RWS Holdings Stock

Welcome to RWS Half Year Results for 2026. I'm Ben Faes, the CEO of the company. And with me today, I have Stephen Lamb, who joined us as CFO -- sorry, we have Stephen Lamb, who joined us as CFO in March; Christina Scott, our Group CTO, who you've met already in December; and Lewis Whiting, CEO and co-founder of Obviously, which RWS acquired last month. Beyond the usual update, we'd like to give you today a view on the progress we are making in the strategy that we've announced in December.

The company went through significant reorganization, and I want to share how this pivot is going. We're operating in a fast-changing environment where innovations are released daily, where our clients need more help than ever to navigate the new reality of an AI-powered world. And in a rapidly evolving market, RWS grew its revenue by an exceptional 7% organically, showing the importance of our work to our clients and also the value of internally having a lot more clarity and focus.

In December, we set out 3 key priorities: a refreshed go-to-market and innovation road map and an efficiency plan. Six months on, all 3 are delivering and crucially, they are reinforcing each other, which said we returned to growth, and we have, ahead of expectations. We've been quite busy. The pace at which we operate has significantly accelerated. So you'll hear a lot about the financial performance in detail in a second.

I just want to insist on a few points here. Our technology road map is very strong and has achieved significant milestone. We signed a unique partnership with Cohere and built together a product that delivers today the highest quality translation across 30 different languages on any benchmark. Our leadership team keeps getting stronger. Stephen is bringing years of PSC experience. Brajesh, our new CEO of the U.S.A., has been leading very large-scale operation at Genpact and Oracle. And Ravi in India was previously leading all offshore operations for News Corp Group. This isn't a turnaround on paper. It's leadership refresh, logos won in every segment, new AI patents, cost down and an organization that genuinely is operating AI first.

As the adage says, what gets measured gets managed. So in December, we've put KPI against each of these priorities. Our top 100 clients are spending more with us, not less. Net repeat revenue of 109%. In an AI-disrupted market, that's the number really that matters. That's our largest enterprise relationships are deepening. On the Net Promoter Score, as expected during a strategic reorganization of this scale, engagement temporarily dipped, but our rapid execution meant that our latest pulse in May bounced back to 53, the highest in 2 years.

1/3 of the business is now AI related and growing. We have actually started 2 new lines of services on the Voice AI front. Voice, I believe, will be the interface of the future and the cultural intelligence that we are building is even more important in the voice environment. We are increasing productivity. Each employee is contributing to higher value creation, which in turn increases our EBITDA margin.

And on that note, I will pass to Stephen.

Thank you, Ben. Good morning, everyone. I'm delighted to be presenting my first trading update as Group CFO of RWS. This morning, I'll take you through our strong financial performance for H1, covering a brief summary of group trading, an overview of performance in each of our business segments, summary of cash flows, our outlook and key considerations for the remainder of the year. This slide focuses on our key financial metrics for the first half. We delivered strong organic revenue growth and double-digit profit growth in the first reporting period under our new operating model.

In December, we provided high-level FY '26 guidance for the group, and I'm pleased to be able to say that we exceeded our expectations for each of revenue, operating profit and cash conversion. On revenue, we delivered 7% constant currency growth, a strong outperformance and our highest growth rate for a number of years. This outperformance was driven by an exceptional growth in the Generate segment, which is linked to a large-scale AI project to support a strategic client through a key phase in the rollout of one of their new solutions. Whilst both Transform and Protect performed in line with our expectations.

On profitability, adjusted operating profit margin improved by 130 basis points with adjusted operating profit up 28% to GBP 26.4 million in H1 compared to GBP 20.7 million in H1 in the prior year. It's worth noting that the year-on-year growth is despite absorbing a couple of large headwinds being the accrual of market rate bonuses this year, which were negligible in H1 '25 and adverse FX movements, which I'll come to shortly. Our operational free cash conversion, which is defined as adjusted EBITDA less working capital movements, lease payments and CapEx was also a little ahead of expectations, reflecting good working capital management together with focused capital investments in our new products.

Turning the page. Looking at our financial highlights. Our first half financial performance resulted in total revenue of GBP 360 million with reported growth of 5%, and as I said, constant currency growth of 7%. It's worth pausing here to touch on how currency impacts the group. Almost 70% of the group's revenue is in U.S. dollars, and as such, a weakening of the dollar reduces our revenue in GBP. The global services nature of our cost base means that less than half our costs are in dollars and the strengthening of other currencies increases our cost base in sterling.

In FY '26, both of these factors have gone against us. We actively hedge around half our net U.S. dollar exposure in the year, but we cannot fully offset these headwinds, either in the current year or the medium term. As we pivot to a technology-first model, we do expect this currency imbalance to gradually correct. Gross margin of 41.6% for the half was below the prior year, with over half of this change being driven by the outperformance of lower-margin TrainAI activities and the balance is related to product mix in the other business units.

At a net margin level, we have seen the cost actions taken over the last 12 months to continue to improve productivity across the group. The combination of strong revenue growth and disciplined cost management has delivered significant adjusted EBITDA progression with GBP 46 million in H1, up 20% from GBP 38 million last year and EBITDA margins expanding from 11.1% to 12.7%. Adjusted operating profit, as I said, was up 28% year-on-year with margins up from 6% to 7.3%, whilst adjusted EPS was up 34% to 4.9p, so good growth at every profit level. Cash and net debt was also well managed, and we are declaring our first interim dividend since rebasing our dividend payout in December 2025. At 1.75p, our interim dividend is consistent with our declared progressive full year policy and a payout at this stage of around 25%. This compares to around 20% for the prior year interims.

Moving over. Over the next few of the slides, I'll walk through trading performance across each of our 3 business segments. Given this is the first time we are reporting using our new segments, I've also included a simple reminder of what each business does together with an indicative size of the market. These market assessments were done by a third-party consultancy for us last year. For each segment, we've included the serviceable addressable market, SAM, which is effect of the elements to which our services and products are relevant, the vendor market, i.e., how much is spent for third-party providers, the medium-term growth rates and finally, our share of that vended market. I've also included adjusted operating profit by segment for the first time.

So now turning to Generate. Generate helps enterprise clients produce intelligent knowledge and deploy trustworthy AI at scale. It comprises 2 complementary businesses. Our content technology business includes enterprise-grade content management solutions, including Tridion, Contenta and Propylon. Whilst TrainAI provides a full range of end-to-end AI data services for all types of AI.

In terms of market context, the total SAM for Generate is estimated at over GBP 5 billion with a vended market of GBP 2 billion. Although given the recent AI investment, this is likely to understate the opportunity. As you can see, the growth opportunity in AI data services is particularly exciting. In the first half, Generate delivered exceptional revenue growth at 52% constant currency, largely due to the key project mentioned previously. Despite tough comparatives from a strong FY '25 performance in the higher-margin content technology business, this resulted in 19% operating profit growth in Generate. The key takeaway from the excellent H1 performance though is that in TrainAI, we've been able to demonstrate we can rapidly scale activity to meet peaks in customer demands whilst maintaining high-quality ratings and also onboarding new strategic clients. We see this as a fast-moving and evolving market segment and are focusing investments in TrainAI to ensure that we build sustainable and scalable business for the future.

Looking to the second half, we've taken some targeted actions in content tech to improve our sales capabilities and margins, but it's also worth noting that activity in TrainAI returned to normal levels at the start of the second half.

Turning to Transform. Transform is our largest segment, representing approximately 60% of the group. Transform combines AI-enabled language technology with human linguistic expertise to help enterprises transform how their content is created, adapted and delivered worldwide. This segment also includes 2 market-leading technology platforms, Trados, which is our translation management solution; and Language Weaver Pro, our amazing new AI-powered translation solution, which you'll hear about shortly.

In terms of market context, the SAM for language services and technology is approximately GBP 17 billion to GBP 19 billion with around 2/3 of this outsourced to specialist language service providers. The broader language services market is declining in low single digits, but we expect this to be offset by strong growth in technology and automation. Despite the global market leader being -- despite being a global market leader, our revenue of GBP 450 million represents only around 4% of a fragmented global vendor market, highlighting the significant headroom remains for us, albeit our focus is on the large enterprise clients where we can really solve their complex needs on a global scale.

In H1, Transform delivered revenue of GBP 210 million, which is a mid-single-digit decline as previously guided. Demand for services revenue remained robust. Pricing is holding up, and we continue to win and onboard new customers and increase our share of wallet with existing clients. We're continuing to invest to pivot to a technology-first approach. The launch of Language Weaver Pro at the end of the half and the development of our new translation management platform is also on track. These will form the foundation of our shift to a recurring technology revenue model over the medium term.

The Transform segment also benefited from increased productivity, reflecting our cost-saving actions, and we are really pleased with the 17% increase in adjusted operating profit. Looking to the second half, we expect to pick up in both revenue and net margin in Transform due to a combination of higher-margin seasonal projects, initial sales of Language Weaver Pro and further productivity benefits.

Finally, turning to Protect. Our Protect business helps enterprises across their intellectual property life cycle from initial idea to commercialization. This enables them to safeguard their brands and maximize the returns from their innovations. In terms of market context, the overall IP services addressable market was estimated about GBP 3 billion per annum with a global vended market share of about GBP 1.5 billion, growing at low to mid-single digit rates. I should add that we believe the Obviously acquisition adds a further GBP 2 billion of addressable market opportunity.

Our revenue of approximately GBP 100 million represents around 5% of the vended market share, and we are really well positioned to increase our share of this, particularly as we broaden our offering. In H1, we delivered revenue of GBP 51 million, representing a growth of 3.3% at constant currency. Within that, the mix was impacted by a higher contribution from our renewals products, which is of course carries a lower margin profile. We also secured a number of new client logos with notable successes in both APAC and the Middle East. We saw a planned increase in our cost base as a result of both investments in our teams and sales capabilities and our processes and technologies.

