Transcript
Good day, ladies and gentlemen and thank you all for joining us for this Innodata First Quarter 2022 Earnings Conference Call. As a reminder, all phone participants are in a listen-only mode. But after today’s prepared remarks you will have the opportunity to ask questions. As a reminder, today’s session is being recorded. And now for opening remarks and introductions, I’m pleased to turn the floor over to Amy Agress. Please go ahead.
Thank you, Jim. Good morning, everyone. Thank you for joining us today. Our speakers today are Jack Abuhoff, CEO of Innodata; and Marissa Espineli, Interim CFO. We will 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 will 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 statements 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, including without limitation, the expected or potential effects of the novel coronavirus COVID-19 pandemic and the responses of governments, the general global population, our customers, and the company thereto; impacts resulting from the rapidly evolving conflict between Russia and Ukraine; that contracts may be terminated by customers; projected or committed volumes of work may not materialize; 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 clients to reduce, delay or cancel projects; the likelihood of continued development of the markets, particularly new and emerging markets that our services support. Continuing Digital Data Solutions segment revenue concentration in a limited number of clients; potential inability to replace projects that are completed, canceled or reduced; our dependency on content providers in our agility segment, continued downturn in or depressed market conditions, whether as a result of the COVID-19 pandemic or otherwise. 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 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. Changes in our business and growth strategy, the emergence of new or growth in existing competitors, our uses 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 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 Form 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. I will now turn the call over to Jack.
Good afternoon. Thank you for joining our call. Starting this quarter, we have shifted our earnings releases and investor conference calls to after market close. We trust that this will prove to be more convenient for investors. Just eight weeks ago in our Q4 call, we shared some important new wins, expansions, and partnerships. Since that time, in just the last eight weeks, we have had even more wins and more expansions. I’m excited to share a few of those with you today, as I believe they clearly illustrate our market positioning and our land of expense strategy delivering results. We are pleased to announce a strong first quarter with revenue up 33% year-over-year, exhibiting an acceleration in revenue growth over the 20% we experienced in fiscal 2021. In the current quarter Q2, one of our largest customers is reallocating data annotation supporting two of its more mature models to less mature AI models that it wants to ramp up. The impact of this transition is that a portion of revenues from this program shifted into the first quarter in anticipation of this transition and we expect a portion of revenues from this program to shift into the third and fourth quarters as reallocations ramp up. Therefore, Q2 may have a lower growth rate likely in the range of 18% to 24%. But this is not expected to change the revenue expectation from this customer for the year, or our overall revenue expectations for 2022. Consequently, we reiterate our 2022 target of 30% year-over-year revenue growth and our long-term 2025 target of approximately 200 million in revenues, and approximately 30% adjusted EBITDA, based on the continued momentum we see in our business. In the quarter, we added 121 new logos across our segments. This is a 96% increase over the 62 new customers that we added on average per quarter in 2020 and a 30% increase over the 93 new customers we added on average per quarter in 2021. The momentum in just the last eight weeks since we last spoke, in terms of landing new customers and expanding business with existing customers has been quite exciting. One of the more notable new logos we signed in the last eight weeks was with a leading multinational consulting firm to provide an ongoing seed of annotation AI data. We expect this win to result in approximately 800,000 or more of recurring revenue per year. From a strategic perspective, we believe this validates for us the opportunity to partner with large consulting firms. It also validates the opportunity to create native AI offerings by combining capabilities. So for this customer, we are combining the output of our agility infrastructure, in which we intake and tag billions of news items each year with our financial services domain subject matter experts and our new data annotation platform. The result is an integrated solution for automatically identifying certain trends in business news that are important to this consulting firm and its customers. We are winning new customers continually. Just yesterday, as I prepared these remarks, we signed a brand new customer with whom we anticipate approximately 250,000 per year of recurring revenue to start. We view these new logo wins like planting seeds. We nurture and tend to them carefully, provide outstanding quality data and product along with a culture of service. And we get to watch as they grow. In the first quarter, we saw lots of our seeds germinate, looking at business expansions with dozens of our existing customers. I will briefly describe several expansions that occurred in just the last eight