Good day, ladies and gentlemen, and thank you all for joining us for this Innodata Q1 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. Now for opening remarks and introductions, I am pleased to turn the floor over to Amy Agress. Please go ahead.
Thank you, Jim. 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 Q1 . 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 statements include, without limitation, any statement that may predict, forecast, indicate, or imply future results, performance, or achievement.
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 the 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 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, 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 in 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 use of and reliance on information technology systems, including potential security breaches, cyberattacks, privacy breaches, or data breaches that result in the unauthorized disclosure of consumer, customer, employee, or company information or service interruptions. 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've shifted our earnings releases and investor conference calls to aftermarket 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 and expand strategy delivering results. We're pleased to announce a strong Q1 , 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 Q1 in anticipation of this transition, and we expect a portion of revenues from this program to shift into the Q3 and Q4 as the reallocations ramp up. Therefore, Q2 may have a lower growth rate, likely in the range of 18%-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. 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 we added on average per quarter in 2020, and 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 feed of annotated 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 validated for us the opportunity to partner with large consulting firms. It also validated the opportunity to create native AI offerings by combining capabilities. For this customer, we're 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're 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 nurse 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 Q1 , we saw lots of our seeds germinate, booking business expansions with dozens of our existing customers. I'll 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 with 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 estimated the revenue value of their subscription to be about $200,000 per year. Based on expansions over the last eight weeks alone, we now estimate the value to be 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 in social media. A key area of focus for us strategically is on AI in the enterprise.
In the last eight weeks, we've seen a number of our enterprise seedlings germinate and flower. A large billion-dollar e-commerce website has agreed to sign with 30% additional scope over the $300,000 initial commitment they made in the SOW we signed with them last year. We can tell similar stories of 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. Again, I emphasize, all in the last eight weeks. We've talked before about the great strides we're 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's 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 the AI models require continuous training given 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. These were both multi-billion dollar financial services companies.
We're feeding this 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. In 2022, we anticipate further increasing sales and marketing spend by approximately $10.4 million or 75% year over year. This year's 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 3-5x 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 clearer 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 GoldenGate 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 $1 million 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 PyTorch algorithms. Many experts in AI now clearly say that the neural network architecture is basically a solved problem, and that for many practical applications, it's 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, tools to augment data and collect data more efficiently, and tools to select the best data to annotate next in order to maximize system accuracy while minimizing annotation cost.
The 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's 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, has 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're 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 on bringing this product to a wider market next year. We've 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. 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 of cash and no debt, we do not presently expect to need external financing to execute our plan. What's particularly exciting is to see our products and services win in 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 wanna 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 a fourth consecutive quarter as a momentum leader by software review site G2, this latest time in its spring PR software report. We're continuing to invest in the product to increase its ASP and the value it delivers to customers.
As a result of the expansion with a 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 since that is top of mind for a lot of investors. There is talk of recession, stagflation, capital markets dislocation, the Great Resignation, et cetera. 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. 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 secular demand for our offerings in these challenging times. The growth of our Synodex business is illustrative of this phenomenon. We're putting our Goldengate 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 digital transformation of underwriting and embracing AI and concluding that the Synodex platform is an ideal tool for enabling this transformation. As a result, our Synodex year-over-year growth was 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. Simply stated, people reading complex documents too expensive, our platform solves this. People that read complex documents hard to find or hard to retain, again, our platform can be an answer to this as well. 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'll now turn the call over to Marissa to go over the numbers, and then we'll 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 32% year-over-year. Net loss for the quarter ended March 31, 2022 was $2.8 million or $0.10 per 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 Q1 of 2022 compared 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. Ladies and gentlemen joining today over the telephones, at this time, if you would like to ask a question, simply press star and one on your telephone keypad. Pressing star and one will place your line into a queue, and we will take your questions one at a time. Also, a friendly reminder that if you're joining us today on a speakerphone, please return to your handset prior to pressing star and one to be certain that your signal does reach our equipment. We'll pause for just one moment to assemble our queue. Again, everyone on the phones, I would like to remind you that if you are joining us today on a speakerphone, please return to your handset prior to pressing star and one to pose your question. We'll hear first from the line of Tim Clarkson at Van Clemens.
Hey, Jack. You know, great quarter, obviously. Pleased with the revenue growth, and obviously there's lots of exciting things going on. One, just a kind of a technical question. When 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?
You're talking about, you know, if we go out.
30% EBITDA, what are we looking at on an after-tax basis?
Yeah, Tim, I'm gonna come back to you with that. I guess we need to check to see if that's, you know, if that's something that we wanna put out there at this stage. If it is, I'll start to include that in, you know, in our next call.
Yeah. I'm guessing it's somewhere between 10% and 20%. I just haven't calculated it out.
I think it's safe to say it's on the high end of that range.
