Good morning. Thanks, David, and thanks everyone for joining our AI in Action webinar, especially to our presenters, Tim Fung, Michael Ivanchenko, and Martin Filz. Our thinking behind this webinar is that AI has brought a lot of uncertainty into the equity markets, and while we can't predict where AI will take us, we thought it was worthwhile looking at how companies that RaaS covers are dealing with the threat, the challenges and the opportunities that AI presents. It's our view that smaller companies tend to be more nimble with technological change because management's closer to the coal face and can gauge and alter their businesses accordingly. It's also our view that some business models have moats that can help them with the onslaught of AI. We see data as a moat, networks and enterprise relationships as a moat.
I think we'll hear a bit about moats today, and so I'll hand back to David to get the presentations underway.
Thanks, Finola, and welcome again to everyone joining us today. Quick housekeeping before we get underway. This session is being recorded, and you will receive a copy of the recording to your email in the next 24 hours. Questions can be submitted through the chat, the Q&A function, which you'll see on your screen. What I would ask, given we have three presenters, is make clear who the questions are for and what companies they're directed to. The Q&A will be run at the end, but you can submit questions at any time. For those who are appropriate, you will receive one CPD point for your attendance today. Now, AI is clearly one of the dominant themes in markets right now, as Finola mentioned. The key question for investors is simple: Where is AI actually driving outcomes?
Not just experimentation, but real impact on revenue, cost, product, and ultimately, earnings. What makes today's session valuable is that we're looking across three very different business models, a marketplace, a regulated compliance platform, and a global data and insights business. Each company will present for around 10 minutes, followed by a live Q&A session at the end. Let's get started with Airtasker. This is an interesting place to begin because it challenges one of the core assumptions around AI, that it replaces labor. In Airtasker's case, the vast majority of jobs are physical, real-world services. The question becomes, how does AI enhance a marketplace like this rather than disrupt it? To walk us through that, I'll hand over to Founder and CEO, Tim Fung. Tim, over to you.
Thanks, David. Thanks everyone for joining us today. If we skip forward two slides. Just as a reminder, Airtasker is building the world's most trusted marketplace to buy and sell local services. Really simple business model. We connect people who need work done with people who wanna work. To the next slide. Our mission at Airtasker is to empower people to realize the full value of their skills. For us, creating jobs for humans isn't a byproduct of the work that we do, it's the core purpose. We've done over 1 billion in jobs now globally and now have passed over 5 million jobs completed in the Australian market. We are carving out a place in this marketplace ecosystem. If we move to the next slide.
I think one of the things that's really interesting about artificial intelligence is that it's gonna make productivity gains really powerful in the white-collar space. It's actually, you know, it's a little bit unintuitive, I guess, to ourselves from 10 to 15 years ago, but AI is really making things like white-collar jobs and jobs that can be done virtually and remotely really disrupting that space. 95% of Airtasker's jobs actually require physical and real-world skills, humans doing things with their hands, things like cleaning, moving, furniture assembly, handyman service, etc. As Jensen Huang, the CEO of NVIDIA says, you know, it's really gonna be the plumbers that win the AI race. W e move to the next slide.
Anthropic has done some really amazing research and released a labor market impact study. If you look at this spider web chart on the right-hand side there, you can really see that jobs like management, business and finance, computer and math, architecture and engineering are really the jobs that are gonna be impacted here with a huge amount, you know, close to 100% of those jobs potentially being covered by AI. In the red, you can see there what we're actually already seeing as the impacts there. That has been considerable in things like software engineering, et cetera. Real-world jobs, I think, have much lower exposure to automation.
As we mentioned before, things like handyman, installation of home garden maintenance, much less likely to be automated. Now, there is also a frontier of humanoid robotics, which I think is worthwhile calling out. We do think that over time, humanoid robotics could become something that is impactful on the labor markets. That said, we always believe that humans are gonna be on the frontier of the creativity and the things that are gonna be done to further humanity. Although some of these jobs, even in the physical world, could be disrupted eventually, new jobs are gonna be created that we can't even imagine right now. If we move to the next slide. There's Airtasker as a provider of actual labor services.
As we mentioned, I think that we are some way away from humanoid robots taking all those jobs 'cause they require a huge amount of physical in-person dexterity. That said, there's also the disruption of Airtasker or the potential disruption of various marketplace platforms and connectors between that. One of the things I think is worthwhile really looking at is the difference between those that are gonna come out with stronger moats post-AI disruption, and those that are gonna come out with weaker moats. There's a great article that Nicolas Bustamante drafted and shared, which sort of talks through 10 moats that are very common in businesses.
There's sort of, this, first list of potential moats in businesses like learned software interfaces, specific business logics that are created, or access to data which is ultimately publicly available. Those moats, because of the, you know, unlimited intelligence that you can get from, artificial intelligence agents, are gonna be largely weakened moats. However, there are some, moats in, businesses which, in an agentic AI world, are actually gonna get even stronger. Examples of that would be proprietary data, regulatory lock-in, and of course, network effects. I think one of the things that Airtasker’s really built on are three of these are key moats, which are network effects. Airtasker is a marketplace.
