Chief Financial Officer, Justin Miles. Following a brief summary of the results released to the ASX this morning, we will have some time for Q&A with the management team.

There'll be a choice of two options. First, research analysts are able to raise their hand should you ask wish to ask a verbal question of the management team.

Or we'll also take written questions via the Q&A function at the bottom of your screen throughout today's presentation. We'll endeavor to get to the majority of questions asked, in some cases, combining questions on the same or similar topic.

With that, I'll pass it over to you, Ryan.

Thanks, Sam, and good morning, everyone. Thanks for joining us today for our H1 FY26 results presentation.

My name is Ryan Collins. I'm the CEO and Managing Director at Appen.

With me is Justin Miles, our CFO. Um today's presentation covers four sections.

I'll start with the results overview. Justin will then walk through the detailed H1 FY26 financial performance.

Then I'll return to cover our strategy and operational update, and we will close with an FY26 outlook and guidance before opening up for questions. Let me start now with an overview of our H1 results on page five of the presentation.

The first half delivered on the themes that we've been building towards. At the group level, we delivered $119.9 million in revenue.

This is 17% growth on the prior corresponding period. Appen China was a standout for the half.

Revenue grew 80% to $76.2 million and achieved an annualized revenue run rate exceeding $175 million in June. That is up from $135 million at the end of 2025.

The sustained growth reflects the strength of our relationships with the Chinese model builders and the ongoing demand for our data services in the region. Appen Global continues to make progress outside of our largest client.

Q2 revenue grew 65% on Q1. We're progressing well in our ambition to expand across frontier AI labs in the USA.

Capturing cost efficiencies via AI-enabled operations remains a focus for Appen Global. We have identified an incremental $12 million in operational efficiencies.

70% of the $12 million will be executed before the end of the year and the remainder in Q1 FY27. It's important to note that the cost outs don't impact the ability to grow within Appen Global.

On profitability, we delivered an underlying EBITDA before FX of $5.3 million for the half, a $7.5 million improvement on the first half of last year. EBITDA margins for the half were 4.5%.

Our cash balance at 30 June was $44.7 million equivalent to 64.8 million Australian dollars. I'll now hand over to Justin, and he'll take us through the financials.

Thank you, Ryan, and good morning, everybody. A reminder that we report in US dollars and that all comparisons are to the half year ended 30 June 2025, unless stated otherwise.

Starting with uh the H1 FY26 profit and loss on page uh seven. As Ryan already mentioned, revenue increased 17.5% to $119.9 million.

Within our operating segments, Appen China revenue grew by 80.4% to $76.2 million with Appen Global down 26.9% to $43.7 million. Appen Global made solid progress during the half.

However, growth in new areas has not yet offset a reduction in traditional work. Gross margin reduced slightly down 30 basis points to 36.7%.

The decrease reflects a change in customer and project mix, noting China margins are traditionally lower compared to Appen Global. Underlying EBITDA before FX improved $7.5 million to $5.3 million.

The increase reflects revenue and gross margin growth, prudent cost management, and operational leverage within Appen China. I won't talk to slide eight, as we have just covered this data.

Over to Appen Global revenue and EBITDA on slide nine. The chart on the left shows quarterly revenue, and on the right-hand side, it is underlying EBITDA.

The charts demonstrate the progress made during the half. Q2 FY26 revenue reflects growth from expanding projects with leading AI labs.

Pleasingly, outside the largest customer, Q2 FY26 revenue grew 65% compared to Q1 FY26. However, as just mentioned, growth in new areas has not yet offset a reduction in traditional work, resulting in revenue of $43.7 million for the half, which is down compared to H1 FY25.

An important point to note is that traditional work has currently stabilized. EBITDA reflects some inves- investment in winning new customer projects and does show improvement during the period.

Ryan has already mentioned this, but again, noting that approximately $12 million in operational efficiencies have been identified in the Appen Global segment with 70% to be executed by the end of this year and the remainder in Q1 next year. Over to slide 10, which shows quarterly revenue and underlying EBITDA for Appen China and reflects the strong market position Appen China continues to hold.

Revenue grew each quarter with Appen China achieving $76.2 million revenue for H1 FY26, which was 80% growth on H1 FY25. Growth continues to be driven by new and expanding LLM-related projects.

