Innodata Inc.

In a minute, I will describe some of the business we have won, some of the new business opportunities we are pursuing. Suffice it to say, we are seeing an increase in both the number and the magnitude of potential customer requirements, which we reflect in our increased guidance. Therefore, we're taking steps to ensure that we have sufficient liquidity to accommodate working capital as our already substantial growth potentially accelerates. First, we have increased our receivables-based credit facility with Wells Fargo from $10 million to $30 million, subject to a borrowing-based limitation, with an accordion feature that enables it to expand up to $50 million, subject to the approval of Wells Fargo. Maryse, in her remarks, will give more color on the terms of this facility. I believe the Wells Fargo facility has now extended will be sufficient to fund our working capital requirements for our anticipated growth. That said, we want to be prepared to react quickly to customer demand that could result in us significantly exceeding our anticipated rate of growth and therefore having additional working capital needs. Toward that end, this afternoon we filed a universal shelf registration statement on form S3 with the SEC. Once the registration statement is declared effective by the SEC, we will have the flexibility to sell up to an aggregate of $50 million worth of our securities in registered offerings pursuant to the effective registration statement. We believe it is prudent and good corporate governance to have an effective shelf registration statement on file with the SEC to preserve the flexibility to raise capital from time to time if needed. As disclosed in the registration statement, we have no specific plans to raise money at this time. The intended uses for the net proceeds from any such offering would be set forth in a prospective supplement. Now I'll give you an overview of the success we're experiencing in the marketplace with both existing and new customers. On June 3rd, 2024, we announced one of our existing magnificent seven big tech customers, had awarded us two significant new LLM development programs. These programs are expected to deliver approximately 44 million of annualized run rate revenue and represents the single largest customer win in ADATA's history. These awards are in addition to the new programs and program expansion with this customer announced on April 24th, 2024 and May 7th, 2024. In the one year that InnoData has been working with this customer, InnoData has landed new programs and program expansions that bring the total value of the account to approximately 110.5 million of expected annual run rate revenue. InnoData aspires to replicate the success across the six other big tech customers already contracted for generative AI development and to land additional big tech accounts. We want several other new assignments in the quarter as well, and we expect to land several others in the near future. Some notables include a big tech company that would be a new customer for us. It is one of the most valuable companies in the world and one of the companies most often talked about in connection with generative AI. Another is an existing big tech customer In connection with this opportunity, we would aim to become certified to work on their premises. We believe being co-located with their engineering and operations teams may potentially enable us to access new attractive opportunities. We also expect to shortly sign a prominent social media platform that is building its own generative AI models and would be a new customer for Interdata. Another noteworthy win was with a clinical provider in the healthcare market. Up until now, we've been focused on the use of the Synodex platform as a tool for supporting insurance underwriting. This new engagement is the first time that we will be applying the platform in a clinical use case. We believe that the Synodex technology roadmap may enable us to expand to support additional clinical use cases in the future. We have also been awarded a deal to provide news briefs and media monitoring to a federal government agency that will be leveraging the new generative AI capabilities built into our Synodex platform. We are seeking to expand into the public sector, so we consider this a strategic win. We have started to integrate agility with what we call PR Copilot. Our purpose builds generative AI layer that enables PR professionals to get more done in less time and at lower costs. While we're only about 30% into our roadmap for PR co-pilot, it is already delivering tremendous business value. This quarter, agility revenue crossed $5 million mark for the first time. Our agility demo-to-deal win rate in the quarter was 36%, significantly higher than the sub-20% win rates we were achieving prior to starting this integration. And we doubled our new business bookings in Q2 compared to the prior year period, even though we're operating with a leaner sales force. Now, before I turn the call over to Mariz, I want to share our perspectives on the generative AI market opportunity and how we have shaped our strategy to capitalize on where we see the market going. In our view, the big tech companies are clearly bullish about how generative AI technology will support their core products and services and enable exciting new opportunities. For the MAG7, capital expenditures in the latest quarter were up 63% year over year. With the bulk of these expenditures tied to generative AI spending, it is clear that the market sees under-investing as a greater risk than over-investing. In the not distant future, we believe the technology will enable computers to reason and plan, to solve hard problems, and to self-organize in complex ways that help people accomplish their goals. Our belief is that generative AI technologies will soon sit deeply and ubiquitously in every tech stack. That's why none of the big tech companies can sit this one out. The shift in experience is destined to be too significant, making the risk of being left behind untenable. Just as the California gold rush began on January 24, 1848, The GenAI gold rush began on November 30th, 2022, when OpenAI demonstrated to the world the power of deep, the power of training