Really important that when you come to Airtasker, you post your job, you get the best quality offers, the best in the fastest possible time with the biggest range of possible services. We believe that's driven largely by having a highly liquid base of customers and taskers. The second thing is embedded transactions. Airtasker actually manages the payment. If you look at some of these sort of like lead generation websites which tend to broker information, you know, connect one person with another, I think those are gonna be very largely disrupted. What Airtasker does is actually takes the payment, provides the insurance and the assurance and the system of record, which makes sure that those services are provided to the level expected. The third thing is proprietary data.
One thing that Airtasker is sitting on is over 10 million reviews from customers, which is really insights into which tasker is gonna be best for which kind of job. By collecting that information and hosting that information for each of our taskers, we've really created a unique piece of inventory, which is very, very difficult to find anywhere else. As agentic AI makes the economy more productive, you're actually gonna see more of a need for accessing that kind of information, that kind of inventory. I think that's gonna bode very, very well for our business. If we move forward one further slide. It's also interesting to look at not just Airtasker as a business model.
We sort of started by looking at the services economy, and I think you know, humanoid robots are pretty far away from that. We then looked at Airtasker as a marketplace platform, and I think the moats in our business are actually potentially gonna get even stronger during this period. Then there's also Airtasker as an organization and how efficient we can make that organization. I think one thing to call out for Airtasker is although we provide our services via a piece of software, we don't actually sell software. In fact, Airtasker is a net buyer of software. As building software becomes faster and cheaper, we can actually deliver even more value to our customers without actually incurring as many costs.
I think that's actually gonna see a widening of margins in this kind of business. I would differentiate that from, say, a SaaS company where sometimes what you're selling is the actual software itself. The second thing that we've observed in our business that's really, really exciting is that artificial intelligence can help us use semantics and human-based language to get better at things like moderation in our community and to address platform leakage. A lot of these things were really hard to do with like deterministic models where you're trying to design algorithms to work out, you know, what is good behavior and what's bad behavior.
Using generative models, we can actually much more easily identify what intuitively is a good marketplace behavior that we want to encourage and what is marketplace behavior that we want to be able to address. AI has really helped us to be able to do that. Then the third thing is across our business, and so much of this is coming from the bottoms up, which is really, really exciting. AI is already driving huge operational improvements. For example, one aspect of Airtasker was that it's really difficult to identify customers at the precise moment at which they need a job done.
Using agentic AI, we can actually go and watch almost an unlimited amount of Instagram and TikTok reels and actually work out when is a customer actually in the market to buy a service. By being able to stay on top of that in real time in intuitive semantic human language, we can actually meet those customers at that exact moment with an offer or a discount, and we're seeing incredible conversion rates. It's also helping us with aspects of our business like marketing. Onto my last slide, and just summarizing here, I think in this if we can move forward a slide. This agentic AI era is really gonna compound some really important competitive advantages for Airtasker. First of all, network effects.
I think ultimately this is something that is a hugely powerful moat that we are gonna be able to build our business upon and makes very hard to disrupt. The second thing is embedded transactions. Airtasker actually running the payments and being part of that economic value exchange, very hard to disrupt. The payments provides us with the opportunity to be able to hold people accountable and provide that transparency and accountability you need to run a powerful marketplace. The third is proprietary reputation data. People create their profiles on Airtasker, giving us unique inventory that we can use to distribute the ability to buy local services through many of these agentic AI protocols and frameworks. Fourth is regulatory responsibility.
Airtasker provides a service to the tax office, for example, making sure that all of our users are ID checked, provide tax information, et cetera. This is generally something that agentic AI type models are probably not gonna wanna take on the responsibility of. This provides us with a huge competitive advantage. In summary, thanks everyone, and onto the last slide. Thanks for hearing me out, and I appreciate and would love to hear your questions on what we've just spoken about. Thank you.
Thanks, Tim. Quite a nice handoff to the next presenter, 'cause if we shift from marketplaces into regulated environments, the dynamic clearly changes. AI can clearly drive efficiency, but it also introduces accountability, risk, governance, and that's exactly where Kinatico comes in. What's particularly interesting here is their approach, embedding AI across the business while keeping humans firmly in control of decision-making. To take us through that, I'll hand over to CEO Michael Ivanchenko. Michael, over to you.
Thanks, David. Just a reminder before we get into the presentation, Kinatico is a provider of simplified workplace compliance solutions, where we aim to remove the administrative burden and the time and distraction effort required while maintaining secure optics of compliance at all times, along with pre-employment and ongoing lifecycle credential verification. Next slide, please. Today, I'll go through quickly, and I suspect there'll be plenty of questions at the end. Our approach to AI, what we've done, how we do it, realizing that the fundamental part of Kinatico is the involvement of PII or personal information and personal data across not only individuals, but corporates, et cetera. Everything we do is about privacy by design. Everything we do, irrespective of technology, irrespective of approach, firstly takes that into the primary consideration.
when we've looked at how we've approached AI, how we've done it, we've actually seen it first and foremost as how does it fit into our security frameworks? How does it make sure that we don't violate any of the trust and confidence that is provided to us by our customers? Also making sure that things like accountability, certainty, all of the things that you have to have in an environment where you're dealing in data systems is actually prominent across the board. What that also means is that the adoption of AI, I think in any company, but certainly in Kinatico, is actually a change management piece just as much as it is a technology piece.
