Kinatico Ltd ($KYP)

Good morning, everyone, and welcome. I'm David Tasker from Chapter 1 Advisors, and I will be your host today. Thanks for joining us for this RAS Research Group webinar titled AI in Action, how ASX Companies are leveraging artificial intelligence. Before we get into today's session, I'd like to hand over to RAS Research Group Managing Director and Founder, Finola Burke, who will say a few words. Finola, over to you.

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 is closer to the coal face and can engage in all of 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 in a mode. 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 e-mail in the next 24 hours. [Operator Instructions]. Now AI is clearly one of the dominant themes in markets right now, as Finola mentioned. But 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 3 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. So 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. So 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. So if we skip forward 2 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 want to work. To the next slide, our mission at Airtasker is to empower people to realize the full value of their skills. And for us, creating jobs for humans isn't a byproduct of the work that we do. It's the core purpose. So we've done over $1 billion in jobs now globally and now passed over 5 million jobs completed in the Australian market. So we are carving out a place in this marketplace ecosystem. If we move to the next slide. So I think one of the things that's really interesting about artificial intelligence is that it's going to make productivity gains really, really powerful in the white collar space. And it's actually -- 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 is 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. And as Jensen Fang, the CEO of NVIDIA says, it's really going to be the plumbers that win the AI race. If we move to the next slide, and Synoptic has done some really, really amazing research and released a labor market impact study. And if you look at this Spiderweb chart on the right-hand side there, you can really see that jobs like management, business and finance, computer math, architecture and engineering are really the jobs that are going to be impacted here with a huge amount, close to 100% of those jobs potentially being covered by AI. And then in the red, you can see there what we're actually already seeing as the impact there. And that has been considerable in things like software engineering, et cetera. Real-world jobs, I think, have much lower exposure to automation. And as we mentioned before, things like handyman, installation of home gun maintenance, much less likely to be automated. Now there is also a frontier of humanoid robotics, which I think is worth while calling out. And we do think that over time, humanoid robotics could become something that is impactful on the labor market. That said, we always believe that humans are going to be on the frontier of the creativity and the things that are going to be done to further humanity. So although some of these jobs even in the physical world could be disrupted eventually, new jobs are going to be created that we can't even imagine right now. If we move to the next slide. So there's Airtasker as a provider of actual labor services. And as we mentioned, I think that we are some way away from humanoid robots taking on those jobs because 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 going to come out with stronger moats post AI disruption and those that can't come out with weaker moats. There's a great article that Nicolas Bustamente drafted and shared, which sort of talks to 10 moats that are very common in businesses. And there's sort of this first list of potential moats in businesses like learn software interfaces, specific business logics that are created or access to data, which is ultimately publicly available. And those moats because of the unlimited intelligence that you can get from artificial intelligence agents are going to be largely weakened moats. However, there are some moats in businesses which in an agentic AI world, we're actually going to get even stronger. So examples of that would be proprietary data, regulatory lock-in and, of course, network effects. And I think one of the things that Airtasker is really built on 3 of these 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. And we believe that's driven largely by having a highly liquid base of customers and taskers. The second is embedded transactions. So Airtasker actually manages the payment. So if you look at some of these sort of like lead generation websites, which tend to broker information, connect one person with another, I think those are going to be very largely disrupted. But 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. And the third thing is proprietary data. So one thing that Airtasker is sitting on is over 10 million reviews from customers, which is really insights into which tasker is going to be best for which kind of job. And 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. And so as Agentic AI makes the economy more productive, you're actually going to see more of a need for accessing that kind of information, that kind of inventory. And I think that's going to 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. So we sort of started by looking at the services economy, and I think 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 going to get even stronger during this period. But then there's also Airtasker as an organization and how efficient we can make that organization. And 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. So as building software becomes faster and cheaper, we can actually deliver even more value to our customers without actually incurring as many costs. So I think that's actually going to see a widening of margins in this kind of business. And 