After its listing, the primary challenge for DEEPZERO (02723) is how to transform its technological narrative into sustained growth. According to information from Zhitong Finance, DEEPZERO went public on the Hong Kong Stock Exchange in May 2026, becoming the "first stock for enterprise decision AI agents." Rather than simply focusing on the AI concept, the company wants the capital market to understand that generative AI is reshaping the growth boundaries of enterprise service firms and is also expanding the commercialization radius DEEPZERO has built over the past seventeen years.
In an interview, Huang Xiaonan stated that the company is currently in a "land-grabbing" phase. She is more focused on new customer acquisitions, monetizing new products, and migrating customers from purchasing a single product to multiple products, rather than the fluctuations of short-term single financial indicators.
AI Lifts the Ceiling
Historically, DEEPZERO was primarily focused on two high-value decision-making scenarios: digital advertising placement and CRM user operations. These scenarios naturally rely on data and algorithms, helping the company form core platforms like AlphaDesk and AlphaData. However, from a business boundary perspective, the ceiling for traditional digital marketing software and intelligent placement services remained relatively clear.
Generative AI has changed this. Huang Xiaonan believes this wave of AI is not just a technological iteration but an industrial revolution. The understanding, generation, and reasoning capabilities of large models, when combined with the predictive models, industry models, and customer scenarios that DEEPZERO has accumulated, allow the company to expand from two scenarios to many more, including product innovation, GTM, social marketing, sales training, intelligent guidance, customer service quality inspection, and user operations.
This is the significance of the company's proposed "Agentic Software + Agentic Service" for growth: the former enters the internal processes of enterprises, replacing or upgrading traditional marketing software; the latter enters the outsourced service budget of enterprises, transforming tasks originally handled by agencies or manual services into AI-driven outcome delivery. In other words, DEEPZERO's market space is no longer just the original software budget but also includes broader operational budgets for enterprise marketing, sales, customer service, and user operations. Huang Xiaonan said in the interview that AI has "lifted the ceiling" for DEEPZERO.
The 100×100 Growth Framework
The growth framework Huang Xiaonan provided is "100×100": on one end, the number of customers could increase a hundredfold, and on the other end, the number of products purchased by a single customer could also expand a hundredfold. The expansion in the number of customers comes from market penetration. In the past few years, DEEPZERO's end customers have been mainly concentrated in large and medium-sized enterprises, but the number of enterprises in China with the need for AI upgrades in marketing, sales, and user operations is far greater. The listing itself has also increased the probability of the company being seen by potential customers. Huang Xiaonan mentioned that the number of active market leads has significantly increased after the listing, putting the company in an unprecedented window of opportunity.
The expansion of the number of products per customer comes from the scenario matrix. In the past, a customer might only purchase a single product for advertising placement or data management. However, in the era of Agentic AI, the same customer can extend from advertising placement to multiple scenarios like user insights, content hub, knowledge hub, sales training, intelligent guidance, customer service quality inspection, and GEO optimization. This means that the company's growth does not only rely on new customers but also on cross-departmental, multi-level penetration within existing major customers. A brand might start with advertising placement and then expand to the e-commerce department, marketing department, sales department, customer service department, and user operations department, eventually forming continuous cooperation across multiple products, multiple budget pools, and multiple teams. For investors, the key to this growth logic is not the size of a single project but whether DEEPZERO can turn "one entry point" into "multiple scenarios" and "one product" into a "product matrix."
M&A: From Customer Entry Point to Platform Reuse
Within this growth framework, Huang Xiaonan views M&A as a very important path for DEEPZERO's future. To B companies naturally have long sales cycles, slow customer onboarding, and a heavy focus on scenario understanding. This is both a moat for DEEPZERO and a constraint the company must face during its own expansion. Therefore, relying solely on self-built sales teams and organic expansion may not be enough to fully capture the industrial window of opportunity for AI Agents.
In the interview, Huang Xiaonan stated that the core logic of DEEPZERO's M&A is not simply to buy revenue, nor just to buy products, but to buy customer entry points, deep relationships, and synergistic business lines. "In the past, the core of To B M&A was actually to buy customers and products. We basically don't need to buy products now," she said. The reason is that AI is significantly improving DEEPZERO's own product R&D efficiency. The company values more whether the target company has already penetrated key departments of quality customers, especially those related to consumers, marketing, sales, e-commerce, customer service, and user operations. If the target company serves scenarios like finance or HR, which have weak synergy with DEEPZERO's existing capabilities, the collaborative value is limited. However, if it serves the marketing department, e-commerce department, sales department, or customer operations department of a brand owner, it could become a new entry point for DEEPZERO's Agentic Software and Agentic Service.
The potential of such M&A lies in the fact that the acquired company might have only served a single point of a customer's needs, while DEEPZERO can expand horizontally on this basis, bringing DeepAgent, AlphaData, AlphaDesk, and a series of marketing agent products into the customer's internal operations, expanding from one entry point to multiple products, departments, and budget pools. The value of M&A is not only reflected at the customer level but also at the operational level. In the past, many To B companies fell into a growth dilemma: they could be profitable serving a few customers, but when pursuing scale, they had to invest in R&D, products, and delivery, quickly eroding profits. If DEEPZERO incorporates such companies into its system, it can use their customer relationships to open entry points on one hand, and on the other hand, consolidate redundant R&D and scattered product capabilities onto a unified platform, improving efficiency through AI-powered products and delivery systems. This means M&A could become an important way for DEEPZERO to release scale effects after listing: acquiring customers and scenarios at the front end, and achieving reuse at the back end with a unified AI product platform, industry models, and delivery system.
The Window of Opportunity for Land Grabbing
The window of opportunity for AI applications is often very short. The underlying large models iterate rapidly, and single-point tools are easily replaced. What can truly remain in a company's budget over the long term is a system that can enter processes, form a data loop, and continuously review and optimize. Huang Xiaonan judges that DEEPZERO, compared to many competitors, has already completed several months of product and organizational preparation. On one hand, the company's seventeen years of accumulated customer relationships, data capabilities, algorithm models, and industry know-how are being transformed into assets in the AI Agent era. On the other hand, the company is also reorganizing internally, shifting from traditional internet-style R&D to a small-team, rapid-incubation model of "AI architect + AI product manager + business consultant."
This change directly impacts product supply efficiency. Huang Xiaonan mentioned that in the past, an enterprise software product might require 50 to 100 people for long-term polishing. Now, with the aid of AI, some products can have their prototypes and commercial validation completed by a smaller team within a month. For an enterprise service company, this means the speed of product matrix expansion, delivery efficiency, and M&A integration efficiency could all be redefined. From an investor's perspective, the indicators to watch next are relatively clear: the signing and renewal of DeepAgent-related products, the multi-product penetration rate of Agentic Software within customer organizations, the gross profit and delivery efficiency of Agentic Service, the speed of customer base expansion, and whether the company can form a broader customer entry point through M&A.
DEEPZERO's growth story is not about chasing short-term AI trends. Its core is to use generative AI to re-amplify the decision-making AI capabilities accumulated over the past seventeen years; to turn single-point products into a product matrix; to turn customer entry points into cross-departmental penetration; and to turn traditional services into AI-driven outcome delivery. If this path is successful, DEEPZERO will not just be a marketing technology company, but could become an AI operating platform for enterprise marketing, sales, and user operations scenarios.
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