One of the reasons we appointed our Chief AI Officer, it was also our chief people officer in making sure that the way we look at AI across the company is about accountable AI governance, and including extending our existing ISO/IEC 27001 accreditation to the newly formed ISO/IEC 42001, which is the international management of AI. All right, next slide, please. Where are we at in our journey? We actually adopted Anthropic as our primary LLM within the organization early in 2025, and adopting it across all parts of the organization to the point today where 88% of our employees are confidently utilizing AI in day-to-day work.
We've seen a 50% increase in features being deployed across all of our product set, but also used in all components of the development, whether it is the product management specifications, the coding of course, but then also the QA, the testing, and most importantly, also, I think in any product company, the measurement of success and iteration that feeds back into that product framework. We've also developed our own proprietary LLMs to use within our product. It's very much a case of where there is a solution that exists that can enhance you, of course, use it.
A lot of cases, what we're dealing with is proprietary understanding, historical knowledge, marketplace, and sector knowledge, all of that building into some of the models that we've developed that further allows us to enhance the product offering for our customers. One of the things that I think this really focuses and what we end up looking at in the software provision is the ongoing value of the service you provide to an organization. This isn't about providing a toolset, it's about ensuring that you can deliver the value that customers see and need at an increasing velocity and an increased basis generally. Next slide, please. In terms of, you know, this is. We talk about Kinatico's AI model, but I actually suspect most companies would be able to look at this at some form or another.
At its core, realizing and certainly a theme I'll keep coming back to, the fundamentals of business don't change in any way when utilizing or looking at AI. Why you're in business, the value you provide has to be sound. AI is an opportunity to accelerate, et cetera. The domain level expertise, in our case, 17 years of domain expertise and proprietary data source access. Access to data sources back to government, et cetera, that we use in all our verifications that are not publicly available. They are certainly not searchable across the net and not something that governments, et cetera, allow anybody to connect to. The data sovereignty and protection, again, not only the experience and knowledge, but everything we do is not data sources that are then made available and broadcast.
Anything we do is contained and maintained within our platforms.
There is no feeding b ack into language models to help them learn or other things like that. Everything we do is contained within our sphere. The looking forward and how this is going as a competitive advantage across marketing and sales, making sure that taking advantage of AI puts us in a position where we can start capturing the AI budget of organizations. When you look at all of the data that is coming out of how much companies are looking to spend and budget in their various departments, the one that is actually increasing is their automation across AI. Making sure that we are part of that ecosystem.
We're not only embedded into it, but actually facilitating it and leveraging it and being at the hub, if you will, of a hub and spoke type arrangement is a strong advantage for us.
The one that everybody talks about, which I think is, quite frankly, the very much the skin on the overall totality of the body, is that all the things that you can do from an operational point and the operational leverage of actually using AI for what it is particularly good at in the automation and time reduction of certain processes, as long as you have the controls in place. Because what is always important, and we've seen plenty of examples already in the press of where AI deployments have gone awry, is that looking at the technology and the deployment of technology is the victory, rather than losing sight of the core KPIs, the reason you're trying to do those things in the first place, and making sure that AI is delivering those advantages. You're not just delivering AI.
Next slide, please. Our core product, as an example, when we started building over two years ago, Kinatico Compliance is built from the ground up with AI. We've already taken the investment in doing that. It is a platform that is envisaged to be connected to from AI agents, not just people. Significantly, even the pricing model that we've then looked at is we don't charge for admin users because that's where the AI agents are going to be accessing the platform within constrained company environments. What we charge is on the data generation, the value provided by monitoring and managing the compliance of the workers that are there. Next slide, please. We've also looked at overlaying it across our existing product space. An example of something that we're now rolling out, expected results across that.
Resolution of customer inquiries, 60% automated with a response time of less than two minutes versus potentially hours previously, and a cost per ticket reduction of around 40%. Next slide. Some of the features that you then allow us with that AI-native architecture that we have in the platform, you start to get into the enablement of effectively AI as a UI to your customer interfaces. One is the integration and access from AI agents, but the other one is allowing people to interact with computers in a way that is far more logical to themselves. For instance, the example on the right-hand side to read quickly a prompt of, "I have a potential new starter named Frank Hyde. Their email is," whatever it is there. "Their role would be HK worker at our Springfield facility.
Commence the process." That's all they need to do. The platform takes care of all the things that need to be done. Always with a confirmation, though. You notice it says, "This is what I'm gonna do. Are you happy for me to proceed?" Next slide, please. Just in summary, you know, what does this look like? That's where we are now. We're already embedded, as I say, over 80% of our staff using it daily. What we think we get to is this idea that even in the physical ecosystem in the bottom right-hand corner of the slide, we will end up with AI agents on our org charts. This idea that when we're looking at what a department makeup is, how much of it is people-based, how much of it is AI agent-based? How do they interact?
What is the structure? The overall construct of organizations changes. From a service model, you've got AI support and routing agents taking care of that, coordination, access, presentation, querying, refinement, et cetera, processes, but very significantly, always with human oversight and human control. All AI agents, all people for that matter, you know, are not infallible. Making sure that the same safeguards we put in place today exist in an AI framework are also critically important. 'Cause again, the benefits of the software, the benefits of AI technology doesn't negate the fundamentals. Whether that is fundamentals of how you deal with your data security, your company security across all of the workforces, your policy and obligations, your liabilities as a company.