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. So a lot of these things are really hard to do with like deterministic models where you're trying to design algorithms to work out 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. And AI has really helped us to be able to do that. And then the third thing is across our business, and so much of this is coming from the bottoms up, which is really, really exciting, but AI is already driving huge operational improvement. So 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. But 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. And 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 rate. So it's also helping us with aspects of our business like marketing. On to my last slide, -- and just summarizing here, I think in this -- if we can move forward a slide, this Agentic AI era is really going to compound some really important competitive advantages for Airtasker. So first of all, network effects. I think ultimately, this is something that is a hugely powerful moat that we are going to be able to build our business upon and makes very hard to disrupt. The second thing is embedded transactions. So 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. And then fourth is regulatory responsibility. Airtasker provides a service to the tax office, for example, making sure that all of our users are ID check, provide tax information, et cetera. And this is generally something that Agentic AI type models are probably not going to want to take on the responsibility of. And so this provides us with a huge competitive advantage. In summary, thanks, everyone. And on to 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. And quite a nice handoff to the next presenter because 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 press, 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 preemployment and ongoing life cycle credential verification. So next slide, please. So 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 AI or personal information and personal data across not only individuals, but corporates, et cetera. And everything we do is about privacy by design. So everything we do irrespective of technology, irrespective of approach, First takes that into the primary consideration. And 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 and 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. And 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. And so 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 27001 accreditation to the newly formed 42001, which is the international management of AI. All right. Next slide, please. So 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. So it's very much a case of where there is a solution that exists that can enhance, you, of course, use it. But 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. So this isn't about providing a tool set. 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 -- this is -- we talk about Kinatico's AI mode, but I actually suspect most companies would be able to look at this at some form or another. And 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 back into language models to help them learn or other things like that. Everything we do is contained within our sphere. 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. So 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 and 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 the hub-and-spoke type arrangement is a strong advantage for us. And then the one that everybody talks about, which I think is the -- 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. So our core product, as an example, when we started building over 2 years ago, Kinatico Compliance is built from the ground up with AI. But 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. And 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 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 2 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. So 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 logic to themselves. So for instance, the example on the right-hand side to read quickly a prompt of I have a potential new started name Frank, their e-mail is whatever it is there. Their role would be aged care worker at our Springfield facility, commence the process. That's all they need to do, the platform takes over of all the things that need to be done. Always with a confirmation though, you notice it says, this is what I'm going to do. Are you happy for me to proceed. Next slide, please. So just in summary, what does this look like? That's where we are now. We're already embedded, as I say, 80% of our staff using it daily. What we think we get to is this idea that even on your -- in the physical ecosystem in the bottom right-hand corner of the slide, we will end up with AI agents on our org charts. And 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, not infalable. And making sure that the same safeguards we put in place today exist in an AI framework are also critically important because, 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, yes, 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. But 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. And the -- that comes back to the headline there that the fundamentals remain the same. What is it that you -- within your organization that makes your customers want to interact with you and stay with you. And at the same time, it's an opportunity to increase internal staff engagement. And I think that's somewhat counterintuitive with all of the press and everything that gets talked about the decimation of workforces. And yes, there are considerations that -- around what that's going to look like. But 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 assistance for them to actually assist in doing their role. And 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 by third-party integrations will be the overarching technology model moving forward. David, back to you.

