Okay. I think we're ready to kick things off. So I'm DJ Hynes. I'm the senior software analyst here at Canaccord. This is the 44th year we've done this conference, so thank you to the companies that participate and bring all the content, the investors that show up and ask smart questions. We couldn't do it without you guys, so we appreciate it. We have iLearning Engines here with us this morning. Harish, the CEO, is joining via Zoom. These guys are doing some really interesting stuff, leveraging AI for corporate knowledge management and learning automation. I think the plan is Harish is gonna run through some slides to give everyone an introduction to the business, and then we should have time at the end to field some Q&A. With that, Harish, hopefully you can hear us, and I'll kick it over to you.
Sure. Thank you. Thank you, everyone here. That it's a real pleasure and an honor to be here to present iLearningEngines. You know, my apologies, I wish I was able to be there in person, but just had some conflicts and... But it was a real pleasure here to be there. You know, I just wanted to, you know, just start out by saying, you know, we just announced our quarterly results yesterday, and really for us, this was our Q1 as being a public company. You know, very significant milestones and achievements. You know, in April, we became a public company. We are de-SPAC with Arrowroot Corporation.
Since then, we were added to the Russell 3000 Index and its related indexes, and really secured an additional $20 million in funding, you know, just fund our growth plans here. So, you know, we generated $136 million dollars in revenue in the quarter. This was a 33%+ increase over last year, $4 million in Adjusted EBITDA. Added 100 new end customers and 176,000 end users. You know, so at its core, what we are, and we are a leading applied AI platform for learning and work automation. You know, we enable enterprises to rapidly productize and deploy a wide range of AI applications and use cases.
You know, people talk about as AI agents or, you know, we call them hyperautomation apps or AI engines, but really help them do this at scale. And our platform is really powered by proprietary, very vertical-specific AI models. So we have a strategy of verticalization and a No-Code AI Canvas that really drives rapid out-of-the-box deployment, while really offering very low latency, high levels of data security, sovereignty, and compliance. You know, in sum, we are a pure-play AI company. Our AI platform is really solving real customer problems today. You know, we're a company at scale. You know, we've been solving use cases and problems well before the gen AI craze. And it's been over four years, five years now, since we've been delivering AI solutions for learning and work automation.
Now, we built our proprietary AI technology and vertical-specific, what we call Enterprise Language Models, across various vertical markets. So these are dedicated models per vertical, including healthcare, education, insurance, retail, energy, etc. And really, our platform allows enterprises to connect to all the different systems in the enterprise, collect the content and data that is in there and put it all into the Knowledge Cloud. That AI Knowledge Cloud and our No-Code AI Canvas then powers various agents and use cases to solve high-impact problems for our customers. And really, what we are doing is helping companies make better use of their data and institutional knowledge, right? These AI engines can be deployed very quickly.
They can be dealt with in a matter of weeks to months, versus, you know, what we see mostly in companies is really internally developed custom AI solutions that take longer, millions of dollars, and years to do. We've been doing this at scale. You know, we generated, in the end of, last year, in 2023, over $420 million in revenue. You know, much of our revenue is recurring revenue, so 97% of that. A strong Net Dollar Retention. At the end of Q2, we were at close to 130. You know, our contracts are all typically long-term contracts, you know, ranging from 1 to 3 years. They're all long-term contracts. You know, had a very track record of strong growth.
You know, we have more than 1,000 enterprise end customers, 4.9 million licensed users. And really, for us, since 2020, we have been adjusted EBITDA positive. So, you know, we became profitable well before it became fashionable to be profitable. Okay? Next slide, Farhan. And sure. So, you know, we play at the intersection of three very large markets. You know, the global artificial intelligence market, $hundreds of billions, at the global e-learning market, as well. You know, these are all- and the market for hyper-automation. So these are really large markets growing fast, but also with very strong tailwinds. Sorry, I just had an interruption.
No worries, no worries.
Okay. Perfect, I got it. Yeah, thank you. And so, so for us, we have. These are, you know, large industries, very strong tailwinds, and for us, we really have an opportunity here to build a category-defining company. Next slide. You know, in many ways, the AI industry, from our perspective, parallels the SaaS industry from, you know, 2010 onwards. And, you know, as the AI market evolves much like the SaaS market does. We think we have a chance to be an important player, you know, similar to the company that were created at that time, like Workday, Salesforce, ServiceNow. Next slide. So we have always been about operationalizing AI for the enterprise. You know, when we go into any enterprise, you know, we see solutions in three broad buckets.