Saying to somebody, "Yeah, look, that's really bad, but it was our AI agent who did it." Doesn't get you away from the liability and accountability for you as an organization and what you're providing. Therefore, understanding and the controls in place around all of that become absolutely critical. What I think we'll see across productivity in organizations more so is that the size of orgs won't necessarily scale with more employees, but you start to look at how that people organization can be supplemented with AI agents. On the customer side, and we've seen this with every technology that's ever come out, whether it was the original internet, big data, take your pick, the expectations around speed of innovation change, what customers can get when and how and how much it costs just continue to increase.
I don't think there's anything new here just because it's AI. The key one in terms of the ongoing relationship there is ensuring that we're delivering value and service, not just tool sets. I think any software company that is providing just tool sets has the risk of being commoditized or replaced. That comes back to the headline there that the fundamentals remain the same. What is it that you're within your organization that makes your customers want to interact with you and stay with you? At the same time, it's an opportunity to increase internal staff engagement, and I think that's sort of somewhat counterintuitive with all of the press and everything that gets talked about the decimation of workforces. Yes, there are considerations around what that's gonna look like.
I think for the staff that then remain in organizations, there is a genuine ability for a tool set and the coordination within that actually allows them to increase their engagement with the organization. It is almost like providing every worker a number of personal assistants for them to actually assist in doing their role. Then, of course, finally, just quickly to finish in the technology ecosystem, the combination of proprietary large language models or AI models, but also acquired ones, proprietary data, secure data, all wrapped up with AI-enabled processes and management to deliver, and then the idea that the appropriate interfaces to exist to provide secure, appropriate access via third-party integrations will be the overarching technology, model moving forward. David, back to you.
Thanks, Michael. Our final presenter, Pureprofile, sits in a very different part of the AI landscape. This is a data and insights business where AI is not just enhancing workflows, it's reshaping the product itself. The key question becomes. How does AI drive both growth and margin expansion in a data-driven business? To talk us through that, I'll hand over now to CEO Martin Filz. Martin, over to you.
Yeah. Thank you very much, David, and thank you very much for everybody who's attending. I've taken a slightly different approach, sort of talking generally about AI before we get into a bit of detail about Pureprofile and what we've done with AI. Bear with me, everybody, but thank you very much, David. Next slide. For those of you who don't know, we're a data company, as sort of David said. We have nearly 1,000 clients around the world who have business problems. They come to us and we recruit millions of people around the world to be a source of truth. That source of truth could be behavioral data, what people do, what searches they're doing, what websites they're going to, et cetera, and clients can just analyze that data.
For example, in the last few weeks, we've seen the searches for electric vehicles go up over 50%. Again, our clients who are looking to answer questions about what sort of automotive products people are interested in can do that. People can actually run surveys, again, going back to those audiences to understand how maybe the sentiment, interest, desire, et cetera, change. Millions of people around the world, the true source of data, lowest cost. Governments, education companies, ad agencies, research agencies, as well as direct to brands across every vertical. That's what we do. Thank you, David. We do that today, 14 offices around the world, about 260 staff. We'll talk about staff in a moment.
A lot now in platform revenue, so getting on for, you know, nearly 1/3 of our revenue is now platform revenue, and we'll talk about that and AI in a moment. Getting on to about half our revenue is in long-term annuity revenue. Could be SaaS, could be long-term contracts. Again, AI driving that as well. A truly global company. Thanks, David. Now, we've been on a journey. Journey one, to be global. Two, technology-led as a company, and then AI. Similar to Michael talked about, we've been working with AI for about three years now across our business, and then to absolutely focus on being a data company. Thanks, David. A couple of things investors are really excited about for us as a company today.
Number one, rest of the world overtaking our home base of Australia, where we've been for 26 years, so being a truly global company. EBITDA margin expansion, so seeing that. True NPAT profit expansion, seeing that, and again, driven by platform, driven by efficiencies, driven by rolling out technology, not necessarily AI, to be a faster, better, more efficient company for our clients. Again, investors love to see top-line growth, EBITDA margin expansion, true NPAT margin expansion, and then actually what's your big story? Big story about the rest of the world. Ticking the boxes on that. Thanks, David. Then we grew our guidance at the half year, so AUD 646.5 million, 10%-11% EBITDA as a company. Thanks, David.
Let's get into some AI stuff. This is where I'm sort of talking generally about AI. The first thing AI's been really brilliant at is actually it's driven innovation in companies. Because I can do it in AI doesn't mean I'm necessarily going to do it in AI, but it means I start talking in the company more about innovation. As an investor, you really want to see companies who aren't necessarily just talking about AI but are maybe accelerating their product development line, or maybe external clients, internal clients. Because in every executive team meeting, in every leadership team meeting, and in day-to-day conversations, companies are talking about innovation because you're seeing competitors change. We're all talking about AI. You know, we may be writing birthday cards using AI.
We're doing children's homework using AI, teaching skills using AI, and hopefully yourselves you're using AI, but it's driving innovation and the conversation of innovation. First off, what are your investments doing? How are they moving forward? The second thing really that AI has done is that separation of coming up with an idea, that really product teams do. What are clients asking for? What problems are we solving? Coming up with that idea, and then that gets thought out. You've got ROI, you've got planning, and then that gets, if you like, thrown over the fence to an engineering team that says, "Okay, now go and build this, please." Guess what? The engineering team goes back, looks at the specification.