Appen China exited the half with annualized revenue exceeding $175 million. Pleasingly, in addition to revenue growth, profitability has improved with increased gro- gross margins due to a greater mix of GenAI projects and increased revenue from high-margin, pre-built datasets.

Appen China is also capturing scaling efficiencies due to tight OpEx controls as revenue expands. Turning to slide 11 for the profit and loss summary.

I won't talk to all line items. However, there are a few additional points to highlight.

There was a decrease in employee and other expenses in Appen Global, and this is highlighted later in the presentation. Employee expenses for Appen Global were down 19% on PCP and other expenses down 29% on PCP.

The decrease was achieved through technology innovation and automation. The decrease in Appen Global was offset by additional expense from the Appen China segment to enable the delivery of strong revenue growth.

The $14.9 million NPAT improvement and $8 million improvement to underlying NPAT reflects the improved performance for the half, as well as a decrease in amortization. I'll finish up with the cash flow summary on slide 12.

The cash balance at the end of the period was $44.7 million. The Australian dollar equivalent of the cash balance is $64.8 million.

Despite the decrease in balance compared to the prior period, a strong balance remains. Cash flow used in operations was $2.7 million.

In comparing to the prior period, it is important to note that H1 FY25 was positively impacted by the receipt of a payment from a major customer in the first week of January '25 versus December '24, as scheduled. Cash flow used in operations for the period was impacted by the timing of customer receipts, annual payments due during the period, and working capital required to support strong Appen China growth.

Cash used in investing activities was $1.9 million higher compared to H1 FY25 due to higher investment in product development and new facilities for the Appen China division. Cash used in finance activities of $2.7 million reflects lease payments.

Cash was used to fund operations and CapEx. That concludes the financial performance slides.

I'll now hand back to Ryan.

Thanks, Justin. Um I'll now cover our strategy and operational progress that we've made in the half.

Turning to page 14, um to understand Appen's services, it helps to start where the value sits in AI development. There are three fundamental bun- uh building blocks for AI development: compute, algorithms, and data.

Compute is abundant and commoditizing. Algorithms are increasingly open and largely commoditized, while unique data is becoming scarce and is a major source of differentiation for AI model performance.

Not all data is equal. There are many facets of data used to train models, all with different uses.

Public data is largely exhausted, and it's already been captured in existing models and offers little ability to differentiate. Synthetic data is relying on other models to produce.

Does not solve new or novel situations and can result in model collapse if overused. Real-world, bespoke data, the kind that Appen creates, enables new AI approaches.

It brings human expertise and interaction that improve and evaluate models in ways that alternatives can't replicate. This is the market that Appen serves.

Primary way that we serve our clients is through a managed services approach, where we build custom and high-value datasets that are specific to the AI model needs. The usual first step is that a researcher comes to us with their data need.

Can be a description of the task, expertise requirements, quality rubrics, data volumes, and timelines. Our delivery experts work very closely with clients to deeply understand their intentions and translate that into a data workflow that typically coordinates a set of complex and iterative handoffs between humans and AI models to generate the data.

We deliver through a combination of our research and delivery experts, our proprietary software stack, and our expert workforce marketplace. The output we provide is high-quality data for leading AI organizations, including the global tier-one AI labs.

What differentiates Appen is the combination of the platform, people, and our global reach. Our workforce is a core competitive asset.

More than 1 million contributors across 200-plus countries and over 500 dialects and languages covered. We focus on building out our domain expertise in our workforce.

We now have contributors covering more than 100 specialist fields, from computer science and mathematics to law, medicine, and the creative disciplines. Generative AI demands a different type of contributor.

It requires people who can reason, evaluate, and provide expert-level feedback. Our workforce has rapidly evolved to support the nee- the new expert-level requirements of our customers.

Our service offering continues to expand and covers nine categories um and is evolving rapidly with the needs of our customers. Some of the areas that we're working in include LLM training data, covering supervised fine-tuning, RLHF, and preference annotation, multimodal data across text-to-image annotation, aesthetic scoring, video labeling, and embodied intelligence, speech and audio across a broad set of languages, domain-expert annotation in medical, scientific, legal, and financial fields amongst many others, model evaluation covering LLM and vision benchmarking and search quality performance, computer vision and physical AI for autonomous driving, robotics, smart home, AR/VR, and embodied AI, and we also offer reinforcement learning environments, off-the-shelf datasets, and platform and tooling solutions.