a deep neural net on enormous quantities of data and utilizing massive compute for inferencing. As a result, the world's largest tech companies went on the offense, committing to massive GenAI programs solving for the next big market opportunity while simultaneously defending their hegemony. One analyst has forecast $1 trillion of Gen AI CapEx over the next several years. We prescribe wholeheartedly to the notion that in a gold rush, you want to be the person selling the shovels. The shovels required by the big tech companies in the Gen AI gold rush take the form of compute. and data compute is expensive and hard to come by which is why we believe nvidia's market cap has skyrocketed over 7x to 2.6 trillion dollars since the beginning of 2023 data is also expensive and hard to come by once more we believe the data that is likely to be required to train tomorrow's gen ai is going to become even more expensive and even harder to come by. And we believe that is in a data's opportunity. The next generation of LLMs will be trained to handle more complex tasks and to be more agent-like. The complexity will take the form of models that handle difficult multi-turn tasks. For example, asking an LLM to find out how much vacation I have left and book me a trip. Complexity will also take the form of deep domain-specific tasks, like helping doctors diagnose disease or helping banks sort out complex regulation. And complexity will also take the form of models that enable users to work with audio, video, and text interchangeably. You'll hear this referred to as multimodal capabilities. Training data will be required to build models that can handle this complexity. Unlike web data that gets users halfway there for today's LLMs, these more complex LLMs are going to require a high quantity of high quality data to be specifically developed to show the models how they're supposed to function. Right now, this data does not exist anywhere. It isn't on the web. It isn't on the cloud. It isn't on premises of enterprises. Because it is neither input nor output, it exists only in a transitory, unpreserved state. It's impermanence perhaps justified by its nature as byproduct. In other words, when we solve hard problems, we don't save our work. When we began building our own AI models and applying them to our managed services work in legal data and medical data, we had to build new workflow platforms to capture and preserve this interim knowledge in an organized way to be used to train our models. This was our eureka moment when we realized that our breakout opportunity would be in creating this byproduct of human thought in order to train other people's models. Doing this as a science in a way that is repeatable and scalable is a huge opportunity, and we are still in the early days. We intend to be the preferred provider of complex demonstration data at scale, required to train models for complex reasoning, multimodal use cases, agentic retrieval augmented generation, or RAG, and for domain specificity across all languages. Our competitive advantage is that for decades we've been providing high quality data across domains such as medical, law, regulatory, science, and finance. We're encouraged by the feedback from our customers who already recognize that no single factor has as much influence on LLM performance as the quality of customized data for supervised fine tuning. We will always be looking for ways to drive continuous improvement in how we operate, ensuring that our training data is both the best quality and the most economical. Now, on the enterprise side, we believe that in 18 to 24 months from now, enterprises will dramatically accelerate their generative AI adoption. We believe the catalyst for this will be generative AI that can tackle multi-phase tasks without losing its way, now often referred to as agentic RAG, in combination with advanced open source models which significantly lower the bar for experimentation. These smaller but highly trained language models will likely prove ideal for enterprise applications that require high accuracy for specific tasks. Like the big techs, we believe enterprises will drive both offensive and defensive strategies to support their investments. The offensive play will be defining new product experiences, while the defensive play will be keeping pace with competitors who we anticipate will work to enable their current products and reengineer their operations to be AI first. Just as with the big techs, we believe enterprises will come to recognize that you've got to be all in, even with uncertain near-term ROI. A few years from now, we envision enterprises will face a shortage of experienced talent and may struggle to manage their internal data. Thus, the shovels for enterprise will be the people with the experience to help them choose the right architectures, the right approaches, and the right models and to help them manage and deploy their internal data. InnoData's enterprise strategy is focused on this. Specifically, we see the opportunity to respond to these anticipated emerging ways, needs in three ways. First, for enterprises building their own capabilities, we will be ready to assist across the entire continuum of integration types and levels. from fine-tuning custom models to building agentic RAG applications. As enterprises move to NAI services from development to production, they will need to know, how are the models working? Are they performing as intended? Are they, as they were intended, helpful, harmless, and honest? We see a big opportunity in helping them monitor their LLMs for alignment and safety. We are developing both services and platforms to respond to this need, proven by high-quality custom data. Second, for enterprises that prefer to outsource, we will make available managed services that are engineered to leverage the technologies. And third, for enterprises that prefer generative AI encapsulated in industry platforms, We will provide platforms specifically designed for industry-specific, knowledge-intensive workflows. In this way, we intend to serve enterprises at their highest point of value. I'll now turn the call over to Maryse to go over the numbers, and then Maryse, Anish, and I will be available to take your questions.