It changes it maybe a little bit, and it always takes longer, and it always costs more money than you expected it to do at the beginning. Well, now we're seeing because of AI and because of this innovation, you're actually seeing product design and engineering coming much closer together. Prototypes that are coming out of product actually are 80% good enough. Our what as we say, our minimum viable products are 80% good enough to actually launch across the company. Quite often for internal tools, what comes out of a product team allows you to actually start using something, maybe do some reiterations or iterations of that tool you're using before you've got a final release. Companies should be faster with new solutions. Again, I think Michael talked about change management being really important.
How are companies geared up to actually see more changes faster because I'm rolling out tools faster across my organization. Perhaps efficiencies because I'm not so heavy in the engineering side. Expensive resources if they're onshore, obviously less if they're offshore, but I need less of those to do more work. The third thing, as I just touched on, everything is accelerated. Going from problem, design, prototype to launch is faster. Again, you should start to see companies doing more work, but equally keep an eye on companies' employee satisfaction. Keep an eye on companies' client NPS scores, satisfaction scores.
Because what you can see is if organizations start to do too much too quickly, then they're gonna put a strain on their internal clients, and they're gonna put a strain on their external clients, and start to lose track of, what am I actually in business to do? What problem am I trying to solve? Really I should just be using AI and technology to solve that problem faster and better. I'm not trying to develop something newer. Sometimes technology can get in the way of what I do as a core business. Finally, the speed that AI is now moving at is incredible.
Today, AI has gone from where it was really six to eight months ago, where it was aiding development, it was enabling development to be faster, to now, if I'm careful about it, actually AI can do all of the work for me, and I've got people overseeing it, but really all the code is being generated, the problems are being solved just by AI. We're up to version 5.3 of ChatGPT as an example, doesn't matter what AI you use. They actually have in their latest release of 5.3 that some of the code and product was totally developed by AI. We now have this agentic AI, which is this terminology where you can actually take.
Rather than using AI as an expert to speed up what I'm doing, you can actually say to AI, "Be the expert. The problem you are trying to solve, you are a customer service manager. How would you do it faster in my company? You are a salesperson. How would you build systems to do it faster and better and more efficiently in my company?" Actually, the AI agents will run off and build that themselves. Got to have a word of caution about that, but we're seeing acceleration in AI. Thank you, David. Now, what does this mean about clients? We're all using AI. We're all using AI in our day-to-day life. People have downloaded onto iPhones.
Within companies you have perhaps safe, and you should have safe enterprise versions where you're maybe uploading documents or asking business questions in a safe ring-fenced environment. That's enterprise AI. What are all clients expecting? Number one, they're expecting delivery to be faster. I'd want the same answer you were giving me yesterday. In our worlds, it's data, it's problems to a business question they may have. It might be views on an advertising campaign, or if I'm the government, it might be looking at are people talking about should we drop excise on. Is the problem about excise on petrol versus having petrol at the bowser? Let me keep talking to voters to understand how long I can push making extra money from excise before I have to drop that.
I want those decisions faster, I want that information faster because I've got information now at my fingertips in my day-to-day life. The second point is companies are expecting it to be cheaper. Again, when you look at your investments and you look at your clients, your portfolios, where actually are their gross margins tracking? Because what you might see if they're not ahead of the technology curve, or at least with the technology curve, they're gonna start to have pricing pressures which are going to start eroding gross margins because they're behind the eight ball on actually rolling out efficiencies of technology, because clients expect it to be cheaper. Thirdly, why do we do anything if it's not gonna be better? As a company, every stage of what we do should be improving.
We should be better today than we were yesterday, and these are client expectations. Even if an organization says, "Not me." An organization says, "I'm good. I provide," let's say, "legal services. It's a certain way we do this. It has regulatory ways that we're held to, and so I'm okay with what I do. I don't have to respond now to AI and technology." It's not true. Because that client maybe uses 10 or 15 other services around that one service you deliver, and all of those are getting faster, all of those are getting cheaper, and all of those are getting better. If you as a company are not improving, then you as a company start to look cumbersome and expensive. Your ecosystem around you might actually be making you look like you're falling behind. Company opportunities.
Thank you, David. Firstly, companies need to be more efficient. How can I replace manual tasks with automation and do more with less? However, and this is a huge however, you are just as a company changing your risk profile. We've got tried and tested risk. Lots of us and companies we use Amazon Web Services or we might use Microsoft Hosting. We say the cloud, our data, our systems are held in the cloud. Again, companies you invest in will talk about the cloud. Well, Amazon has been in the cloud and doing this for 20 years. Their servers run around the world. They have natural efficiencies and they have natural rollover, should there be outages from one system to another. All of these new AI companies are really very, very new.
They're burning a huge amount of cash. They're private equity led in many instances. They raise money. They're not profitable, so they raise money to keep the lights on. They're setting up server farms, they're setting up data farms. We've heard they take huge amounts of data and water and electricity to run what they do. The jury's slightly out on the reliability of these companies. What you have to be really careful about is I'm outsourcing what I do as a company today, a process maybe, and I'm saying, "This is great. This is now run by an AI agent," and I outsource that process to a third party. Could be Anthropic, could be OpenAI, et cetera. What if they have a data outage? What if they have downtime?