Looking to the second half, we expect Protect to show improved margins with a focus on selling higher-margin products, onboarding new customers and simplifying our delivery model. Clearly, we'll also be focusing on welcoming our new Obviously colleagues and building a combined sales pipeline, and you'll hear from Lewis about this shortly. In terms of financial contribution, we expect Obviously to have a GBP 1 million negative impact on PBT this year after cost of funding before becoming profitable at an operating profit level next year, probably breakeven after funding costs and making a strong net profit contribution thereafter.

Moving on to cash flow. We started the year with GBP 25 million of net debt, excluding leases. The adjusted EBITDA contribution for the first half was GBP 46 million, reflecting the strong trading performance discussed. Working capital saw an outflow of GBP 5 million, which is broadly in line with our revenue growth trajectory and reflected a solid working capital performance for the period. Total CapEx payments of GBP 7 million in H1 reflected both investments in new products and process automation and the exceptional cash items totaled GBP 11 million in the period, mainly related to further payments for productivity investments, both people and IT related. We also paid the final FY '25 dividend, which together with the above brings us to a period-end net debt position of GBP 33 million. Net debt to adjusted EBITDA level is below 0.5x after the Obviously acquisition actually, which we paid GBP 16.5 million just after the period end.

Finally, just looking to our outlook. As noted in our interim announcement, whilst we expect profit in FY '26 to be H2 weighted, it is much more balanced than we saw in the prior year, which was 30-70 split, and that reflects a strong H1 performance. The H2 profit weighting reflects a combination of the seasonality of the Transform business, good sales pipeline momentum and further benefits of productivity initiatives coming through.

Given we expect the TrainAI business to moderate in H2, we expect the group to return to a normalized level of revenue growth. Across the full year, this is expected to result in mid-single digit organic constant currency growth. I've also got a few messages to call out on full year profit expectations. On trading profit, we are not expecting any significant change to current expectations for our full year outcome. Clearly, the exceptional TrainAI revenue will dilute group-wide margins for the full year and provide an interesting headwind for H1 next year, but we expect to be on track overall.

On nontrading activity, currency will be a full year headwind at current rates. When we set out our expectations in December, the dollar rate was GBP 1.32. Since then, the dollar has weakened to around GBP 1.34 to GBP 1.35. We managed to hedge about half of this in year, but this is still expected to represent a GBP 2 million to GBP 3 million headwind on net profitability for the full year based on the currency explanations I gave earlier. Clearly, it will also depending on where the rates land impact FY '27 and beyond. And as noted earlier, we expect a small negative impact this year from the Obviously acquisition, but this will be building great momentum going into FY '27. Finally, I've included some further balance sheet details and modeling guidance in the appendix.

And with that, I think I'm handing back to Ben.

Great. Thank you, Stephen. Now I suggest we'll dive a bit into each of the pillars of our strategy and illustrate really a bit more clearly what we've been doing. So at the beginning of this year, we set ourselves 3 strategic objectives. I'm a strong believer of the power of focus and executing at pace. We revamped our go-to-market to focus on key growth segments, and we are at an extraordinary moment in the industry where enterprise are rethinking everything they do. To do that, they need to rely on strong partners, partners they trust can bring the work they need, but with the fuel of AI. I will walk you through some of the examples of this work we're doing for our clients across our segments.

We rebuilt the technology team and the product team basically from the ground up to do 2 things fundamentally: bring innovation to our clients and build the internal AI platform to consolidate our assets and help us scale faster. Christina will explain in more detail the incredible achievement, for example, of launching Language Weaver Pro in 9 months, which is now the most advanced enterprise translation engine by any measure. You'll hear as well from Lewis on the story of Obviously and hopefully understand how powerful an addition it is to our Protect segment.

Finally, we started a very strict efficiency program, which we -- which is delivering visible results. We have a clear plan to continue this throughout '26 and '27. And this is really a combination of modernization of our workflows, significant automation and reliance on more offshore skills. Stephen will share more details about this. The work we do is really hard and really important for our customers. Really hard because it requires complex orchestration of technology solutions and very skilled human intervention at scale. Really important because it's work that help our clients grow their revenue, reach new customers in a safe and compliant manner.

And I'll share with you three examples to illustrate this point. In December '25, one of the most prominent AI companies revolutionizing customer service by building Bespoke Voice AI agent came to us with a very hard brief. They wanted their agent to sound like home in the U.K. This meant being able to spin up agents that could adopt local accents from 12 different regions in the U.K. And they tried that with others. They failed because it's really, really hard to do. Anyone will say they can, but in reality, it means casting hundreds of people, building scripts, building an operating platform that guarantees the security of their data. And that's what our TrainAI team delivered. Today, we operate this with the amazing clients in many more countries, helping their agents sound more human everywhere, more like home everywhere.

The second example is from probably one of the most successful fintech company in Europe today. They're expanding incredibly fast, and they needed to adapt their video ads to 11 new markets. In this occasion, we work initially with their creative agency, and we delivered 200 different assets localized using our production platform with carefully chosen artists for the dubbing of their videos. Our solution was comprehensive end-to-end managed service, and we didn't just execute. We took full ownership of that work. This included managing voice talent, localizing scripts, leading recording sessions and acting as a guardian of their brand throughout the process. The client was so pleased that we expanded even further. We now work directly with them. And the following year, we delivered 350 assets for them.

And finally, this last example is from our Protect segment and more specifically, the work through our new platform, Obviously. In the previous example, I showed how RWS is assisting clients expand their brands abroad. And when those brands are successful at doing this, they often expose themselves to counterfeit. Counterfeit is a problem and potentially can destroy their brand. Beyond the missed economic opportunity, it is potentially destroying the image of the brand, exposing customers to bad quality and even dangerous product. Obviously helps fight this.

With this incredibly successful plush toy company, which is growing at exponential rate, they get copied a lot. Fighting counterfeit is a bit of a whack-a-mole game, and that's where AI comes quite handy. Obviously's Vision AI technology is able not only to recognize the brand attribute, but also the detail of the product and the key features of that product. And then they're able to make a judgment call to decide whether it's a true or a fake product to initiate a takedown notice of the counterfeit. The system is actually so good that it makes counterfeit economically unviable. So counterfeit and move to the next brand potentially.

Finally, we don't just sell AI solution. We run on them. This is a brilliant example of how we've modernized our marketing function at RWS. Less than 6 months with actually half of the team that we used to have, the team has achieved phenomenal results. Today, we're the #1 seated localization brand across AI search engine, well ahead of any competitor on generative engine optimization. We've replaced also 5 tools creative tech stack with 1, which represents really an 80% saving for us. We've also built a set of very smart AI agents used by our sales team working 24/7 to help them perform better in their job, anticipate demand, understand customer needs, detect RFP signals before they are even published. This is the AI-first culture that's spreading in each function.

Now I hand over to Christina to show you more about the technology.

Good morning, everyone. I spoke to you in December and set out our new product vision and how I've restructured the product and technology team and also disclosed the deal with Cohere. I'm delighted to say that we're on track with delivering our new products. But today, I want to really focus on the Cohere deal that we did and that great partnership that Ben has mentioned a couple of times already.

In March, we launched Language Weaver Pro, which is built on Cohere's Command A+ model. This model was purpose-built for enterprises, and it has 100 billion parameters. So to put that into context, a small model of, say, less than 10 billion parameters would be like a linguist, junior linguist who is proficient in one domain, but they would guess outside of their domain. 100 billion parameter is like a senior linguist decades of experience who is proficient across finance, law, marketing, technology, understands regulatory texts and knows when not to improvise.

As the world of Gen AI has evolved, enterprises are beginning to understand the opportunities, but also the pitfalls. I was at Tech London Week yesterday speaking to a lot of senior leaders. And there are a number of challenges in really truly adopting AI at enterprise level. And the truth is that generic LLMs are simply not good enough for enterprise use for translation. While powerful, they just lack the nuance that enterprises really need. And we see four main barriers to entry to adopting LLMs for localization. The first barrier is quality. How do you get consistent quality across languages, across inputs and that you ensure that the fluently written outputs are actually accurate. Customers try to solve this by using lots and lots of input in the prompt engineering. And then suddenly, you're processing 500 tokens to translate 10 words, and the cost is going up exponentially and the processing is slowing down.

The next challenge we see is scale. How do we provide the throughput that customers were able to achieve through neural machine translation. LLMs were typically much slower to do the processing. We have one customer who translates 1 million words per minute. How do we meet those sort of expectations and not just by throwing more compute power at it, but how do we actually do it by achieving more with less.

The next area is security. How do you offer the quality and scale I've mentioned without packing all your data over to the APIs of the foundational providers and therefore, breaking some of the guarantees you may have given your customers around privacy. A number of our customers today work on on-premise solutions and particularly popular obviously with governments and legal companies, financial institutes. And the last area people are really worried about is safety, and it remains a big challenge. And it's not just the biases that are built into a model, but it's also ensuring that the output is accurate and true to source. Hallucinations can be a simple odd extra word, so not so harmful or it can be something that sounds really fluent and confident, but it is totally wrong. So when we did the deal with Cohere, we didn't just license a model. We forged a strategic alliance with them. And we codeveloped and fine-tuned their model. But on top of this, we've built the product, and we've added the capabilities to offer something that is unique in the market, and it addresses the shortcomings of the off-the-shelf models. So let's just think about how this has broken down the 4 barriers I've described.

So to address the quality issues, we worked with Cohere on training and the evaluation, both machine and human evaluation and making sure that we've created something was the best quality available globally. We chose different languages, different domains, working at paragraph and sentence levels and removing English as the target or source. And this quality is built into the underlying model, which means that you can build a lot less into your prompts, ensuring that you can increase your processing speed.

