Innodata is a company that has lumpy growth yet is also centered in the surging trend of AI data engineering, known as data-as-a-service (DaaS) which offers curated and synthetic data to augment large language models (LLMs). Notably, the company is a small cap, high risk stock.
Complex reasoning models require an expanded data set, such as dozens of foreign languages or multi-step problems within math and chemistry, for example. This is in contrast to a static data set, which often produces too many hallucinations and can be inaccurate at times. For example, if a Big Tech company only used its proprietary social data to train LLMs, this may not be broad enough to prevent hallucinations since social data is limited in its context and scope. In many cases, additional data points are sought out to improve the accuracy of the model.
In order to move toward general artificial intelligence (AGI), which is defined as AI models that think for themselves similar to a human, companies like Innodata are also tapped for their ability to augment accuracy through reinforcement learning and direct preference optimization, which utilizes subject matter experts to annotate data and to also stress-test the models for accuracy.
Overview of Innodata’s Solutions:
The problem Innodata aims to solve is to help generative AI improve its multimodal reasoning skills and to help the accuracy of agentic models. The definition of an agentic model is when the model is more proactive, has multi-layered memory for knowledge across sessions, and eventually will work across a multi-agent ecosystem with an orchestrator. Although very few enterprises use agentic models today, Big Tech and other enterprises rely on data solutions such as Innodata’s to build out the next level of complex problems that AI can solve.
The CEO stated the following on the most recent earnings call in terms of the problem their solutions are aimed to solve: “As models grow more sophisticated, their performance hinges not just on raw computational power, but also on the breadth, depth and quality of the data they are trained on. Continuous data acquisition enables the models to better understand nuance, context, and intent across languages and domains.”
Here is an overview of Innodata’s solutions and how they’re used:
- Fine tuning is using curated and synthetic data to expand the list of tasks and subtasks to where Innodata offers hundreds of capabilities through its data sets, such as programming tasks (coding), content creation (emails, papers, checklists), logic and semantics (sentiment analysis), multi-modal reasoning (input from audio, visual and text for more nuanced comprehension), etc. The list is quite long as to how synthetic data can augment proprietary data.
- Model scoring, risk mitigation and red-teaming refers to stress-testing AI systems for vulnerabilities. It’s a common practice in cybersecurity that Innodata provides for generative AI models to help surface any biases or inaccuracies. Model scoring helps to rank a model compared to frontier models (i.e., your model is X% less accurate than Chat-GPT 4o).
- Reinforcement learning from human feedback (RLHF): Generative AI requires human feedback to spot inaccuracies with expert annotators to help LLMs reflect the complexity of human interactions. The company advertises that it has 5,000 subject matter experts located globally who oversee a reward model.
- Direct Preference Optimization (DPO) also uses feedback but is a more refined process due to optimizing models by assigning high probability or low probability to two outcomes. This offers a faster feedback loop as the model can more quickly learn from the higher probabilities to improve accuracy.
Partnership with Nvidia’s NIM Microservices:
Although very early stage and still in beta testing, Innodata announced a new platform at Nvidia’s GTC 2025 Conference. The company is partnering with Nvidia’s NIM microservices to help facilitate LLM development across enterprises.
Nvidia’s NIM microservices is essentially an app store for LLMs, which offers foundation models, inference engines and APIs in out-of-the-box software containers for enterprises to easily build and deploy customized LLMs. Innodata helps by providing stress-testing and reinforcement learning/direct preference optimization to fine tune the models.
Meta Invests $14.3 Billion into Scale AI
Scale AI is a major competitor to Innodata that also annotates data with a global team of contractors. Scale AI was recently in the news following a $14.3 billion investment by Meta, which helps to underscore the importance of data engineering platforms and Data-as-a-Service (DaaS) for the purpose of fine-tuning large language models.
Scale AI has a particular specialty in autonomous vehicles as the company helps companies like Waymo and Tesla label objects from lidar sensors and video frames. Reinforcement learning from human feedback (RLHF) — discussed above – is then used to improve the quality of the response.
Following Meta’s investment, it was rumored that Google, OpenAI and Tesla are looking elsewhere to avoid strengthening Meta at the cost of their proprietary data. Although it’s speculative, the exodus of major players from Scale AI could become a tailwind for Innodata.