How is my business going to be affected? Again, when you look at your investments, and there's a lot of AI washing that's going on at the moment, companies trying to perhaps boost their share price by saying they're AI companies or doing a lot, but actually, what is their core business and what are they risking by putting this out to third parties now that, again, have been around for a few years versus 20+ years, and how stable is that? As a company though, you really need to look at whether you. It 's traditional technology or outsourced technology, how am I being faster? How am I improving my quality, and how am I deepening and improving my analytics? How am I being better every single day?
To do that, get the whole company involved because as Mike talked about, it's a change management piece. How am I getting the company behind this, coming up with perhaps new ideas, new ways of working? I'm not necessarily rolling them out, but they're on the journey with me, so actually they feel part of the solution. It's easy just to get caught up on internally doing it better because they're the easiest ROI. However, look at external client revenues. There's opportunities and low-hanging fruit out there for new client solutions, new clients that I can work with, so us, Pureprofile as a company. Data is at the heart of all of the AI, LLMs that you hear, those terminology, but all of the AI companies, they're nothing without data.
We have the purest form of data right back to the humans, right back to the person, so they're all new clients for us who need access to our data. Companies should be thinking about new clients, and then think about how am I adjusting my interactions and my relationships? How am I letting clients have a frictionless response with me, interaction with me? Thank you, David. At Pureprofile, our product strategy certainly is AI acceleration. As a company, and we talked about this in our releases, for the first time in our history, our salary growth is lower than our revenue growth.
As a growth company, you're investing normally ahead of the curve, and that's because we can see natural attrition can occur, and we're not having to replace the people that are doing those manual tasks because either innovation, technology, or AI, we've seen actually a speeding up of those manual process or replacement of some of those manual processes. Product team innovation. It's really key that companies look at that product team investment ahead of the engineering and understands, can I launch something that is right for the company? Pureprofile, we just made a change in our company that our CTO's left the company, and actually our head of product, who's ex-Facebook, ex-Meta, she now heads up the CTO role and the product role, bringing those two together, so being faster.
Thought leadership, thinking about how can I help the industry and the company? There's change going on in every industry. How can I be positioned to farm that? Part of what Pureprofile does is all of our internal AI development, we make available free of charge through our hub to actually our clients because we believe a rising tide lifts every boat. We're all trying to do the same things, and actually allowing our AI innovation to be made free of charge available, our clients can use it. It's more sticky with our clients, but they also come back with feedback. We're innovating iterations faster, and we have a closer relationship with our clients. We're making their processes and their jobs better. We all need to recognize it's an evolving client journey. Let's not be left behind.
Thank you, Dave. Some of the internal ones we've rolled out, again, took big data, AI fraud detection across all of our data that we have. It's really key about the responses that we see, the data, and we pay incentives to people to share data or to answer surveys. Wherever money's involved, fraud can be involved. AI's fantastic at seeing patterns. We've seen some key tools that we've evolved open-ended pre-screening, in-survey product checks, translations and others. That dollar has flowed directly down to the bottom line, and we've seen 1%-2% EBITDA margin expansion over the three years because of these tools we rolled out. Keep an eye on your investments, that they should be now showing an EBITDA margin expansion because of these efficiencies. Thank you, David. I'm conscious of time as well.
The client journey is changing. All client journeys are changing. The important point here is company moats. You're gonna hear this phrase a lot, and we've heard it on a couple of presentations so far. The moat is changing company, i.e., what is a moat? A moat is really what is defensible by a company and what makes you famous. What we're seeing is that, investments you might have made in companies where they were talking about technology as a barrier to entry, it is no longer a barrier to entry. Your reputation, how long you've been in business, the number of clients you have. We've been in business 26 years. We have nearly 1,000 clients. We have embedded intelligence and knowledge, 260 people around the world. At our core, we have irreplaceable data that is updated daily by millions of people around the world.
They're our moats as a business. Technology, no longer a moat. You need to look at companies and what are their moats. What are their defensible points of business? Because we're seeing companies like Atlassian, where their share price is being decimated at the moment, all right, there are other reasons for that, but is it replaceable SaaS technology? You might have something like WiseTech, which actually the defense moat of them is the relationships and how they're embedded across the whole shipment plan, the delivery, the route to market from product being put on a tanker to tankers delivering it to end case. That's the defensible part, not the technology. Think about that as investments. Thank you, David. We can nip through a couple of slides. Only support clients. Thank you, David.
Most companies you see should start to talk about great interfaces they have, new products they have with clients, how are they delivering what they did yesterday. The same thing, they're not changing the company, but how are they doing that faster, better, perhaps cheaper. Thank you, David. Here again, you can see platform SaaS has grown to over 30%. In summary, think about a company that's moved their risk profile. They've gone from people doing the tasks that are managed by talent and culture, HR, whatever you want to call it. They've got servers, server farms, maybe AWS in the cloud. They've moved that externally. The second point you should bear in mind, you should now start to see EBITDA margins expand within companies, so keep an eye with that. It changes with moats. People really shouldn't be talking or keep.
