Trading sentiment in the Hong Kong stock market's AI sector has recently been straightforward. On one side, pure-play model companies are being aggressively bid up by capital. For instance, MiniMax saw its peak阶段性 increase approach 90% in March, while Zhipu AI has surged 72% since March, hitting a record high and rapidly breaking away from traditional valuation frameworks. On the other side, enterprise-grade AI application companies are experiencing a rebound following their earnings reports, as the market begins to reassess how AI translates into revenue and profit. One listed AI data governance company, which surpassed the 1 billion yuan revenue threshold, saw its stock price more than triple in a single month. Judging solely by short-term stock performance, the former seems to trade on imagination, while the latter appears to trade on improving fundamentals.
However, taking a step further, the critical question for capital markets should be: Which company is most likely to advance AI from merely "answering questions" to "executing tasks," and thereby build a lasting competitive advantage within complex organizations? This is precisely why HAIZHI TECH GP deserves re-examination within a more advanced framework.
First, consider the market sentiment itself. A March report from the Financial Times highlighted that Hong Kong's two best-performing AI IPOs this year had already gained over 400% year-to-date, helping drive the city's first-quarter IPO and refinancing volume to its highest level since 2021. Jason Lui, Head of Asia-Pacific Equity and Derivative Strategy at BNP Paribas, noted in the report that while investors were buying into large-cap Chinese tech index constituents in 2025, by 2026 the market began actively seeking companies with "pure AI exposure." This indicates that the capital market's valuation logic for Chinese AI has shifted from broad tech heavyweights to more vertical, pure-play AI assets.
This sentiment is first reflected in foundational model and agent platform companies. Their advantages lie in straightforward logic, clear themes, and ease of understanding for international capital: keywords like large models, agents, autonomous evolution, and platform gateways naturally suit a market environment with rising risk appetite. From a secondary market perspective, the rise of these companies is reasonable, as they embody the imagination surrounding the "capability ceiling" of Chinese AI.
Another group being repriced consists of enterprise-grade AI application companies. Public information shows that one Hong Kong-listed enterprise large-model AI application company achieved revenue of 415 million yuan in 2025, a year-on-year increase of 70.8%. Its adjusted net loss narrowed by 71.4% year-on-year. Within this, revenue from AI solutions reached 254 million yuan, surging 181.5% year-on-year and becoming the primary revenue source. In early April, the company's stock surged over 40% in a single day, driven by renewed market expectations of a gradually clarifying "profit inflection point."
From a capital market perspective, these companies are also commendable. They demonstrate a crucial point: enterprise clients are not unwilling to pay for AI; the key is whether AI can integrate into real workflows and assume specific responsibilities, rather than remaining solely at the demonstration or Q&A level. In other words, the commercialization path for enterprise AI is being validated, and the market's assignment of higher valuations is justified.
However, the issue lies precisely here. Whether pure model platforms or enterprise-facing AI applications, most companies currently remain closer to opportunities at the "reasoning layer" or "interaction layer." They can generate content, assist decision-making, and connect parts of processes, but the truly difficult step is enabling AI to understand the world according to rules, reason within boundaries, and execute tasks following processes within complex organizations. This step determines whether enterprise AI remains merely a smarter interface or evolves into a genuine productivity system.
HAIZHI TECH GP's scarcity lies in its closer alignment with the latter. According to its prospectus, the company's core product is not a single model or agent but a composition including the DMC Data Intelligence Platform, the Atlas Knowledge Graph Platform, the AtlasGraph graph database, and the Atlas Agent Solution built upon them. In 2025, the company reported revenue of 621 million yuan, up 23.4% year-on-year; adjusted net profit was 24.15 million yuan, increasing 42.6% year-on-year; overall gross margin rose to 43.3%. Revenue from the Atlas Agent Solution specifically was 145.7 million yuan, growing 68.4% year-on-year, with a gross margin reaching 53.2%.
Viewed in isolation, HAIZHI TECH GP's revenue growth may not be the most aggressive in the sector. However, when measured by "depth of technology stack" and "control at the execution layer," it more closely resembles the type of company capital markets are willing to assign a long-term premium to. The reason is that HAIZHI TECH GP's core logic is not simply "graph + large model," but rather ontology + graph-model fusion. These terms may seem technical, but they directly address the most critical value proposition of enterprise AI.
General large language models excel at generalization, induction, and linguistic reasoning; their weakness is a tendency to lose boundaries within complex business environments. A corporate internal environment is not an open internet corpus but a system composed of roles, objects, permissions, relationships, processes, timelines, and business rules. Issues like reporting structures, customer-product line mappings, valid approval paths, conclusions requiring database verification, and actions that must reach system execution layers are not reliably solved merely by how articulate a model is.
This is precisely the significance of graph databases, knowledge graphs, and ontology modeling. They first provide a structured representation of entity relationships and business rules within the corporate world, then allow the model to reason within this constrained, interpreted, and traceable scope. In other words, HAIZHI TECH GP does not attempt remediation after the large model produces a conclusion; instead, it defines boundaries, provides structure, and establishes pathways for the model before reasoning occurs. The result is not just reduced hallucination but, more importantly, enhanced controllability and accuracy of execution.
This is where HAIZHI TECH GP operates at a "deeper" level than many upper-layer AI application companies. Many enterprise AI products essentially focus on process encapsulation, Q&A enhancement, or task orchestration—valuable, but with control points mainly at the front-end interaction and light collaboration layers. In contrast, HAIZHI TECH GP's graph-ontology and graph-model fusion embed control points deep within the enterprise knowledge structure and task execution logic itself. It doesn't just make AI "a bit smarter"; it enables AI in complex environments to "know what to believe, what to connect, which step to take, and what can ultimately be achieved."
This approach closely mirrors the core methodology of Palantir Technologies Inc.. The reason Palantir Technologies Inc. commands an extremely high strategic valuation from capital markets long-term is not merely its AI concept, but its ability to genuinely integrate heterogeneous data, relational networks, task processes, and decision execution, forming an enterprise or institutional-level operating system. By this standard, what HAIZHI TECH GP possesses is not a single-point application entry, but a more稀缺 piece of execution-layer infrastructure.
Public information indirectly supports this. According to Frost & Sullivan data cited in HAIZHI TECH GP's prospectus, based on 2024 revenue, the company ranked first among China's "graph-core AI agent providers," holding approximately a 50% market share. Furthermore, the market size for "industry-grade AI agents integrated with knowledge graphs" is projected to grow from 200 million yuan in 2024 to 13.2 billion yuan by 2029. This indicates that HAIZHI TECH GP occupies not a crowded AI application track, but a capability layer where the market space is still opening up, yet the technical barriers are already well-defined.
Therefore, if the first category of AI companies represents the elasticity of China's model capabilities, and the second category of enterprise AI companies signifies the beginning of commercialization scaling, then HAIZHI TECH GP represents the direction where AI might genuinely solidify long-term value in the next phase: not making models more human-like, but making models function truly like systems within organizations. Capital markets invariably chase the most dazzling concepts first, then gradually return to the most robust barriers. The AI sector is no exception.
Pure-model companies deserve valuation, and enterprise applications deserve reevaluation. But if searching for a Hong Kong-listed stock that more closely resembles a "Chinese Palantir," the one warranting serious attention might not be the noisiest, but rather the company that genuinely integrates graph ontology, knowledge structures, reasoning boundaries, and execution closed-loops. Following this线索, the value of HAIZHI TECH GP may still be far from fully appreciated by the market.
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