Fourth Quarter Revenue Up 35% Year-Over-Year

NEW YORK, NY / ACCESSWIRE / February 22, 2024 / INNODATA INC. (NASDAQ:INOD) today reported results for the fourth quarter and the year ended December 31, 2023.

• Revenue for the quarter ended December 31, 2023 was $26.1 million, up 35% from revenue of $19.4 million in the same period last year. The comparative period included $0.5 million in revenue from the large social media company that underwent a significant management change in the second half of last year, as a result of which it dramatically pulled back spending across the board. There was no revenue from this company in the three months ended December 31, 2023.
• Net income for the quarter ended December 31, 2023 was $1.7 million, or $0.06 per basic share and $0.05 per diluted share, compared to a net loss of $2.0 million, or $0.07 per basic and diluted share, in the same period last year.
• Total revenue for the year ended December 31, 2023 was $86.8 million, up 10.0% from revenue of $79.0 million in 2022. The comparative period included $8.5 million in revenue from the large social media company referenced above. There was no revenue from this company in 2023.
• Net loss for the year ended December 31, 2023 was $0.9 million, or $0.03 per basic and diluted share, compared to net loss of $12.0 million, or $0.44 per basic and diluted share in 2022.
• Adjusted EBITDA was $4.3 million in the fourth quarter of 2023, compared to Adjusted EBITDA of $0.2 million in the same period last year.*
• Adjusted EBITDA was $9.9 million for the year ended December 31, 2023, compared to Adjusted EBITDA loss of $3.3 million in 2022.*
• Cash, cash equivalents and short-term investments were $13.8 million at December 31, 2023 and $10.3 million at December 31, 2022.

* Adjusted EBITDA is defined below.

Amounts in this press release have been rounded. All percentages have been calculated using unrounded amounts.

Jack Abuhoff, CEO, said, "We are pleased to announce fourth quarter 2023 revenues of $26.1 million, representing 35% year-over-year growth and 18% sequential growth. We exceeded our guidance of $24.5 million by 6.5% as a result of strong customer demand for generative AI services and our ability to ramp up quickly to meet customer demand. In 2023 overall, we grew revenues 10%.

"It is worth noting that our Q4 2023 year-over-year revenue growth was 39%, versus 35%, and our year-over-year revenue growth was 23%, versus 10%, if we back out revenue from the large social media company that went through a highly-publicized take-private in 2022 in conjunction with which it terminated our services (as well as services from many of its other vendors) and laid off 80% of its staff. This customer contributed $8.5 million in revenue in 2022 and $0.5 million in revenue in Q4 of 2022. Beginning in Q1 2024, revenue from this customer will no longer provide a drag on year-over-year comparisons.

"We are also very pleased to announce fourth quarter Adjusted EBITDA of $4.3 million, exceeding our guidance of $3.7 million by 16%.

"Growth in Q4 was driven primarily by ramp of generative AI development work for one of the Big Five tech companies we signed mid-2023 and also benefited by the start of the generative AI development program with another of the Big Tech customers we announced late last summer.

"In late Q4, the first customer I mentioned signed a three-year deal with us for our current, initial program, with an approximate value of $23 million per year for each of 2024, 2025, and 2026, or $69 million for the three years, based on the not-to-exceed value of the statement of work. We're very proud of this achievement. It came with customer kudos for the work we've done and expressions of interest in expanding the partnership further. That said, and as a cautionary note, investors should understand that there are a number of ways under the SOW that the customer could terminate early or reduce spend if it chose to. We believe the quality of our services will always be the key to enduring customer relationships, not the stated term or value of a contract.

"We're off to a strong start to 2024. We entered the year with master service agreements in place with five of the so-called Magnificent Seven technology companies. With two of these companies, we are now solidly underway. A third also contributed to Q4 growth, with a more significant ramp-up from this customer starting this month. We are optimistic we will grow revenues with all three of these customers in 2024.