Thanks, Michael. Our final presenter Pure profile 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. So 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.

Yes. Thanks 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 Pure Profile and what we've done with AI. So bear with me, everybody, but thank you very much, David. So next slide, for those of you who don't know, we're a data company, as sort of David said. So we have nearly 1,000 clients around the world who have business problems. So they come to -- and we recruit millions of people around the world to be a source of truth. So 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. So for example, in the last few weeks, we've seen the searches for electric vehicles go up over 50%. So again, our clients are looking to answer questions about what sort of automotive products people are interesting can do that. Then also people can actually run surveys again, going back to those audiences to understand how maybe the sentiment, interest, desire, et cetera, change. So millions of people around the world, the true source of data, lowest point and governments, education companies, ad agencies, research as well as direct to brands across every vertical. So that's what we do. Thank you, David. And 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 nearly 1/3 of our revenue is now platform revenue, and we'll talk about that in AI in a moment. And then getting on to about half of our revenue is in long-term annuity revenue, could be SaaS, could be long-term contracts, again, AI driving that as well. So a truly global company. Thanks, David. Now we've been on a journey, journey to be global; two, technology-led as a company and then AI. Finola and Michael talked about, we've been working with AI for about 3 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, 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. So again, investors love to see top line growth, EBITDA margin expansion, true NPAT margin expansion and then actually what's your big story. So big story about the rest of the world. So ticking the boxes on that. Thanks, David. And then we gave out our guidance or reconfirmed -- or actually, sorry, grew our guidance at the half year, so $64 million, $65 million, 10% to 11% EBITDA as a company. Thanks, David. So let's get into some AI stuff. And this is where I'm sort of talking generally about AI. So the first thing AI has been really brilliant at is actually it's driven innovation in companies. So 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. So 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. We're may be writing birthday cards using AI with 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. So 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. So 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. And guess what? The engineering team goes back, looks at the specification, 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. So prototypes that are coming out of product actually are 80% good enough as we say, our minimal viable products are 80% good enough to actually launch across the company. And 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. So companies should be faster with new solutions. So 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. And then 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. Third thing, as I just touched on, everything is accelerated. So going from problem, design, prototype to launch is faster. So 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 going to put a strain on their internal clients and they're going to 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? And really, I should just be using AI and technology to solve that problem faster and better. I'm not trying to develop something new. And sometimes technology can get in the way of what I do as a core business. And then finally, the speed that AI is now moving at is incredible. And today, AI has gone from where it was really 6 to 8 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 the code is being generated, the problems are being solved just by AI. And we're up to version 5.3 of ChatGPT as an example, it doesn't matter what AI you use, but they actually have in their latest release of 5.3 that some of the code and product was totally developed by AI. And so 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. And actually, the AI agents will run off and build that themselves. I have a word of caution about that, but we're seeing acceleration in AI. So 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 on to iPhones within companies. You have perhaps safe and you should have, say, enterprise versions where you're maybe uploading documents or asking business questions in a safe, ring-fenced environment, that's enterprise AI. So what are all clients expecting? Number one, they're expecting delivery to be faster -- so I want the same answer you were giving me yes, Dave. In our world, 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 browser. So let me keep talking to voters to understand how long I can push making extra money from excise before I have to drop that. But 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. So again, when you look at your investments and you look at your clients, your portfolios, where actually are the 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 going to 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. And then thirdly, why do we do anything if it's not going to 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. And even if an organization says, not me, organization 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. So if you as a company are not improving, then you as a company start to look cumbersome and expensive. So your ecosystem around you might actually be making you look like you're falling behind. Company opportunities. So thank you, David. So 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. So we've got tried and tested risk. Lots of us and companies we use Amazon Web Services or we might use Microsoft hosting. And so we say the cloud, our data, our systems are held in the cloud. And 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 to -- 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. So the jury is slightly out on the reliability of these companies. So 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. I outsource that process to a third party. It could be Anthropic, it 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? So 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 their AI companies are 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-plus years and how stable is that? As a company, though, you really need to look at whether it's traditional technology of 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. And to do that, get the whole company involved because as Mike talked about, it's a change management piece. So 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 of low-hanging fruit out there for new client solutions, new clients that I can work with. So us pure profile 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. So 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, Dave. So Pure Profile, our product strategy certainly is AI acceleration. So as a company, and we talked about this in our releases, so for the first time in our history, our salary growth is lower than our revenue growth. And 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 replacements 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 and Pure Profile, we just made a change in our company that our CTO has left the company. And actually, our Head of Product, who's ex Facebook, X, Meta, she now heads up the CTO role and the product role, bringing those 2 together, so being faster. Thought leadership, so 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 Pure Profile 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 bone. We're all trying to do the same things. And actually by leaving -- 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. So we're innovating iterations faster, and we have a closer relationship with our clients. We're making their processes and their jobs better. And then 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, to big data, AI fraud detection across all of our data that we have is really key about the responses that we see the data and we pay incentives to people to share data or to answer surveys. So wherever money is involved, fraud can be involved. AI is fantastic at seeing patterns. We've seen some key tools that we've evolved open-ended prescreening, in-survey product checks and translations and others. That dollar has flowed directly down to the bottom line. And we've seen 1% to 2% EBITDA margin expansion over the 3 years because of these tools we rolled out. Keep an eye on your investments, but they should be now showing an EBITDA margin expansion because of these efficiencies. Thank you, David. I'm conscious of time as well. The journey is changing of clients, all client journey is changing. The important point here is company moats. You're going to 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. And what we're seeing is that investments you might have made in companies where they were talking about technology and 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. And at our core, we have irreplaceable data that is updated daily by millions of people around the world. There are moats as a business technology no longer a moat. So 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 or 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 Encase. That's the defensible part, not the technology. So 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. And here, again, you can see platform as us has grown to over 30%. So in summary, Think about a company has 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, survey 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 going to change businesses overnight. watch AI watching. What I do as a company, pure profile, we answer business questions that companies have. They want to understand from their consumers. I've not sudden become an AI company. I just use AI to do that better to really, again, dig deeper if companies are changing what they do. And then finally, AI is now disrupting AI. So be really cautious about investing in AI leading companies because maybe they're doing something that could today be disrupted by AI tomorrow. So thank you, David. Thank you all.

Thanks, Martin, and thanks to all presenters. We'll now move into the Q&A section where each of our presenters will now come back and answer any questions that have come through. Now we have had a number of questions come in, and we want 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. So 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?

