An AI “Traffic Router” at a Turning Point in the Era! Xunce Launches TokenRouters: Building an Enterprise AI Core Platform Centered on Dedicated Tokens as Exchange Assets

On October 28, Xunce Technology (3317.HK) launched TokenRouters, a one-stop platform for the exchange, matching, and service of real-time AI data and specialized tokens. Unlike general-purpose tokens, TokenRouters focuses on serving as a specialized token exchange platform, connecting the intelligent needs of industries across all sectors. It forms a closed-loop service ecosystem centered on enterprise model optimization and the implementation of enterprise-level small models, helping enterprises connect, match, and exchange data, models, and intelligent assets.

Enterprise AI has entered uncharted waters, and the focus of model competition has shifted from general-purpose conversational capabilities to task-specific performance. Gartner predicts that by 2027, more than half of the generative AI models used by enterprises will be industry- or function-specific models built for particular domains. While general-purpose models address the question of “whether a conversation is possible,” specialized models address “whether tasks can be accomplished”—professional scenarios such as financial analysis, contract review, and industrial operations and maintenance have become the focus of the next wave of optimization and implementation. The “fuel” driving these specialized models is precisely the specialized tokens refined from enterprises’ private data.

Small models, meanwhile, offer a more cost-effective and on-premises-friendly implementation path for enterprise AI. According to NVIDIA’s estimates, running a small model with 7 billion parameters is 10 to 30 times cheaper than running models in the 70 billion to 175 billion parameter range. The combination of “small models + specialized tokens” makes on-premises deployment economically viable: data remains within the organization, responses are faster, and inference is more closely aligned with business scenarios. This is also the fundamental distinction between specialized tokens and general-purpose tokens—specialized tokens do not carry general language capabilities, but rather the specific enterprise’s data assets and business knowledge.

As enterprise AI enters uncharted waters, model supply becomes increasingly tiered, and small models emerge as the primary implementation form, the bottleneck in enterprise intelligence upgrades has shifted from “whether computing power is available” to “whether the right resources are selected.” Dedicated tokens typically circulate within a single enterprise and are difficult to obtain across organizations: the quality of publicly available data varies widely, and industry-specific private data is hard to source; models lack cross-platform evaluations, so they must be tested and deployed one by one; there are still gaps in the transition from demonstration to production for vertical applications; and experience in specific scenarios is often locked within individual experts. Consequently, there is no common foundation for exchanging and matching data, models, and applications.

To address these pain points, TokenRouters has built a five-in-one AI resource circulation system comprising “dataset marketplace × AI models × AI applications × scenario library × intelligent customer service.” Within this system, the dataset marketplace, AI models, and AI applications address the discovery and comparison of specialized resources: they make data comparable through content overviews, technical specifications, and scenario analyses; make models understandable through capability overviews and detailed specifications; and align applications closely with business needs through product tags and feature breakdowns; The Scenario Library and Intelligent Customer Service address scenario assessment and operational inquiries; the former transforms vague intelligent intent into actionable development goals, while the latter uses natural language question-and-answer capabilities and human fallback to streamline the consultation process.

From a strategic perspective, TokenRouters is currently in the 3.0 phase of Xunce’s four-stage evolution. Previously, Xunce used TokenOS to refine enterprise private data into specialized tokens and provided end-to-end cloud services via TokenCloud, thereby completing the construction of the “data–token–intelligent agent” end-to-end infrastructure. Building on this foundation, TokenRouters facilitates cross-entity token exchange and paves the way for the private deployment of 4.0 enterprise-level small models—thereby extending Xunce’s narrative from “creating tokens for enterprises” to “enabling the circulation of specialized tokens.”

For Xunce, TokenRouters signifies not only an extension of its business model but also a shift in its strategic role. The pricing model has become multi-tiered: in addition to charging based on Token call volume, it now includes fees for exchange and circulation processes, covering Token routing, pricing and matching, compliant circulation and auditing, as well as services that assist enterprises in valuing tokenized data assets and capitalizing them on their balance sheets.

The essence of this upgrade is the platform’s shift from “delivering resources” to “facilitating circulation.” Xunce no longer merely delivers data and tokens to individual clients; instead, it establishes rules for cross-entity exchanges, facilitates these exchanges, and provides a foundation for compliance and pricing. Consequently, the company’s positioning has evolved from an “AI data infrastructure provider” to a “token economy infrastructure operator.” Its growth potential is no longer determined by the volume of requests from individual clients, but rather by the scale of token circulation across the entire ecosystem.