"With the remaining two of the five Mag Seven customers, we've barely gotten out of the gate, but we are optimistic about making significant inroads this year. We are also in conversations with several additional companies, including some of the most prominent leaders in generative AI today.

"We believe we have the strategy, business momentum and customer relationships to deliver significant revenue growth in 2024. We will stick with our annual growth target of 20% in 2024 with the intention of over-achieving this."

Abuhoff continued, "In 2024, we will target two broad markets. The first is Big Tech companies that are building generative AI foundation models and we believe are likely to spend significantly on generative AI development. For these Big Tech companies, we provide a range of services they require to support their gen AI programs. One of these services is the creation of instruction data sets. You can think of instruction data sets as the programming used to fine tune large language models. Fine tuning with instruction data sets is what enables the models to understand prompts, to accept instruction, to converse, to apparently reason, and to perform the myriad of incredible feats that many of us have now experienced. We will also be providing reinforcement learning and reward modeling, services which are critical to provide the guardrails against toxic, bias and harmful responses. In addition, we are also involved in model assessment and benchmarking, helping ensure that models meet performance, risk and emerging regulatory requirements. Based on my conversations with several of these companies, as well as public remarks they have made, we believe they are likely to spend hundreds of millions of dollars each year on these services. This spend is separate from and in addition to their spend on data science and compute, the other essential ingredient of high-performing large language models.

"Our second target market is enterprises across a wide range of verticals that seek to integrate and fine-tune generative AI models. These are still early days in terms of enterprise adoption of generative AI, but we believe that a decade from now virtually all successful businesses will have adopted generative AI technologies into their products and operations. For enterprises, our offerings including business process management, in which we re-engineer workflows with AI and LLMs and perform the work as ongoing managed services. We also offer strategic technology consulting, where we work with customers to define roadmaps for AI and LLM integration into both operations and products and build prototypes and proofs-of-concept. We also fine-tune models, both in isolation and as part of larger systems that incorporate other technologies. For enterprises, we are capable of going soup-to-nuts, everything from initial consulting to model selection to finetuning, deployment, and integration, as well as testing and evaluations to ensure that the LLMs are helpful, honest, and harmless.

"Also for enterprises, we offer subscription-based platforms and industry solutions that encapsulate AI - both our own models and leading 3^rd party models. Much the way data is at the heart of the programming-like work we do for Big Tech, data is similarly critical to enterprise deployments. Enterprise use cases tend to be highly specific and targeted, requiring models that are trained with industry-specific or domain-specific data or that require significant prompt engineering efforts and in-context learning utilizing carefully curated and organized company data.

"The bottom line here is that data engineering is important for the big tech companies building generative AI foundation models and the enterprises adopting these technologies. Data engineering has been our focus for the past two decades, and we believe we are quite good at it."

Abuhoff concluded, "In response to some questions we've recently been asked by investors:

• Several investors have asked whether we currently anticipate needing to raise additional equity.
  • The answer is no, we do not currently anticipate needing to raise additional equity. We ended Q4 with $13.8 million in cash and short-term investments, slightly down from $14.8 million last quarter, but that was largely due to timing, as we had $2.4 million in cash receipts from major customers collected right after the New Year, and we generated over $4 million of Adjusted EBITDA in Q4 alone. Nonetheless, to support our growth and future working capital requirements, we have a revolving line of credit with Wells Fargo that provides up to $10 million of financing, 100% of which was available under our borrowing base as of the end of Q4. We have not yet drawn down on the Wells Fargo line. We anticipate generating enough cash from operations in 2024 to fund our capital needs without having to draw down on the Wells Fargo facility.
• Several investors have asked why we have no Chief Technology Officer.
  • In a sense we actually have four chief technology officers, or at least their equivalents, each of which manage a specific technology area: we have a PhD in computer science and AI who heads our AI labs research team and data science teams; we have an SVP of engineering overseeing product and platform engineering; we have another VP focused on software development and product evolution for our Agility product; and we have a Chief Information Security Officer who heads security and infrastructure. Under these leaders, we have close to 300 developers, architects, infrastructure managers and data scientists. We have found that this structure best supports the breadth and scale of our business.
• Investors have asked us to share our recent spending on software and product development, and why do we not separately disclose it, and to comment on whether we have a significant spend on cloud infrastructure.
  • In terms of our spending across software and product development, over the last five years, we spent about $26 million. This peaked in 2022 at $8.9 million and came down to $6.4 million in 2023. However, since roughly 80% percent of our business is managed services, we do not view the aggregate spending across these areas as a focal point for investors. In terms of cloud, we spend a couple of million dollars per year, mostly for software, infrastructure and data hosting. It is our Big Tech customers, not us, that spend massively on GPUs for training foundation models.
• Other investors have asked us how they should think about our comps. Specifically, they asked whether our comps are the largest technology and software companies in the world and whether they should compare our R&D spend and Cloud compute spend to these companies.
  • These companies are absolutely not our comps. Rather many of these companies constitute part of our target market. We are not in their business and, to state the obvious, we are not of similar scale. Players in this market are building foundation models, and we are providing services to this market that help them on their journey. Therefore, we do not believe that comparing our R&D spend and Cloud compute spend to theirs is especially useful. We view our competition as companies focused on AI data engineering services to this market.
• Another question we've gotten is how did we manage to pivot to AI without having to raise substantial capital?
  • There are essentially three reasons we were able to pivot to AI without having to raise capital. The first reason, which we believe is by far the most important, is that the massive spend we read about being required to build foundation models is incurred by our large tech customers, not by us. Our customers are deploying extensive amounts of capital for cloud compute, for data science, and for data engineering - three crucial ingredients to an LLM, if you will. We provide the kinds of data engineering services they need, and providing data engineering does not require that we separately incur compute costs. The second reason we were able to transition to AI data engineering without incurring massive upfront costs is that we have been a data engineering company for over 20 years, and we were able to repurpose a lot of what we already had in place, including management, resources, facilities, and technologies, to serve the AI use cases. The third reason is that when we began exploring AI back in 2016 and developing our Goldengate infrastructure we incurred manageable investment. From a data perspective, because we were already employing large teams of resources doing customer work, we did not have to incur incremental additional costs for humans-in-the-loop. We simply had to rearchitect our operator workbenches and to create the right data lakes. The objectives we initially set for the models we built were to enable us to reduce costs associated with maintaining rules-based data processing technologies. We were not seeking to automate the work of humans, but to augment it. Over the years, Goldengate, one of our proprietary platforms, became, we believe, state-of-the-art at things like entity extraction, data categorization and document zoning - all important aspects of what we do. We use the technology in customer deployments and within our own platforms with great results. That said, Goldengate is not ChatGPT - you can't converse with it or ask it to perform magical feats like writing poetry. Goldengate has 50 million parameters, while ChatGPT is reputed to have 1.7 trillion parameters. Nevertheless, Goldengate demonstrates that AI can be trained to perform specific tasks very well without incurring massive spending; that AI deployments leveraging open source algorithms and models can be within reach for many enterprises for industry-specific datasets; and that for business implementations especially, data engineering is more important than sheer model size as a predictor of performance.
• A question we got recently is "How does revenue per employee compare in your different lines of business?"
  • The answer is that revenue per employee is lowest in our managed services business, while it is multiple times higher in our AI data engineering scaled services. Regardless, we target an adjusted gross margin of 35 to 37% across these business lines, so we believe adjusted gross margin is the better metric to track. In our software business, our targeted gross margin is anticipated to be about 73% this year, and we intend to target a consolidated adjusted gross margin of between 40 and 43%.
• Another question we've gotten several times recently is "Is Agility now profitable?"
  • The answer is yes. In this quarter, Agility posted Adjusted EBITDA of $1.2 million. This was a 69% sequential increase over Q3. We think we executed the Agility business very well in 2023, growing it 15% in a difficult macro environment. It had a strong adjusted gross margin of 69% over 2023 as a whole and 74% in Q4. We also love what we've done with the product - we believe we've taken a leadership position as the first end-to-end public relations and media intelligence platform to integrate generative AI."