The next challenge is scalability. Most models are just too slow to be able to handle the 1 million words per minute requests that our customer needed. Cohere architected into their model, the ability that the input gets routed to just one part of the model and expert in the model and therefore, reducing the processing time it takes. And most frontier models would need a whole data center to host, meaning that it's cost prohibitive for enterprises to go to that solution. Our model runs on just 2 GPUs, allowing us to offer a hosted cloud solution, a private cloud solution or a fully airgap solution. This is the only product on the market, allowing enterprises this flexibility to deploy exactly as their security policy dictate. So we have our cloud solution with 0 data retention or you can have your own edge solution complete within your own environment. And again, particularly important for many of our clients who need to ensure that they keep in control of their data.

And to ensure the output is not just at the highest quality, but in terms of fluency, it is true to source, we've developed a series of hallucination and overgeneration detection capabilities. These work to ensure the output is safe and can be trusted by our customers. The Language Weaver Pro is a product, but it also produces the machine translation capabilities across all our language technology portfolio. And so the result of this partnership has created a really unique and marketing-led product. An evaluation shared by Cohere on LinkedIn a couple of weeks ago shows that the Command A model was second, beating all of the competitors, so Google and everyone else. But actually, RWS is at #1, and that's because of the capabilities we've built on top of their model.

So as we look to the future, our partnership with Cohere will remain a cornerstone of the strategy. But it means developing further models, whether it's specific for industry or whether it's a 1 GPU model for efficiency, we will continue to push the boundaries of what's possible with AI to deliver solutions that will empower our clients to navigate the complexities of the global market.

And I'm now delighted to hand you over to Lewis to talk about the Obviously platform.

Thank you, Christina. Good morning, everyone. Lewis, Co-Founder and Chief Executive of Obviously. IP is hugely valuable. AI is accelerating its pace and growth and where content has never been easier to create, our clients have to work harder and spend more to keep their own unique propositions clear in a crowded landscape. We started obviously with a simple belief that the right mix of expertise, understanding of the market and new technology could create something that is better for the IP industry, a way to manage IP, trademarks, patents, commercial data together in one place far more efficiently than ever before. We had to invest heavily. We built 3 connected solutions into one platform. This started with my career as an IP attorney. So I trained, I qualified as a trademark attorney, and we built the Obviously business out of an award-winning law firm, Stobbs.

As such, this is an industry that we really do understand. This deep domain expertise and our first-hand grasp of the problems that our clients face, coupled with our talented engineering team has really helped us to solve our clients' problems at scale. Through doing so, we've built in a relatively short period of time, our own brand, the Obviously brand. We've worked hard to meet the needs now of around 50 of the world's largest brands across tech, financial services, fashion, entertainment, consumer goods. We've made sure that our tech works across all of these different challenges.

So looking at the fit of Obviously in RWS. A couple of things stand out for me. RWS' sales team, their customer success team and the scale at which they're able to solve really complex problems for the world's biggest brands is super exciting for the Team Obviously. In return, Obviously can deliver an accelerated ability to support the road map from a tech innovation point of view at RWS and bring our ability to quickly deliver AI native products into effectively the same customer base that RWS currently serves.

So one month in to life at RWS, it's been really great to see the teams working really closely together. We have incredible people at Obviously, and really incredible people at RWS. And this has given me increased conviction that we can continue to attract world-class talent, and we can drive forward with our plans to scale the most comprehensive AI-powered IP productivity platform and continue to serve all of our customers across the full IP life cycle.

So what is Obviously? Clearly, being an IP professional, we take this for granted, but I'll spend a bit of time to tell you in a bit more detail what it is. So as I said, one platform with 3 connected parts built by IP experts, powered by AI. If you take one of our customers, one of -- think about one of the largest brands in the world, it brings all of their intellectual property needs, their management into one place. It breaks down the silos between departments, finance teams, legal teams, brand creation teams, all of the same teams that RWS is serving, creates a single space for them to manage their most valuable assets, and it provides something fit for purpose for their continued journey from an AI adoption point of view.

Our modular approach across the 3 pillars gives clients the ability to start with us in one area and grow our relationship across the different services and technologies that we offer, where the solution is highly automated, it scales really well. It produces long-standing relationships with our clients, and that revenue by nature is reoccurring. Quickly to move through the product. So Obviously Manage, this is the home of our clients' intellectual property. It's the main system of record that brands rely on as a single source of truth. It's the operating system. It's the key component that allows us to replace old tech, siloed tools, manual processes.

Importantly, from an AI point of view, that system of record provides the context that then drives agentic workflows, AI automation and kind of revolutionizes honestly, how our clients are able to work within this global ecosystem of protecting intellectual property. Obviously, Protect, Ben mentioned, is our AI native, I would say, industry-leading brand protection, antipiracy content protection system. We pioneered our own computer vision. So this is using the kind of legal concept of what makes a brand strong, legal concept around how you protect your brand and the identity of your corporation and coupling that with machine learning techniques to create really powerful tech.

As Ben mentioned, it's AI that understands the DNA of a client's product. One of our clients, famous manufacturer of shoes, they have a very distinctive yellow stitch. And a real-life example is building AI that can detect a yellow stitch running across the wealth of a pair of boots and then think through how that would scale to process millions of images a day to find copies or dupes or replicas, lookalikes of that same product.

Finally, Obviously Discover. So this is a really interesting area for us. This is about connecting commercial data with IP rights. Naturally, this happens day by day anyway. So this is the real world. People sell products. Those products are protected by IP rights, and that generates revenue for our customers. The Discover platforms connects those data sets together and allows legal teams to make the same data-driven decisions that the rest of their organizations rely on across all of their different departments.

So in combination, use of Manage, Protect and Discover provides a very powerful engine when it comes to our clients' ability to see where their brand lives and act where it matters. So hopefully, you're all seeing where this opportunity leads us to. It fits right into the strategy that Ben and Christina shared with you back in December. Obviously fills a trademark and brand size gap in the Protect segment.

As you know, the Protect segment has a deep heritage in terms of its patent capabilities where I believe the business started, but it's never served customers from a brand and trademark point of view. That's now changed. Where is our focus? Accelerated delivery. The work undertaken by RWS to build the AI-powered IP attorney productivity platform, a bit of a mouthful, fits extremely well with the Obviously technology. From the work we've already been able to do in May, we can say that in combination, working together, we can speed up the existing road map and deliver growth sooner. The experience our engineers have in terms of building AI native solutions for this industry is also invaluable in terms of reducing the risk of that rapid pivot or program of development that we've set out to the market. What this means is growth and market expansion. So naturally expanding into a new segment of intellectual property comes with a new addressable market, the trademark space itself, the online brand protection space and across our wide range of kind of service offering, we unlock an additional market of GBP 2 billion.

In addition to that, we can also use our technology to better serve customers in relation to the existing IP services delivered by the Protect segment. That unlocks again further addressable market and increases the amount of customers we can serve.

So finally, on to kind of a real guiding proposition for us, brand guardianship and the trademark life cycle. Brand guardianship really matters to our clients. Brands take a lot of energy to create, nurture, grow, reach customers and clients use those brands to ultimately drive revenue. Where we have -- sorry, it's an existing proposition of RWS that customers create content and generate, localize and transform and protect and protect. The step from patents into trademarks mean we can continue serving customers through now into the brand and trademark space.

Why this is really exciting? Where that content and localization work is brand heavy, and I would say for a business like RWS serving 85 of the world's top -- or 85 of the top 100 or biggest brands in the world, much of that work is brand heavy. And by continuing to be able to deliver services throughout the life cycle and beyond the current capacity of the business, that will naturally lead to increased growth. So I think that's covered all I wanted to share with you today. But generally, to wrap up, I think the most important message for me is this acquisition allows us to continue solving some of the world's kind of largest and most complex problems at scale.

And yes, happy to take questions later. Thank you.

Thank you, Lewis. Such a great business. I have one slide on efficiency. So I just share this with you now. So as I mentioned earlier, a key part of our H1 trading performance was delivered -- was driven by the delivery of our productivity improvements. This is actually becoming a core part of the DNA at RWS. There is no single lever we can pull to reduce our cost to serve, but I set out a few examples here to show how we are achieving this goal.

On people, as technology improves, we've been able to simplify and automate more, and this has allowed us to reduce our overall number of FTEs by about 6% in the last 12 months. And you see on the chart here the improvement of revenue per FTE. We've also been making more use of our offshore shared services hubs to standardize processes in lower-cost locations.

On products, we are simplifying our portfolio through a program to actively end-of-life, end-of-sale legacy platforms with a significant progress being made transitioning towards our core Trados solution.

On IT, we have a global program to rationalize our legacy infrastructure, and that's allowing us to move to a more secure, more scalable and efficient cloud platform across the group. Not only does this save in IT, it also makes it much easier for us to exit a large number of our legacy locations.

And finally, we're also championing the use of AI across the business. We've given AI tools to our teams, challenge them to learn and encourage them to show off their successes. Ben has already mentioned our achievements in marketing, but AI is now starting to benefit all areas of the business. One notable success has been how much faster we've been able to develop our new products across the group and a significantly lower cost. A brief summary on productivity.

And with that, I shall hand back to Ben.

Thank you. So let me close for you. When I stood before you 6 months ago, I just didn't -- I didn't just outline a vision. I laid out a strict blueprint for this company, a new go-to-market strategy, an aggressive innovation road map and a rigorous efficiency plan, all tied to hard KPI. Today, you're seeing the results of that execution. We delivered 7% organic growth, significantly ahead of expectation. Profits are up 28%.