While Innodata’s partnership with Nvidia is a boon, one reason that Innodata may struggle to capture the business is the company is vintage with an inception date in the 1980s. The other data labeling/tooling companies are native AI companies with API-first data pipelines. To contrast, Innodata has roots in legal, healthcare, publishing and PR content whereas these other companies were founded with natural language processing (NLP) in mind.
For example, there are other private companies that stand to benefit as well, such as Labelbox, Appen (public company in Australia) and SuperAnnotate. From there, startups such as SnorkelAI also compete by relying on automated labeling, although it’s likely the workforce behind companies like Scale AI and Innodata is what's attractive to Big Tech given automation is an area where they lead.
ScaleAI is valued at $29 billion compared to Innodata’s $1.5 billion market cap with last year’s reported revenue of $870 million last year. If we assume Scale AI is at $1 billion revenue now, that would be a 29X compared to Innodata’s 6X forward sales.
Big Tech Seeking Data Quality as Differentiator
If we read between the lines on the Meta $14B investment into Scale AI, then what we see is an emphasis on data quality as a key differentiator for frontier LLMs, such as Meta’s Llama, OpenAI’s Chat-GPT or even proprietary models for Waymo and Tesla’s autonomous vehicles. While we’ve heard companies like Palantir state LLMs will become commoditized, I will stick my neck out here to say I think Alex Karp is oversimplifying the quality of LLMs.
Last month, I asked a question of Chat-GPT 4.1 about export licensing under the Trump Administration to help ascertain if a specific semiconductor was subject to export licensing due to manufacturing partners in Hong Kong and this was the response:
Pictured above: Chat-GPT4o hallucination on simple, basic facts from a query dated May 20th, 2025
Chat-GPT updates its training data about once per year with this example showing the limitations of lower quality data in terms of frequency of updates and/or limited resources for new data.
As with all technologies, we are in the hype cycle for LLMs which precedes a period of mass consolidation. Meta knows it must be competitive on data quality, and clearly, its proprietary social data is not able to produce a broad level of intelligence in order to compete with a company like Google or OpenAI when comparing recent benchmarks
Source: CapeStart
Innodata’s Financials: Triple-Digit but Lumpy Growth; Anything Could Happen
Innodata is a high beta stock with a $1.5B market cap and $241M estimated for fiscal year 2025 revenue. The company reported three consecutive quarters of triple-digit topline growth in Q1 with revenue rising 120.1% YoY to $58.3 million, marginally ahead of estimates for $57.6 million. Although revenue growth slowed by over 6 percentage points sequentially, it is expected to decline even more sharply in the coming quarters.
For Q2, analyst estimates point to revenue growth decelerating nearly 50 points to the 73% range, before slowing to the low double-digits against peak growth comps. Management did not provide any quarterly guidance for Q2, though they maintained FY25 revenue growth guidance of 40% YoY, suggesting that with what management knew at the time of the earnings call, revenue growth is expected to follow this trajectory of a sharp back-half deceleration.
However, it is important to keep in mind the fluid nature of Innodata’s business, and that any new contractual agreements or expansions could have a large and/or immediate impact on revenue. For example, in FY24 Innodata had originally guided for 20% YoY revenue growth, before raising that to >40% in Q1, then >60% in Q2 and ultimately to 88% to 92% YoY by Q3. Such a dynamic occurring again this year cannot be quickly written off, given that management is upfront about current engagements and prospective discussions with Big Tech customers.
Customer Update: “Mag 7” and “Big Tech” Mentioned Repeatedly on ER Call
Management provided a handful of updates on existing Big Tech customer expansions (which includes five of the Mag 7) and discussions with prospective customers in Q1. Keep in mind, the fiscal year revenue estimates right now are for $241 million yet discussions around SOWs present a strong case for Innodata exceeding this estimate as the year plays out:
- Innodata signed a second statement-of-work (SOW) with its largest customer, which as of Q4, was contributing revenue at a $135 million annualized rate, up more than 22% in two quarters on new expansions in Q4 and January.
- A Big Tech customer (noted to be one of the most valuable software companies in the world) was said to have a late-stage pipeline potentially valued up to “more than $25 million of bookings this year and continued growth over the next several years.” This customer began working with Innodata in Q2 ’24 and contributed just $0.4 million in revenue in FY24.
- Another Big Tech customer recently signed one expansion deal and is expected to soon sign a second expansion, worth a combined $1.3 million in potential revenue. Management said there is another opportunity with this customer worth up to $6 million, and for comparison, the customer generated just $0.2 million in FY24.