Thank you, Jack, and good afternoon, everyone. Let me briefly share with you our 2024 second quarter financial results. Revenue for Q2 2024 reached $32.6 million, reflecting a year-over-year increase of 66%. On a sequential basis, we observed a 23% increase of $6 million from Q1 of 2024 revenue of $26.5 million. Adjusted growth margin for Q2 2024 was 32%, reflecting a sequential decrease from 41% we achieved in Q1 of 2024. This reduction is attributable to the $3.6 million of recruiting costs we incurred in the second quarter to support a substantial expansion for our organization to prepare for a significantly larger revenue base. When you separate these unusually high recruiting costs, adjusted gross margin in the quarter would have been approximately 44%. Similarly, adjusted EBITDA for the quarter was $2.8 million, a reduction from $3.8 million in Q1 of 2024. But without the $3.6 million of recruiting costs, adjusted EBITDA would have been $6.4 million, or 20% of revenues. There are three things noteworth noting. First, as Jack mentioned, we expect our adjusted EBITDA next quarter to be approximately triple the 2.8 million of adjusted EBITDA reported this quarter. Second, we have since enhanced our captive recruiting engine to enable us to reduce the cost of future larger-scale recruiting. And third, we expect weak payback on recruiting costs, typically within just a few months, and strong ROI. Our cash position at the end of Q2 was approximately $16.5 million, up from $13.8 million at year-end 2023. Let me also elaborate a bit on credit facility extension that Jack mentioned earlier. We are indeed very pleased to announce that Wells Fargo has increased on receivable back credit facility from $10 million to $30 million with an accordion feature that enabled it to scale up to $50 million subject to Wells Fargo's approval. The amount drawable under the facility at any point in time is determined based on the borrowing base formula. The facility has an attractive cost of capital for amounts drawn under the line set as software plus 2.25%. We greatly appreciate Wells Fargo's confidence in our business and believe the extended facility will be sufficient to fund our working capital requirements for our anticipated growth. That said, we want to be prepared to react quickly to customer demand, which could result in InnoData significantly exceeding the rate of growth we have guided to today. With this in mind, this afternoon we filed a universal registration statement on Form S3 with the FCC, Once the registration statement is declared effective by the FCC, we will have the flexibility to sell up to aggregate of 50 million worth of our securities in registered offering pursuant an effective registration statement. We believe it is prudent and good corporate governance to have an effective shelf registration statement on file with the Security and Exchange Commission to preserve the flexibility to raise capital from time to time if needed. We have no specific plan to raise money at this time. The intended uses for the net proceeds from any such offering would be set forth in a prospective supplement. In terms of preparing for accelerated growth, Our expanded Wells Fargo line of credit and the flexibility provided by the SHELF registration statement are expected to allow us to finance our short-cycle, growth-driven working capital need for a revenue base significantly higher than our current projection. Thank you, everyone, for joining us today. Mike, we're open for questions.

At this time, we will be conducting a question and answer session. If you would like to ask a question, please press star 1 on your telephone keypad. A confirmation tone will indicate that your line is in the question queue. You may press star 2 if you would like to remove your question from the queue. For participants using speaker equipment, it may be necessary to pick up your handset before pressing the star key. One moment, please, while we poll for questions. Okay, we do have our first questioner. Alan Key from Maxim Group.