Dig a bit deeper if they're talking about technology moats as a company, because I can build it in seconds. It's not gonna change businesses overnight. Watch AI washing. What I do as a company, Pureprofile, we answer business questions that companies have. They want to understand from their consumers. I've not suddenly become an AI company. I just use AI to do that better. Really, again, dig deeper if companies are changing what they do. Finally, AI is now disrupting AI. Be really cautious about investing in AI-leading companies, because maybe they're doing something that could today be disrupted by AI tomorrow. Thank you, David. Thank you all.
Thanks, Martin. Thanks to all presenters. We'll now move into the Q&A section, where each of our presenters will come back and answer any questions that have come through. Now, we have had a number of questions come in, and we aim to get through as many as we possibly can in the time allocated, and I'll group them to try to keep them together as well. Tim, maybe firstly to you, where are you already seeing AI improve conversion or matching, and how should investors think about that flowing into GMV and take rate?
In terms of, like, how it's improving the marketplace, I think the first thing we're seeing is massive surge in traffic coming in through LLM- based chatbots. Like basically traffic coming through ChatGPT, through Gemini, through all of those ways that you discover, you know, the way that you're gonna get your job done, that's coming through us. Why is that? Why are we seeing such a surge in that? Basically for two reasons. One is I think brand is gonna play a bigger part in that. Where is Gemini, OpenAI, et cetera, gonna send their customers? They're generally gonna send them to someone who's trusted and who's got authority in the space. I think Airtasker's consistently invested into building that brand trust with our users.
The second thing is that we have much richer data than, I guess, alternatives in the space. When you go to Gemini and you say, "Hey, how much does it cost to find a handyman in Parramatta, you know, this Saturday?" Because Airtasker actually has all of that proprietary data, and we're now exposing it, for example, by, you know, doing server-side rendering, like all of our UI is moving to server-side rendering so that the bots can actually go through and capture all that data, read all of that data, and present it back to their users. That's probably the biggest area that we've seen. The second area that I think's been really interesting is content moderation and basically removing leakage and bad behavior in the marketplace.
You know, being a community marketplace with 250,000 tasks a month on the platform, you see lots of interesting conversations happen. One of the things that we really wanna do for our customers is create the most trusted place for you to get that job done. Using semantic and human language-driven search, we can actually get rid of any of that content that isn't exactly on point. I think that's having an implication of having higher completion rates and assign rates. That's helping that part of the funnel.
Then lastly, you know, talking about monetization and take rates and things like that, we don't really think of it so much as, like, take rate, as much as, like, how are we gonna get our customers to have more happy transactions and come back more frequently. I think the biggest impact there is just the velocity of software development is just insane. You know, we wanted to launch a membership program this year. That was sort of idea to in-market and ship to users buying memberships in, you know, five weeks. There is just the pace of development has been, you know, massively improved, and I think that's just gonna be better for business overall.
Is AI more of a cost reduction story today, or are you already seeing it unlock incremental demand?
We are seeing a bit of both. You know, as we mentioned, like huge traffic coming in through these refined ways of being able to discover a solution to your problem, and that's benefiting Airtasker massively. To the point around cost savings, I think one of the things that we're really proud of as a business is that we were able to scale revenue post, you know, COVID era in a pretty lean way. We haven't scaled our headcount massively. We're about 200 people overall, of which, you know, 80 of those are in customer service and ops. I think, you know, there's obviously some efficiencies that could be created there over time. We didn't blow up our headcount.
You know, we're doing close to, you know, over AUD 200 million of sales with a team of about 130 people. We're not looking at so much as cost reduction as just, like, massive abilities to be able to ship more value to our customers without having to hire people.
Just lastly, if demand increases, how do you ensure supply keeps pace? Does that create pricing power?
Generally, we are fortunate that the way that we've constructed the marketplace, we're predominantly demand constrained, meaning that we have a lot of people wanting to be able to work in this way. I think that as we're seeing more disruption in white-collar work and ultimately greater demand for human in-person skills are really expanding. I don't necessarily think we're gonna have a supply-side issue because we've always been a demand constraint. That may change into the future depending on how quickly that demand growth accelerates. Overall so far we're feeling pretty good about being able to deliver value for our customers and keeping as many workers in Australia as we can, you know, in a good position.
Thanks, Tim. Michael, I've got a couple for you that have come in. You've positioned this as AI recommends, people decide. How does that impact scalability versus more automated competitors?
The people decide aspect actually comes back to the organizations themselves. It isn't that we decide on a customer's behalf. We would never tell a customer when we think they are compliant versus they know they're compliant. We give them the info. The entire concept there is that you've got all of the tools in the platform, the further insights, the accelerated insights that we are able to deliver with the aid of AI, further providing more detailed, nuanced information for organizations and the administrators within those organizations to make their decisions.
A good one's just come through. Which do you see as the biggest AI opportunity for your business? Revenue, revenue growth or cost reduction containment over the short, medium, longer term?
Yeah. It's. Look, we see them very distinctly. The obvious answer is to say both. However, it's not that simple. There is ongoing in any business the opportunity for further refinement, optimization, operating leverage, et cetera. We have had programs around that since I joined, and that will continue to be so. AI and the things that I talked about that we've done with AI have materially benefited that. We've seen from our most recent results the evidence of the increasing or the widening of the acceleration of the margin of the EBIT line versus revenue, et cetera, is apparent of that developing leverage to the point where, you know, at the start we had 134-odd heads, we're now down to 73, and we doubled the revenue at the same time.
