Appen Limited

Thank you very much Rachel and good morning everyone. Welcome to Abbin's H1 FY24 results presentation. Today I'm joined by our CFO Justin Miles. There are four sections to the presentation as per the agenda on page three. Firstly, I'll share an overview of our H1 performance. Second, Justin will provide greater detail in the financial performance for the half. Third, I'll share an update on how we are tracking against our strategy. And finally, we will provide an update on July trading and discuss our FY24 outlook. Moving to page five in the presentation. Including the impact of the Google contract loss, we're very pleased that Appen is returning to revenue growth. In Q2, revenue grew 16% compared to the same period in FY23 and was up 22% on Q1 this year. For the half, H1 revenue was up 13% on H2 last year if we exclude Google. H2 revenue has historically been higher than H1, so we are particularly pleased with this result. Much of the growth has been supported by new generative AI-related projects, especially from our global and China customers. We're pleased with the revenue growth experience so far. Now onto page six. We achieved underlying EBITDA positivity in Q4 last year, However, the Google-related revenue reduction impacted our profitability in Q1, resulting in a loss of $2.9 million. We acted swiftly to control costs and now have completed the previously announced $13.5 million cost reduction. While we were reducing costs, we were also winning new business, which resulted in an underlying EBITDA of positive $0.6 million in Q2. This was a $7.8 million improvement compared to the same period last year. For the half, group underlying EBITDA before FX improved $13.4 million. The significant improvement is due to the cost our programs executed with our operating expenses decreasing 33% compared to H1 2023. Profitability remains a key focus and something we will continue to manage towards. Turning now to page seven. Generative AI continues to evolve rapidly. A major component of generative AI is high-quality human data annotations, both for the training of the models and to evaluate performance. Our customers are investing heavily in generative AI, and this in turn has been a major driver of revenue growth for Appen. In H1, 15% of our revenue was from generative AI-related projects. This was up from 6% in H2 last year. Throughout H1, the proportion of our revenue from generative AI projects has grown. In June, 28% of group revenue was from generative AI projects. This is up from 8.3% in January 2024. Finally, the number of customers we are working with continues to grow. We have 42 generative AI customers in H1, up from 28 in H2 last year. We remain very bullish on the impact of generative AI, and have strong foundational capabilities to deliver high quality data to our customers. I'll now hand over to Justin who will take us through the H1 financial performance.

Thank you Ryan. Good morning everyone. A reminder that we report in US dollars and that all comparisons are to the half year ended 30 June 23 unless stated otherwise. Starting with the H1 snapshot on slide 9. Total revenue decreased 18% to $113.4 million, reflecting the termination of the Google contract. Excluding the impact of Google, revenue decreased by a modest 2%. Looking at our operating segments, global services revenue decreased 36% to $63.6 million. This also reflects the termination of the Google contract. New markets revenue increased 28% to 49.8 million due to strong growth in China and global product. This growth is pleasing as it reflects significant traction in multiple generative AI projects. Our gross margin percentage, which is revenue less crowd expenses, increased 0.4 percentage points to 37.7%. The increase was mainly due to a change in project and customer mix during the period. Underlying EBITDA before the impact of FX improved $13.4 million to a $2.3 million loss. The significant improvement is due to cost out programs executed during FY23 and H1-24. I won't talk to slide 10 as we cover revenue in further details at later slides. Over to underlying EBITDA on slide 11. As I just mentioned, group underlying EBITDA before FX improved $13.4 million. The significant improvement is due to cost our programs executed with our operating expenses decreasing 33% compared to H1-23. The Global Services Division reported EBITDA of $6.7 million. down 23% on the prior corresponding period. The decrease reflects lower revenue and gross margin, partially offset by the benefit of the cost out. New markets EBITDA improved by $13.8 million to a loss of $7.9 million. The improvement is driven by growth in revenue and gross margin for China and global product, as well as the benefit of the cost out. Slide 12 shows monthly group revenue, underlying EBITDA and underlying cash EBITDA, both before FX. Revenue for Q2 shows positive momentum, which is pleasing as the early positive indicators of LLM-related growth have started to develop into significant opportunities. As you can see, EBITDA improved month-on-month in Q2. Turning to slide 13. This slide shows global revenue with Google excluded. Reduction in spend from a large customer experience during FY23 stabilized in H2 23 with growth returning in Q2 24. Q1 24 was pleasing given Q4 23 includes some expected seasonality. Global product growth is driven