On one end of these small point solutions that really for the enterprises are a challenge because, you know, they go through the whole pain of doing an implementation, and all you get is really solving a small problem. On the other side, which is what we see almost always, is highly complex custom in-house development. You know, this is a do-it-all yourself AI infrastructure, so built on top of, you know, an AWS or an Azure with a large language model and a data infrastructure, but really requires very expensive AI programming. You know, we feel like we are having the sweet spot here. You know, everything is out of the box for us, everything is configurable, so our platform gets deployed in an enterprise, we're up and running in 8-12 weeks.
It comes packaged with, you know, existing use cases for a vertical. And then, companies can add new verticals and new use cases in a matter of weeks to months because there's no expensive programming. It can be configured. You don't need AI engineers to do this, you can do this with business analysts. And so we see the ability here for enterprises to be able to deploy use cases at scale, and I think that's really what they're looking for. And everything can be very ROI-driven, so if you find that a use case has very strong ROI, you can scale that up. If it's not working, you can shut it down. And so that really gives this opportunity. And this is what enterprises are looking for.
I mean, almost every CEO of an enterprise, you know, their main challenge is: How do I bring AI into my enterprise? What does AI mean for my business? And really, an option that is being presented to them is, "Hey, you must use AI and spend millions of dollars and, you know, otherwise, you're gonna be left behind." And that's not how enterprises make decisions. Next slide. So, you know, we talked about this before, but we are really about companies productizing their institutional knowledge, so leveraging their internal data and their knowledge. And like I said, we deploy our platforms, help create these Knowledge Clouds, and from the Knowledge Clouds, we are able to power the various workflows and applications inside the enterprise.
What's really important to understand is we have our own proprietary models that we have been building over time. You know, we have our own language models. We also have more than just language models, but functional models and decision-making models here. And these are really designed to be very efficiently fine-tuned within the enterprise. So these things, you know, really continue to get better and better over time. You know, for every vertical we are in, we're building our own models, and the big focus on that is make us very efficient, but also, you know, create very strong unit economics. You know, the ability to have this with very low inference costs and fine-tuning costs.
And then the other part of the ability for, ability for this to run on-prem, you know, data sovereignty is a very important thing for most enterprises. And really, so we really built this for the enterprise. You know, we also have an MLOps feature that allows enterprises to bring in a large language model like this. So please, now, today there is a surplus of models, some models do better on certain functions versus others. So this is the ability for this to be able to route functions to an appropriate model if they need to, et cetera. But ultimately, like I said, this was being designed for the enterprise, for really knowledge automation, learning, and work automation. Next slide.
Now, some of our other capabilities that we have around this is, you know, the ability for our AI, and we have spent over 30% of our revenue on R&D. And the ability for us to take content of any kind from within the enterprise, convert them into learning artifacts by scanning paragraphs, generating questions, looking for reviews, having the answers. So really building at scale, learning artifacts for learning automation, being able to do this for documents, audio, video, et cetera. And really, the ability to embed AI tags and really make all the content and data AI-ready is really what this is talking about, and really be able to generate insights and even some recommendations.
So why this is important is because we are able to connect to all the systems in the enterprise, and through those systems, we can extract, process gaps and human competency gaps, and then these learning prescriptions can be delivered to remedy those gaps. So that's a really strong example of, learning automation, for example. Next slide. Like I talked about, you know, once we created this Knowledge Clouds, our No-Code AI Canvas is really able to automate these workflows. So at its core, like I talked about, this is about building use cases around learning and work automation, at scale, and being able to do so at a, you know, significantly disruptive, price point and also from a time to, production rate. Next slide. You know, a core element of our go-to-market strategy is verticalization. Most...
There is a, you know, in software, typically, you would see people taking a horizontal approach. But when it comes to AI, I think understanding an enterprise deeply is very critical. So for every vertical, we have dedicated enterprise models. So these are language and decision-making and functional models created for that vertical. So for if you think about insurance, you know, you can have out-of-the-box dedicated model for that vertical, but also use cases. Use cases such as claims processing, claims intake, smart risk management, loss prevention. If you are in healthcare, this could involve disease management programs for, you know, the big health conditions like diabetes, heart disease, et cetera, or care orchestration, care management, et cetera. And so for every vertical, you're coming out of the box.
Why this is important is, let's say you go to an insurance company with these models, we know that almost every insurance company will need many of these models. So these apps are already production-ready. It is, you know, and so from our standpoint, it, you know, this is about... From an enterprise standpoint, it's not about just, you know, experimenting on gizmos and things like that, but really talking about day-to-day meat and potatoes problems that we're solving. Next slide. You know, we are in 12 verticals today. You know, our strategy is to continue to add new verticals, while also going deeper into these verticals. What that really means is building new models to go to new verticals and use cases, and also adding use cases within our existing verticals and going deeper. So it's a very much a very replicable rinse and repeat process.