- Management said they signed a deal in Q1 with “one of the most highly regarded generative AI labs” worth $0.9 million, with expansion potential worth double that figure.
To note, Innodata’s largest customer is by far its most important, as a $135 million annualized rate implies this customer is contributing nearly $34 million quarterly, or around 58% of Q1’s revenue. This is a rather significant customer concentration, in that any lost revenue from this customer would not easily be made up from others, as deal sizes touted by management in Q1 pale in comparison.
With that said, the shakeup around Scale AI and the growing importance around data engineering, plus Innodata’s partnership with Nvidia would help level out the customer concentration by attracting more large customers.
On the call, it was stated that Innodata is working on building 200 autonomous agents with its largest customer worth approximately $6 million at the onset:
“With one of our smaller big tech relationships, one that I discussed a few minutes ago, we have begun a collaboration around both AI agent data set creation and AI agent building. The work we are hoping to kick off with them this quarter will involve creating approximately 200 conversational and autonomous agents across multiple domains.”
Key Segments
Innodata’s Digital Data Solutions (DDS) segment is the primary driver of this sharp growth acceleration and improvement in profitability in FY24 and FY25. The segment handles AI data preparation, labeling and annotation, AI training and related services.
The Synodex segment transforms medical records into usable digital data for customers, while its Agility segment provides a platform for PR and communications professionals to target and distribute content to journalists and influencers globally.
- DDS revenue in Q1 rose 158% YoY to $50.8 million, accounting for more than 87% of revenue. This marked the third consecutive quarter with revenue growth above 150% YoY. However, given that Innodata’s revenue is expected to decelerate sharply by Q4, it’s likely DDS is behind this as the core growth driver, and could see growth return to Q3 23’s levels.
- Synodex revenue rose 7.6% YoY to $2.0 million, decelerating from 14.6% YoY growth in Q4.
- Agility revenue rose 11.6% YoY to $5.5 million, decelerating from 24.9% YoY growth in Q1.
GAAP Profitable with Adjusted EBITDA Growth of 236%
Considering Innodata has a mere $58.3 million in estimated quarterly revenue, plus $241B in estimated annual revenue, the margin profile is quite impressive since most companies operate at a loss until they reach scale.
Margins weakened slightly sequentially in Q1, though the rapid ramp of DDS revenue that really accelerated in Q2 has driven margins down the line much higher on a YoY basis.
- Q1 GAAP gross margin was 39.9%, down 5.3 points sequentially but up 3.5 points YoY. Adjusted gross margin was 43.2%, up 1.8 points YoY. Innodata shared that it is targeting an adjusted gross margin of 40%, with this result being above expectations.
- GAAP operating margin was 14.4%, down 4.8 points sequentially but up more than 9 points YoY.
- GAAP net margin was 13.4%, down 4 points sequentially but up nearly 9.7 points YoY, benefiting from the increased operating leverage driven by improving DDS profitability.
Innodata did not provide any clear guidance on Q2’s margins, though management noted that they plan to invest ~$2 million in Q2 to support the new SOW with its largest customer, which will occur ahead of associated revenue and thus impact margins.
Turning to adjusted EBITDA, management forecast YoY growth for the metric, though it is not clear to which degree, given that there was no supporting commentary. Adjusted EBITDA for FY24 was $34.6 million for a 20.3% margin, with Q1’s 21.8% margin already positioning Innodata for growth. Adjusted EBITDA was up 236% YoY (although on small numbers).
- DDS adjusted EBITDA was $11.5 million for a 22.7% margin. This marks a substantial improvement from the 11.0% margin a year ago.
- Synodex adjusted EBITDA was $0.4 million for a 20.8% margin, down nearly 4 points from a 24.7% margin a year ago.
- Agility adjusted EBITDA was nearly $0.8 million for a 13.7% margin, down nearly 10 points from a 23.3% margin a year ago.
EPS
Despite Q1 starting off with triple-digit topline growth and a rather strong >40% guide for FY25, EPS growth is expected to be negative this year. This is primarily due to two factors: a $5.9 million income tax benefit in Q3 and strong outperformance in margins in Q4.
In Q1, Innodata reported $0.22 in GAAP EPS, ahead of estimates for $0.17 and representing growth of 633.3% YoY.
