Innodata Inc. (NASDAQ:INOD) Q1 2023 Earnings Call Transcript May 11, 2023

Operator: Greetings. Welcome to Innodata’s First Quarter 2023 Earnings Call. [Operator Instructions]. I will now turn the conference over to your host, Amy Agress. You may begin.

Amy Agress: Thank you, John. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata; and Marissa Espineli, Interim CFO. We’ll hear from Jack first, who will provide perspective about the business, and then Marissa will follow with a review of our results for the first quarter. We’ll then take your questions. First, let me qualify the forward-looking statements that are made during the call. These statements are being made pursuant to the safe harbor provisions of Section 21E of the Securities Exchange Act of 1934 as amended, and Section 27A of the Securities Act of 1933 as amended. Forward-looking include, without limitation, any statements that may predict, forecast, indicate or imply future results, performance or achievements.

These statements are based on management’s current expectations, assumptions and estimates and are subject to a number of risks and uncertainties and including, without limitation, the expected or potential effects of the novel coronavirus, COVID-19 pandemic and the responses of government of the general global population, our customers and the company there to impacts from the rapidly evolving conflict between Russia and the Ukraine; 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; acceptance of our new capabilities, continuing Digital Data Solutions segment reliance on project-based work and the primarily at-will nature of such contracts and the ability of these customers to reduce, delay or cancel projects; the likelihood of continued development of the market, particularly new and emerging markets that our services and solutions support; continuing Digital Data Solutions segment revenue concentration in a limited number of customers, potential inability to replace projects that are completed, canceled or reduced; our dependency on content providers in our Agility segment; a continued downturn in or depressed market conditions; changes in external market factors; the ability and willingness of our customers and prospective customers to execute business plans that give rise to requirements for our services and solutions; difficulty in integrating and driving 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; changes in our business or growth strategy; the emergence of new or growth in existing competitors, our use of and reliance on information technology systems including potential security regions, cyber attacks, privacy breaches or data breaches that result in the unauthorized disclosure of consumer, customer, employee or company information or service interruptions and various other competitive and technological factors and other risks and uncertainties indicated from time to time in our filings with the Securities and Exchange Commission, including our most recent reports on Forms 10-K, 10-Q and 8-K and any amendments thereto.

We undertake no obligation to update forward-looking information or to announce revisions to any forward-looking statements except as required by the federal securities laws, and actual results could differ materially from our current expectations. Thank you. I will now turn the call over to Jack.

Jack Abuhoff: Good afternoon, everybody. Thank you for joining our call. As you probably saw in our announcement earlier today, we have quite a lot of exciting news to share with you. Over the last couple of weeks, we received verbal confirmation from 2 of the largest 5 global technology companies that we have been selected to provide data engineering for their innovation programs in generative AI, the technology behind ChatGPT. One of these companies is an existing customer and the other one will be a new customer. In addition, a third company, also a new customer and another of the largest 5 global technology companies, has indicated that they are likely to choose us and we have just reached agreement with them on terms of a master services agreement.

We believe these accomplishments are potentially transformative for Innodata. For these companies, we expect to be performing a range of data engineering work required to build cutting-edge generative AI. We expect this potentially to include creating training data sets that are used to train the models, providing instruction data sets that teach the models to follow instructions, providing reinforcement learning, a process by which we align models with human values and complex use cases and providing red teaming and model performance evaluation. This involves potentially working in multiple languages and across several data modalities. I will be focusing today’s call on first: Our view of the industry landscape and the tremendous change, we believe generative AI will unleash on how businesses operate worldwide; second, how we are positioning Innodata to take advantage of this tremendous change; third, why we believe we have been able to get rapid traction with the largest tech companies as they seek to build out their generative AI capabilities and our growth drivers going forward; fourth, how we’re thinking about the potential cadence of the financial impact from these wins, both this year and beyond; and fifth and last, our Q1 results and forward guidance.

We believe that generative AI will be even more transformative for businesses and consumers than the Internet. And that because of the tremendous productivity advantages, we believe the technology will provide as well as the innovative customer experience we believe it will provide. Virtually every company in the world will have to incorporate this technology to be competitive. When the Internet busted into the collective consciousness with Netscape’s IPO in 1995, most people could see the Internet’s potential, but the basic infrastructure, like broadband, ad servers, logistics, et cetera, were not yet been in place resulting in massive capital destruction subsequently as revenues were late in coming and monetization capabilities were slow to develop.

Of course, when that infrastructure was built over time, it created several of the most valuable companies in the world and revolutionized how business is conducted. But by contrast, in the case of generative AI, we believe the adoption cycle and benefits of adoption will potentially be much more quickly realized. We expect this will be driven by the massive productivity increases, adoption of generative AI will likely provide. Recent paper by MIT referenced to 37% increase in productivity among workers using ChatGPT versus using their legacy processes. This is an industrial revolution level leap in human productivity. The adoption of generative AI in our opinion, may do for the service sector what the steam engine and the electric motor did for the industrial sector.