You know, we can think of AI from our perspective as, you know, there's a software layer, and then there is a data and a model layer, and the software layer is the same for all verticals, and the dedicated models per vertical really allows you to go deep within that vertical. We think the real strategy for winning is verticalization at scale. Next slide. Our go-to-market is really designed to support our verticalization scale strategy. So we work closely with value-added resellers who are building these solutions on our platforms. So these value-added resellers are bringing all the domain expertise. So when we are in, say, healthcare, we would work with value-added resellers in healthcare who are good at building disease management and care orchestration solutions. If you are in insurance, we would work with value-added resellers who build those solutions.
So they bring the domain expertise. We work with them to build these models and these, solutions. You know, those solutioning and the models are often sometimes co-developed, or we provide the support to the value-added resellers to build this. And why this is, and each of these resellers has several hundreds of enterprise end customers. And so, you know, from our standpoint, we, we don't want to have our own professional service and solutioning arm. We work with these value-added resellers instead, and we focus on our, ability of building these enterprise models and, you know, the platform. You know, Andrew Ng at, Google talked about electricity, AI being the new electricity, period. And I think we find some truth in that.
And if you think about it, the earliest producers of electricity didn't go around knocking on businesses' doors and asking: "Hey, would you like kilowatt hours of electricity to buy?" They instead went to appliance makers and, you know, heating system, air conditioners, et cetera, and really built solutions, these appliances powered by electricity, right? And so these value-added resellers, from our perspective, are the appliance makers, powered by AI. Next slide. Like I mentioned, these value-added resellers have several hundreds of end customers, and we power over 1,000 enterprise end customers. Some of our major verticals we are in are education, healthcare, enterprise hyperautomation, and workforce upskilling. Our top four value-added resellers contribute about 52% of our revenue today.
You know, this was a number of around 70 a few years ago, but we keep steadily bringing it down. But, like, we do not have any end customer concentration here with these things. Next slide. You know, just wanted to summarize here. You know, we have a very deep global enterprise base here, thousands of over a thousand end customers. Our contracts are all between 1-3 years, really showing how important we are to these enterprises.
Strong net dollar retention, a recurring revenue base, and, you know, today, we power over 4.9 million licensed users, and like I said, continue to have very low growth churn and high net dollar retention. We are a very sticky system, you know, when the more. As people build these hyperapps and we get integrated with the workflows, we become very hard to replace. With that, I'll stop here, and just open it up for any questions from the audience.
Thank you, Harish. I don't know if there's any questions from the audience. I can ask a couple to tee you up.
Sure.
Give you guys a chance to think.
Sure.
Harish, maybe you can talk a little bit about competitive dynamics in this space, who you see directly from a competitive standpoint. And I remember, we covered C3 for a long time, and I remember Tom saying, you know, oftentimes these challenges, folks come at that and trying to do it themselves before they turn to a commercial vendor. Are there similar dynamics in this space where, you know, maybe they take a stab at solving the problem, you know, with piecemeal solutions and say: "Hey, this is really, you know, far more complicated than we thought," and then they turn to iLearningEngines? Or kinda how does that go-to-market motion work?
You know, that's, that's very right. You know, so we often see every enterprise we go to, they are always having, you know, they have a team of AI engineers, working in a particular group and, you know, doing a lot of experimentation. Nothing really going to production because it gets very, very expensive. So they do try to turn to people like us. You know, one of the big strengths that we have over almost any other player is this, everything is out of the box. You know, our implementation cycles are 8-12 weeks. If you look at it, companies that are doing it themselves, you know, we're talking about a spectrum of multi years that they take to build. They try to reach out to other vendors, but they too have pretty long implementation cycles.
You know, I think this ability for us to drive 8-12 weeks of implementation and building use case in a weeks to month is a huge strength for us, and I think that's really where we go, you know? So from our standpoint, most of the time, the competition we are seeing is do-it-yourself, built on top of, like I said, an AWS or Azure, plus a large language model, plus, you know, some kind of a Snowflake-type infrastructure. But having custom development built on top of that is what we often see. But yeah, I think our out-of-the-box capabilities, we think, is the key to success. You know, we have several times where enterprise look at this and say, "Okay, you know, we have these people working on this, but we really like what you have, and we're going to go with this." And that's a pretty much good enterprise buy-in.
Yeah, makes sense. I want to ask about net revenue retention. I mean, 130% is best in class, relative, especially what we're seeing in the current environment. Can you just talk about what drives such strong expansion in the base and maybe the sustainability of those types of metrics?
