Apr 21, 2024

Sentiment: Sideways

Type of Trade: High Growth

Industry: Technology - AI Related

Sector: Information Technology

As per (CY2023) Investor Presentation and Financial Report

Appen is a company specialising in creating high-quality training data for artificial intelligence (AI) and machine learning (ML) applications. Services that provide high-quality training data, like those from Appen, are critical for developing accurate, reliable, and unbiased AI systems. They play a central role in enabling advanced AI capabilities, supporting the entire AI development lifecycle, and accelerating the time-to-market for AI products and services. Without these services, the development of effective AI would be significantly hindered.

Here’s a summary of what Appen does:

Diverse Training Data for AI

Appen provides training datasets designed to be highly accurate and reliable, which are essential for building effective AI models. The datasets are tailored to meet the requirements of various deep learning and traditional AI applications.

High-Quality Data with Human Involvement

Appen has a global network of over one million contributors who generate and annotate data in various formats, including text, audio, image, and video. The company uses advanced data processing and quality control measures to ensure that the datasets are consistent and diverse, reflecting real-world scenarios. This approach helps to maximize the performance of deep learning models and ensures a high level of accuracy.

Natural Language Processing (NLP)

Appen is also known for its expertise in natural language processing. The company offers curated datasets and services for collecting, annotating, and evaluating text-based data to support the entire AI and ML lifecycle. They have a team of experts, including linguists, project managers, and language specialists, who can assist with text annotation, text generation, evaluation, and benchmarking. Pre-labeled datasets are also available for immediate use.

Here are several reasons why high-quality training data is important for AI:

1. Foundation for AI Models

Training data is the foundation upon which AI and machine learning models are built. The quality and diversity of the training data directly influence the accuracy and effectiveness of the resulting AI models. If the data is flawed, incomplete, or biased, the AI models will reflect these issues, leading to poor performance and potentially harmful outcomes.

2. Improves Accuracy and Reliability

High-quality training data ensures that AI models can learn from accurate examples and generalize effectively. This leads to AI systems that are more reliable and perform well across different scenarios. By providing diverse and richly annotated data, companies like Appen help AI systems become more robust and dependable.

3. Reduces Bias

Bias in training data can lead to AI models that make unfair or discriminatory decisions. Diverse training data from a global pool of contributors can help reduce these biases by including a broader range of perspectives and experiences. This diversity is essential for creating AI systems that are equitable and inclusive.

4. Enables Advanced AI Capabilities

Advanced AI applications, such as natural language processing (NLP), computer vision, and speech recognition, require specific types of training data. Services like Appen can create and curate these specialized datasets, enabling AI developers to build complex models that can understand and process text, images, and audio accurately.

5. Supports AI Development Lifecycle

AI projects go through various stages, from initial data collection to model training, evaluation, and ongoing improvement. Appen’s services can support the entire AI development lifecycle, providing data annotation, evaluation, and benchmarking to ensure that AI models are continually refined and updated with high-quality data.

6. Accelerates Time-to-Market

Having access to pre-labeled datasets and efficient data annotation services can significantly reduce the time required to bring AI products to market. This allows AI developers to focus on building and improving models rather than spending extensive time on data preparation.

Appen partners with top companies and institutions to deliver high-quality data for their AI projects.

🚩 The shift of giant partners such as Google and Meta from outsourcing to in-house services has been a significant challenge for companies like Appen. When major clients decide to bring data annotation and other AI-related tasks in-house, it can have a noticeable impact on revenue and business stability.

Google Impact was quickly overcome with action to reduce costs. Google revenue was $82.8m at a gross margin of 26%. The company’s response was to continue to manage costs in line with revenue.

On 12 February 2024, Appen announced $13.5m of cost out initiative, with 80% to be achieved by end of March FY24 and remainder complete by end of June FY24.

✅ The company expects full year benefit of cost savings expected to be realised in FY25.

Impact on Appen

For Appen, this shift poses significant revenue loss and requires strategic adjustments. Losing major clients can lead to:

  • Reduced Revenue: As major partners move services in-house, Appen faces decreased revenue, impacting financial performance.
  • Restructuring: Appen may need to reorganize its business model to focus on other markets or services to compensate for the revenue loss.
  • Customer Diversification: To mitigate the risk of losing large clients, Appen might seek to diversify its customer base, targeting smaller businesses or new industries.

As large partners shifted to in-house data services, Appen’s revenue streams faced a sharp decline. This reduction in business led to investor concern over the company’s ability to sustain growth, driving down the share price. The drop from over $40 per share to a low of around 20 cents represents a significant loss in market value and investor confidence.