We believe our immediate opportunity is with large mega cap tech companies, as we announced today that are in a race to build the generative AI foundation as well as new entrants, companies like Anthropic, Character AI and that are raising massive amounts of capital to enter the race. These companies plan on generating revenues from providing the foundational layer of generative AI and licensing that technology to third parties to build their own AI applications for their specific niches and use cases. We believe that the adoption by these licensees will potentially be very rapid, potentially much more rapid than the Internet because the application of this technology is primarily focused on enhancing productivity and because the infrastructure and ecosystem needed for its implementation is readily available and proven.

Moreover, companies may likely fear that if they do not incorporate these capabilities, they’re resulting in relatively higher cost structures may render them uncompetitive to say nothing of the customer experience which will seem so yesterday. The second opportunity to do for Innodata is to work with these large technology companies, providing data engineering services to their end customers that help their end customers build solutions on these foundation models or to fine-tune their own versions of the foundation models. We see the opportunity to provide these services on both a side-by-side basis and on a white label basis. We are in active discussions with 2 of these companies about doing just that. We hope to have progress to report on this front by our next call.

The third opportunity is to help large businesses build their own proprietary generative AI models. We see this as likely becoming increasingly attractive to large businesses that have unique large data sets. We believe this will become increasingly a viable path based on the likely current evolution of 3 predictions we are making. First, that high-performing commercially usable open-source generative AI models will become increasingly available. Second, that progressively more effective techniques for model fine-tuning will become available. And third, that the marginal cost of compute will, over time, significantly reduce, thanks to innovations in GPUs and other systems. We’re already seeing evidence of this. We believe, for example, we are close to signing a master services agreement with a leading investment bank that earlier this year deployed 40 data scientists to team LLMs in the context of their data.

Their vision is to enable their people to quote chat with their data, much the way that people query databases today. There are essentially 3 main areas of cost in building generative AI; the costs of building the model, the costs of compute and the cost of data engineering. As models commoditize, fine-tuning cycles become less expensive and compute costs come down over time, we expect that large businesses will see the remaining costs, data engineering, as a high ROI investment. Moreover, we expect it will become economic for them to address progressively more ambitious and sophisticated use cases. We believe that the data engineering work that will be required, including data collection, creation, curation, preprocessing for LLM training and testing will become the key to unlocking the potential competitive advantage that their proprietary data represents.

This transitions to our third point, why we believe Innodata has been able to gain such rapid traction with leading companies focused on this new arms race. I would say sometimes it is better to be lucky than smart. We did not predict that generative AI would burst onto the scene as quickly as it has, nor are we architects of the system, but our long history of building capabilities for other applications which are now directly applicable to generative AI, we believe has put us in an enviable position. We haven’t placed scalable domain expertise in diverse areas, including material sciences, agriculture, biology, math, legal and pharma, with thousands of civic matter experts around the world. We have a proven reputation for creating consistently high-quality data sets in complex subject areas right with subjectivity.

We have a global reach, enabling us to work in 40-plus languages. We have the technical acumen we have developed over the past 7 years around AI model training and deployment. And we have developed flexible platforms to digest and ingest data to auto annotate data with zero-shot learning and to track quality metrics in real time. We believe the technology choices we made starting back in 2016 and 2017 turned out to be tailor-made for generative AI. For example, we chose encoder-based transformer architectures that supported generative AI. At the time, this was hardly an obvious choice. While our first models only had 50 million parameters versus, for example, today’s GPT-4 that is reported to have 1 trillion parameters, the essential architecture is what we settled on 7 years ago.

Moreover, our experience taught us how to deploy models into real-world production environments in ways that are safe and value creating. So maybe it was not all luck. In addition, over the past several years, we have been integrating both generative and classical AI into real-world customer operations, workflows and platforms. We have developed recipes and technologies for building and configuring fine tunable LLMs and overcoming safety and trust challenges. More recently, we have developed deep expertise in prompt engineering and prompt chaining, important skills required to deploy foundation models. As we work with our largest customers on the implementation side as we hope to and with the large businesses on a going alone basis, as we plan to, we believe our experience will both be repurposable and invaluable.

Fourth, I want to give a framework for the customer wins and positive indication of a win we announced today and broadly speak to the potential magnitude of these accomplishments and how we believe they could show up in our results. These 3 customers — notified us over the past 10 days. As I mentioned earlier, but it’s worth repeating, each of these 3 customers is in the list of the top 5 global tech firms. One is an existing customer while the remaining 2 are new. With our existing customer, we are in the process of putting in place the work order for a new win. With the 2 new customers, we are now in the process of putting in place formal agreements and finalizing initial scope. While we are confident that agreements will be inked until this happens, there is always the chance that it does not.

The deals are potentially large. To illustrate, one of the new customers has indicated that it will cut an initial purchase order for $2.5 million to get us started, but it will be supplementing that as we move forward. They also shared with us their vision for where the initial scope of work might go, which if fully realized, we believe could result in approximately $12 million of new quarterly revenues at maturity. Moreover, the draft contracts that are now being worked on are at our customers’ requests, framework agreements that enable them to easily add scope. Again, I want to emphasize that deals with the 2 new potential customers have not yet been signed, but we’re expecting to get them signed and for the details to be worked out over the next few weeks.

