, which Singhal said entered beta during the quarter. Singhal said the platform serves as a “control plane for agentic systems,” enabling enterprises to evaluate agent behavior, inspect traces, monitor performance, catch regressions, and maintain audit trails, while helping optimize resource usage. He said Innodata signed its “first major platform opportunity,” a $1 million engagement with a hyperscaler customer, and that 15 other companies are actively evaluating the platform. He also said the company is in discussions with two hyperscalers about potential channel partnerships to distribute the platform.
On profitability, Abuhoff highlighted operating leverage, noting that in Q1 revenue grew 54% year-over-year while adjusted EBITDA grew about 96%, which he described as adjusted EBITDA growing “roughly 1.8x faster than revenue.” In response to a question about investment levels, he said he did not expect a “step change” in investment, adding that the company expects to continue increasing investment while benefiting from operating leverage.
Espineli also noted a reporting change: effective this quarter, Innodata is reporting as a single operating segment rather than three segments (DDS, Agility, and Synodex). She said the shift reflects the company’s “transformation of our business strategy and operating model,” driven by a focus on agentic AI technologies and the integrated way it manages and delivers services. In Q&A, Abuhoff confirmed results are now reported on a consolidated basis.
In closing remarks, Abuhoff reiterated that Q1 set records across key metrics and said customer concentration is improving as growth broadens beyond the largest customer. He also highlighted research investment, citing Innodata researcher Esther Derman’s two accepted papers at ICML 2026, including one spotlight paper, as evidence of what he called a “meaningful competitive advantage” in research talent.
About Innodata NASDAQ: INOD
Innodata Inc NASDAQ: INOD is a digital services and technology company that specializes in data engineering and artificial intelligence solutions. Founded in 1988 and headquartered in East Brunswick, New Jersey, the company provides structured content and digital transformation services to publishers, media companies, legal and compliance organizations, and other information-intensive industries. Innodata's platform enables clients to convert unstructured text, images and multimedia into high‐quality, machine‐readable formats that support search, analytics and AI model training.
The firm's offerings include content enrichment, metadata management, taxonomy development, digital asset management and data annotation services.
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