据招股书披露，滴普科技专注于为企业提供企业级大模型人工智能应用解决方案，助力企业大规模高效 整合数据、决策及运营。滴普科技的FastData Foil数据融合平台及Deepexi企业级大模型平台两大基础设 施，实现Agentic人工智能应用在企业的部署及实施。以2024年的收入计，滴普科技在中国企业级大模 型人工智能应用解决方案市场排名第五，市场份额为 4.2%。 中国企业级大模型人工智能(AI)应用提供商滴普科技(01384)10月20日至23日招股。据市场消息透露， 滴普科技的市场申购倍数已突破7590倍，涉资2721亿港元，23.9万人认购，荣登港交所主板史上超购第 一位。 滴普科技循上市规则18C特专科技公司规例申请上市，计划发行2663.2万H股，5%于香港作公开发售， 发售价为每股26.66港元，集资7.1亿港元。滴普科技每手200股，一手入场费5385.8港元。滴普科技预期 将于10月28日挂牌买卖，中信证券、民银资本、国泰君安国际、浦银国际、交银国际为联席保荐人。 滴普科技的解决方案助力各行业的企业优化决策、提升运营效率并提高生产力。公司已经实现了在多个 垂直行业的规模商业化落地，包括消费零售 ...

中国企业级大模型人工智能(AI)应用提供商滴普科技(1384.HK)今日中午截飞，有数据显示，券商为其 借出2721亿港元孖展，超购逾7590倍。以初步数据计算，滴普科技的超购倍数已超越今年9月上市的大 行科工(超购7557.4倍)，成为香港历来超购王第二位，同时也是今年港股新股AI领域的超购王。 滴普科技此次IPO发售2663.2万股H股，香港公开发售占5%，其余为国际配售，每股招股价26.66港元， 集资最多7.1亿港元。一手200股，入场费5385.77港元。该股预期10月28日挂牌买卖。 滴普科技是企业级大模型人工智能应用解决方案提供商，可助力高效整合企业的数据、决策运营及生产 制造等领域专业知识，构建企业级大模型AI应用，为客户在运营决策和生产力提升相关核心场景，基 于客户专属数据和业务知识逻辑提供符合企业级高精度零幻觉的专业AI数字员工。 同时，针对企业AI价值落地的核心痛点，滴普科技打造了FastData Foil数据融合平台、Deepexi企业级大 模型平台为两大基础设施，并以此为基础构建FastData(企业级数据智能)与FastAGI(企业级人工智能)两 大解决方案，分别聚焦于面向AI的数 ...

据招股书披露，滴普科技专注于为企业提供企业级大模型人工智能应用解决方案，助力企业大规模高效 整合数据、决策及运营。滴普科技的FastData Foil数据融合平台及Deepexi企业级大模型平台两大基础设 施，实现Agentic人工智能应用在企业的部署及实施。以2024年的收入计，滴普科技在中国企业级大模 型人工智能应用解决方案市场排名第五，市场份额为 4.2%。 滴普科技循上市规则18C特专科技公司规例申请上市，计划发行2663.2万H股，5%于香港作公开发售， 发售价为每股26.66港元，集资7.1亿港元。滴普科技每手200股，一手入场费5385.8港元。滴普科技预期 将于10月28日挂牌买卖，中信证券、民银资本、国泰君安国际、浦银国际、交银国际为联席保荐人。 滴普科技按18C章申请香港上市，按上市规则第18项应用指引第4.2段（经上市规则第18C.09条修订）要 求，需要设置回拨机制。 若香港公开发售部分认购股数，占初步发行规模达到10倍和50倍或以上，滴普科技分配至公开发售部分 的股数，将增至初订发行量的10%和20%，相当于266.32万股（13316手）和532.64万股（26632手）。 滴普科 ...

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]