Our top 100 clients are spending 9% more with us. In a market where the consensus was that AI would shrink this industry, we're proving the exact opposite. We aren't just participating in the AI revolution. We are leading it. We built and launched Language Weaver Pro in just 9 months and today, it outranks the engine built by the biggest tech clients in the world. With the acquisition of Obviously, we are forcefully expanding our footprint into a GBP 2 billion market that we didn't play in last year. Internally, we are at our own base case study.

We're running an AI-first playbook across every function, starting with the marketing that I showed you. This disciplined operating model is exactly why we can accelerate growth while driving margin expansion. On the outlook, we have upgraded our revenue guidance to mid-single digit growth, whilst our margins will still expand this year. The mix is shifting, and it's shifting because we are aggressively capturing market share with TrainAI at an exceptional rate. More top line growth, higher profit and rising margin will make that move every single time. This company is executing at a pace it hasn't seen in years. The strategy is locked in. The culture is revitalized. We said we return to growth, we did. Now we intend to win.

Thank you very much. I'd like to invite my colleagues for Q&A.

Katie Cousins from Shore Capital. So 2 questions from me. First one, obviously, talking about the GBP 2 billion TAM. Just kind of what's your market share of that and the competitive landscape? And also if there's a crossover of RWS clients that you feel that you could upsell and make better use of the customer wallet. I'll go with that first.

Thank you. So I'll start and Stephen can chip in if you'd like to. So Obviously is a small company. We're focused hard on innovating, investing, building the technology. Our market share is small in terms of the competition, but our scale and breadth is -- or our breadth rather is large in terms of where we can service the market. Yes, there is a really strong overlap both in terms of our existing clients, the clients in the Protect segment, but also importantly, the clients in the Transform and Generate segments.

So we know our product serves those clients well, and we have lots of use cases in terms of serving similar clients already in the business. The competitive landscape is interesting. There's a good market there. It's an interesting market to play in. As we said, AI is pushing forward in terms of the necessity of our products to the clients. I would say what triggered us to build the Obviously business in the first part is our clients' frustration over legacy tech. And what we benefit from is a fresh business that's built this product from the ground up in a relatively short period of time. And by definition, that means our tech is really new, it's innovative, and that's where we compete most effectively with our competition.

I think actually the only thing to add is that we did this deal in record time, and we did that deliberately to land just ahead of the trade show, the biggest trade show for the industry in the U.K. And I think over that weekend, we've generated some phenomenal leads, both from Obviously clients, RWS products, RWS clients there, but also a much broader set of global clients that we can now target as well. So it's really exciting opportunity. I'm really hoping my guidance on it is very conservative.

Dividend. If we kind of think about a standard phase of 1/3, 2/3, doesn't kind of look like it's going to get to that progressive stance without either a lot higher second half or final dividend and also that implies a higher payout ratio, which I think was -- is probably not consistent with your rebased and previous language around being aligned to earnings?

So we -- after the rebasing in December, I think we settled to 7p for last year in total. Traditionally, we've paid 20% in the first half and 80% in the second half for some historic RWS reason. This time around, we are expecting a progressive dividend for the full year compared to that 7p. And so we've gone with 1.75p as a conservative quarter. We're kind of nudging in the right direction is probably the way to think about it, but it should be progressive for the full year.

We can use that 7p.

Yes, the [ 7p ] is the base.

James Beard at Deutsche Bank. I've got 2 questions, please. Firstly, on Language Weaver Pro. Can you talk about the early progress that you've made in terms of marketing and selling that product? And also how we should think about that product in terms of differential, I don't know, pricing or service versus previous iterations of Language Weaver and therefore, how this product might contribute to future growth expectations?

And then secondly, on TrainAI, I'm going to ask a question that I think I asked about a year ago and see how different your response is now. But a year ago, I asked about your -- wanted to get your perspective on the longer-term viability and sustainability of the revenue model within TrainAI. What has happened in the last year to have changed, if anything, your viewpoint on that? Obviously, we had in H1, what appears to be a very large contract project, which has since finished. Is that the sort of catapult to further growth? Just interested to get some more thoughts on that as well.

Okay. On Language Weaver Pro. So it's software solutions that takes a bit of time to install. It's not just a click to play. So we have already signed a few of our customers. There's 2 motions. One is upgrading our existing customer from Language Weaver. And that's in terms of price, roughly double the price, the Pro version. So a great upsell opportunity. And on the new customers, I mean, we have now a very strong pipeline of people that are interested because they want to try it, because they are curious about this benchmarking in quality.

And more importantly, from what Christina was saying, it's -- we see a lot of companies now being a bit more wary of their token consumptions. And if you translate hello on Claude, it will burn through a lot of tokens that you don't need to burn through because it's not meant for purpose. So those are the 2 reasons why we see a lot of attraction and upsell potential.

On TrainAI, I remember your question. I remember my answer. I feel actually a lot more confident today about the perspective. That's confirmed by all the market studies on the Data Services segment. I think the 2 elements I'd like to point out. One, you're probably aware of the CapEx level of the large LLM builders, which I don't -- I think last year, when Google said they would spend -- I think it was GBP 60 billion last year, everybody thought it was outrageous. They are going to spend GBP 180 billion this year and that's meant to deploy more AI models. So there's a commitment from more builders to invest in future models.

We've signed actually 2 more foundational model builder so that we're diversifying also our customer base. And I think the new models are fundamentally harder to train because they are multimodal, they are voice and that requires a whole level of input that is hard to find on an open source model. All the models trains on text on the Internet. The voice and the spatial models are very much harder to train. So we're investing a lot in what's called the world models and tomorrow, the robotic agentic also.

Thomas Callan from Investec. I've got 2 as well. So just on the headcount reduction during the period, I think you took about 500 heads out of the business. Can you just give a bit more color in terms of where those reductions actually landed and also where you think head count might be by the end of the year?

And then just on Transform for Christina actually. I just wondered to what extent do you think clients at the moment are paying for that sort of outcome risk piece as opposed to just words or licenses? And how do you see that potentially evolving over time? Do you think more clients are going to turn to you guys for absolute guarantee that they're getting a safe translation and an accurate translation and therefore, can push back on you if the translation is inaccurate?

So the head count savings are -- they're spread quite broadly across the business actually. As I touched on, we're making -- we've made some productivity improvements in our translation area, in our client services area, in some of our overhead areas and also in terms of our business product development areas as well. So there's no one go-to spot. I think as I said, it's in our DNA. We are making the right decisions. We're not doing anything that doesn't feel right for the wider business. And so I think the head count will slowly tick down over time, but we've not got any dramatic changes coming.

Just on LXD specifically to use the old terminology, I think we're at, what, 1,500 heads at the end of last year, something like that. Where are we today on that?

A little down. Yes, it's down. I can't give you the exact number, but it's...

And if I can pick up the second question. Yes, it's obviously very dependent. There's lots of different use cases. And so for use cases where enterprises really care about quality, that is exactly the conversation that we're beginning to have. And with the testing of new products, so obviously, there's more product than just Language Weaver Pro, we are speaking to early alpha customers and testing with early alpha customers. And so some customers are already making that transition and thinking in that way. Others will be further behind. So what we think it will be a phased approach, there'll be early adopters who we are now working with to make sure our solutions are fit for purpose. And then we see the rest of the market will start to move in that direction.

James Bayliss from Berenberg. Three, if I may. On the Cohere partnership, can I ask how the unit mechanics work? The output model is yours, but the development was with their input. How does that all come together as you look to push that out into the wider market? And then perhaps specifically on that one, given it's their own model -- sorry, they have their own model, is there competition there? Or are they now fully kind of pushing the RWS output given it is superior to their own offering?

Then Lewis, one for you perhaps. I thought there was a really clear run-through of the strategic fit and the opportunity for, Obviously, as part of RWS. But I'd expect it was quite a competitive space when you were looking at who the kind of the natural owner for the business might be. Is there one kind of overriding reason why you went with RWS as opposed to anyone else? And then to round it off, one for Stephen, just a quick one on CapEx. I think your guidance evolved from a modest increase as a percentage of revenue back in December to a modest decrease this year. Is there anything specific to call out on that? Or is that just evolution now that you've started executing the strategy and you're landed in business?

I start with Cohere. So I think the important thing to remember is that the model, the LLMs, they are not a final project. And so what Cohere do is they build a model and we've built a product, and it's a product that we're selling to customers, and that's a really important distinction. And so we worked on Cohere on improving the model, but the model is completely hosted for us, and we have built our product on top of it. So they can -- the Command A model, there's some exclusivity clauses in the contract. They can sell it to other people, but actually, it's the product that is most valuable. So all the hallucination detection, identity access, all the things that are built around the outside, those are the things that make it really important for enterprises and fit for purpose for enterprises.

From an Obviously point of view, yes, the process was competitive, and it was a great experience in terms of being -- looking at the competitive landscape through that lens. Why RWS? Why was that such a compelling choice for Obviously, and for RWS. So really, I think it comes down to the fit of the 2 businesses. From a competitive landscape point of view, Obviously has driven hard to change the way that our clients operate. So from a legacy existing competition point of view, we have the choice in terms of being acquired by one of those legacy players in our market or moving into a business where actually the vision is so clearly aligned, but we do not have competing legacy technology. So that offers a really fresh approach to our clients, to our team, to the market. And I think that probably answers the question in a snippet. But yes, it's a great result for everyone involved.

And just on CapEx, I think the -- probably the biggest change, and I think we spent about GBP 7 million in the first half this year is just focus really. So we're spending less than we were on back-office systems. We're spending less than we were on IT platforms because they're moving everything to the cloud. And we're being very disciplined in where we put our dollar in terms of investment in our product development. Questions online?

So the first question is from Chris Morrison of Jupiter. What is the profile of the exceptionals and how long will they last?

