XUNCE (03317) TokenRouters makes a strong debut, building an enterprise AI platform with dedicated Tokens as the core exchange asset, transforming AI infrastructure from "pipelines" into "networks"

As the AI industry moves from technological breakthroughs to large-scale commercial applications, the focus of industry competition is also shifting. In the past, when enterprises deployed AI, their first considerations were whether computing power was sufficient and whether model capabilities were leading. Today, as large models gradually mature, what truly affects the efficiency of AI implementation is shifting toward data quality, model adaptation, application scenarios, and efficient coordination among resources. Looking further, what enterprises need is no longer just a data pipeline or a model invocation entry point, but an enterprise AI core platform capable of connecting data, dedicated Tokens, models, applications, and scenarios, and continuously optimizing enterprise models. For enterprises, the market is not lacking data, models, or AI applications; what is truly scarce is a mechanism that can effectively connect these resources. Data is scattered across different systems and platforms, model capabilities are difficult to accurately match with specific business needs, and applications moving from technical demonstrations to production environments face challenges such as scenario adaptation, system integration, and performance validation. These issues not only raise the cost of enterprise AI applications but also, to a certain extent, constrain the pace of industry intelligentization. Against this backdrop, on October 28, XUNCE Technology (03317) launched TokenRouters, positioned as an enterprise AI core platform and a one-stop exchange, matching, and service platform for AI real-time data and dedicated Tokens, seeking to further bridge the connections among data, models, applications, and business scenarios. Compared with simply providing data processing or model inference capabilities, what is more noteworthy about TokenRouters is its attempt to integrate the discovery, evaluation, matching, and exchange of AI resources into a complete system, and to form a closed service loop around enterprise model optimization and the implementation of enterprise small models. This means that XUNCE Technology's strategic layout is extending from helping enterprises produce and use Tokens to further promoting the circulation and value realization of Tokens and related intelligent assets. For the company, this is not only an expansion of its product matrix but may also become an important step in the evolution of its business model from point services to a platform-based ecosystem service. From "resource availability" to "resource exchangeability": TokenRouters enters the key link of enterprise AI implementation As AI applications gradually enter actual business links such as financial analysis, industrial quality inspection, and intelligent manufacturing, enterprises' requirements for AI are no longer limited to whether models can answer questions, but focus more on whether models can understand industry contexts, accurately process professional data, and operate stably within existing business processes. Here, two types of Tokens need to be distinguished. General Tokens mainly address general capability issues such as language understanding, common-sense question answering, and content generation; their core is "understanding language," and their advantages are strong generalization and low barriers to acquisition. But for enterprises, general Tokens can be purchased, yet it is difficult for them to form differentiated competitiveness. Dedicated Tokens are formed from high-quality industry data through governance, processing, and tokenization, carrying industry terminology, business rules, process knowledge, and scenario constraints; their core is "understanding business," and they can support more precise decision-making in vertical scenarios such as financial risk control, medical diagnosis, and industrial quality inspection. TokenRouters is not a simple aggregator of general Tokens, but takes dedicated Tokens as its core exchange asset, making high-quality vertical Tokens discoverable, comparable, matchable, circulable, and priceable, thereby supporting the optimization of enterprise models and the training and deployment of enterprise small models. Therefore, for enterprises, even with sufficient computing power and advanced general models, if they cannot find suitable data, match applicable models, and transform them into applications that can solve practical problems, it will still be difficult for AI investment to generate sustained business value. The main contradiction facing enterprise AI implementation is gradually shifting from insufficient resources to insufficient resource allocation efficiency, especially insufficient adaptation efficiency between dedicated Tokens and enterprise models. On the one hand, datasets, models, and applications are scattered across different vendors and platforms, making it difficult for enterprises to compare and screen them under unified standards. On the other hand, the same business problem often has multiple technical implementation paths, but there is a lack of intuitive reference for model capability boundaries, data adaptation levels, and application effects, forcing enterprises to complete selection through repeated testing. At the same time, industry experience is usually accumulated in project teams or among professionals, lacking reusable scenario references, which