The breadth of our offering allows us to evolve with the needs of the leading AI labs. We've recently expanded our dataset offering significantly.

These are the existing datasets that we either own or resell with value-added services on top. We're seeing an increase in the demand for these datasets, and we're building out our catalog to meet the specific needs of model builders.

Some of the areas where we've where we've recently added datasets include reinforcement learning tasks, code repositories, book corpuses, enterprise data for agentic AI, and many other standalone datasets covering unique areas like medical dictation, STEM Q&A, and infographics. These products accelerate time to value for our customers and often also come alongside managed service projects that add value to the datasets.

We anticipate this to be a solid growth driver in the near future. A unique proposition of Appen is our coverage of the two AI epicenters, namely China and the USA.

We operate two dedicated businesses to cover these markets, each purpose-built for the specific customer requirements. Appen Global serves the USA and Europe, targeting hyperscalers, foundational AI companies, and vertical AI builders.

Demand drivers include AI capabilities, new customer expansions, and new data modalities. Appen China serves China, Japan, and Korea, targeting Chinese big tech, foundational AI, and vertical AI builders.

In addition to the demand drivers, international expansion is an increasing driver of Chinese model builders look to complete in the go- global markets. Each business has its own dedicated operations and technology stack.

That separation allows us to optimize for the distinct requirements of each market without compromise. As Justin mentioned earlier, Appen Global's technology roadmap has continued to deliver operational efficiencies.

Employee expenses in H1 FY26 came in at $14.4 million, down from $17.7 million in H1 FY25. Other expenses were $8.1 million compared to 11.4 million in H1 FY25.

We continue to be highly focused on driving technology-led efficiencies across our operations, particularly through the use of AI. We have identified approximately $12 million in incremental annualized cost efficiencies.

Around 70% will be executed over the remainder of FY26 with the balance by the end of Q1 FY27. Importantly, there's been no operational impact from the cost-out work executed to date.

We're capturing efficiencies through AI-enabled operations, not by reducing our abilities to deliver high-quality data at speed. Let me now turn to our outlook and guidance statement for the full year.

We remain confident in the AI data market and Appen's ability to contribute meaningfully to the development of leading foundation models. We continue to see positive signals on LLM-related growth from both Appen Global and Appen China customers.

We're winning new work with leading AI labs and expanding existing programs. We remain focused on driving technology-led efficiencies across our operations.

As in previous years, Appen Global's revenue is predominantly project-based, and seasonality continues to skew revenue towards H2. Considering all of this, we reaffirm our FY26 guidance of group revenue of $270 to $300 million and underlying EBITDA before FX margin of 5 to 10%.

That concludes our presentation for today. Thank you for your time and your continued interest in Appen.

Justin and I are now happy to take questions.

Great. Thank you very much, Ryan and Justin.

As a reminder, research analysts can ask questions via raising your hand on Zoom, and I'll aim to get to you shortly, while the remainder of the audience can submit written questions via the Q&A function at the bottom of your screen. Uh we'll kick off with some pre-submitted questions before getting to any analyst questions today.

First on profit sustainability, can you cap- clarify how much of the recent EBITDA improvement is driven by a permanent structural cost reduction versus temporary project-based revenues?

Thanks, Sam. We're definitely focused um you know on a sustainable cost base and you know profitable growth across the business.

The good thing is that um particularly for China where we're seeing um improved margin, that's coming through the uh gross margin at China uh improvings, but also we're getting that the leverage of um scaling efficiencies in the China business. As we've called out, uh we've continued to drive OpEx improvement in Appen Global.

Multitude of factors, but we see this as a sustainable uh and ongoing um trend in the business.

Okay. Great, thank you.

Just a follow-up there. What baseline quarterly revenue is currently required to maintain positive EBITDA through the second half of FY26?

I think it's um you know fairly similar to where we're at with that um in in Q2. Uh we've as we said, we've made efficiencies across the business.

There's not a material uh uplift required to deliver profitability through the remainder of uh at a quarterly basis.

Great, thank you. On cash runway and free cash flow, given the cash balance, what's the current projected timeline to achieve consistent uh positive free cash flow?

I'll throw that one to Justin.

Thanks, Ryan. Thanks for the question, Sam.

