Good News on Listing | Frost & Sullivan Assists Dipu Technology Co., Ltd. to Successfully List in Hong Kong (1384.HK)

Dipu Technology Co., Ltd. (hereinafter referred to as 'Dipu Tech') successfully went public on October 28, 2025, planning to issue 2,663.2 million H shares, of which 95% will be international offerings and 5% will be public offerings. The issue price per share is HK$26.66, raising approximately HK$610 million in net proceeds.

During the process of listing in Hong Kong this time, Frost & Sullivan mainly undertook the following tasks: helping the issuer accurately and objectively understand its positioning in the target market, using objective market data to discover, support, and highlight the issuer's competitive advantages, assisting the issuer, investment banks, and other intermediaries in completing the relevant parts of the prospectus (such as overview, competitive advantages and strategy, industry overview, business, and other important chapters), helping the issuer complete communication with the Hong Kong Stock Exchange and investors, assisting investors in quickly understanding the market ecosystem and competitive landscape, and assisting the issuer in completing feedback on various industry-related issues from the Hong Kong Stock Exchange, etc.

Frost & Sullivan has always been a leader in helping companies go public in Hong Kong. According to LiveReport's big data (statistical data as of September 30, 2025), from January to September 2025, as well as during the past 12 and 36 months, Frost & Sullivan provided listing industry advisory services to 47 (market share 72%), 62 (market share 69%), and 162 (market share 70%) Hong Kong-listed IPOs respectively, ranking first in terms of number. It has a wealth of industry experience and communication skills with regulatory authorities, exchanges, investment and financing institutions, and various related institutions.

• Based on its 2024 revenue, DeepSpeech.AI is the fifth-largest enterprise-level large model AI application solution provider in China;

   

• Dipu Technology's proprietary large model is the industry's first general enterprise operation decision-making large model to complete dual regulatory filings for deep synthesis algorithms and generative artificial intelligence services;

   

• Dipu Technology provides enterprise-level data intelligence and artificial intelligence solutions based on its self-developed FastData Foil and Deepexi platforms, achieving a synergistic effect between data governance and intelligent decision-making.

According to the Frost & Sullivan report, in terms of revenue for 2024, DTP Technology:

• Ranked fifth in the Chinese enterprise-level large model AI application solutions market, with a market share of 4.2%.

Artificial intelligence has undergone significant evolution since its inception. With continuous technological progress, AI capabilities have been increasingly enhanced, from discriminative AI to large model AI, from narrow AI (ANI) to general AI (AGI), and the emerging potential of superintelligence (ASI), with the ultimate goal of achieving AI that surpasses human intelligence. As AI increasingly integrates into workplaces, it is reshaping various industries, driving disruptive innovation, and significantly improving productivity.

Against this backdrop, AI application solutions have become an important tool for organizations of all sizes, helping them address unique challenges and achieve strategic goals. Enterprise-level AI application solutions refer to integrating AI algorithms into hardware, software, and services to provide enterprises with various intelligent solutions. Enterprise-level AI application solutions seamlessly combine AI with enterprise systems, ensuring scalability and compatibility with existing infrastructure. At the same time, they provide tangible value by solving complex and industry-specific problems and support long-term business growth.

Utilizing the popularization of artificial intelligence and the continuous trend of digital transformation, the market size of enterprise-level AI application solutions, calculated by revenue, has grown from RMB 10.7 billion in 2020 to RMB 38.6 billion by 2024, with a compound annual growth rate (CAGR) of 37.8% from 2020 to 2024. Looking ahead, driven by the widespread adoption of enterprise-level AI application solutions across various industries and the continuous innovation in AI technology, the market size of enterprise-level AI application solutions in China is expected to reach RMB 239.4 billion by 2029, with a CAGR of 44.0% from 2024 to 2029.

Source: Frost & Sullivan report

Over the decades, artificial intelligence has undergone significant technological evolution, from early rule-based systems to discriminative AI driven by machine learning and deep learning, and now to large model AI that utilizes advanced foundational model techniques (such as fine-tuning and distillation). Therefore, enterprise-level AI application solutions can be further divided into enterprise-level discriminative AI application solutions and enterprise-level large model AI application solutions. Although discriminative AI is still widely used in many applications, large model AI is rapidly emerging and becoming a transformative force with great potential for enterprises.

The development of large model artificial intelligence is one of the most significant advancements in the field of artificial intelligence in recent years. It has completely transformed the way businesses operate, innovate, and interact with customers. With the rise of models such as GPT, BERT, and others based on Transformer architectures, large model artificial intelligence application solutions have become an important tool for various enterprises, driving operational transformation and enhancing user experiences in various application scenarios.

Source: Frost & Sullivan report

Since the birth of large model artificial intelligence, it has developed rapidly, and people now have a clearer understanding of its evolution from generative artificial intelligence to more advanced Agentic artificial intelligence. Although both generative artificial intelligence and Agentic artificial intelligence are built upon the foundation of large model artificial intelligence and represent important progress in the field of artificial intelligence, they differ significantly in their core focus and application goals.

