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. [Operator Instructions] As a reminder, today’s session is being recorded. And now for opening remarks and introductions, I’m please 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, continue downturn in or depress market condition, whether as a result of the…

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 and our Q4 call, we have 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 revenue growth over the 20% we experienced in fiscal 2021. In the current quarter Q2, one of our largest customers is a 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 three allocations ramp up. Therefore Q2 may have a lower growth rate likely in the range of 18% to 24%. But it 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…

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. [Operator Instructions] 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 kind of 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 we are 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 gust I 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. Okay and 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, casino proverbially probably Microsoft, and Amazon. You can’t mention the names. I’m not so much interested in the names as how does enter data’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 they 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 their accustomed to receiving or able to get from other places. But they do experience is that their AI performs better and it gets there faster. And that enables them to begin new initiatives, enables them to obtain 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 if they try to, 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 bed training, it is like you pour a little bit of a 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 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 that is what will 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, and I think that you can take a version of those numbers or get to where you are going even the issue with that. I think, we look at the revenue increase in agility this quarter. 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 that you have to 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 phenomenal job Martin is leading the charge, of course, Tom specifically is leading the sales teams and he has got great experience that he has brought to the table and we are starting to see the all the upside that 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 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’m believed now on about 17 or 18, different specific AI initiatives. And what we are bringing them is consistent, high quality data that is enabling them. And we saw it, just a week-ago, they shared with us that the algorithms we are working on are performing better 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. Sounds like, we are on plan. So keep going. Thank you.

Thank you Tim.

[Operator Instructions] 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 here 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 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, they were able to deploy efforts and funds to other things and reallocate. That is 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 like agility [indiscernible]?

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 - as I said, added 121 new logos in the quarter 96% increase over Q1 30%, or, excuse me, Q1 2020, 30% over Q1 2021. So, 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 fines. Along those lines, then would 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. And I’m faced with the problem of being excited, about everything that we are doing right now. I’m excited about some things we are talking about doing right now. So, I can’t really, you know, 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. 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 here is we are building our AI algorithms that we have trained, deployed, manage, 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 re-imagine how they work, and the way that their work can be augmented through this technology. And where we see an opportunity that can go, you know, outside of the single customer, but Reno really across the market. That is super cool. And we will build what we call, an industry solution around that. So, I’m excited, but 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 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 intimidating send index or that 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, you are selling it, there is an application, it is a cloud based of 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 year which was the high quality data that we use 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 a separate business units? How 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 for a wonderful quarter.

Dana, thank you.

And 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 is the critical questions that need to be asked, and the rest of that should be able to be processed quickly. And 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. But there is still tremendous inefficiencies out there that are really are difficult. So with that, I will comment on that or not so.

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 series is in essence, and 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 can 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 any data. 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 it is 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. And I just want to emphasize, it is been just a eight-week 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, are 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 will be looking 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.