by multiple generative AI projects. It is important to call out, given the early stage for some projects, volumes may be inconsistent during H224 as customers adapt to their evolving needs and budgets. Global services growth is driven by an increase in projects and volumes across multiple customers, including some early stage LLM projects. Over to slide 14. Our China business achieved consecutive quarterly revenue records during the half, with revenue for the half of $25.4 million up 66% on H1-23. The growth is driven by expansion in existing large tech customers as well as new customer wins. Growth includes significant traction in LLM projects and we continue to support leading LLM model builders. Slide 15 has revenue for the balance of the new market segment, being enterprise and government. The decrease in revenue was driven by lower volumes within some existing large enterprise projects. The impacted projects remain active and the customers continue to be key Appen customers. Despite the disappointing H1 result, we have conviction in the revenue opportunity. However, timing is unclear around how enterprises will proceed with their generative AI investment. The enterprise market for generative AI labelling software remains nascent and we're yet to see material traction, but market signals remain positive. We have a healthy government pipeline and remain optimistic about the federal market. Turning to slide 16 for a summary of the profit and loss. We've already covered most line items, however there is additional data on this slide worth noting. Employee expenses are down 39% and all other expenses are down 21% compared to H1-23. This is due to the cost out programs. Statutory NPAT has improved by $25.5 million due to the cost out, lower restructure cost compared to the prior period and a reduction in depreciation and amortization. For the balance sheet on slide 17, noting the comparison here is to December 23. The cash balance of 30 June 24 was $34.7 million up $2.6 million from December 23. The cash balance at the end of July was $30.6 million. The decrease compared to June is due to the working capital cycle. Receivables decreased due to lower revenue in Q2 24 compared to Q4 23. Current liabilities were $2.6 million higher, reflecting the timing of trade payables. and the decrease in net assets to 79.5 million primarily reflects trading during the period. Turning to the cash flow summary on slide 18. As just mentioned, the cash balance at the end of the period was 34.7 million. Cash flow from operations increased to 10.6 million, which includes a positive impact from strong Q4 23 trading. Cash flows from investing activities were down $5.9 million to $6.5 million compared to H1-23. The decrease is due to a lower investment in product development and reflects the cost-out program executed during FY23. Cash has primarily been used to fund operations, some CapEx and one-off costs associated with the H1 cost reduction program. That concludes the financial performance slides. I'll now hand back to Ryan.

Thank you, Justin. I'll now talk to our strategy. Moving to slide 20, Appen plays a crucial role in the AI ecosystem. We specialize in providing high-quality data that brings human expertise into AI model development. We support our customers across data sourcing, data preparation, and model development. These are all critical steps in AI model development. Slide 21 provides a much more granular view of our capabilities. I won't step through all the elements on this page, but I did want to share more detail about the role that we play. The top section of this diagram outlines some of the AI efflusions that we support. The common element here is that most of these models are imitating human behaviors, and therefore human-generated data is a critical element to ensure that the models act as close as possible to humans. The blue box in the middle outlines some of the core services we provide across data sourcing, data preparation, and model evaluation. Each project that we work on is typically unique and involves one or more of these types of services. Technology is a critical component of everything we do at Appen. Our data annotation platform enables us to deliver high quality data for our customers and includes sophisticated annotation capabilities quality and workflow management, and specialized LLM features. A key part of our platform is the ability to include AI automation in the data development process. Finally, we support a diverse set of languages and workforce models that are tailored to suit the needs of our customers. This is particularly beneficial for customers that are operating on a global scale. There's a lot on this page, and it's reflective of the broad set of capabilities that we offer our customers. Onto slide 22. The breadth and scale of our global workforce is a key differentiator for Appen. We have over one million contributors that speak over 500 languages and have specialization in over 100 domains. The benefit of our crowd is that we can create highly specialized and customized workforce to provide AI data, but also deliver it very fast and at high quality. Speed and quality are the most important elements for our customers. and our workforce enables us to maintain our competitive advantage. On to slide 23. We've spoken about the impact of generative AI