That is just an ongoing effort to continue to do that. The bigger impact opportunity, though, is on the revenue side to accelerate as you can continue to deliver the features that customers value and are willing to pay for. The ability for us to deliver that, those insights and all the things that scale around, you know, a promise of simplified compliance, time saving and efficiency, et cetera, is the thing that has opened up our market space, where what we were providing was a solution to companies that had 500 or 1,000 workers up. We're now everything from your small, medium business, you know, your corner garage shop, all the way up to the largest companies in one product.
You touched on in your presentation that your pricing is based on outcomes and data rather than users. Does that protect you from AI-driven pricing pressure?
Yes. To be really succinct. Like the whole reason we structured the pricing in that way was that this is all about delivering ongoing value to customers. The purpose of the platform is to ensure your workforce, whatever it may be, however big it may be, however complex it may be, you have instant visibility of compliance. Who has access to that information in your organization that you determine is more of a tool set functionality, which is the reason why we don't charge for it.
You're already seeing strong efficiency gains. When do those start to materially flow through the margins?
Well, I think they have already. That evidence has begun. You know, we've seen our you know the half year NPAT we've just released is up 107%, and we see that trend continuing.
Thank you. Martin, I do have a couple of quick questions for you. How much of your platform revenue growth is now AI-driven, and how quickly does that become dominant?
That is a very good question. Of the platform revenue, it's the motors behind it, the core systems that are AI. Actually, we've been able to generate a platform because of AI. It's not AI generating per se. The simple answer is 100% because AI enables you to develop it, but actually AI generating revenue is zero on it. But what you're seeing is clients now are able to connect computer to computer. They're able to analyze data that they couldn't before. That's the growth of platform. It's because of AI, not AI that's driving it, if that makes sense.
Yeah. Where do you see the biggest margin upside from AI over the next 12-24 months?
Well, in any company it's the people, and Michael just touched on that. You know, it's doing more with less, and we've got the same metrics as anybody should have. You've got NPAT increasing, EBITDA increasing, at a faster rate than your revenue line. Again, as a company you've got to be really careful and mindful of just getting caught up on the internal margin savings, because you can try to get the last 1% or 2% out of a saving, but in actual fact you're better off stopping at 80% of saving, so that's the low-hanging fruit, and then actually going to innovation and revenue driving. You've got to find the balance between revenue coming in and margin expansion.
The last one for you. How is AI changing pricing dynamics and client expectations?
Yeah. It's, as I said, they want it faster, cheaper, and from a higher quality. Whether they're asking that today of your business or in every sector of your business, they're going to. As a company you need to be really prepared that your margins are gonna drop. Where are you more efficient today and you're able to charge a higher price until it comes down? Because the worst thing is you wake up in six months' time and actually your margins have been eroded and you haven't put the building blocks in place to cut salaries, to speed up efficiencies, et cetera. It's coming, if companies aren't already being affected by it.
On that note, I might just finish with one for the group, and I'll throw it to Tim, then Michael, and then yourself, Martin. What's one AI initiative you're investing in today that you believe materially or will materially impact earnings over the next 12-24 months? Nothing like a crystal ball question there for you, Tim.
Well, I always get pinged on not providing guidance to anyone. Look, I would have to say probably the area that I'm most excited about is being able to get on top of content moderation and leakage in our marketplace. One of the things that people have talked a lot of, you know, that is really aligned with our long-term vision is building a really trusted high-quality marketplace, and doing that at scale is very, very challenging. I think one of the things that we have is now millions and millions of tasks completed and what a good task looks like.
Being able to train our models to be able to go and ensure that the content being created in our marketplace aligns with what good looks like is really exciting, and I think that will have material impact to margins and completed tasks ultimately.
Michael?
There's no one initiative I'm excited about or would call out because AI is embedded in across all of them. What I am excited about within the organization or, you know, very pleased with how it's progressing is we've taken the philosophy, as I mentioned, our Chief People Officer is our Chief AI Officer, is one of our big commitments is the investment in all of our staff in education training and knowledge increase as it pertains to AI. Making sure that that change management piece, that embedding in the organization, doing it, you know, first and foremost, as I mentioned, with security and responsibility, but being able to leverage it to its full capability, and I think that overall will give us the biggest impact.
Martin?
Yeah. Similar to Tim mentioned, it's about the scale of the business. You've got much more business intelligence than you ever had before, and you're able to leverage that and then scale at the top end, so adding more clients faster, more efficiently without having to add more at the back end. Your acquisition of client dollar has come down dramatically. Top-end scale is the key thing, and similar to as Tim touched on.
Thanks, guys. We have exhausted all our time. There were a few questions that we'll get to via email and directly with the presenters, so we will leave it there. A big thank you to Tim, Michael and Martin, so thanks gents.
Thank you.
Thank you.
Thank you to Finola.
Yeah.
The RaaS team for hosting.
Cheers.
Today's session. Of course, thanks to everyone who joined us. We look forward to seeing you at the next session. I hope everyone has a great day. Thanks for your time.
Thank you.