So we are guiding to around GBP 25 million this year in terms of exceptionals. The 2 big items in there are the people-related exit costs for the head count reduction we touched on and also there's a big element of the IT replatforming to the cloud. I think that will continue into next year, possibly ticking down a little bit, and then we expect to see it slowing a bit after that. But I do think as we continue this journey, we will continue to evolve.

Next question is from Gareth Evans from Progressive Equity Research. It's interesting that a lot of what you do relates to large customers building trust and belief in their own brands. Could you talk a bit about how you're evolving RWS to make sure that these customers trust RWS to help them manage their AI implementations?

I think I would reframe. The RWS is one of the greatest assets that we have is this trust from our large customer. We have relations that span 20, 30 years with some of the largest brands around the planet. And that trust comes from this very, very high benchmark of quality that RWS has always delivered in core localization or IP management. And I think we are building on that trust platform. We're very careful on how we deploy new products because we don't want to risk any of that trust we've built over the years. But this is one of the strongest assets that we have in the company is a long-lasting relationship with some of the most prestigious brands.

Another question from Gareth. Post the acquisition of Obviously, please, can you talk a bit more about your future M&A pipeline?

I mean I don't want to talk in detail about the M&A pipeline. We have -- it's a moment of massive change. And I think we see 2 things. One, as we build this internal AI platform that's becoming more fit for purpose to be an AI consolidation platform. We're adding more services into that platform helps us scale without increasing cost. So there's an appeal there. And the second one is we'll make an informed decision this time acquiring Obviously, we acquired Momentum. They demonstrated that top brands were voting for their solutions. And that would be -- that's a much faster access for us to the trademark industry than building a platform on our own. And there are, I think, a lot of appealing technology out there. But getting to our list of customers is something that's really hard for a new tech company. And so I think there's probably more opportunities there also.

Another question from Hamish Adam from Patchwork Investment. If Cohere was to be acquired by a larger model company, where would that leave the business relationship with RWS?

I mean we've built some very strong wording in the contract that guarantees us this access for many years. And at the same time, we are also working with other foundational models. The architecture that Christina has built allows us to have this level of agility and flexibility also in how we use different models.

Last question online from Hamish again. You've answered some of this on the TrainAI side, I think, because there was a question around whether you might be sort of sidelined by clients once those models become stronger. He's asked, once there are lots of AI models with, for example, flawless regional accents, how do you continue to generate fees for that work? Now that future models and clients can copy from what's already out there, can you structure the deals as royalty deals instead of one-off fees?

So I guess there's 3 elements. One is we are building also unique data sets for ourselves, unique methodology, which is building on our IP. The second thing is the work that those companies are asking is really changing. The voice work that I mentioned didn't exist a year ago. I think that's -- we're building more capability of finding skill set to render a task rather than building a specific thing that will be obsolete in the future.

And to that point, I think the third element is, let's imagine a world where, okay, AI is built and there's no more training needed. The trends that are growing more and more are ones of trustability of AI, inspectability of AI. And so we already work with some company on helping them test and inspect the AI output rather than helping the input. So I mean, AI is going to be central to our lives if it's not already. And so the services that need to be built around how we manage AI at an enterprise level, I'm not worried about the future.

That completes the questions online.

Thank you very much. And so I think we should close that meeting. Thank you.

Good morning, everyone, and thank you for joining us this morning. We're here today to mark a definitive shift in the RWS story. Over the next hour, we're going to walk you through how we are accelerating our evolution from a service-led company into a technology-first future. This isn't just a plan. It's a shift that's already underway.

I'm Ben Faes, the CEO of RWS. And joining me today is Candy Davies, our CFO, who's been instrumental in navigating this year of change. And I'm also pleased to have Christina Scott here, our Chief Product and Technology Officer, who is driving the innovation that you will hear about.

In terms of our agenda, Candy will kick us off with the financial detail for FY '25. I'll then return to deep dive into the new growth strategy we've put in place, including a closer look from Christina at our product and technology road map, which is central to this pivot.

And finally, we'll wrap up with our outlook and guidance for FY '26 and beyond. Before I hand over to Candy, let me set the context for the journey that we've been on, particularly since we last spoke in June. Looking back, FY '25 was a definitive inflection point for RWS. While we navigated a complex environment, the headline here is our trajectory, we saw a strong exit rate and a distinct positive shift in dynamic during the second half. We haven't stood still. We fundamentally reshaped the company to be fit for the future.

First, we refocused our strategy. We redesigned our operating model to be more agile, accountable and efficient. And this new structure is not just a concept. It's already been fully operational since the start of FY '26 in October. We've also injected fresh energy into the leadership team with new talent and successfully relaunched our brand to reflect our new direction. Crucially, we see strong momentum in AI services, a trend that we see accelerated into FY '26.

Our acquisition of Papercup is fully integrated, giving us leading capabilities in the vital AI for video space. To win in this fast-paced tech environment, you cannot work in isolation. And I'm proud of the deep collaboration that we've established with major tech players. Partnership that really allow us to build, to develop and to distribute superior products. And finally, this is key to our -- this is really key. Our SaaS revenue is continuing to climb. It now accounts for 46% of our license revenue, up from 39% last year. This shift is vital because it builds a predictable recurring revenue base, and Christina will show you shortly how our robust product pipeline is fueling this transition.

Looking ahead, FY '26 is all about accelerating that momentum and demonstrating a clear return to profitable growth. Our focus is simple and threefold. We're targeting high-growth opportunity. We are executing an ambitious product innovation road map, and we are driving efficiencies to fuel this transformation. To put it simply, we're focusing our sales effort on the most attractive market. We are building products that our clients need, and we're running a leaner and smarter organization.

With that context, I will hand over to Candy to walk you through FY '25. Thank you.

Thanks, Ben. So let me take you through the financial performance for the year ended 30 September '25. Overall, it was a challenging year financially, but one where management took decisive action on cost control whilst also focusing on building a refreshed operating model designed to lead the shift to a technology-first AI solutions partner. As you can see, we reported revenue of GBP 690 million, reflecting an organic constant currency decline of 0.7%. Gross margin declined year-on-year by 350 basis points to 43.4%, primarily impacted by adverse mix. I'll give you some more color on that shortly.

Adjusted profit before tax was GBP 60.4 million with a strong delivery of GBP 42 million in the second half, following GBP 18 million in the first half. This translates to an adjusted basic EPS of 12.1p. Adjusted EBITDA was GBP 101 million and adjusted EBITDA margin 14.6%, down 500 bps on the prior year. Capital expenditure was GBP 26 million versus GBP 46 million last year, which reflects both lower spend related to the transformation initiatives and the change in the capitalization treatment of some of our technology R&D. Operational free cash flow was GBP 80 million, a 46% increase year-on-year, supported by improvement in our net working capital as well as the lower CapEx.

Finally, I can confirm that the Board is recommending a final dividend of 4.6p, bringing the total dividend per share for the year to 7.05p following a reset of the dividend in line with the adjusted profit performance in the year.

As I'm reporting on full year '25 results, this slide shows the legacy structure, and Ben will talk to the new segments and their performance later in the presentation. The full year '24 OCC revenue adjusts full year '24 reported revenues by subtracting the predisposal PAT-based revenue and restating these numbers at full year '25 FX. Year-on-year in organic constant currency, the full year '25 revenue at the group level was broadly flat with repeat revenue stable at 95%, a slight headwind from price and promising new logo wins.

From a divisional standpoint, IP services revenue was flat on a constant currency basis, with strong growth in renewals, offset by the decrease in Eurofile on the back of lower grant applications. Language Services grew 3% in constant currency with our data services AI-led proposition TrainAI performing strongly within enterprise clients, and we continue to win new business across the division. From a core localization perspective, we saw clients grow more confident in machine-first translation for nonspecialized services.

In Regulated industries, the OCC decline of 10% is primarily driven by our linguistic validation business due to a reduced number of projects and client delays as well as some softer trading conditions and spending cuts in both Life Sciences and Finance and Legal. Fresh Management and the new operating model, which places RI within the broader Transform segment, which was put in place from October 1, is expected to resolve some internal operational issues whilst also moving forward on a leaner cost base.

Finally, our Language and Content Tech division was flat year-on-year in organic constant currency terms. SaaS revenues grew 14% versus prior year. And as Ben mentioned, this now represents 46% of license revenue versus the 39% last year. Moving to gross margin, which declined 350 bps year-on-year to 43.4%, driven largely by some unfavorable mix dynamics, and I'll highlight the 4 main areas.

Firstly, we have a number of new growing services, which are currently lower margin, such as renewals and IP services, TrainAI and the geographic area of APAC within Language Services. As these areas grow, we expect to see margins improve through both automation and economies of scale. Secondly, as mentioned at the half year, we experienced some teething issues as a couple of larger clients moved to newer automated delivery models. The majority of these issues have now been resolved through tech and process improvements and to some degree, increased pricing.

We also continue to drive SaaS revenues to secure recurring revenue that come with slightly lower margins. And finally, this year, we also experienced a lower-than-expected revenue in the higher-margin areas of Eurofile within IT services and Linguistic Validation in regulated industries.

Looking at price. Price was a tailwind in our Language and Content Tech division by increases through renewals. We did experience modest downward price pressure in our translation services as clients expect continued efficiencies from AI and automation. But you can see that in-year efficiencies supported 150 bps margin expansion, which more than offset this pressure. As machine translation consumption increases, we continue to adapt our processes, tooling and overall cost structure to drive the benefit of tech first and protect and grow our gross margin.

As mentioned briefly on the previous slide, we're actively pursuing operational and functional efficiency through a combination of process improvements, automation and simplification, leading to headcount offshoring and reduction. The total FTE at the end of full year '25 was just over 7,600, a reduction of over 400 or 6% versus a year ago.

