Core Viewpoint - Dipu Technology has officially listed on the Hong Kong Stock Exchange, becoming the first "enterprise-level large model AI application" stock in the market, with a significant oversubscription and a notable increase in share price post-IPO [1][2]. Group 1: Company Overview - Dipu Technology was established in 2018 and provides enterprise-level large model AI application solutions, covering the entire process from data governance to model training and industry applications [2]. - The company has achieved a compound annual growth rate (CAGR) of 55.5% in revenue over the past three years, with revenue increasing from 100 million yuan in 2022 to 243 million yuan in 2024 [2]. - As of the first half of 2025, Dipu Technology's revenue accelerated to 132 million yuan, representing a year-on-year growth of 118.4% [2]. Group 2: Financial Performance - In the first half of 2025, Dipu Technology's gross margin was 55.5%, an increase of over 25 percentage points compared to 2022, and the adjusted net loss rate significantly narrowed to 39.5% [2]. - The FastAGI enterprise-level AI solution generated 73.07 million yuan in revenue in the first half of 2025, a year-on-year increase of 191.04%, accounting for 55.3% of total revenue [2]. Group 3: Market Position and Strategy - Dipu Technology is positioned uniquely in the market as a rare "AI + cloud-native data intelligence" pure play, filling a market gap [1]. - The company focuses on a "vertical deep cultivation + horizontal replication" strategy, which is seen as a model that can be easily valued by the capital market [3]. - The enterprise AI application market in China is expected to grow from 38.6 billion yuan in 2024 to 239.4 billion yuan by 2029, with a CAGR of 44.0% [4]. Group 4: Research and Development - Dipu Technology plans to allocate 40% of the funds raised from its IPO to enhance R&D capabilities, including strengthening its two main technology platforms and building a computing power platform [4]. - The company currently employs 143 R&D professionals, making up 44.3% of its total workforce [4]. Group 5: Leadership and Expertise - The founder and CEO of Dipu Technology, Zhao Jiehui, has held key technical roles in several Chinese technology and internet groups, and the founding team members have an average of 10 years of industry experience [5]. Group 6: Future Outlook - Analysts suggest that Dipu Technology's ability to embed R&D into its business model and transform its technology stack into repeatable software revenue will be crucial for its valuation [6]. - The company is expected to transition from project-based revenue to standardized subscriptions, with a clear path to profitability if it continues to deepen industry know-how and expand internationally [6].

Group 1 - Dipu Technology (01384) is set to launch its IPO from October 20 to 23, with a market subscription multiple exceeding 7590 times, involving HKD 272.1 billion and 239,000 subscribers, marking the highest oversubscription in the history of the Hong Kong Stock Exchange [1] - The company plans to issue 26.632 million H-shares at a price of HKD 26.66 per share, aiming to raise HKD 710 million, with 5% allocated for public offering in Hong Kong [1] - Dipu Technology focuses on providing enterprise-level large model AI application solutions, helping businesses efficiently integrate data, decision-making, and operations [1] Group 2 - The company's solutions assist various industries in optimizing decision-making, enhancing operational efficiency, and increasing productivity [2] - As of June 30, 2025, Dipu Technology has served a total of 283 enterprise users across multiple verticals, with 94 repeat customers, representing 33.2% of its client base, indicating high customer loyalty and satisfaction [2] - The company ranks fifth in the Chinese enterprise-level large model AI application solutions market, holding a market share of 4.2% based on projected revenues for 2024 [1]

Core Viewpoint - Dipu Technology (1384.HK), a provider of enterprise-level large model AI applications, has seen its IPO heavily oversubscribed, raising significant interest in the AI sector within Hong Kong's stock market [1] Group 1: IPO Details - Dipu Technology's IPO raised a total of up to 710 million HKD, with a share price set at 26.66 HKD [1] - The company offered 26.63 million H-shares, with 5% allocated for public sale and the remainder for international placement [1] - The stock is expected to begin trading on October 28 [1] Group 2: Market Performance - The IPO was oversubscribed by over 7,590 times, surpassing the previous record set by KGI Technology in September 2023, which was oversubscribed by 7,557.4 times [1] - This makes Dipu Technology the second highest oversubscribed IPO in Hong Kong's history and the highest in the AI sector for 2023 [1] Group 3: Company Overview - Dipu Technology specializes in providing enterprise-level large model AI application solutions, focusing on integrating data, decision-making operations, and production knowledge for businesses [1] - The company has developed two foundational platforms: FastData Foil for data integration and Deepexi for enterprise-level large model applications [1] - Dipu Technology's solutions, FastData and FastAGI, aim to standardize data output for AI and support model training, addressing the efficient deployment of models and their application in business scenarios [1]