further increases the time cost of AI projects from solution design to actual deployment. It is understood that TokenRouters is precisely laid out to address these pain points. The platform is centered on five major modulesdatasets, AI models, AI applications, scenario library, and intelligent customer serviceestablishing a complete chain from identifying scenarios, discovering resources, evaluating adaptation, learning through reference and validation, to full-process service. Among them, datasets help enterprises more effectively identify and evaluate data resources through information such as content summaries, technical specifications, and applicable scenarios; AI models provide references around model capabilities, technical specifications, and application scenarios, promoting mutual matching between model capabilities and business tasks; AI applications further connect product functions with actual workflows, helping enterprises determine what problems specific applications can solve and where they are suitable for deployment. The significance of this system is not just to display different resources together on the same platform; more importantly, it seeks to reduce the search cost, comparison cost, and decision cost for enterprises in acquiring AI capabilities, making the selection process that originally required cross-platform and cross-vendor work more centralized and transparent. For enterprise models, this mechanism not only solves the question of "what to choose" but also provides scenario references for subsequent adaptation, fine-tuning, validation, and deployment, essentially continuously optimizing enterprise models at the platform level. At the same time, the scenario library and intelligent customer service fill in the two links of business understanding and service support for resource matching. Among them, the scenario library starts from actual enterprise tasks, breaks down business pain points, explains expected value, and presents how technical capabilities are integrated into business through system screenshots and architecture diagrams; intelligent customer service provides natural language Q&A, FAQ guidance, and historical session management, while retaining an entry point for manual assistance for complex issues. This product design reflects an important change: the starting point for enterprises to introduce AI may no longer be to first determine a certain technology and then look for application scenarios, but to first clarify business problems and then reverse-search for suitable data, model, and application resources. For enterprises lacking large AI R&D teams, this resource-matching approach starting from business needs is expected to lower the threshold for exploring AI applications and also shift enterprise model optimization from "blind trial and error" to "scenario-driven." More notably, TokenRouters incorporates enterprise small models and dedicated Tokens into its overall strategic framework. General large models have broad knowledge coverage, but in professional scenarios such as financial risk control and industrial quality inspection, enterprises often models trained on industry data that can understand specific business rules. Vertical domain data that has been governed and refined can become an important foundation for adapting models to specific businesses, and the role played by TokenRouters is to further promote the discovery, matching, and exchange of these data, Tokens, and model capabilities, helping enterprises obtain domain small models that can be privately deployed at lower cost and in shorter cycles. Of course, platform value ultimately still needs to be verified by supply and demand scale, resource quality, actual transactions, and customer usage. What the launch of TokenRouters demonstrates is XUNCE Technology's strategic judgment on this market opportunity; its subsequent performance still depends on whether the platform can continuously gather high-quality resources, form effective matching, and establish a service mechanism that enterprises are willing to use over the long term. From Token production to value circulation, accelerating the layout of a new AI infrastructure ecosystem If TokenRouters addresses the question of how enterprise AI resources are connected, then from the perspective of the company's overall strategy, its deeper significance lies in promoting XUNCE Technology's evolution from an AI data infrastructure provider to an operator of Token economy infrastructure. Looking back at the company's business development path, a strategic mainline that gradually extends upward can be seen: the first stage is data governance, forming high-quality, usable data assets by processing heterogeneous data inside and outside enterprises; the second stage is tokenization, combining enterprise data with specific business scenarios through products such as TokenOS to form scenario Tokens oriented to professional tasks; the third stage is Token exchange, using TokenRouters to connect supply and demand sides across enterprises, industries, and scenarios; the fourth stage further points to the training and large-scale deployment of enterprise small models, enabling AI capabilities to penetrate deeply into enterprises' own business processes. The four form a progressive relationship around the layered release of data value: data governance solves the question of whether data can be used; tokenization solves how data is transformed into professional knowledge usable by models; TokenCloud solves how relevant models can achieve training and inference with the help of