Innodata Inc.

Greetings. Welcome to InnoData's fourth quarter and fiscal year 2023 earnings call. At this time, all participants are in a listen-only mode. A question and answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. Please note, this conference is being recorded. I will now turn the conference over to your host, Amy Agris. You may begin.

Thank you, John. Good afternoon, everyone. Thank you for joining us today. Our speakers today are Jack Abelhoff, CEO of InnoData, and Maryse Espinelli, interim CFO. We'll hear from Jack first, who will provide perspective about the business, and then Maryse will follow with a review of our results for the fourth quarter and the 12 months ended, December 31, 2023. We'll then take your questions. Before we get started, I'd like to remind everyone that during this call, we will be making forward-looking statements which are predictions, projections, or other statements about future events. These statements are based on current expectations, assumptions, and estimates, and are subject to risks and uncertainties. Actual results could differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release in the risk factor section of our Form 10-K, Form 10-Q, and other reports and filings with the Securities and Exchange Commission. We undertake no obligation to update forward-looking information. In addition, during this call, we may discuss certain non-GAAP financial measures. In our SEC filings, which are posted on our website, you will find additional disclosures regarding these non-GAAP financial measures, including reconciliations of these measures with comparable GAAP measures. Thank you. I'll now turn the call over to Jack.

Good afternoon, everybody. We're very excited to be here with you today as we have a lot of good news to share. 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%. Now, it's 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 were 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 up of generative AI development work for one of the big five tech companies we signed mid 2023, and also benefited by the start of 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 that 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 value or term of a contract. We're off to a strong start in 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're optimistic we will grow revenues with all three of these customers in 2024. With the remaining two of the five MAG7 customers, we've barely gotten out of the gate, but we're optimistic about making significant inroads this year. We're 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 to our annual growth target of 20% in 2024 with the intention of overachieving this. 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 A to I 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 ingredients 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. We believe that a decade from now, virtually all businesses will have adopted generative AI technologies into their products and operations. For enterprises, our offerings include business process management, in which we re-engineer workflows with AI and LLMs and perform the work on an ongoing managed service basis. 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 fine tuning, 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 third-party models. Much the way data is at the heart of programming like what 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. I'm going to take a few minutes now to respond to some questions I've been asked by investors recently. Number one, 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. Number two, several investors have asked why we have no chief financial officer. Well, in a sense, we actually have four chief financial, excuse me, chief technology officers, or at least their equivalents, each of which manages 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 products 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 I've asked why we do not separately disclose it to comment on whether we have a significant spend on cloud infrastructure. So there are three separate questions there, and I'll address each. 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% 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 spent 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 companies like OpenAI, Google, and Meta, and whether they should compare our R&D spend and cloud compute spend to these companies. These companies are absolutely not our comps. Rather, 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 that 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, like Scale AI and others, and companies more broadly focused on technology services, but also focused on AI data engineering, like Accenture and Cognizant. Another question I've gotten is, how do 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 LLO, 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 golden gate 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 re-architect 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, as 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. The technology is deployed in customer deployments and within our own platforms and yields great results. That said, GoldenGate is not ChatGPT. You can't converse with it or ask it to write 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 data sets, and that for business implementations especially, data engineering is more important than sheer model size as a predictor of performance. The question I 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 percent across these two business lines, and we believe gross margin is the better metric to track. In our software business, our target gross margin is anticipated to be about 73 percent this year, and we intend to target a consolidated adjusted gross margin of between 40 and 43 percent. The final question I've gotten several times recently and that I want to respond to on today's call is, is agility now profitable? The answer is yes. In this quarter, Agility posted adjusted EBITDA of $1.2 million. This was a 69 percent sequential increase over Q3. We think we executed the Agility business very well in 2023, growing at 15 percent in a difficult macro environment. It had a strong adjusted gross margin of 69 percent over 2023 as a whole and 74 percent in Q4. We also love what we've done with the product. We believe we've taken leadership position as the first end-to-end public relations and media intelligence platform to integrate generative AI. I'll now turn the call over to Maryse to go through the numbers, and then we'll open the line for some questions.