Group 1 - Dipo Technology (01384) is focused on providing enterprise-level AI application solutions and has launched its IPO from October 20 to 23, with a public offering ending on October 23 at noon [1] - The company recorded a subscription of HKD 216.3 billion, with the public offering portion amounting to HKD 35.5 million, resulting in an oversubscription of 6093 times, surpassing other recent IPOs [1] - Dipo Technology plans to issue 26.632 million H-shares at a price of HKD 26.66 per share, aiming to raise HKD 710 million, with a listing expected on October 28 [1] Group 2 - The company’s public offering shares will increase to 10% and 20% of the initial offering if the subscription reaches 10 times and 50 times, respectively, translating to 266,320 shares and 532,640 shares [2] - Dipo Technology ranks fifth in the Chinese enterprise-level AI application solutions market with a market share of 4.2% as of 2024 [2] - The company has achieved commercial deployment across various verticals, including retail, manufacturing, healthcare, and transportation, serving 283 enterprise users as of June 30, 2025, with a customer retention rate of 33.2% [2]

Core Viewpoint - Dipu Technology Co., Ltd. plans to issue up to 115 million overseas listed ordinary shares and convert 300 million domestic unlisted shares into overseas listed shares for trading on the Hong Kong Stock Exchange [1][3]. Company Overview - Dipu Technology focuses on providing cutting-edge artificial intelligence solutions to help enterprises efficiently integrate data, decision-making, and operations [3]. - The company has developed two major infrastructures: the AI-Ready FastData Foil data fusion platform and the Deepexi enterprise-level large model platform, facilitating the deployment and implementation of Agentic AI applications in enterprises [3]. Market Potential - According to Frost & Sullivan, the market size for enterprise-level large model AI application solutions is projected to reach RMB 38.6 billion by 2024 and RMB 239.4 billion by 2029, with a CAGR of 44.0% from 2024 to 2029 [3].

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The Chinese enterprise-level GenAI market is experiencing explosive growth, with daily model invocation reaching 101,865 billion tokens in the first half of 2025, a 363% increase from 21,999 billion tokens in the second half of 2024 [8][18][11] - The market is transitioning towards a dual-track development of open-source and closed-source models, with open-source models gaining traction due to their cost-effectiveness and flexibility [13][16] - The focus of enterprise-level model application is shifting from seeking a single powerful model to finding optimal solutions tailored for specific business scenarios, emphasizing cost-performance ratio, system flexibility, and security [6][20] Summary by Sections Introduction - The report, published by Frost & Sullivan in collaboration with the Head Leopard Research Institute, surveys 700 IT department heads, technical directors/managers, and AI project leaders across various industries including finance, manufacturing, internet, consumer electronics, and automotive [4][28] - The study aims to assess the deployment of open-source and closed-source models in the enterprise-level GenAI market and to provide structured insights into the current application status and trends [4] Section 1: Overview of Enterprise-Level GenAI Development - The development of enterprise-level GenAI is characterized by the parallel growth of open-source and closed-source models, with open-source models becoming the preferred choice for low-cost implementation and autonomy [13][16] - Open-source models are increasingly recognized for their adaptability and long-term value, while closed-source models are favored for their reliability and performance [13][16] Section 2: Current Status and Trends of Model Invocation - The daily invocation of enterprise-level models has surged, indicating a shift from pilot testing to large-scale implementation, with significant implications for resource consumption and industry restructuring [18][19] - Key drivers of this growth include the expansion of model and computing power supply, accelerated deployment in various sectors, and the emergence of ecosystem effects that enhance efficiency [19][20] Section 3: Analysis of Model Invocation Behavior - The choice between open-source and closed-source models is primarily driven by business value, with open-source models offering greater flexibility and control, while closed-source models provide reliability and ease of use [24][26] - The top factors influencing the selection of open-source models include performance, customization ease, and knowledge ownership, whereas closed-source models are chosen for their reliability and brand reputation [25][26][27]