Please note, these figures exclude contractors, freelancers and other casual workers. This, alongside continued offshoring, equates to annualized overhead savings of about GBP 14 million, 50% of which was realized in the year. As a result, revenue per FTE increased 5% in full year '25 to GBP 90,000. So moving to the adjusted profit before tax bridge, where the waterfall summarizes the main impacts year-on-year.

And firstly, looking at the trading items, where the decline in volume mix and price has been fully offset by the efficiency initiatives. These include the 150 bps seen in gross margin from headcount reduction, offshoring and other noncomp and initiatives, the reduction in overhead headcount and offshoring and some additional one-off items such as reduced sales commission and other discretionary cost control measures.

And then the nontrading items for GBP 32 million, there was a GBP 10 million overall impact from increased amortization related to historic investments and the change in the capitalization treatment of technology R&D. Whilst gross R&D investment levels remained broadly flat year-on-year, as explained at the half year, we have expensed more technology investment this year rather than capitalizing it and the offsetting impact is seen in the CapEx number.

And overall, FX impact in the year was GBP 20 million adverse with the strengthening of the pound versus the dollar. The full year '25 average of the cable was GBP 1.31 versus GBP 1.27 last year and the EUR 1.18 versus EUR 1.17. Adjusting items totaled GBP 160 million in full year '25. This is primarily due to a noncash goodwill impairment charge of GBP 88 million relating to our core localization services within both RI and Language Services and the weaker performance of linguistic validation.

It also reflects the shift in direction towards a technology-first offering and more technically, the increase in WACC rates. Exceptional items are mostly restructuring and integration costs, enabling the new strategic direction and leaner operating model. And finally, acquisition costs in full year '25, which were primarily the deferred consideration for Propylon acquisition.

Moving to cash. The business remains strongly cash generative with operational free cash flow of GBP 80 million, up GBP 25 million year-on-year. This increase reflects working capital improvements and tighter CapEx discipline more than offsetting the decline in adjusted EBITDA. The group had a net debt position of GBP 25 million at the 30th of September, an increase of debt of GBP 12 million versus a year ago. Working capital focus delivered a GBP 13 million improvement, driven by enhanced receivables collection and payables optimization and the increase in deferred revenue as SaaS licenses grow.

Capital expenditure reduced this year to GBP 26 million of revenue, as I said, reflecting both the reduction in the spend in internal transformation initiatives and the change in capitalization treatment. And the acquisition exceptionals and other includes primarily exceptional items and acquisition costs as discussed in the previous slide, whilst the dividends reflect the interim dividend for full year '25 and the final dividend for full year '24.

Finally, I'm happy to confirm that we completed an amend and extend of our RCF in early October, extending the facility to $285 million through to October 2029. And with that, I'll pass back to Ben.

All right. So turning now to our growth strategy. I want to share the vision we have for RWS and the specific pillars that we are executing to get there. RWS has evolved significantly over the last few years, and it's a shift that isn't always obvious when you look at the numbers quarter-on-quarter. But with the strategy we're presenting today, we are accelerating that evolution to become a true technology-first leader. The best way to understand this evolution is to look at our revenue mix.

Today, more than 1/4 of our revenue is already derived from AI-related products and services, whether that's from data services to fine-tune client models or AI workflows to adjust the output of AI. Sorry. We expect this to become our dominant revenue stream in the future, exceeding 50% of our revenue share. We see the same trajectory in SaaS. We've moved rapidly from 24% to 46% today of our license revenue, and we project this will climb to over 75%. To give you a sense of scale, this year, our technology processed 1 trillion words. That's roughly the equivalent of half the text of all of the public Internet out there. And to do that manually, to give a sense also of that scale, you would need about 1 million linguists working full year, full time for a year to do this. Yet even 1 trillion words is a fraction of the demand that we see coming. We see a clear path to our technology translating over 5 trillion words in the future.

Conversely, traditional localization where humans handle every step used to be our largest segment. Today, it's less than 1/3, and we expect this to settle just below 20%. Now we do see a natural floor there. There is a stable baseline of regulated work that will always require expert human validation. And this creates a natural high value for this segment because there are clients who actually restrict us to use machine translation for policy reason. And there's also a long tail of rare languages where AI isn't quite ready. The direction of travel is inevitable, but we see this flow.

So why is this shift happening? And it all starts with our client. Their fundamental goals haven't changed. They still need to reach new market, new audience to protect their IP, to ensure compliance, to do this in a safe and secure way. What has changed is the speed and scale required to do this. Speed is now really the currency. Content breaks daily. And if localization lags, that content becomes irrelevant. Time to market is really everything. At the same time, the environment that they're operating in is exploding. We are past the tipping point where actually half of the Internet is AI generated. Content is becoming multimedia first. It's hyper-personalized. It's hyper-personalized by audience and by channels like TikTok or Instagram.

We are also seeing shifting in trade routes. We see, for example, a surge in demand from our Chinese customers who now export directly to other Asian markets. And that creates demand for new language pairs that were previously niche. And finally, what we hear from our clients is they're facing a flood of AI innovation. New models appear every week, and it is incredibly complex for a CMO or a CTO to know which tool to use. They're looking to us to help them navigate this use of AI efficiency without risking their brand reputation or their data security. And this is really what makes RWS a strategic partner rather than just a service provider. That brings us to our vision.

Fundamentally, our mission at RWS is to build the cultural intelligence layer to enterprise AI. Let me break this down. We know that very soon, already today, our lives will be surrounded with AI-powered experiences. Imagine your alarm clock that briefs you in the morning, your autonomous car that drives you to work, probably very soon, your robots that will make your dinner. Can't wait for that. But for those experiences to be truly adopted, for them to be wonderful rather than just functional, they need to adapt to who we are. They need to understand our culture, our nuance and the context they operate in.

The current enterprise AI focus heavily on maximizing the IQ. They want to make large language model as smart as possible. But there is a deficit, especially as soon as you move to non-English market or to complex cultural context, that intelligence drops off. Our mission, to put it simply, is to bring the EQ, the emotional intelligence to that tech stack by solving 3 critical deficits in particular. Those 3 critical deficits that hold AI back today are -- sorry, first, the data deficit. There is a gap between generic raw data and the high-quality domain-specific data needed to TrainAI for mission-critical tasks.

Second, the culture deficit. Generic model today struggle to build genuine relationship, and we bridge the gap between robotic output and culturally resonant communication. And third, the trust deficit. Speed is useless without safety, and we closed the gap between automated speed and the expert validation required for regulated industry and IP protection. If AI is going to play a central role in our lives, it must understand how we are, how we behave, how we communicate. And this is an immense opportunity ahead of us. To capture this opportunity, our strategy is simple and focused on 3 growth pillars.

First, a new go-to-market where we're focusing our firepower on large enterprise and high-growth vertical; an ambitious innovation road map where we're really building that cultural intelligence product at scale, blending our tech with human expertise and operational excellence. We're implementing an efficiency plan to deliver faster, leaner experience to our clients.

Let's take each pillar in turn, starting with our go-to-market. As I signaled during our interim results in the summer, we've moved this strategy now into full execution. And we're shifting from transactional sales to solution selling, where we're integrating directly our technology into our client tech stack that creates stickiness, scalability and recurring relationships. We're also being much more selective, and we're focusing on industry that need us the most. Those industries that are global by nature, that are highly regulated or are managing high-value brands.

We do this from a position of strength. RWS already works with half of the Fortune 500 company, including the world's largest tech company out there. And they trust us because of our global footprint and our heritage of quality. To leverage this, we've reshaped our sales team. We now have dedicated focus on nurturing our most valuable existing clients and hunting for new strategic logos. We're expanding also our reach through partners. And a prime example of this partner-led approach is Microsoft. I'm delighted to share that RWS is one of the very first partner integrated directly into Copilot.

At their Ignite conference a month ago -- less than a month ago, Microsoft outlined a vision where Copilot becomes the orchestrator of the enterprise, the single entry point for all the tasks that you need. So instead of toggling between different applications, a user simply ask Copilot to get the job done. And I'm proud that all localization technology were part of that launch. This means that if you're a Trados or a language weaver customer, you can now access our technology directly within Copilot. You don't have to leave your workflow and you find right there a secure approved translation tool. It is right there using your company's tone of voice and your company's glossary.

Now of course, with over 0.5 billion professionals using Microsoft 365, this is a formidable distribution channel for us. As of today, we're the only the sole third-party translation agent, including in Copilot. That for me stands really as a powerful testament to the quality of our product, but also to the agility of our tech team.

And on that note, I'll hand over to Christina to take you deeper on to our product road map.

Thank you. Good morning. I'm Christina Scott, and I joined RWS as Chief Product and Technology Officer in May. I've been in technology my whole career, and my passion is really driving business growth and value through technology and building fantastic products for customers. I've had the privilege of building world-class digital products at companies like BBC, Financial Times and Pearson. I've used AI and automation to drive efficiencies in companies like News Corporation and OVO Energy and the latter whilst doing a very complex transition away from legacy technology for 5 million customers.

And I also spent 18 months doing an AI fertility start-up, which had the challenge of AI in a regulated industry. And I spend a lot of time speaking to my counterparts in different industries, and there are some very key trends that everyone is talking about.

Gen AI has driven the explosion of content, explosion of different formats. It's never been easier to create content. But actually, it's also driven a change in consumer behavior where and how consumers look at that content. We've had the hype curve. Everyone is talking about AI, and people are beginning to come down into the trough. CEOs and Boards are really thinking how do we drive the investment from AI and how do we get those efficiency gains that we were promised.

At an AI roundtable I went to recently, it seems to be consensus that actually the efficiency gains will come from domain-specific use cases as opposed to roll out generic AI capabilities. And accountability is becoming more and more important for customers. It's no longer good enough to say you're not responsible for the AI that you're using. Last year, we saw Air Canada was ruled against by a judge for saying that they weren't responsible for a robot chatbot on their website.