🚩 Business Reset due to major customer reduction with revenue down by 30%.

🚩 Negative underlying EBITDA for the full year.

✅ Nevertheless, the company has been implementing various cost reduction initiatives, resulting in an impressive positive EBITDA by the end of the year.

The question in this key fundamental metric is whether it will be reflected into the next full year as well or only for the November and December months.

✅ Positive Q4 revenue momentum

Q4 on Q3 revenue growth as decline from large customer exit has stabilised in Q4, which shows the first positive signs of a turnaround.

✅ China market achieves Q4 record revenue growth with breakthrough recorded in LLM related revenue with multiple LLM projects from eight customers.

✅ New Markets (ex China) Q4 growth across Enterprise, Quadrant and Government.

✅ Appen’s latest financial report highlights the growing role of generative AI in the company’s operations. Appen is using generative AI models to handle tasks like LLM (Large Language Model) prompt-response annotation across various languages. This integration of generative AI helps Appen deliver high-quality data for the AI lifecycle, supporting organizations in creating cutting-edge artificial intelligence systems. With generative AI technology continuing to evolve, its influence on Appen’s services and products is expected to increase, keeping the company at the leading edge of the AI industry.

The future looks promising for Appen, as outlined in its latest investor presentation. The company is seeing growth in revenue from generative AI, and projections suggest that this trend will continue to climb in the coming years with the worst behind. This growing revenue stream indicates a positive outlook for Appen as it further integrates generative AI into its offerings and contributes to the development of innovative AI solutions for its clients.

Generative AI related revenue has significantly increased in 2H FY23 (+410% vs 1HFY23), and it is expected to continue to grow.

✅ Cash position improved in FY23 to $32.1 million, mostly from net proceeds of equity raised.

Outlook for 2024 and beyond

The company is placing significant focus on generative AI services, which have demonstrated substantial improvement in the second half of FY23, as noted earlier. The generative AI industry is experiencing favourable momentum, with forecasts suggesting a remarkable growth from $40 billion in 2022 to a projected $1.3 trillion by 2032. This surge underscores generative AI’s rising status as a major investment priority for leading technology companies.

AI market opportunity: Generative AI has expanded Appen’s TAM (total addressable market) by $4-8 billion.

✅ Management team is highly focused on ongoing cash positivity.

Achieving cash EBITDA profitability in FY24 will largely depend on revenue growth from Appen’s non-global customers, the timing of which remains uncertain.

Technical Analysis

Appen Limited (ASX: APX) has experienced significant price fluctuations throughout the year, indicating a highly volatile trading environment. Here’s a summary of the technical analysis:

  • Overall Decline: APX has fallen 73% over the year, dropping from $2.4 to its current price of 63c. This sharp decline reflects broader challenges within the company and the industry.
  • Lowest Point and Rally: In February 2024, APX reached its lowest point at 25c. Afterward, the stock staged a dramatic rally, surging 346% on speculation of a potential buyout by Innodata. However, this rally was curtailed when Innodata announced the withdrawal of its non-binding indicative proposal.
  • Current Position and Discount: The stock is currently 44% down from its recent peak, indicating a significant retreat from the high following the rally. APX is trading at the SMA40, suggesting a potential support level for short-term traders. However, it’s also 41% below the SMA250, positioning it at a technically discounted price for long-term trend, but not for short term yet.
  • Bearish Indicators and Trends: The stock price is below the EMA9, with bearish intersection levels 1 and 2 indicating downward pressure. This underscores a potential risk of further decline.
  • RSI and Divergence: The RSI bullish divergence trend has been broken, requiring attention as it suggests a shift in momentum. This break could indicate a weakening trend and a need for careful observation.
  • Positive Momentum and Volume: Despite the bearish trends, there is strong short-term momentum to the upside, with high volumes during the upside moves. This indicates constructive accumulation, suggesting that some investors are seeing buying opportunities at current levels.
  • Formation and Caution: The recent lower high formation is a cautionary signal. This pattern could suggest the continuation of a downward trend unless broken by stronger upside momentum.

Given these mixed technical signals, investors and traders should approach APX with extreme caution. While the stock shows signs of accumulation and momentum in the short term, the broken RSI divergence, lower high formation, and long-term discount below the SMA250 point to potential volatility and continued uncertainty. Any fundamental update should have a profound impact on the stock price on both sides. We suggest traders buy at a technically discounted price upon solid signs of reversal to avoid being burned.

Should I Buy (ASX: APX) Now?

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