weeks. A Fortune 500 Life Insurance Company increased its go-forward annual projected spend from about 1.3 million per year to approximately 2.4 million per year. We significantly expanded our scope at one of the world’s largest social media companies, a company that we announced in our last earnings call had become a new subscription customer for our media intelligence platforms and solutions. At the time of signing, we estimate the revenue value of their subscriptions to be about 200,000 per year. Based on expansions over the last eight weeks alone, we now estimate the value to bring approximately 500,000 per year. The social media company has been ranked as one of the fastest growing brands in the world over the last two years. It chose our product to monitor how their brand is being depicted globally in news and social media. A key area of focus for us strategically is on AI in the enterprise. In the last eight weeks, we have seen a number of our enterprise seedlings germinate and flower. A large billion dollar e-commerce website has agreed to sign with a 30% additional scope over the 300,000 initial commitment they made in the SOW we signed last year. We can tell similar stories about business expansion over the past eight weeks with respect to a leading credit agency, a large ad tech vendor, a startup mortgage processor, and a large research organization. And again, I emphasize all in the last eight weeks. We have talked before about the great strides we are making in expanding our services and solutions across a number of Silicon Valley big tech companies. We have pilots underway for potentially large programs at these companies. We estimate that they collectively spend billions of dollars on products and services that we believe we are well positioned to provide. Here is what we believe is working in our favor. First, we provide the highest quality data engineering, which results in AI models that perform better, faster. Encouraged by the result, customers often agree to start new programs to create new AI models to help their businesses in other ways. For each existing AI model, our work is not done. Maintaining AI models requires continuous training, different data drift, which is the tendency for real-world data to change over time. From a business perspective, we believe the cycle of success results in recurring engagements that build over time. I can think of at least two customers that told us recently that they extensively evaluated the vendor landscape before deciding to go with us, and that their technology groups had tried unsuccessfully to duplicate our capabilities in-house. And these were both multibillion dollar financial services companies. We are feeding the strong momentum by continuing the investment in sales and marketing that we started last year. In 2021, we increased our sales and marketing spend by 7.4 million or 116%, year-over-year, and in 2022, we anticipate a full-year increase in sales and marketing spend by approximately 10.4 million or 75% year-over-year. This year, these investments are designed to accelerate our growth in 2023 and beyond. As we mentioned on our last earnings call, we target the long-term value of new customers from these investments to be 3x to 5x our customer acquisition costs. Two weeks ago, we had our President’s Club trips for high performers in our sales team. The enthusiasm and energy were palpable, and I heard about a number of new exciting pipeline opportunities. It becomes clear to me all the time how well-positioned we are to help businesses through their AI journeys, which is doubly exciting because AI is still very much in its earliest innings. On the product front, we also made great progress in just the last eight weeks. We launched our AI-enabled document intelligence platform in late March as planned. This new customer-facing platform uses our proprietary Golden Gate AI technology to automatically extract meaning from complex documents. It can be utilized across domains from healthcare to financial services to media and entertainment, essentially any business that employs people to read or manage complex documents. Initial feedback from our customers has been positive and we are in the late stages with two opportunities which taken together we believe will be worth approximately a million dollars per year in likely recurring revenue. Our annotation platform embodies what is becoming increasingly known as data-centric AI. It solves the data curation, annotation and introspection challenges, rather than trying to compete with TensorFlow, Torch or general algorithms. Many experts in AI clearly say that the neural network architecture is essentially a solved problem. For many practical applications, it is now more productive to hold the neural network architecture fixed, and instead find ways to improve the data. Taking this exact approach, our platform will include tools to enforce data consistency and learnability tools to analyze blind spots, bias and noise, choose to automate data and collect data more efficiently, and choose tools to select the best data to annotate next in order to maximize system accuracy while minimizing annotation costs. Last eight weeks also saw good progress on a new platform that we are on track to deliver by the end of June that will be sold as an extension to our existing agility platform. Its purpose will be delivering deeper analytical insights into social media. It will be the first of two related platforms to be launched with a second planned for the end of the year. Both platform releases are expected to support our strategic drive toward increased initial average selling price for agility subscriptions, and offer new entry points for new customers. You