And finally, the thing that people are really grappling with is it's not actually creating more content. It's how do you stay globally relevant? How do you connect with your customers in an environment that is changing so rapidly in technology, geopolitically and in regulatory landscape.

I've also had the privilege of speaking to many of our customers since joining, and they've given me some very clear messages. They want partners to help them for the long term and to make sense of the AI journey that they're going on. They want to launch faster, quicker. I spoke to a hotel group last week, and they're using our capabilities in Trados to launch new websites. They launched in Japanese and Korean. And they have no hotels in those regions, but they saw actually a huge growth in sales, meaning that actually experiences that resonate culturally are really important for buying experiences.

LLMs have promised scalability in content creation, localization, removing humans. But actually, there's a lot of ongoing investment you need to do, and it's not trivial and it's not core to a lot of our customers. There's a real shift of focus to cost, so cost per word translated to actually what is the business outcome you can drive? What is the customer conversion? How do you engage more with your customers?

And technology teams like mine have been building systems for years where actually we've made you learn the technology. But with the new generation of AI, it's more human-like. And actually, it means it's now more accessible to nonexperts. So actually, the content creators are not the localization teams can do the content transformation. And all companies are having to think about how they verify and explain their AI output. It's particularly pertinent for our customers in regulated industries.

So if you're producing instruction manuals for hazardous equipment, you need to make sure that it's not only correct, but understandable in all the different languages you're producing it in, and it's not excusable to say that something is lost in translation. Listening to our customers and understanding what's happening in the market means that I've come up with a number of very strong design principles upon which we're building our new products.

We need to be embedded where our customers work. So Ben talked about Copilot, actually move and stop making people flick between lots of different capabilities. We need to use technology to deliver what it can and then humans become the value added on top, removing the mundane from the human workflows. We need to make sure that we are orchestrating those technology and human agents in flexible workflows. And in doing that, we're really focusing on the business outcomes for our customers. It's no longer one size fits all.

We need to make sure that we're truly multimodal and working across all content types. And as we build out, we need to make sure that we remain open and agile and that we are looking at as the technology changes, we are able to partner with the right companies and make sure that we avoid any vendor lock-in.

And security is definitely a nonnegotiable and verification has to be built into the process, whether by machine or human. So I'm delighted to say that we're already building our next generation of products using the design principles that I just mentioned. We have a lot of key strengths at RWS. And so we're not starting from scratch. I'm taking the assets that we already have. And so we have years of investment in our language AI and including our AI dubbing acquisition. We have a fantastic expertise in our linguists and networks of experts. We have a real understanding of our customers and their domains and the challenges that they're working with.

We have experience of creating and managing the content needed to fine-tune the AI. And we have a lot of experience at managing complex workflows on behalf of our customers. And security and compliance, we build in by design. So we're taking our RWS assets, and we're looking at the new capabilities that AI is now producing, the LLMs, the agents, the Agentic workflows and working with some of our key tech partners, we are creating our AI intelligence layer.

So we are taking our existing assets and our AI intelligence and this really builds in the efficiency, not just replicating what humans do, but going beyond human capability. These capabilities will then be surfaced in our products that are tuned for domain-specific use cases. In Generate, we're bringing an AI-native enterprise knowledge platform that empowers knowledge workers to research, draft and manage change with accuracy, auditability and governance at scale.

For transform, it's an AI platform that's delivering the future of multimedia content that really drives growth for our customers, the culture intelligence that Ben mentioned earlier. And for Protect, it's an AI-powered platform that improves IP attorney productivity and make sure that we are automating the critical practice management functions for them. And again, we will surface these capabilities and products where our customers are working, whether that's in existing workflows, in our own workflows that we're producing or in places like Copilot. And we're building this in a slightly different way.

With me joining, we brought all technology teams together so we can build out this AI intelligence layer once and surface it through all the products. We're working really closely with customers, getting feedback as we're building out. And these -- we're working in a fast and agile way. So these products will be launching in '26, and then we'll be evolving from then. Sorry, I forgot to click and so to give you some examples, why is this different? So we talk about scalability, efficiency. But actually, let me give you a few examples we're talking to customers about.

With one customer, they want to launch a product to market, and it takes 3 to 6 months to come localize. We're trying to do it in a day. With another customer, they have an Uber spreadsheet where they capture all the latest compliance regulatory requirements for their products, and they have to manually update all their product manuals. We want to automate that. And so it's really delivering value to customers and the customer needs that we talked about earlier.

So finally, why can RWS win and how are we going to win in this space? We have a long heritage ourselves, but we're also very keen to look at the right partners. RWS have been building linguistic AI since 2017. We have a significant number of patents. I think we got a new one yesterday. We're constantly winning awards. And so we have that capability. But LLMs have come in and they have a huge promise of the scale, the efficiency, but they need to be really tuned on appropriate data and they need to be verified to be domain specific. They're also really expensive to build and train.

Cohere is a Canadian AI company. It's currently, I think, valued at around GBP 7 billion. And they were founded in 2019 and have focused on LM model building. They created their multilingual model in 2023, and we've been testing it for the last 3 months, and it beats competitors. So I'm thrilled to say that RWS and Cohere have agreed exclusive partnership by which we bring together Cohere's capability of model building with RWS' capability to do the fine-tuning, have the right data and then linguistic expertise. And these capabilities together will be surfaced within our products, making sure that we're meeting our customer needs now and in the future. And the technology doesn't just create better products for our customers. It also drives efficiency that I believe Ben is going to speak about next.

So this brings us to our third and final growth pillar, strategic efficiency. The challenge our clients face today is straightforward. They only localize a fraction of their content in a subset of language. And this is because traditional costs are still a barrier to scale. Yet content volumes are exploding and new market opportunities are opening up every day. So our goal is to break that constraint. And to do that, we're deploying a scalable technology-led delivery model that makes content expansion faster and more cost effective for our clients.

We aren't just cutting costs. We're removing that bottleneck to their growth. We're driving this efficiency through 3 specific levers. First, the rationalization. We're streamlining our systems, and we are migrating towards a digital AI-optimized operating system. Second, we are offshoring more. We are leveraging our global footprint more aggressively. We already have 20% of our colleagues in India alongside key hubs in Czech Republic and Romania. And we'll continue to scale this center of excellence.

And third, AI efficiency. This is about deployment. We're working in close partnership with two major tech companies to build an agentic platforms for our production systems. And we're already seeing the results. We've recorded a 15% improvement in productivity in our developers through the deployment of AI coding tools. Globally, we expect these initiatives to drive a 10% productivity increase over the next 18 months. This creates fundamentally a faster, more valuable service for our clients while improving our margin. '26 will be a year of acceleration.

And before I share the financial guidance, I want to show you the operational KPIs that we are tracking to measure the success of this strategy. We're looking at this through the lens of our 3 pillars. In go-to-market, success means deepening our relationship. We aim to grow the net retention score with our top 100 clients to be greater than 100%, means keeping all of them, cross-selling, growing those top 100 clients, which are so strategic. And crucially, we intend to achieve this while maintaining our exceptional customer satisfaction score and keeping it above the 46% that we are today.

For innovation, we'll measure the shift to tech-led revenue, and we expect our SaaS revenue to climb to 60%. For AI-related products and services, we expect this to grow to 40% of our mix. And for efficiency, we are tracking the revenue per employee, which we aim to increase to GBP 105,000. And ultimately, this flows down to the bottom line, where we are targeting an EBITDA margin gradual improvement. This is really becoming cultural within the company, and this is a good measure of the efficiency of our work.

So that brings us to the financial guidance. As we execute this strategy, transparency is key. And so we are moving from our legacy 4 segment structure to 3 distinct pillars: generate, transform and protect. And these slides provide a clear map of that evolution. So you can see exactly how our historical reporting translates into this new simplified model.

Before I dive into the number, at a high level, this is the dynamic of the group moving forward. Think of generate and Protect as our high-growth engine. These divisions are operating in expanding markets and are prime for acceleration. We've isolated them with creating autonomy, and we want them to grow as fast as possible. Transform is the foundational core of the group. It is where we're pivoting and modernizing to shift from human-centric service to technology-led model. And underpinning all 3 is our group-wide efficiency program, which is optimizing our cost base to fuel this transition, okay?

So what does it look like for a number in FY '26? In terms of revenue, we expect our new go-to-market strategy to return the group to low single-digit growth on an organic constant currency basis. This growth is driven by 3 distinct components. First, generate. We expect good momentum here with growth in the mid-teens. This is our AI and data powerhouse. Protect, we see mid-single-digit growth. This business is robust. It's recurring. It's growing steadily, and we feel confident with our FY '25 exit run rate. We see the benefit also of cross-selling our new products to our existing clients.

Finally, in transform, we expect a low to mid-single-digit decline. And I want to be clear why. This is a managed consolidation. It reflects our strategic decision to step away from low-margin noncore legacy work as we replace the traditional service with higher-value technology. Looking to the medium term, beyond '26, we expect the group growth to accelerate. And the dynamic behind this is that in transform, we will reach a crossover point where our technology-first offering and our new business lines with resilient pricing fully offset the decline of traditional services.

For Generate and Protect, we expect them to continue powering the group forward with their underlying market and boosted further by our strategy of diversification across products and vertical.

Now turning to profitability and cash. The story here is one of gradual improvement in gross and operating profit margin and the normalization of our operational free cash flow conversion at a strong level. So for '26, we expect to see immediate benefit of the operational changes we've already executed. We are targeting a gross margin expansion of about 150 basis points. We expect this to flow through to an adjusted operating margin expansion of approximately 100 basis points.