Based on our experience, we believe that these engagements will ramp up over the course of several months. Typically, once an agreement is finalized, we work with the customer to create detailed specifications and run pilots to ensure that the specifications are yielding the intended results. Oftentimes, this requires several iterations. Once the specification is locked down, our next step is to put in place the required infrastructure. This includes custom-configuring technology systems, finalizing process designs and assembling human resources, data engineers and subject to matter experts. This can take 2 to 3 months typically. Once this is completed, ramp-up begins. We typically ramp up slowly so that we can continue to test and refine as necessary with the customer.

Ramp-up itself can take 3 to 6 months to achieve steady state. The duration of the engagement will depend upon many factors, including the size of the model being built and the amount of ongoing updating and tuning that will be taking place. These are often not knowable in advance. This is a dynamic process with customer dependencies and checkpoints throughout, which makes it tough to do quarterly forecast. But based on our experience, the end result that gets us to full ramp-up is typically achieved in a roughly 12-month period. Finally, let’s talk about our Q1 results and guidance. In Q1, revenue was $18.8 million, and adjusted EBITDA was $800,000. There was no revenue in the quarter from the 3 deals we are announcing today, all of which happened in just the last couple of weeks, well after Q1 close.

It is also worth knowing that there was no revenue in the quarter from the large social media company, which contributed $4.4 million in revenue in Q1 of last year, but dramatically pulled back spending in the second half of last year as it underwent a significant management change. If we back out revenue from this large social media company, our revenue growth in the quarter over Q1 2022 would have been 12%. We believe that it is possible that business from this customer could resume in the second half, but our 2023 business plan does not account for this upside. Our 2023 business plan also does not account for revenue from the 2 new customers we have spoken about today. Even without these elements incorporated, we expect that exiting this year our revenue growth rate could potentially be high teens or in the 20s, if we back out from 2022 of the large social media company we just discussed, and that our adjusted EBITDA run rate could potentially exceed $15 million on an annualized basis.

We ended the quarter with a healthy balance sheet, no appreciable debt and $10.8 million in cash and short-term investments on the balance sheet. I’ll now turn the call over to Marissa to go over the numbers, and we will then open the line for questions.

Marissa Espineli: Thank you, Jack. Good afternoon, everyone. Allow me to provide a recap of our Q1 results. Revenue for the quarter ended March 31, 2023 was $18.8 million compared to revenue of $21.2 million in the same period last year. And as Jack mentioned, the comparative period included $4.4 million in revenue from large social media company that underwent a significant management change in second half of last year, as a result, it dramatically pulled back spending across the board. There was no revenue from this company in the quarter ended March 31, 2023. Net loss for the quarter ended March 31, 2023 was $2.1 million or $0.08 per basic and diluted share compared to net loss of $2.8 million or $0.10 per basic and diluted share in the same period last year.

Our adjusted EBITDA was $0.8 million in the first quarter of 2023 compared to adjusted EBITDA loss of $1 million in the same period last year. Our cash and cash equivalents, including short-term investments, were $10.8 million at March 31, 2023, and $10.3 million as of December 31, 2022. Again, thanks, everyone. John, we are now ready to take questions.

Q&A Session

Operator: [Operator Instructions]. And the first question comes from Tim Clarkson with Van Clemens.

Operator: [Operator Instructions]. The next question is from Dana Buska with Feltl.

Operator: We have reached the end of the question-and-answer session, and I will now turn the call over to Jack for closing remarks.

Jack Abuhoff: Operator, thank you. So yes, I’ll quickly recap. Today, we’re announcing that we have received verbal wins from 2 of the top 5 global tech companies and that we have a strong indication of a win from a third of these 3 companies, 2 our new customers. These are all companies that are widely expected to forge the path forward in generative AI development over the next several years. Last quarter, we cautioned you that these were just pipeline, and the pipeline often does not close this quarter. We’re proud to say that 2 are now verbally confirmed wins and that we got a strong indication from the third that it also is likely a win. We hope to have each of these papers in the next few weeks. We believe that these new deals, perhaps individually, but certainly in the aggregate, present a potential transformative opportunity for our company.

As you know, we have a solid track record of land and expand with large tech companies. And now with the additional tailwind of generative AI, we think we are extraordinarily well positioned. We believe the revenue growth opportunity with these companies is significant in the near, medium and long-term perspectives. We’re also excited about the endorsement. We believe these new wins and accomplishments represent for us. We believe virtually every company out there will need to become an AI company over the next several years, and we believe that we will be well positioned to help them do just that. I also want to say that we plan on stepping up our Investor Relations activities in the second half of the year. We plan to be presenting at several investor conferences, and we will announce these once plans are firmed up.

So again, thank you, everybody, for joining us today. We’ll be looking forward to our next call with you.

Operator: This concludes today’s conference, and you may disconnect your lines at this time. Thank you for your participation.