Okay. Sure. Okay.
Yeah.
Just in terms of, you know, you mentioned in your letter to the shareholders that you're doing, you know, business, it sounds like just developmental business so far with some cloud players, I guess, 'cause, you know, proverbially probably Microsoft and Amazon, you know, you can't lose these names. I'm not so much interested in the names as, you know, what. 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 wanna do business with you?
Sure, Tim. Well, I think there's a lot. You know, it starts with kind of a fundamental appreciation for the fact that AI algorithms are essentially programmed with data. They perform better, faster, meaning you get to better performance the higher the quality data is, all other things being equal. When we're able to provide AI training data to any company that is of higher quality than they're 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. It enables them to obtain the results of, you know, the algorithm, faster. There's a pretty significant return for them there.
Right. From a negative point of view, since, you know, apparently 80% of the failures are from bad data, it's an insurance policy that they're not wasting money on these projects.
Yeah, for sure. I mean, that's an excellent point.
Yeah.
I think what we're 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. You know, the early adopters are seeing how potentially these technologies could be used within their businesses. 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, you know, 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's a garbage in, garbage out phenomenon.
Even more so because you can't easily extract that bad training. It's like you pour a little bit of, you know, poison into a chalice. Well, then you need twice as much of to dilute that, and it becomes invidious. Having consistent high-quality data is critical to achieving or, you know, exceeding the ROI expectations that people have of the technology.
Right. On the social media deal, the Agility, I know that your initial quota, and this is kind of a crude goal, is, you know, $400,000 revenue annually per salesperson. 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, you're at $2.5-$3 million. With 89% gross margins, that's what will bring that division closer to breakeven to profitability. I mean, are my numbers correct, or is that the way you're thinking about it?
Well, yeah. I think that you can take a version of those numbers or get to where you're going even easier than that. I think, you know, if we look at the, you know, revenue increase in Agility this quarter, you know, 13%, okay. But if you look at the increase in bookings quarter-over-quarter, that was 130%. The reason I shared, if you look at the increase in bookings quarter-over-quarter, that was 130%. Bookings are an early indicator of revenue growth.
Okay.
In other words, you have to book the business, and then you start billing for a subscription, you know, $112 per month of the value of that subscription. But that's booked business.
Right.
you know, in the Agility business, we look very, very carefully at bookings. We look, you know, at you know, annual, you know, our backlog, our annual recurring revenue in the business. bookings are the earliest leading indicator and, you know, I'm very happy with that 130% number.
Sure. You've got a first-class guy running that division, and you've got really good salespeople who are working in it, right?
Yeah. You know, Martin's doing a phenomenal job. Martin's leading the charge, of course. Tom specifically is leading the sales teams, and he's got great experience that he's brought to the table, and we're starting to see the, you know, all the upside that we expected to see when we brought him onto the team. You know, I'll go even broader than that. I've got a great executive team right now and, you know, we're firing on all cylinders, and it's an exciting time at the company.
Right. 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. What's the key service that Innodata is bringing to that company? Why are they doing business with Innodata?
We're working across several engineering teams, now on about 17 or 18, you know, different specific AI initiatives. What we're bringing them is consistent high-quality data that's enabling them. We saw it, you know, just, you know, a week ago, you know, they shared with us that the algorithms we're working on are performing better, faster than they expected. And that
Wow.
You know? Exactly what we wanna hear.
Right. Well, listen, you know, that's as much as I need to know at this point. A great quarter. Sounds like we're on plan, so keep going. Thank you.
Thank you, Tim.
Once again, ladies and gentlemen, that is star and one if you have a question. We'll hear next from Dana Buska at Feltl and Company.
Hi, Jack.
Dana, Good afternoon.
Congratulations on a wonderful quarter.
Thank you.
It sounds like things are going extremely well for you.
Well, thank you so much. They are indeed.
I have a question around, or my first question is around, the client that's switching between models, your services. Could you talk a little bit more about that? Is that something that you might expect happening going in the future that the future that a client move from model to model with your services?
Sure. You know, I guess I can kind of conflate my answer to your question, what I just said to Tim, because it applies equally. You know, if we get to a level of accuracy and efficacy faster with less data in a model, then we can start other initiatives, you know, arguably, you know, within the same recurring program within the customer's budget. You can imagine customers, you know, certainly embrace the idea of being able to do more for the amount of money that's budgeted, you know, around a technology.
You know, we view that in the medium and long term as a, you know, great thing as bragging rights that we will be talking to others of our customers about, like, you know, well, here's what they got to achieve based on, you know, working with our data. They exceeded their plans. They're able to deploy efforts and funds to other things and reallocate. That's, you know, a tremendous calling card for us.