So 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. So like basically traffic coming through ChatGPT, through Gemini, through all of those ways that you discover the way that you're going to get your job done, that's coming through us. Why is that -- why are we seeing such a surge in that? Basically, for 2 reasons. One is I think brand is going to play a bigger part in that. So where is Gemini, OpenAI, et cetera, going to send their customers? They're generally going to send them to someone who's trusted and has got authority in the space. And I think Airtask has 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. And so when you go to Gemini and you say, hey, how much does it cost to find a handyman in Parramatta this Saturday because Airtasker actually has all of that proprietary data, and we're now exposing it, for example, by doing service side rendering like all of our UIs moving to service 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. So that's probably the biggest area that we've seen. The second area that I think has been really interesting is content moderation and basically removing leakage and bad behavior in the marketplace. So being a community marketplace with 250,000 tasks a month on the platform, you see lots of interesting conversations happen. And one of the things that we really want to do for our customers is create the most trusted place for you to get that job done. And so using semantic and human language-driven search, we can actually get rid of any of that content that isn't exactly on point. And so I think that's having an implication of having higher completion rates and assign rate. So that's helping that part of the funnel. And then lastly, 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 going to get our customers to have more happy transactions and come back more frequently. And I think the biggest impact there is just the velocity of software development is just insane. We wanted to launch a membership program this year. That was sort of idea to in market and ship to users buying memberships in 5 weeks. So there is just the pace of development has been massively improved, and I think that's just going to be better for business overall.

And 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. So 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 COVID era in a pretty lean way. So we haven't scaled our headcount massively. We're about 200 people overall, of which 80 of those are in customer service and ops. And I think there's obviously some efficiencies that could be created there over time. But we didn't blow up our headcount. So we're doing close to over $200 million of sales with a team of about 130 people. So we're not looking at so much as cost reduction. It's just like massive abilities to be able to ship more value to our customers without having to hire people.

And 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. And I think that as we're seeing more disruption in white collar work and ultimately greater demand for human in-person skills really, really expanding. I don't necessarily think we're going to have the supply side issue because we've always been demand constrained. That may change into the future depending on how quickly that demand growth accelerates. But 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 in a good position.

Thanks, Tim. And 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?

So the people decide aspect actually comes back to the organizations themselves. So it isn't that we decide on the 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. So 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.

Good one has just come through. Which do you see as the biggest AI opportunity for your business, revenue growth or cost reduction containment over the short, medium, longer term?

Yes. Look, we see them very distinctly. So 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. And we have had programs around that since I joined, and that will continue to be so. And 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 at the EBIT line versus revenue, et cetera, is apparent of that developing leverage to the point where I started, we had 134-odd heads. We're now down to 73, and we've doubled the revenue at the same time. So that is just DNA 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 at scale around our promise of simplified compliance, time saving and efficiency, et cetera, is the thing that has opened up our market space that we can now -- where what we were providing was a solution to companies that have 500 or 1,000 workers up, we're now everything from your small medium business, your corner garage shop all the way up to the largest companies in one product.

And you touched on 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. 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.

And you're already seeing strong efficiency gains. When do those start to materially flow through to margins?

Well, I think they have already. That evidence has begun. We've seen our -- the half year NPAT we just released is up 107% and we see that trend continue.

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's a very good question. So of the platform revenue, it's the motors behind it, the core systems are AI. Actually, we've been able to generate a platform because of AI, it's not AI generating per se. So the simple answer is 100% because AI enables you to develop it, but actually AI generating revenue is 0 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. So that's the growth of platform. It's because of AI, not AI that's driving it, if that makes sense.

And where do you see the biggest margin upside from AI over the next 12 to 24 months?

In any company, it's the people, and Michael just touched on that. 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 savings. So that's a low-hanging fruit and then actually going to innovation and revenue driving. So you've got to find a balance between revenue coming in and margin expansion.

And the last one for you. How is AI changing pricing dynamics and client expectations?

Yes. As I said, it's -- they want it faster, cheaper and from a higher quality. And whether they're asking that today of your business or in every sector of your business, they're going to. And so as a company, you need to be really prepared that your margins are going to drop. So 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 6 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. So it's coming if companies aren't already being affected by it.

And 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 to 24 months? Nothing like a crystal ball question there for you, Tim.

Well, I always get pinged on I'm 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 -- 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 -- so 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 a material impact to margins and completed tasks ultimately.

Michael?

So there's no one initiative I'm excited about or would call out because AI is embedded across all of them. What I am excited about within the organization or 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 -- so making sure that, that change management piece that embedding in the organization, doing it, 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.

And Martin?

Yes. Similar to Tim mentioned, it's about the scale of the business. So 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. So your acquisition of client dollar has come down dramatically. So top and scale is the key thing and similar to as Tim touched.

Thanks, guys. So we have exhausted all our time. There were a few questions that we'll get to via e-mail and directly with the presenters. So we will leave it there. A big thank you to Tim, Michael and Martin. So thanks, James.

Thank you. Thank you.

And thank you to Finola and the RAS team for hosting today's session. And 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.