Obviously, um part of the strategy, we've been talking about that, the efficiencies and the performance of the business and and the growth um in Appen Appen Global. We're we're well towards, heading in the right direction and well towards uh achieving that.

That is the goal, sustained um sustained profitability and and free cash flows. Um so we've got enough cash, we've got a strong cash balance, there's enough working capital, we're confident that the runway is there.

There's no additional funds required um to to manage the growth. We're definitely heading towards it.

We're heading in the right direction, and we're not too far off.

Okay. Another follow-up there.

Should shareholders expect the current cash reserves to be sufficient to fund operations until self-sustainability is reached?

Based on everything we know today, yes.

Thank you. Just one more pre-sequel pre-submitted question before we get to the analysts.

Um on revenue diversification outside Appen's core hyperscaler clients, what specific momentum or contract wins are we seeing in the broader enterprise AI AI market? How long are these uh sorry, are the typical sales cycles for these newer revenue streams?

We're very focused on um yeah the the large foundation model builders and the near labs that are you know popping up typically spun out of the research division of these of these large AI labs. Um so that that remains the focus of of Appen at the moment.

That's where the bulk of the spend is in the market, um and it's highly aligned to the capabilities that we're building. In terms of the the deal cycle time, it can you know vary quite extreme.

Um some of the deals uh are very very, short lead time. Some yeah if researchers are you know contemplating specific areas they want to work through and there's a good amount of back or forth, that can introduce um you know a longer a longer sales cycle.

It's it's certainly trending towards much much shorter uh sales cycles.

Okay. Great.

Thank you. Next question comes from Nikola Willment at Barrenjoey.

Nikola, please unmute your line and go ahead. Uh Nikola at Barrenjoey, would you like to ask your verbal question?

Please unmute your line.

Hi, guys. It's Josh Cannarozzo here.

Can you hear me?

Yeah. Hi, Josh.

Ah cool. Sorry, Nicole is just looking at me a bit funny.

[laughter] All good. Um no worries.

Just a first couple of questions to get kicked off. Um first one, uh just just around in the global business.

I know, obviously, there was some reasonably chunky customers in terms of that you had contracts for that you ended last year on, um some of those contracts were potentially coming back. Like what what have you seen in terms of that start, and I know visibility is obviously not high, but what are some of the um I guess the conversations and scope for work that you see from both some of the traditionally big customers that you have, but also as we said some of the foundational other customers into the second half, and just to talk a little bit about, yeah, what confidence you have in in in that delivery ability to deliver into the second half there.

Thanks, Josh. Um so there are a few things that are giving us some really good confidence at the moment.

The first is um we've been able to to penetrate into some new areas within existing customers that are really focused on foundation model build. We started in, specific areas that um, starting with a first project in a specific domain.

Now what we're seeing is much broader set of conversations around growth opportunities not just within the projects that we're working on, but across a broader set of domains. A lot of these growth opportunities um are in areas that we've been strategically investing our capabilities in.

Some of the things that we've called out, like coding, finance, healthcare, really pushing um into the the more valuable part of the uh the market at the moment to support AI development in in specific data modalities.

Got it. That's that's helpful.

Traditionally the margin on those sorts of projects as well versus maybe where the historical gross margin is, can you give a bit of context on that?

It it can vary, but you should think about them broadly similar to the to the um traditional margins that we've seen in the in the business.

Got it. Um and just while we're on global, just the competitive um the competitive environment in terms of what you're seeing out there.

Obviously, there's a number of players. What are you guys seeing in terms of when you are in those new markets?

What the competition is? Is their price?

Is price coming into it or is it more around the deliverability or the quality? Maybe just to talk through some of those frameworks that you think customers are using to choose the [demand is so that?]

space?

The ability to deliver high-quality data is is always number one. Um we don't see price as a major factor, particularly for the AI labs.

We see quality and speed being, the two primary considerations. We we, as you call out, there are some competitors that are, growing very rapidly.

That's built on a lot of um they're established within the companies, they you know've got the trust of the researchers, and the researchers go directly for them to them because, they trust their ability to deliver quality, and they trust that, it speeds up the cycle rather than running an RFP process. That's what's giving me a lot of confidence in, the Appen Global uh momentum that we're seeing is because of the conversations that we're having in specific labs, and it's across multiple labs.