Marissa Espineli, Interim CFO, added, "Other questions we've gotten recently from investors have been:

• We've been asked about why we keep cash overseas.
  • The reason we keep cash overseas is to cover operating expenses in these locations. We do not plan to repatriate these funds nor do we foresee the need to.
• We've been asked recently about our cost-plus transfer pricing agreements with our offshore subsidiaries.
  • Companies that have revenue in, say, North America or Europe, but have offshore delivery centers in countries like India and the Philippines, put in place what's called transfer pricing arrangements to satisfy the arm's length transaction principle. Under a transfer pricing arrangement, a percentage of revenue is allocated to the delivery center. The percentage allocated is often determined by statute or regulation in the foreign country. We understand that the reason the foreign country does this is to make sure there are profits at the local level for it to tax. When the consolidated enterprise is losing money, and would not otherwise have to pay taxes, it unfortunately ends up having to pay taxes offshore. Obviously, paying taxes when you are losing money is not a good thing and is referred to as "tax leakage" - but even in this situation, the tax we pay is insignificant versus the money we save by operating offshore.
• We've been asked whether there is any structural reason that Innodata would be expected to lose more money as it generates more revenue?
  • The answer to this is absolutely not. As Innodata revenue increases, we expect that its Adjusted EBITDA will increase at an even higher percentage. This is because there is some operating leverage in our direct costs, for things like production facilities, and significant operating leverage in our general and administrative operating costs. We saw clear evidence of this in both Q3 and Q4. In Q3, revenue grew sequentially by $2.5 million and Adjusted EBITDA grew sequentially by $1.6 million. Similarly, in Q4, revenue grew sequentially by $3.9 million and Adjusted EBITDA grew sequentially by $1.1 million. There will however, be quarterly fluctuations in how much revenue falls to the EBITDA line based on how we flex our operating expenses, particularly our sales and marketing efforts, based on market dynamics."

Timing of Conference Call with Q&A

Innodata will conduct an earnings conference call, including a question-and-answer period, at 5:00 PM eastern time today. You can participate in this call by dialing the following call-in numbers:

The call-in numbers for the conference call are:

1-888-506-0062 (Domestic)
+1 973-528-0011 (International)
Participant Access Code - 383451

1-877-481-4010 (Domestic Replay)
+1 919-882-2331 (International Replay)
Replay Passcode - 49773

It is recommended that participants dial in approximately 10 minutes prior to the start of the call. Investors are also invited to access a live Webcast of the conference call at the Investor Relations section of www.innodata.com. Please note that the Webcast feature will be in listen-only mode.

Call-in or Webcast replay will be available for 30 days following the conference call.

About Innodata

Innodata (NASDAQ:INOD) is a global data engineering company delivering the promise of AI to many of the world's most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers. Visit www.innodata.com to learn more.

Forward Looking Statements

This press release may contain certain forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended. These forward-looking statements include, without limitation, statements concerning our operations, economic performance, and financial condition. Words such as "project," "believe," "expect," "can," "continue," "could," "intend," "may," "should," "will," "anticipate," "indicate," "predict," "likely," "estimate," "plan," "potential," "possible," "promises," or the negatives thereof, and other similar expressions generally identify forward-looking statements.