may have seen on our website that a few weeks ago, we launched an e-commerce portal where users can purchase on-demand datasets to accelerate AI/ML model building and training. At the same time, we partnered with Snowflake, a leading data warehousing company to provide access to some of our synthetic data through its marketplace. These initiatives are designed to increase our brand awareness and lead generation. Perhaps saving the best for last, because it is something I’m particularly excited about. We launched the initial version of our new banking industry platform on March 31, on time and on budget. Our charter customer, one of the world’s largest banks, is committed to $11 million subscription spend with us over five years, and already has 65 people using the product and giving us valuable feedback. The bank’s leadership is very excited by how we are able to augment their existing team's work through our AI-enabled product. We hired a new product manager with extensive experience in financial services to lead the charge and bring this product to a wider market next year. We have made and this year are continuing to make strategic investments in product development to expand our SaaS platforms and our industry solutions. This year, we plan to take six new platforms to the market. In 2021, we increased our product development spend by 2.4 million or 65% year-over-year, and in 2022, we anticipate increasing product development spend by approximately 7.2 million or 115% year-over-year. We expect these investments to significantly increase our addressable market and high-margin recurring revenue. Because our book of business is cash generative, we can fund these substantial investments from our internal resources. Based upon current assumptions and expectations, we are budgeting to be cash flow positive by the end of 2022, with significant increases in cash flow expected thereafter. With $15 million in cash and no debt, we do not presently expect to need external financing to execute our plan. What is particularly exciting is to see our products and services within the marketplace against our competition. We feel that our three-tiered product service architecture is helping us intersect with enterprise customers, regardless of where they are in the AI adoption cycle. If they have data science teams, we can provide the highest quality data annotation and related data engineering services across multiple domains. If they do not, we can provide applied AI services in the form of custom trained models or API access to our models. For companies that want to do their own data annotation, we now have a licensable platform. For others, we have domain experts on hand. For companies that want to manage their own AI models, we now have a document intelligence platform that enables them to do so. Lastly, for companies with discrete workflows that can benefit from AI, we provide end-to-end platforms that embed AI to augment their knowledge workers. We invested in building out agility. And now agility just last month was recognized for the fourth consecutive quarter as a momentum leader by Software Review Site G2, this latest time in its spring PR software report. We are continuing to invest in the product to increase its ASP and the value it delivers to customers. As a result of the expansion of the social media company I spoke about a few minutes ago, we now have a new largest agility customer ever. In Q1, we enjoyed a 127% year-over-year increase in subscription bookings and a year-over-year increase in our average selling price. Let me say a few words on the macro environment. Instead, it is top of mind for a lot of investors. There was talk of recession, stagflation, capital markets, dislocation, the great resignation, etc. We believe we are well-positioned to withstand these evolving economic challenges for two main reasons. One, our services lower costs for our customers, which becomes a core focus in tough times. And two, we make our customers more efficient, enabling them to tackle the labor shortages they are experiencing. The increased momentum we see in our business is a testament to the growing sector's demand for our offerings in these challenging times. The growth of our Synodex business is illustrative of this phenomenon. We are putting our Golden Gate AI to work within our Synodex medical data extraction product that changes the way underwriters work. They need many fewer underwriters than they used to. This is a good thing because underwriters are expensive and hard to find. Indeed, large life insurers are becoming increasingly focused on the digital transformation of underwriting and embracing AI, concluding that the Synodex platform is an ideal tool for enabling this transformation. As a result, our Synodex year-over-year growth of 64% in Q1, and we anticipate this accelerating to over 100% in 2022 over 2021. With our new document intelligence product, we enable businesses to use this AI capability not just for medical records, but for virtually any kind of document. We believe we changed the game in terms of both enabling people to avoid having to read large documents, either by producing summaries or generating structured data feeds that are then fed into decision engines. So simply stated, people reading complex documents are too expensive. Our platform solves that challenge. We are quite pleased with our execution, product validation, and the tailwinds from the growth in our markets. We believe more firmly than ever in the blue sky growth opportunities in front of us and remain laser-focused on execution and shareholder value creation. I will now turn the call over to Marissa to go over the numbers, and then we will open the line for questions.