computing power; and TokenRouters further explores how these capabilities can be matched and circulated across organizational boundaries, while continuously optimizing enterprise models in the process. This also makes TokenRouters not merely an extension of the product matrix, but a key pillar for XUNCE to build an enterprise AI core platform. It is understood that the business models TokenRouters may explore in the future include Token routing fees, Token pricing and matching services, compliant circulation and audit services, and value-added services for data assetization. Its core logic is to further extend value originally scattered across data production, model invocation, and application delivery links to the links of resource circulation and transaction services. It should be emphasized that AI cybersecurity and compliance security are underlying capabilities in which XUNCE has long invested. In the process of data governance, tokenization, and enterprise services, the company continuously covers links such as data classification and grading, access control, privacy protection, model and Token circulation auditing, and private deployment security. TokenRouters continues this security and compliance approach, embedding cybersecurity, data loss prevention, model access control, and full-process auditing into the dedicated Token exchange and matching process, so that "exchangeability" is built on the foundation of "auditability, controllability, and compliance." XUNCE has previously signed a strategic cooperation agreement with the Shenzhen Data Exchange and entered into a cooperation framework agreement with the Beijing International Big Data Exchange, which also reflects its continuous layout in compliant circulation and the secure use of data elements. However, the imaginative space of this business model does not mean that platform revenue has already been formed. Whether TokenRouters can truly establish ecosystem operation capabilities still depends on whether supply-side resources are sufficiently rich, whether demand-side enterprises continue to participate, and whether the platform can establish an effective balance among resource quality, transaction efficiency, data security, and compliance requirements. Only when the platform can continuously facilitate matching between real demand and high-quality resources can circulation scale possibly be further transformed into sustainable commercial returns. Today, through its previous business accumulation, XUNCE Technology has already formed a certain commercialization foundation in data governance, industry solutions, and Token business, also providing realistic support for its further expansion of the product system. Enterprise-grade AI differs significantly from general consumer-grade AI: enterprises need to process complex internal data, need to follow existing business rules, and often impose higher requirements on data security, deployment methods, and system stability. This determines that competition in enterprise AI infrastructure is not only competition in technical capabilities, but also competition in understanding industry needs, engineering implementation, and the ability to adapt to customers' business processes. Previously, XUNCE Technology's data processing experience accumulated in fields such as financial asset management, as well as its practice extending to industries such as telecommunications, power, high-end manufacturing, and biomedicine, is expected to provide a foundation for its understanding of vertical scenarios, organization of professional resources, and advancement of model adaptation. On top of this, TokenRouters has the opportunity to further transform resource connections previously completed mainly through projects and customer relationships into platform-based discovery, matching, and service processes, and to continuously optimize enterprise models in the circulation of dedicated Tokens. Looking ahead, as enterprise AI applications move from pilots to broader production deployment, the industry needs not only more computing power and stronger models, but also a resource allocation mechanism that can connect data, models, applications, and business needs. What TokenRouters enters is precisely this gradually emerging market demand. For XUNCE Technology, its strategic value lies in that its growth logic is expected to extend further from data processing and Token invocation to resource matching, platform services, and enterprise small model deployment. If this system can be continuously improved, the company's service targets will no longer be limited to individual enterprises' data processing needs, but may have the opportunity to cover a broader AI resource supply and demand network. From "data pipeline" to "Token value network," XUNCE is attempting to broaden the boundaries of enterprise AI infrastructure. Whether TokenRouters can become the key pillar of this leap ultimately still depends on the speed of platform ecosystem formation, the growth of real transaction demand, and whether the business model can be continuously delivered. But what is certain is that as competition in the AI industry shifts from single-point technical capabilities to full-chain implementation efficiency, infrastructure platforms that can connect data, computing power, models, and applications are ushering in a new development window; and XUNCE's pathpositioned as an enterprise AI core platform, anchored in optimizing enterprise models, and taking security and compliance as its bottom linealso seeks a clearer position for itself within this window.