These forward-looking statements are based on management's current expectations, assumptions and estimates and are subject to a number of risks and uncertainties, including, without limitation, impacts resulting from the continuing conflict between Russia and the Ukraine and Hamas' attack against Israel and the ensuing conflict; investments in large language models; that contracts may be terminated by customers; projected or committed volumes of work may not materialize; pipeline opportunities and customer discussions which may not materialize into work or expected volumes of work; the likelihood of continued development of the markets, particularly new and emerging markets, that our services support; the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services; continuing reliance on project-based work in the Digital Data Solutions (DDS) segment and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay or cancel projects; potential inability to replace projects that are completed, canceled or reduced; continuing DDS segment revenue concentration in a limited number of customers; our dependency on content providers in our Agility segment; difficulty in integrating and deriving synergies from acquisitions, joint ventures and strategic investments; potential undiscovered liabilities of companies and businesses that we may acquire; potential impairment of the carrying value of goodwill and other acquired intangible assets of companies and businesses that we acquire; a continued downturn in or depressed market conditions; changes in external market factors; changes in our business or growth strategy; the emergence of new, or growth in existing competitors; various other competitive and technological factors; our use of and reliance on information technology systems, including potential security breaches, cyber-attacks, privacy breaches or data breaches that result in the unauthorized disclosure of consumer, customer, employee or Company information, or service interruptions and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission.

Our actual results could differ materially from the results referred to in forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, the risks discussed in Part I, Item 1A. "Risk Factors," Part II, Item 7. "Management's Discussion and Analysis of Financial Condition and Results of Operations," and other parts of our Annual Report on Form 10-K, filed with the Securities and Exchange Commission on February 24, 2023, as updated or amended by our other filings that we may make with the Securities and Exchange Commission. In light of these risks and uncertainties, there can be no assurance that the results referred to in the forward-looking statements will occur, and you should not place undue reliance on these forward-looking statements. These forward-looking statements speak only as of the date hereof.

We undertake no obligation to update or review any guidance or other forward-looking statements, whether as a result of new information, future developments or otherwise, except as may be required by the Federal securities laws.

Company Contact

Marcia Novero
Innodata Inc.
Mnovero@innodata.com
(201) 371-8015

Non-GAAP Financial Measures

In addition to the financial information prepared in conformity with U.S. GAAP ("GAAP"), we provide certain non-GAAP financial information. We believe that these non-GAAP financial measures assist investors in making comparisons of period-to-period operating results. In some respects, management believes non-GAAP financial measures are more indicative of our ongoing core operating performance than their GAAP equivalents by making adjustments that management believes are reflective of the ongoing performance of the business.

We believe that the presentation of this non-GAAP financial information provides investors with greater transparency by providing investors a more complete understanding of our financial performance, competitive position, and prospects for the future, particularly by providing the same information that management and our Board of Directors use to evaluate our performance and manage the business. However, the non-GAAP financial measures presented in this press release have certain limitations in that they do not reflect all of the costs associated with the operations of our business as determined in accordance with GAAP. Therefore, investors should consider non-GAAP financial measures in addition to, and not as a substitute for, or as superior to, measures of financial performance prepared in accordance with GAAP. Further, the non-GAAP financial measures that we present may differ from similar non-GAAP financial measures used by other companies.

Adjusted EBITDA

We define Adjusted EBITDA as net income (loss) attributable to Innodata Inc. and its subsidiaries in accordance with U.S. GAAP before interest expense, income taxes, depreciation and amortization of intangible assets (which derives EBITDA), plus additional adjustments for loss on impairment of intangible assets and goodwill, stock-based compensation, income (loss) attributable to non-controlling interests, non-recurring severance, and other one-time costs.

We use Adjusted EBITDA to evaluate core results of operations and trends between fiscal periods and believe that these measures are important components of our internal performance measurement process.

A reconciliation of Adjusted EBITDA to the most directly comparable GAAP measure is included in the tables that accompany this release.

INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS
(Unaudited)
(In thousands, except per-share amounts)

INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED BALANCE SHEETS
(Unaudited)
(In thousands)

INNODATA INC. AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF CASH FLOWS
(Unaudited)
(In thousands)

INNODATA INC. AND SUBSIDIARIES
RECONCILIATION OF GAAP TO NON-GAAP FINANCIAL MEASURES
(Unaudited)
(In thousands)

INNODATA INC. AND SUBSIDIARIES
CONSOLIDATED REVENUE BY SEGMENT
(Unaudited)
(In thousands)

SOURCE: Innodata Inc.