Thank you, Jack. Good afternoon everyone. Allow me to briefly recap our Q1 2022 financial results. Revenue for the quarter ended March 31, 2022 was 21.2 million, up 33% year-over-year. Net loss for the quarter ended March 31, 2022 was 2.8 million or $0.10 for basic and diluted share compared to net income of 0.4 million or $0.02 per basic share and $0.01 per diluted share in the same period last year. Adjusted EBITDA loss was 1 million in the first quarter of 2022, compared it to adjusted EBITDA of 1.3 million in the same period last year. Cash and cash equivalents were 15.4 million at March 31, 2022, and 18.9 million at December 31, 2021. Again, thank you, everyone. Operator, we are now ready for questions.
Thank you. We will hear first from the line of Tim Clarkson at Van Clemens.
Great quarter, obviously pleased with the revenue growth and obviously there is lots of exciting things going on. Wanted to start with a technical question. Can you talk about 30% EBITDA, what kind of net margin are we talking about? I know this is down the line a little bit, but are we talking about 10%, 15%, 20% net? What kind of number are we looking at there?
So you are talking about, you know -.
30% EBITDA, what are we looking at on an after-tax basis?
Yes. Tim, I’m going to come back to you with that. I just need to check to see if that is something that we want to put out there at this stage. And if it is, I will start to include that in our next call.
Yes. I’m guessing it is somewhere between 10% and 20%. I just haven’t calculated all.
I think it is safe to say it is on the high end of that range.
Okay, sure. Just in terms of, you mentioned in your letter to the shareholders that you are doing business sounds like this developmental business so far with some cloud players, I guess, probably Microsoft and Amazon but you can’t mention the names. I’m not so much interested in the names as how does Innodata’s accuracy and proficiency? How would it play out with companies like that? Can you give us a little more color on exactly what would be the skill set that would make them want to do business with you?
Sure, Tim. Well, I think there is a lot and it starts with kind of a fundamental appreciation for the fact that AI algorithms are essentially programmed with data. And they perform better and perform better faster, meaning you get better performance, the higher the quality data is all other things being equal. So when we are able to provide AI training data to any company that is of higher quality than they are accustomed to receiving or able to get from other places. What they do experience is that their AI performs better and it gets there faster. That enables them to begin new initiatives and attain the results of the algorithm faster, so there is a pretty significant return for them there.
Right. And from a negative point of view, since apparently 80% of the failures are from bad data, it is an insurance policy that they are not wasting money on these projects.
Yes, for sure. I mean, that is an excellent point. And I think what we are seeing in the market too, and I made this point just a few minutes ago, is that the technology is becoming more stable, becoming more available, better tested. People are now seeing - the early adopters are seeing how potentially these technologies could be used within their businesses. But they read statistics, much like the ones you just cited, that are scary, like, well, a lot of projects fail. Now, why do they fail? If they read a little further into those studies, what they find is that they fail because the training data isn’t accurate. If you train AI with bad data, you will get bad results; it is a garbage in, garbage out phenomenon. But even more so, because you can’t easily extract that bad training, it is like you pour a little bit of poison into a chalice, well, then you need twice as much to dilute that and that becomes invidious. So, having consistent high-quality data is critical to achieving or exceeding the expectations, the ROI expectations that people have with the technology.
Right. So on a social media deal, the agility, so I know that your initial quota, and this is kind of a crude goal, is 400,000 revenue annually per salesperson. And I’m just thinking, well, if you scale that back and say you get 200,000, and then you divide it into a quarter 50,000, you multiply that by 50, 60, or 2.5 million to three million. And with 89% gross margins, that would bring that division closer to break-even to profitability. I mean, are my numbers correct or is that the way you are thinking about it?