They're really satisfied with our work. The quality is really great.

That's leading to bigger opportunities, but it is backed up. Is it on the back of their confidence that we are delivering high quality, and that we can turn around the data really quickly for them.

Um so the the the thematic that we're seeing with some of our competitors is really starting to play out within Appen Global.

Right. Um and just moving on to Appen China, another stellar result, obviously, um but when we look at that business, now it looks and we've seen, I think, what probably one thing that's changed a little bit since we last talked is just the the rapid release, including, I think, even yesterday, one of the sort of um GLM-3 coming in, one of the Jap- Chinese models coming in at sort of record things.

Then Qwen and the like as well coming out with some fantastic models. There's been a lot of talk around the geopolitical aspects here, but, it does feel like the Chinese models are definitely trying to um accelerate into, the US and other markets as fast as possible.

I think some of the open router stuff is saying it's over 60% of the tokens are now coming through from those models there. Like, I'm just interested in maybe under the hood what the trends you're seeing and how should we sort of think about the breakup of that work, um the continuation of that trend.

It looked like, obviously, Q4 there was a bit of an acceleration and then margins are also tracking. Maybe just a bit of a con- context around the type of work you're seeing, the type of customers, your confidence in revenue momentum, and and then confidence around, further margin upside there.

Yeah. Look, it's it's yeah it's so incredibly exciting and and impactful work coming out from the the the open source model builders in China, which I think, we all get a lot of visibility into, and, we can all kind of predict um that that's going to be a continued focus.

I think some of the things that we see on the ground in China, which, don't get as much exposure outside, is, the real focus on, I will call it, consumer-based AI, where, through the super apps, um the the companies in China, the AI labs, have focused on things like healthcare advice, financial advice. We're starting to see a lot of uh video generation, AI video generation, particularly in short-form videos.

I think they're a fair way ahead of the US on on the video generation side, or at least getting the the applications out that are supported by these models. Then the the third really big driver in, what we're seeing in China is the international uh support required to support Chinese technology companies that are heavily reliant on AI.

Um so you can think about social media companies, e-commerce companies. There's a really big driver for um you know supporting their international ambitions.

Okay. Great.

Just final commentary there, just on margins, um, obviously, that fourth the sorry, the second quarter margin was very strong. Um do you think that can continue, and do you how how should we think about, I guess, the cost base versus, margin perspective there in terms of what further expansion we could see across this year from China?

We certainly um expect that trend to continue. Um, we're seeing good operating leverage come out of the China business, and and that's some a trend we expect to continue, also.

Okay. Great.

Thanks very much. Appreciate it.

Thanks, Josh.

Thanks, Josh. Uh next question, sticking with China.

Um is your Chinese lab revenue recurring in nature, evaluation and data that's refreshed every model cycle, or one-off dataset builds? A follow-up to that, roughly how much of the $175 million run rate would repeat if customers shift to new models next year?

Thanks, Sam. It is a mix of what we do.

Um there is work that is directly um related to the development of new models. There's a lot of work that we do, which is related to you the evaluation of existing models, making sure that they're working in applications, etc.

Um there's also a lot of work that we do that is very experimental with with the researchers that actually may never make it into a model. It it's a it's a difficult one to dissect because the needs at a project level uh change and vary uh on a on an ongoing basis.

I think, what we're seeing in the in, both the Chinese and the US market, there's certainly no slowdown in the model advances and the model release cycles. If anything, it's speeding up.

I think the notion of um if if China stopped building models, what would happen is is not one that we're too worried about. We're very focused on supporting them as they evolve um into into new models, um but as I called out, also different applications, particularly on uh you know things like short-form video, robotics, speech.

There's there's a coding development. There's a very rich ecosystem of applications that sit on top of the existing models.

Okay. Great, thank you.

Just to follow up there. Does anything built in China, like datasets, tooling, capacity, get sold back to global customers?

Or do sovereignty and uh customer requirements keep the two segments commercially separate? Uh it is

it is largely separate, um and the data export controls um in China kind of mean that there's no data that's sold back into the into the global customers.

Okay. Great, thank you.

Switching to Appen Global, um can you just elaborate on the board's long-term strategy for the global business? Will growth be primarily organic, or is there a the ability for acquisition-led growth?

Um and if organic, which higher-value services and initiatives will drive growth to improve gross margin?