That's it. Yeah, that sounds wonderful. Now, one of the questions, you've announced a lot. In your press release, you had a lot of new customers. Could you talk a little bit about what percentage of those are like data annotation and what part of them are for like Agility and Synodex?
You know, I'd love to share that, and we'll probably add that as a metric that we will track or that we will share, you know, 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. You know, goes along kind of what I'm saying about bookings. You know, these are early indicators of revenue growth that, you know, we're very happy to share.
Okay. Okay, great. That's 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're really excited about, or is it like the whole your all your different business lines? Could you talk a little bit more how the data annotation fits into all your other businesses?
Sure. Yeah, no. I mean, I try not to choose what I'm most excited about because then I'm faced with the problem of being excited, you know, about everything that we're doing right now.
Uh.
I'm even excited about some things we're talking about doing right now. 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. There's data annotation, which is programming AI models. That's the Tier-1 . Tier-2 is deploying those models or building those models and managing those models for other people, or we're enabling people to access our models. That Tier-2 we think of as, you know, enabling AI, applied AI, if you will. The Tier-3 is where we're building our AI algorithms that we've trained, deployed, managed.
We're building those into applications that help take kinda legacy workflows, things that people have done for lots and lots of years in the same way they've 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 the single customer, but, you know, really across the market. That's super cool, and we'll build what we call, you know, an industry solution around that. You know, I'm excited about that architecture because I think it enables us to target enterprises regardless of the investment that they've made to date in AI and provide value to them that results from AI. Is that helpful?
Yeah, that's helpful. Just a clarifying question. Where would you put Agility and Synodex in your wedding cake analogy?
Agility and Synodex are that Tier-3 .
Okay.
Applications in which we've encapsulated AI to create a better outcome, you know, better value for our customers. The banking application that I've referred to is going to be another industry solution, Tier-3 level, thing, you know, sold by subscription. The AI is the engine under the hood, but you're selling it in, you know, holistically. You're selling it as an application. It's a cloud-based, you know, SaaS application that performs as well as it does by virtue of the AI, Tier-2 , which performs as well as it does by virtue of the Tier-1 , which was the high quality data that we use to train it. These all three are very intimately related and benefit from each other.
Okay. With your banking application, are we gonna see that revenue in the Digital Data Solutions or are you gonna break out as a separate business unit?
No, we're gonna see.
How are you thinking about that?
We'll see that in Digital Data Solutions.
Okay. Okay, great. Excellent. Well, that does it for me. Thank you so much for answering my questions, and congratulations again on a wonderful quarter.
Dana, thank you.
We will take a follow-up from Tim Clarkson.
Hey, Jack. You know, 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 5-10 minutes, sometimes 15 minutes to process a simple rental car deal. You know, I gotta believe there's gotta be an AI way of, you know, turning that in. It should be a one-minute or a two-minute deal, you know, where you ask the critical questions or, you know, what's the critical, you know, questions that need to be asked, and the rest of that should be able to be, you know, processed quickly. I mean, there's still so much inefficiency, you know, out there that just, you know, costs money and it just aggravates everybody.
It goes everywhere in things as simple as getting a rental car. That's just a comment, but you know, there's still tremendous inefficiencies out there that really are difficult. With that, you can comment on that or not.
Well, Tim, I'm sorry to hear about your experience, but I'm relieved that your complaint has nothing to do with the service or product that we're providing. Your point is absolutely well taken. I'll respond to it with two observations. The first of which is that we're, you know, in the earliest of innings. You know, we've got applications that are AI, you know, based applications on our phones, you know. Our Siri is in essence an AI based application. Recommendation engines are AI based engines, but we're 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, well, you know, How might you reinvent that experience with AI? I mean, you know, it's pretty obvious. You could have, you know, computer vision algorithms that are, you know, understanding who's in line, you know, facial recognition. You could have robotics and applications that are bringing your car to you. You could have, you know, the, you know, image recognition algorithms that are helping you know, check in that car to validate that there's no damage and to make that seamless. Of course, you know, we're talking about an industry that itself might be supplanted when AI-based autonomous driving becomes more available. So clearly it. You're describing an experience that's there to be reimagined. These technologies, I believe, will help do exactly that.
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, you know, 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." These days, that's exactly how we feel at Innodata. You know, our plan was to position our company kind of front and center for the data-centric AI paradigm, which is all about data engineering, our specialization. We're ideally suited to help, you know, these leading businesses embrace AI to do more with less, to carry on business with less dependency on human staff, that we all know have 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.
Of course, it aligns well with market projections and analysts who have declared AI to be at the heart of the next fundamental technology revolution. I just wanna emphasize, you know, it's been just eight weeks since we last reported, but we literally had pages of progress to report just from that eight-week interval. You know, our strategic position coupled with these investments that we're making, that I've described, are showing the returns we saw when we committed to them. So it's 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 Q1 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.