Well, yeah, I think that you can take a version of those numbers or get to where you are going even with that. I think we look at the revenue increase in agility this quarter, of 13%. Okay, but if you look at the reason I shared - if you look at the increase in bookings quarter-over-quarter, that was 130%. And bookings are an early indicator of revenue growth. Now, you have to book the business, you booked the business, and then you start billing for a subscription, you know, 112 per month of the value of that subscription. But that is both business. So, in the utility business, we look very, very carefully at bookings. We look at our backlog or annual recurring revenue in the business, but bookings are the earliest leading indicator. And I’m very happy with them under 30%.
Sure. And you have got a first-class guy running that division, and you have got really good salespeople who are working in it, right.
Well, yes, and Martin is doing a phenomenal job. Martin is leading the charge, of course, Tom specifically is leading the sales teams and he has great experience that he has brought to the table. We are starting to see all the upside that we expected to see when we brought him onto the team, but you don’t go away from Bourbon that we have got a great executive team right now. And, you know, we are firing on all cylinders and it is an exciting time at the company.
Now, I know you got the - right so far the major relationship is with the proverbial social media company. I guess Facebook doesn’t matter from my vantage point, but what is the key service that Innodata is bringing to that company? Why are they doing business with Innodata?
So we are working across several engineering teams, working, I believe now on about 17 or 18 different specific AI initiatives. What we are bringing them is consistent, high-quality data that is enabling them. We saw just a week ago, they shared with us that the algorithms we are working on are performing better and faster than they expected. And that is exactly what we want to hear.
Right. Well, listen, that is as much as I need to know at this point. But a great quarter. It sounds like we are on plan. So keep going. Thank you.
Thank you, Tim.
We will hear next from Dana Buska at Feltl.
I have just a question around - my first question is around the client that says switching between models and your services? Could you talk a little bit more about that? Is that something that you might expect happening going in the future that clients move from model-to-model with your services?
Sure. So, I guess, I can kind of conflate my answer to your question with what I just said to Tim because it applies equally. If we get to a level of accuracy and efficacy faster, with less data in a model, then we can start other initiatives, arguably, within the same recurring program, within the customer’s budget. So you can imagine customers certainly embrace the idea of being able to do more for the amount of money that is budgeted around technology. So we view that in the medium and long term as a great thing, as bragging rights that we will be talking to others of our customers about, like, well, here is what they got to achieve based on working with our data. They exceeded their plans and were able to deploy efforts and funds to other things and reallocate. That is a tremendous calling card for us.
That sounds wonderful. Now, one other question, you have announced a lot of your - in your press release had a lot of new customers. Could you talk a little bit of what percentage of those are like data annotation and what part of them are for agility?
I would love to share that and we will probably add that as a metric that we will track or that we will share going forward. I don’t have that in front of me. What I do have in front of me is that we added 121 new logos in the quarter, a 96% increase over Q1 2020, and a 30% increase over Q1 2021. So, it goes along kind of what I’m saying about bookings; these are early indicators of revenue growth that we are very happy to share.
Okay, great. That is fine. Along those lines, when we look at your business, how do you see the data annotation segment of the business going in terms of, is that where you are really excited about or is it like the whole all your different business lines? Could you talk a little bit more how that data annotation fits into all your other businesses?
Sure, so yes, I mean, I try not to choose what I’m most excited about. I’m faced with the problem of being excited about everything that we are doing right now. So, I can’t really choose one over another. I think the way to think about what we do is as three tiers; you know, like, you might imagine a wedding cake having three tiers. So there is data annotation, which is programming AI models, that is the first tier. The second tier is deploying those models or building those models, managing those models for other people, or enabling people to access our models. And that second tier, we think of as enabling AI, applied AI, if you will. The third tier is we are building our AI algorithms that we have trained, deployed, managed, we are building those into applications to help take kind of legacy workflows, things that people have done for lots and lots of years the same way they have always done it. But where we get to reimagine how they work, and the way that their work can be augmented through this technology. Where we see an opportunity that can go, you know, outside of the single customer, but really across the market. That is super cool. And we will build what we call an industry solution around that. So, I’m excited about that architecture because I think it enables us to target enterprises, regardless of the investments that they have made to date in AI, and provide value to them that results from AI. Is that helpful?