However, for Q2, analysts are currently expecting EPS of $0.11, down (50%) sequentially, before ticking higher to $0.17 in Q3. This would be a decline of nearly (67%) YoY versus $0.51 in Q3 2024, due to the income tax benefit. Q4 is not expected to bring any relief, with current estimates pointing to a (38.5%) YoY decline to $0.19.
For the entire year, Innodata is expected to report a (22.0%) YoY decline to $0.69, before rebounding 46.3% in FY26 to $1.02.
Cash and Balance Sheet
Cash flows have improved significantly as revenue ramped, allowing Innodata to add significant cash to its balance sheet through 2024. As a result, Innodata has a relatively healthy balance sheet with no debt and an undrawn $30 million credit facility.
- Operating cash flow was $10.9 million for an 18.6% margin. This was lower than the 25.5% margin in the year ago quarter, with the strong print driven by a $3 million QoQ increase in deferred revenue.
- Free cash flow was $8.5 million for a 14.6% margin. This was lower than the 20.5% margin from the year ago quarter due to the relatively stronger OCF.
- Cash and equivalents on hand were $56.6 million, up from $46.9 million in Q4 and a substantial improvement from $19.0 million a year ago.
- Debt remained zero, with Innodata still having access to its undrawn $30 million credit line should it need extra funding.
- Deferred revenue was approximately flat QoQ at $8.03 million.
Cash flow is a line item to watch as the company stated they plan to re-invest OCF and this could lead to debt or stock dilution: “Accordingly, we intend to reinvest a meaningful portion of our operating cash flow into product innovation, go-to-market expansion and talent acquisition, while still delivering adjusted EBITDA above our 2024 results.”
Earnings Call:
Largest Customer to be down 5%
In the opening remarks, the CEO stated the largest customer would be down 5% going into the next quarter: “Inevitably, customer concentration can result in quarter-to-quarter volatility. For example, with our largest customer, we exited 2024 at an annualized revenue run rate of approximately $135 million. In Q1, we were running higher than this by about 5%, and in Q2, we anticipate that we could be lower by about 5%, but the customers' demand signals are updated continually and are highly dynamic.”
An analyst asked about this in more detail during the Q&A when it was stated the new statement of work with the customer will provide “additional share of wallet that we can tap into.” Management is referring to 200 autonomous agents discussed above under the customer section, yet at the onset this is worth $6 million.
Risks:
There have been short reports on the company that led to a 30% drop in share price in one day. You can read the report from Wolfpack Research here and a second short report from J Capital can be read here. These are worth a read for anyone seriously considering the stock. We utilize proper risk management in these cases, which includes a stop on the position – should we enter. We would also only buy on a breakout when technicals provide a green light.
One of the primary risks to Innodata’s revenue acceleration and growth trajectory is We’ve already seen one large customer termination with Innodata, though that was attributed to Musk’s publicized take-over of xAI (Innodata said this customer “dramatically cut spending after a significant and highly publicized management change” in 2022). There is no guarantee that customer spend with Innodata will expand beyond the scope of the current deals, though the view that a majority of the Magnificent 7 are rapidly adopting generative AI products and will spend hundreds of millions on generative AI and LLM development over the next few years bodes well for future growth, both in terms of expanding the scope of deals and landing deals with new customers.
Another risk presents itself in the volatile swings in share price that Innodata sees – as a small cap, it’s much more likely to see these substantial moves in such a brief period. For example, there have been multiple weeks and many days in which Innodata has seen moves in excess of +/- 10%. This level of volatility is not typically seen with large or mega-cap stocks and requires prudent risk management. Institutional ownership is also relatively low for a high-beta AI small cap at just 36%.
Conclusion:
The takeaway is that as LLMs continue to fiercely compete, companies like Innodata will become force extenders in the race for more accurate and reliable output. Although Innodata has many competitors, consider that Meta’s investment into Scale AI is 14X larger than its acquisition of Instagram at $1 billon, which puts into perspective the importance of data quality for Big Tech companies.
In the closing remarks, the CEO stated “we believe our business right now is on fire. The growth we're seeing year-over-year is just the beginning. What's happening now inside the Company is really like or unlike anything we've seen before.”
Investors will have to get comfortable with early-stage tech given Innodata’s new product-market fit is very early stage. Scale AI provides a decent comp in terms of the value of a strong AI data engineering company. Innodata’s solutions will be put to the test now that Scale AI customers will be unwinding their partnerships. Anything could happen. If we were to enter, it would be with a tight stop, and we would raise our stop as the stock price increases.
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