The main goal of generative artificial intelligence is to generate entirely new content that mimics human creativity by learning massive amounts of data and applying the knowledge it has acquired. It can automatically generate original outputs in various forms such as text, images, code, audio, video, etc., with an emphasis on content generation and expression capabilities. In contrast, AGentic artificial intelligence goes beyond simple content generation; it can make autonomous decisions, take actions, and adapt in a constantly changing environment. Such artificial intelligence systems not only understand task objectives but also autonomously optimize behavior strategies based on feedback to achieve specific goals with less human intervention.

Although still in its early stages, Agentic AI represents a paradigm shift in the conceptualization and application of artificial intelligence. Unlike generative AI, which mainly relies on user prompts to generate content, Agentic AI can autonomously act in dynamic and constantly changing environments to achieve specific goals.

With the continuous development of Agentic AI, it has laid the foundation for the broad potential of General AI (AGI). Its long-term goal is to achieve Super AI (ASI) - that is, AI that can surpass human cognitive abilities and operate at a level higher than human intelligence. The solutions provided by leading market participants have far surpassed the basic functions of traditional generative AI, such as simple data retrieval, office collaboration, or chatbots. They directly address the core business challenges of enterprises by providing decision support and productivity enhancement tools. These solutions help enterprises make wiser and more impactful business decisions by providing employees with a rich knowledge base covering the entire enterprise domain and industry-specific information, thereby promoting the overall intelligent upgrade of organizations.

●The development of agentic artificial intelligence

With the continuous advancement of large model technology, the development vision of Agentic AI is to achieve autonomous intelligent agents capable of executing tasks with extremely high consistency and reliability. These intelligent agents will operate as adaptive independent entities, capable of making real-time decisions and adopting context-sensitive strategies. In addition, Agentic AI agents are expected to seamlessly collaborate with other artificial intelligence systems to jointly handle increasingly complex tasks, drive the achievement of broader goals, and thus fully unleash the potential of enterprise-level large model artificial intelligence application solutions.

●Expand the application scenarios of large model artificial intelligence

With the continuous maturity and commercialization of large model artificial intelligence technology, it is expected that more and more enterprises will adopt large model artificial intelligence application solutions to meet the diverse needs of various industries - covering fields from marketing and sales to R&D, finance, supply chain management, and more. This will further drive the overall demand growth for enterprise-level large model artificial intelligence application solutions.

●Global expansion

Relying on a mature domestic sales and service system, as well as rich technical implementation experience, Chinese enterprise-level large model artificial intelligence application solution providers are accelerating their entry into the international market. Chinese manufacturers have significant advantages in complex scenario processing and rapid technological iteration, capable of effectively meeting the growing global demand for scalable and efficient solutions. By combining their localized service network with a partner ecosystem, they can quickly adapt to market demands in different regions, transform domestic mature best practices into globally replicable deployment strategies, and thus drive continuous growth in overseas markets.

Based on 2024 revenue, DeepSpeechPro ranks fifth in the Chinese enterprise-level large model AI application solutions market, with a market share of 4.2%.

Source: Frost & Sullivan report

●Data capabilities

The data capabilities of large model artificial intelligence application solution providers are the foundation for industry development. This is because related large model applications highly depend on a large amount of high-quality, diverse data for training. Enterprises with advanced data capabilities (especially those that deeply integrate data and artificial intelligence technologies) can handle complex tasks across various industries with higher accuracy, scalability, and adaptability. Strong data capabilities not only enable more efficient data processing and model training but also ensure that the generated solutions can precisely meet the unique needs of different industries.

●Engineering capability

Engineering capability is a key factor for enterprises to succeed in the large model artificial intelligence application market. It involves developing and deploying large model AI solutions in enterprise applications, including building robust technical frameworks, integrating complex systems, and ensuring continuous operations, maintenance, and industry adaptation. Leading enterprises with strong engineering capabilities can better provide customized solutions that meet customer needs. In contrast, newcomers often lack mature technical frameworks and adaptive toolchains, making it difficult to meet the high standards of enterprise customers for stability, security, and response speed. This leads to longer product delivery cycles, higher failure rates, and difficulty catching up with the technical level of mature enterprises in the short term.

●product capabilities

Leading enterprise-level large model AI application solution providers can typically continuously optimize product performance and expand functionality through a wealth of customer feedback. Through continuous updates and iterations, they can develop more advanced AI application solutions tailored to specific scenarios in different industries. However, newcomers often struggle to support the complex business logic and multi-level needs of enterprise-level customers due to a lack of deep industry experience, long-term technical accumulation, and real-world scenario validation.

●Industry knowledge

Top large model AI solution providers possess profound expertise in understanding industry logic, embedding business processes, and complying with regulatory standards. Through long-term collaboration with major clients, they have accumulated a vast knowledge base covering business rules, scenario limitations, and industry best practices, and have translated this experience into AI optimization goals. In contrast, newcomers lacking industry knowledge not only face longer development cycles and difficult scaling from pilots to large-scale operations but also face issues such as low customer trust and limited market share.