Sea Carrier (02706): "Raising Crayfish" for Government and Enterprise Clients, Initiating a New Chapter in Enterprise AI Transformation.

When the "civil servants raise lobsters" hits the hot search, when someone earns 260,000 by "door-to-door installation", it is an undeniable fact that OpenClaw has broken the circle. With domestic large-scale model manufacturers entering the market one after another, OpenClaw is evolving from a "toy" in the geek circle to a "tool" in the mass market, marking the official transition of AI applications from "passive conversation" to "active execution" in a new era. In view, what excites people about OpenClaw is the three qualitative leaps it brings: first, a leap from "talking" to "doing", it is no longer just an assistant for chatting, but a "long-armed" AI that can call tools and perform tasks; second, a "better memory with more use", it can remember user preferences, bidding farewell to the awkward "first acquaintance" of large models in the past; third, the sense of privacy and security brought by local deployment. It is intriguing to imagine that if everyone had such a 24-hour online, intimate personal assistant who understands you, life would be so much better. So, applying this "lobster" to a more complex and larger enterprise side, what kind of assistance will it bring? In addition to anticipation, we must note that applying OpenClaw to the enterprise side is far more complex than the personal side, and it also places higher demands on it. Compared to OpenClaw's three highlights, the demand in the B-end market shows a clear "upgraded version" feature: for "memory and understanding", the enterprise side not only needs to remember preferences, but also deeply understand professional knowledge in vertical fields such as financial risk control, government governance, and be able to execute accurately; for "execution capability", the enterprise side needs to call internal OA systems, professional software, cross-department processes, etc., belonging to highly customized demand; for "local deployment", the enterprise side due to data security concerns, the demand comes earlier and more urgently. In this context, a sharp question is posed to government and corporate clients: can a general open-source framework, although flexible, withstand the rigorous testing of core scenarios such as financial risk control and government decision-making, due to the "hallucination" risk, data security vulnerabilities, and the lack of industry logic of the "layman"? For the B-end market that seeks "zero errors", what is needed is not just a smart "lobster", but a dependable "AI employee factory" that understands business, follows rules, and is reliable. It is in this context that Haizhi Technology (02706) has shown a forward-looking strategic position. Before the popularity of OpenClaw, Haizhi had already seen the deep-seated pain points in the B-end market, and began to nurture a "lobster legion" that understands business, follows rules. Haizhi's privatized OpenClaw platform - Haizhi CollyClaw, uses a "probabilistic integration" architecture to transform AI's "probability" into business "certainty". In a generic framework, the large model is the only "brain", prone to "wild ideas" due to lack of constraints. In Haizhi's architecture, the knowledge graph serves as a "rational skeleton", solidifying business rules into graph logic and setting a "safety red line" for large model reasoning. The strength of CollyClaw is fully demonstrated in specific scenarios. In the field of financial anti-fraud, traditional rule alert is often lagging and difficult to detect hidden groups. In Haizhi's solution, multiple AI employees collaborate: the "clue sniffer" scans massive transaction data 24/7, the "graph analysis analyst" uses AtlasGraph to automatically penetrate "fifteen-degree" relationships to discover groups, the "disposal executive" confirms and triggers business system freezes directly after discovery - from "seeing risks" to "disposal risks", the closed loop is the embodiment of autonomous execution in core enterprise business. In the field of consumer rights protection law enforcement, traditional manual handling often leads to cases exceeding deadlines due to insufficient evidence and stalled processes, leaving consumers complaining without a solution. In Haizhi's solution, multiple AI employees collaborate under the supervision of discipline and inspection officials: the "overdue diagnosis officer" automatically penetrates the case approval log, precisely identifying the "evidence insufficiently stalled" bottleneck; the "evidence analyst" coordinates with payment platforms and monitoring systems to internally generate evidence correction schemes; the "task scheduling officer" dispatches verification instructions to front-line enforcement personnel and submits pre-trial cases to the legal department - from "supervisory accountability" to "closed loop completion", autonomous execution demonstrates the core of AI-driven government efficiency improvement. For government and corporate clients, data security is a lifeline. Haizhi CollyClaw adheres to the "Local-first" principle and supports full-chain private deployment. Data does not leave the domain, models are localized, operations are auditable, hitting the pain points of B-end clients accurately. Behind the hot topic of "civil servants raising lobsters" is a deep-seated desire for digital transformation in government and enterprises. Moving from digital transformation to AI intelligence transformation, the B-end market shows leverage effects far beyond imagination: the huge value base implies that AI efficiency enhancement will release comprehensive and exponential value. The feasibility and security foundation of industrial AI automation established by Haizhi's "probabilistic integration" architecture is the key to unlocking this blue ocean. When industrial AI moves from concept to deep implementation, the value of this track may disrupt people's imagination, and the arrival of the vision it points to may be faster than expected.