Right. So our net dollar retention for the past five years has been between, you know, 115%-135%, so it's been pretty consistently high. And, you know, a big part of it is, like I said, these use cases, right? And so nothing succeeds like success. So when we go into any enterprise, we are out of the box with these use cases, and so once they start seeing the success and ROI, they want to ramp things up, and we can-- we are delivering very strong results as a result. So that makes a huge difference for us. So, you know, once you have one group in an enterprise, you know, delivering learning and work automation, they look very state-of-the-art, other people want to do it. So it really builds those dynamics in.
So for us, like I said, our Net Dollar Retention is driven by expanding the number of use cases within a vertical and also adding... So that, I think, really helps drive Net Dollar Retention. I think it all comes down to execution at the end of the day, meaning the fact is, I think if you're able to be up and running in a matter of, like I said, 8-12 weeks, the business unit people can actually show success, right? But if you get stuck in long implementation cycles, then it's very hard to... You know, nobody else really wants to step on that stuff, and so I think that has been a real critical piece for us. The second thing is really quantifiable ROI, right, for these people.
So I think this is very much an ROI-driven way to do it, so I think that really helps us as well. And the third thing, right, what drives our Net Dollar Retention is, frankly, working with these value-added resellers who bring in this domain expertise. So when we go into, say, a healthcare company, they already know the challenges these organizations are facing. You know, we are not learning on the job here, and so that. And people really get the sense that we understand their business. The number of times we have customers telling us, "You know, you get what we do," really helps.
Yeah. Yeah. I'd love to hear a little bit more maybe about M&A strategy and how you think about inorganic growth complementing the business here.
Absolutely. And I think, you know, for us, you know, we have come here largely on a, you know, really robust organic growth strategy, but we also have made some very successful acquisitions. And so for us, that. And, you know, we made an acquisition almost a couple of years ago, and that was a huge success. You know, we took a business that was flat growth and pretty much turned it into a, you know, double-digit growth type situation. And really, the core metric is at the end of the day, and as you touched upon, Net Dollar Retention is the big driver.
So the key logic for us when it comes to M&A is being able to take businesses that have Net Dollar Retention sub 100s, often sub 90, build both our platform on top of it, on top of it, and turn it into a, you know, 120, 130 Net Dollar Retention business, right? And I think for us, that's really the goal. So, you know, we have a pretty strong M&A strategy. That was the reason why we went down the path as a De-SPAC, because we were working with a private equity-backed De-SPAC, and we felt like we can get a lot of the support or the expertise that we need in closing transactions, identifying opportunities. And there is a huge opportunity. There are tremendous numbers of companies in the $5-$20 million range that are looking for a home, right?
These are companies that have a legacy technology. They're barely servicing their existing customers. And so for us, this represents an opportunity for tremendous customer acquisition. You know, we are already... And then have built our platform on top of it and make them AI-powered, expand the offerings. That's really the playbook that we have in mind. You know, so it's really about around customer acquisition, vertical-specific customer acquisition, and potentially in some cases, you know, AI is a very exciting space, a lot of innovation happening.
Just, you know, and the core team here, we have, we have come from Silicon Valley, building microprocessors, you know, similar to companies like that, with company like Sun Microsystems, Intel, you know, much like what you're also doing with NVIDIA, et cetera. And so really bringing that ability and so identifying potentially an acquisition here or there that has a transformative technology as well. But majority, or I would say, 80%-90% of our M&A would be driven for customer acquisitions.
Yep. And maybe a final question, Harish, in the last minute we have here. Can you just talk about long-term expectations for growth and margins and what this business should look like over the next, you know, five plus years?
Absolutely. You know, so we, you know, this is an industry that is growing. AI, you know, AI is front and center for everyone today. I think this is here to stay. You know, the market itself is growing, you know, 20%+. Our goal is to be growing faster than the market, so definitely growth first is a critical part of it. But we've also been doing this in a very, you know, like I said, profitable way and just a little bit of while at least. And so for us, you know, today, there is, you know... So we'll continue to grow over the next five years, I think... But then also there is room for, improving, operating leverage, right? So our growth, we are a 70% gross margin business.
We think in the long term, we can grow from 70%-75%. R&D, you know, we're spending about 30% of our revenue on R&D. We think we can bring that down to 25%-27%. So as a percentage, we bring it down, but still in absolute dollars, we'll continue to invest. Sales and marketing, I think we're, you know, at closer to 30%. We can bring it down, you know, to a low of 30% or so, where we think there is leverage there also, to grow. But so I think there is room for margin expansion in the long term. But, you know, like I said, growth while being profitable or just a little bit profitable is our focus.
Yep, perfect. I think that's a great spot to lead it, leave it. Harish, congrats on getting iLearningEngines public, and congrats on the continued momentum in the business. Look forward to keeping tabs on progress. Thank you for doing this.
Thank you so much, and really, you know, looking forward to meeting you in person.