on our business, and I'd like to provide more specificity into the type of work that we are doing. At a high level, there are four categories of work we do for LLM model builders. First, we provide data for supervised fine-tuning, called SFT. In SFT, we are creating unique content that is used to train large language models. An example here is when we have our workforce create sample prompts and responses. Next, we perform preference ranking. This is where humans rank multiple generative AI responses across a specific set of criteria. This allows us to use human feedback to align generative AI models with human preferences. Next, We provide human evaluation of generative AI-created content. This is where our workforce evaluates a response for a given prompt. Similar to preference ranking, the criteria for how we assess models is often unique for each project. Finally, we help our customers evaluate the safety of their models. A common technique here is called red teaming. The common factor across all these work types is human subjectivity. Similar to the search relevance work we have been doing for many years, the subjective feedback of humans is a critical component for effective generative AI model development. Now onto slide 24, where I'll provide an update on our growth strategy that we outlined earlier in the year. Firstly, we are replatforming our crowd and project management platform. This is the main interface for our crowd workers and how we deliver projects for our customers. The approach that we have taken is a complete rebuild of the platform that leverages best of breed capabilities in the market. This is an exciting evolution for Appen and will enable us to provide a better crowd experience, reduce project setup time, automated quality monitoring, and advance analytic in all of our processes. This new platform will launch in September. Second, we are optimizing and automating the processes to create data for our customers. A big focus is the inclusion of AI in our data creation and quality management processes. We're increasingly using generative AI to validate responses and to automatically data quality check. This enables us to deliver high quality data and improved unit economics. It is mostly applicable for projects that are being performed on our annotation platform, and like most generative AI automations, we are early in the development with a lot of testing underway. The third element of our strategy is a SaaS platform for enterprise LLM customization. The thesis here is that enterprises will need to connect their data science team with internal experts to train and monitor LLM performance. As an example, if a retailer is building an LLM chatbot for a specific product line, the best person to provide input data are the product experts spread throughout the business. We remain optimistic about this opportunity, However, market timing is uncertain. We are utilizing our annotation platform as the underlying technology, so there is minimal technical investment required. We continue to test the market and will look to allocate more resources as the market shows sign of acceleration. Fourth, we spoke about a modernized sales and marketing function. We have taken a far more technical focus in our marketing approach, including how we demonstrate our expertise and thought leadership. This includes an updated brand presence, but more importantly, providing our go-to-market teams with the content that resonates with our technical customers. Finally, we have continued to have tight controls around our costs. We have reduced our operating expenses by 33% compared to H1 FY23, while also supporting new revenue growth. Justin and I continue to look for additional areas of cost optimization across the business. I'll now provide a July trading update and an outlook statement. Page 26 shows our July performance. Revenue for July was $17.6 million. While this was down from a strong result in June, we're very pleased as it's 26% up on July 2023, excluding Google. As Justin mentioned earlier, it's worth noting that we may see some lumpy revenue on a monthly basis, due to the nature of some exploratory generative AI projects. Underlying EBITDA before FX was breakeven for July. June data included the right back of share-based payment expenses, hence why July is lower than June. Finally, underlying cash EBITDA before FX improved to a loss of only $0.3 million in July. We're pleased that we continue to trend in the right direction to profitability, despite some lumpiness in revenue. On to slide 27, where I'll provide an outlook statement. Excluding the impact of Google, revenue momentum is positive. We continue to see positive signals on LLM-related growth, including from our global and China customers. Tight cost controls remain in place, in keeping with the company's focus on managing costs in line with the revenue opportunity. FY24 will be the full year benefit of the $60 million FY23 cost reduction program. And on the 12th of February this year, we announced a further $13.5 million of cost out initiatives that are now complete. We remain highly focused on ongoing cash EBITDA positivity, and our target is to become underlying cash EBITDA positive on a run rate basis in early H2 FY24. Thank you. That concludes our presentation. I'll now hand back to Rachel, who will take questions.