Yes, that is just a clarifying question. Where would you put agility in an index and in your wedding cake analogy?
Agility in an index are in the third tier, so there patients in which we have encapsulated AI to create a better outcome, better value for our customers. The banking application that I have referred to is going to be another industry solution, third tier level thing, you know. So by subscription, the AI is the engine under the hood, but you are selling it, holistically, as an application. It is a cloud-based SaaS application that performs as well as it does by virtue of the AI second tier - which performs as well as it does by virtue of the first tier, which was the high-quality data that we used to train it. So these all three are very intimately related and benefit from each other.
Okay. With your banking application, are we going to see that revenue in the digital data solutions, how are you going to break out as separate business units? How are you thinking about that?
We will see that in digital data solutions.
Okay, great. Excellent. That does it for me. Thank you so much for answering my questions. And congratulations on a wonderful quarter.
Dana, thank you.
We will take a follow-up from Tim Clarkson.
Jack, one, this is sort of more of a complaint than a question. But I was getting a rental car in Florida. And honestly, it was taking maybe 5 minutes to 10 minutes, sometimes 15 minutes, the process of simple rental car deal. And I got to believe there is got to be an AI way of turning that it should be a one minute or a two-minute deal. Where you ask the critical questions, what are the critical questions that need to be asked, and the rest of that should be able to be processed quickly. I mean, there is still so much inefficiency out there that just cost money and it just aggravates everybody. So, I mean, it goes everywhere, things as simple as getting a rental car. So that is just a comment.
Tim, I’m sorry to hear about your experience, but I’m relieved that your complaint has nothing to do with the service or the product that we are providing. But your point is absolutely well taken. And I will respond to it with two observations, the first of which is that we are in the earliest of innings. We have got applications that are AI-based applications on our phones, our Siri is in essence, an AI-based application, recommendation engines or AI-based engines. But we are at the very early stages of this. I’m firmly committed to the belief that AI is going to be in everything that we do in the next several years. Now, if we take your example, how might you reinvent that experience with AI? It is pretty obvious. You could have computer vision algorithms that are understanding who is in line, facial recognition, you could have robotics and applications that are bringing your car to you, you could have the image recognition algorithms that are helping you check in that car to validate that there is no damage and to make that seamless. And of course, we are talking about an industry that itself might be supplanted when AI-based autonomous driving becomes more available. So clearly, you are describing an experience that is there to be re-imagined. And these technologies I believe will help do exactly that.
And Mr. Abuhoff, that does conclude today’s question-and-answer session. Sir, I will turn it back to you for any additional or closing remarks.
Thank you. Closing remarks. Well, there was a character on a mid-1980s television show that I used to watch who would regularly say, I love it when a plan comes together. And these days, that is exactly how we feel at Innodata. Our plan was to position our company kind of front and center from the data-centric AI paradigm, which is all about data engineering, our specialization. We are ideally suited to help these leading businesses embrace AI to do more with less to carry on business with less dependency on human staff, that we all know has become more and more difficult to retain and recruit. This aligns well with the economy and the challenges presented by what people are calling the great resignation. And of course, it aligns well with market projections and analysts who declared AI to be at the heart of the next fundamental technology revolution. I want to emphasize it has just been eight weeks since we last recorded, but we literally had pages of progress to report just from that eight-week interval. Our strategic position, coupled with these investments that we are making that I have described, is showing the returns we saw when we committed to them. So it is an exciting time. And again, thank you for joining the call today and I look forward to our next call.
Ladies and gentlemen, this does conclude today’s Innodata First Quarter 2022 earnings release conference call. We thank you all for your participation. You may now disconnect your lines and we hope that you enjoy the rest of your day.