Looking to the medium term, we expect this margin expansion to continue driven by the 3 structural levers that unlock value as we grow. In transform, the new technology platform and redesign processes reaches full velocity, and we will unlock economy of scale. We're moving from a linear service model to a scalable tech model, which inherently drives better margin.

In Generate and Protect, we're modernizing the delivery platforms to drive efficiency. And also as our newer high-growth business lines like TrainAI reach maturity, their contribution to the group profitability will increase. Finally, at the group level, we will continue to drive operational excellence, leveraging our offshore hubs and maintaining strict discipline on central costs.

On cash, for FY '26, we expect to deliver another year of strong cash conversion, aided again by our focus on working capital management and disciplined capital expenditure. In the medium term, as growth accelerates, we expect our free cash flow conversion to normalize to around 65%, which is the right profile for a growing company and it reflects a healthy reinvestment in working capital and CapEx to support the revenue expansion that we've outlined. This brings me to our capital allocation policy.

We are resetting the dividend this year, and I want to be clear why. While our cash generation remains robust, we are prioritizing the funding of this technology-led growth. We need the flexibility to deploy capital into investment opportunity as they arise. However, the policy itself has not changed. We are rebasing to a level that aligns with our strategy. But from this new base, we intend to maintain a progressive dividend policy growing year-on-year.

To summarize, let's look at where we stand today. We are 2 months into our new financial year, and we are already seeing solid trading momentum. The strategy is not just a plan, it is in motion and it's bearing fruit. RWS has evolved into an AI solutions company, uniquely positioned to capitalize on the AI revolution. We are building the critical culture intelligence layer for enterprise AI, and we're pivoting our business model to deliver improved sustained quality of earnings. We're confident in the path ahead.

Thank you, and we're now ready for some questions.

James Bayliss from Berenberg. Two questions from me, if I may. Ben, I was hoping you could share with us some of the internal and indeed external feedback you've had with regard to the new strategy you've taken RWS on. You gave us initial thoughts at the time of the launch in June from the internal side, but how have customers been responding to the new go-to-market strategy and how have internal kind of conversations gone since we last caught up.

And then my second question, just with regard to capital allocation and the fourth pillar with the return to acquisitions in due course. Should we be thinking about those being focused on the AI side of the business? Or would there be instances where we might see more professional services type of acquisitions to complement your offering?

Thank you. So in terms of feedback, internally, I think the message has been quite phenomenal. And it's led by two things. One is the influx of new talent, new teams that really make this vision real. We've had our global sales kickoff last month where the energy was really palpable, where people feel confident of the road ahead because they know that the market for this cultural intelligence layer is just immense. It's ours to grab.

We are working a lot more closely with clients. We have a weekly product development update where Christina's team is really sharing all the progress of the new products. They are co-designed with design partners. We're working hand-in-hand with some trusted clients. The partnership that we've talked about with Cohere, for example, there's been a more test partner that we could ever imagine. People want to be part of this innovation. So that really validates the road ahead.

In terms of capital allocation, I really don't have any news to share today. We are looking at our assets, deciding which ones are core, which one may be not so much core. And there are plenty of opportunities. We're being very picky. Now that we have this clear vision that Christina has a clear map of what we want to build, I think it's much easier to detect the company that will help us go into that direction.

James Beard from Deutsche Numis. A couple of questions from me, please. Firstly, on TrainAI. I was just wondering if you could talk about the longer-run growth runway for that business. I think we can see that, that side of the business has grown very strongly over the last couple of years. And I sense a perception from investors that there's a question mark about whether there's a sort of the longer run sort of growth opportunity for that business as large language models become sort of increasingly fluent and knowledgeable.

And then second question sort of slightly related to that was within the Generate division, the division grew 22% organically in FY '25, but you're targeting mid-teens growth rate in '26. What is the driving factor there behind that sort of slightly -- that slightly slower expected rate of growth within Generate in FY '26?

I'll be frank, on TrainAI, when I first joined, I had the same doubt saying this is a fad and once the LLM are trained, what else is there to do? This is not what we're seeing at all. We've moved from a generation of helping LLM understand data, understand context, so image annotation, things quite simple. To putting the best possible expert in the world, imagine you are professor, thesis professor that is teaching the LLM specifically. We're reaching a point where LLMs are extremely intelligent smart, but the influx of data is continuing to pour in. And there's still -- so the -- when we talk to those large LLM builder about this cultural AI, this is definitely something that is extremely hard to understand.

You probably have seen some of the chatter recently about the ceiling that -- of intelligence that those LLMs are reaching. We have a way as human to understand context, to have a heritage of relation that's really hard to replicate in LLMs. And if we want really to reach this general intelligence, there's still a lot more help to bring in. Beyond that, what we see is also vertical-specific need. And beyond the core LLM builder, we have more and more demand by all kinds of clients that need to adapt AI to their own company, to their own way of working, to their own process. And that's really -- this is a worry that's completely disappeared at least from me. There's so much growth to achieve in the short to medium term that we want to focus on that.

When it comes to generate, this year has been quite extraordinary on TrainAI. It's been exploding business. There has been a lot of competition. We've seen the new LLaMA model from Meta, the new Gemini, the new ChatGPT5. And I think we want to be a bit cautious. The level of CapEx from a large tech company has been extraordinary. I don't want necessarily to bank that it's going to be exactly the same for the year forward. We hope -- we think that they are going to be a bit more precise on what exactly they want to achieve and not just use a brute force going forward.

So we probably -- that's why we think our expertise and special expertise and global presence is very relevant, not just brute force, but we want to be a bit cautious on the future.

So starting with in RWS' 2025 Translation Technology Insight report recently published, it was mentioned that market participants were seeing early signs of end clients coming back to more traditional service and having used LLMs. Can you elaborate on that, please?

Yes. Christina, do you want to take that? I mean, I think the slide that Christina has shown around the mix of AI technology to achieve localization between neural machine translation and LLM is interesting. LLM have a much wider context window than the natural -- the neural machine translation that we used to have.

So they end up having a localization that's more fluid, but it's less precise. And so you can put the same text twice in an LLM and get 2 translation. And for enterprise clients, this is a problem. So that's why I think there's been a lot of hesitation. What we're doing with Cohere is really trying to get the best of the neural machine translation and the management of workflows that is really our heritage, but with the fluidity of the LLM. I don't know if you want to add anything?

No, you answered it quite well. But yes, no, it's time to fine-tuning and verification, but also -- so LLMs aren't deterministic. So if you need 100% guarantee that you get the same answer every time, the LLM currently isn't the right answer.

Are you seeing that return...

Sorry, go back to the mic.

Are you seeing the return to traditional service more in certain verticals over others or...

I would say no because the clients need to translate more. They don't have extensive budgets. And so we need to find a more efficient solution. So really, what we're focusing our work on is building the right guard rails for this new technology to be efficient for some clients. But as I said, the traditional translation has a natural flow. There's the work that we do in life science, we need to have, for example, by regulation, two certified doctors doing the translation. This is a regulation. And we live in a world where regulation tend to increase, not decrease.

Dan Ridsdale from Edison. Could you just elaborate a bit on your partnership with Copilot, just in terms of how that's working economically and practically?

So I mean this is -- this is something that we've worked on with Copilot -- with Microsoft, sorry, for quite some time. The system at the moment is quite simple. It might get bigger and more complex in the future. But today, if you are a customer of Trados, so you have the direct relationship with RWS and direct billing relationship with RWS. Trados is available and compatible within Copilot. So your company, all of the employees will access the localization tool from their Copilot in a secure way. So there's no shadow IT with people using whatever systems they want. And it uses your company tone of voice. So we all have our own lingo. There is built in our technologies, the dictionaries or glossary related to companies.

The beauty of it is also it's self-reinforcing. So the more you use it, the more the system learns also about making better translation.

And in terms of sort of uptake, do you have any data around that?

It's very early days. I mean it's been launched, I think it was the 19th of November, the Ignite conference. So it's very, very early days.

What's the target balance sheet structure for the company? And what considerations have been given to buybacks?

To what?

Buybacks repurchase.

So I think I've explained the capital allocation policy. We've done buybacks in the past, which haven't proven very successful. But the -- I think the company reserves the right to do to consider buybacks in the future. We have, in particular, a lot of work going at the moment on the -- how to manage our long-term employee incentive plan, and that might be actually an opportunity for buybacks.

From online. Could you expand on the prospects for linguistic validation?

Yes. So at a high level, so the linguistic validation is a key element in the process of developing a new drug. There's been a lot of flows in the development of drugs because of regulation, because of change of some policies. The reality is going forward, we see more drugs being developed, helped by AI again. But there's more and more projects of drugs being developed. So we see the market solid, probably growing. We have -- I've explained that in our half year result. We've had some change, unfortunately, of management at RWS, which has creating a little bit of a softness. But this is an area where we are investing. We've welcomed 3 new salespeople, which are doing great. And so that's a segment where I personally spend a lot of time with the team because it's a very solid segment for our traditional workflows.

Another one online. Candy, according to Slide 11, the impact of FX on the adjusted PBT was circa GBP 20 million. The question is, could you please clarify where the impact from FX is accounted for on the bridge on Slide 13, so cash generative operations, specifically which bars does it fall in?

Thank you for the question. I think you would find the majority of FX set in the adjusted PBT bar with a very small impact in net working capital.

Another one just come through online as well. Can the modern RWS exceed previous peak group profitability?

Yes. I mean that's definitely our ambition. So I think we're -- I hope you've heard the message today that we are looking to return to profitable growth, expansion of margins. We've used the word gradual because we want to build on solid foundation as we modernize RWS. But this is both a market and an organization that's poised to have a great future.

I think that's all the questions online as well.

I think that's it. Thank you very much for joining us today, and we'll see you in 6 months.