We're certainly focused on um growth in Appen Global, profitable growth. Um we think there is a a significant pathway and runway through organic growth in the business.

Um inorganic growth right now isn't isn't a high priority for us. Um and in terms of the services that we provide, it's it's continuing to work very closely with the AI labs to meet their needs.

We called out in the presentation some of the ways that we're evolving. That is in kind of really close uh response and feedback to what we're hearing and and getting requests for from the AI labs.

We will continue to evolve to to to meet the needs of um you know the data for for AI training, uh for AI evaluation, um across LLMs and and the future variants of AI models.

Right, thank you. Sticking with um Appen Global, how deep and durable are Appen's relationships with core clients?

Is Appen pursuing frontier AI companies such as Anthropic, and how does it convert pilots into recurring larger-scale work?

Mhm. Our our target customers are all of the leading AI labs.

We work with the real majority of them today. There are a few that we're targeting and making really good progress on to break into.

Um you know in terms of how we convert a pilot into a larger project, um it is really um and this is a very typical sales cycle, where we come in and do a small piece of work and based off the ability to you know deliver really high quality at speed for that piece of work, building a trust with the researchers and then showing them our capabilities and the work we're doing across different clients, that uh confidence that we build based off the project, based off what we're seeing elsewhere, and the conversations that we have and with the way that we bring value to the AI labs is is the best way to get up that we'll call the revenue curve as quickly as possible.

Got it. Thank you.

Um just a follow-up question from Josh at Barrenjoey. Uh Josh, please unmute your line and go ahead.

Great, thanks, Sam. Um just a follow-up, guys, on the robotics side of things.

There's been some interesting news this week. Figure, one of the big humanoid robot companies, has come out with um a platform for building real-world training data.

I'm just interested, obviously, they're big, they're very well funded, but what's the opportunity you guys see in robotics? If you think about the capability and the muscle you've had to build, like are there any other options to create a specific sort of more white-labeled product or a specific product for robotics that could be used by some of these humanoid companies both in um, the US, but also in China, which is obviously, a huge focus for the government there.

Yeah. No, thanks, Josh.

Good question. Um so you can think about three sources of data that are needed to train particularly the humanoid robotics, which are getting a lot of focus at the moment.

One is um what's called egocentric data collection, where um data is being collected by humans who would typically have cameras, strapped to their body, sometimes on the forehead, sometimes in different positions on the body. This is capturing what you know what humans are doing in the real world around things like um you know anything that requires some type of physical manipulation, particularly with with, the hands and arms.

That's a really big focus, and that's, some of the data that Figure AI released was around these egocentric datasets. Um this part of the market is very interesting.

It's it's a little bit of a uh um commoditizing really quickly because there are many companies out there going collecting the data, etc. Um the second part of data needs are related to the the annotation of that data.

It's less about the annotation of, what we saw with autonomous driving, which is putting a bounding box around all of the the images. It's more um assessing and filtering uh for quality standards, making sure that the instructions set matches the image, because there is really vast quantities of data.

There are LLMs being run across it. What we're finding is that there is demand for human involvement in the uh quality assessment of that data that's being captured.

The third data source is um more simulation-based, where it's getting humans to describe in a simulated environment the tasks that the robots should be completing as a way to provide the training data. Um there is one fourth bucket, which is related to teleoperations, which we don't really plan too much, but that's another interesting evolution of the market.

It's um like the LLM front, it's evolving very quickly, and the needs of the of the robotics uh builders are changing really quickly. We're trying to find um the best place, as you say, Josh, that's got a, a durable and longstanding value add, but is also um, going to get generate good margins for the business.

Right. Just final one for me.

Um just in terms of, obviously, being quite a bit of movement and um change in strategy from one of your, potentially big social media customers out there. I know you had a fair bit of work from them towards the end of last year.

Have you seen any of those larger projects start to resume or get any signaling on when you expect some of those projects to resume?

We're still I mean close communication with all of our clients on their needs, and what we find is that the these shifts can come very, very quickly. It's um, there's there's a lot going on across all our clients.

They're changing strategy really quickly, and we stay super close to to meet their needs.

Got it. Not hasn't sort of resumed some of those larger ones as of yet?

Just more so a second-half story.

Yeah. Look, there's always a there's a traditional skew to the second half, we're confident that um, we'll see uh that growth come through.

Okay. Thanks, guys.

All right. Thanks, Josh.

Um next question comes from Connor O'Prey at Canaccord. Connor, please unmute your line and go ahead.

Yep. Hopefully, I've managed the technology.

Um Ryan, a couple of questions. Maybe

if we go back to if if

if we go back to the I guess if we go back a few years to the previous peak of the business, one of the defining characteristics was a heavy customer concentration really around two customers driving a lot of the revenue growth. I'm wondering, and I think you and I were both you're in the business, me observing the business from the outside, we're both around that, and I'm wondering what at that time, I wonder what lessons you're taking from that, especially in in the China business, which is going through a sort of similar, analogous kind of growth path.

Are you Are you able to diversify the revenue across more customers to sort of lessen those risks?

Connor. It's It's certainly a focus for us, and I think the difference between what um, that period that you're explaining where there are, a couple of big customers that that contribute a lot of the revenue, the the China AI ecosystem, there are some very dominant players, but there's a decent number of them that it's not market skewed towards two big kind of customers like it was traditionally.

The focus for us is, hey, we want to serve the big accounts to the best that we can. There's a huge amount of growth potential there.

Um we also, work with you know a really large number of customers um in the China business. This covers, the tier-one labs, tier-two labs, um, startups that are getting into the space.

We we um I I'm less worried about that diversification risk that we had previously um vis-à-vis what what's happening in China at the moment.

Thanks. Then just back on global, um is it fair to catego- characterize the revenue trends there as the kind of the legacy business, the old, I guess we would call it, the content relevance piece, is that in a sort of a structural decline, or is that decline How would you characterize that?

On one hand, we're sort of seeing that possibly swamp all the other kind of good stuff that's going on or or or is it I'm guessing it's more complicated than that, but maybe you can sort of break that apart a little bit for us.

There's certainly an element of that, Connor, where we've seen some of our more traditional work um, decline as its, LLMs are are able to replicate some of that work. What we have seen, um, particularly in the first half of this year, some some good stabilization across some of the, um the vast majority of that work that we're doing, and we are seeing the uptick in in the newer areas related to LLM development.

It is it is certainly a factor, what you're what you're describing there, and and and quite accurate. We are as as we called out in the pres- presentation, seeing the stabilization in some of the revenue from more from more of our traditional work.

Thanks.

All right. Thanks, Connor.

Uh next question um just on cost-out. You've identified $12 million in annualized cost efficiencies in Appen Global with 70% to be executed uh by the end of Q4 this year.

At what point does the incremental margin benefit of this program start to visibly throw flow through the P&L?

I'll Just, I'll I'll pass that one to you. Yeah.

Thanks.

Thanks, Sam. There will be some benefit towards the end of the year, but it's not going to be material the way the timing works.

I think there'll be incremental benefits in the second half, then in Q1 with the full benefit from from the start of Q2 next year.

Great. Thank you.

Um and just a couple of final questions here. Is the board considering acquisitions or other strategic transactions?

If so, what capabilities or businesses would Appen specifically target?

Like I called out, um M&A isn't a um we're very happy with the organic capabilities that we're building. The market's changing very quickly, and we need to be dynamic and responsive to to the needs, we're we're um yeah, largely focused on organic growth in the business.

Okay. Thank you.

Final question, as profitability and cash flow recover, would the board consider share buybacks, dividends, or other capital returns?

The board um, will closely look at um capital allocation and consider all options as as we continue to improve the cash reserves in the business, um, yeah, that that's that's certainly for consideration.

Thank you. That's all the time we have allocated for questions today.

If you do have any follow-ups, please feel free to send them through to me via email, and we'll endeavor to get back to you. Uh and maybe with that, Ryan, I'll just pass it back to you for any closing comments.

Thank you, everyone, for your time today. Thank you for your continued interest and support in Appen.

Um, we continue to to play a major role in the AI ecosystem. It's a It's a very fast and rapidly evolving space, um as I'm sure you are keen interest in.

Um the Appen's role is uh for over 30 years now has been at the forefront of AI, and we look forward to continuing to supporting our customers um and delivering great financial results for our shareholders.

Great. Thank you.

Thanks very much for joining. That concludes Appen's first half FY26 results call.

Enjoy the rest of your day. Thank you, and goodbye.