Weimob has officially rolled out its enterprise-exclusive AI customization service, "Xingcheng." By employing a dual-paradigm approach—building products with an AI-native mindset and handling delivery through Forward Deployed Engineers (FDE)—the company has compressed the implementation cycle for enterprise AI projects down to a matter of weeks, leading to a substantial improvement in deployment efficiency. For businesses, Xingcheng links proprietary knowledge, operational data, model capabilities, and business systems together, constructing an AI technology foundation capable of understanding, executing, and continuously evolving.
Xiao Feng, the technology vice president at Weimob, stated that the bottleneck for enterprise AI adoption lies not in the models themselves but in the "final mile" of delivery. Consequently, the ability to implement AI is replacing raw model parameters as the new benchmark for evaluating AI companies. Weimob is currently restructuring its entire business logic around AI, aiming to build an intelligent business engine that can make autonomous decisions and continually improve. This signals that Weimob is redefining business boundaries through an AI-native approach, potentially establishing a fresh value anchor for the company.
Bridging the "Final Mile" with AI-Native Products and FDE Delivery
In Xiao Feng's view, the value of AI is shifting from isolated tools to intelligent agents embedded within business processes. As large model capabilities rapidly converge, an enterprise's proprietary knowledge, industry know-how, and operational context become the only assets that truly accumulate. Furthermore, AI must evolve from a mere assistant into a business role with measurable performance, complete with indicators, profiles, and attribution capabilities. What clients need are digital employees who drive growth, and that is precisely the rationale behind Xingcheng.
Xingcheng is designed to start from real business problems and integrate AI into actual production environments. It delivers value for enterprises in several key ways. First, it creates products in an AI-native form, using agents to directly complete tasks and managing context by integrating knowledge bases with multi-source data. The models are replaceable through intelligent routing, avoiding dependence on any single platform, and a data flywheel is established in each scenario. This allows every agent role to continuously improve using the client's private data, with results that can be measured. Second, it adopts the FDE model for delivery, removing the lengthy chain of requirement relay. Alignment translates directly into development, and on-site presence enables immediate validation. For deployment, Xingcheng supports three formats—SaaS, client-exclusive, and fully privatized—balancing efficiency with security and compliance.
Xingcheng has already secured recognition from industry partners and works in close collaboration with ecosystem players such as Tencent Cloud, Alibaba Cloud, and Volcano Engine. It is important to highlight that Xingcheng delivers more than just isolated functions; it builds a technology foundation for clients that can continuously grow and develop new capabilities. The application layer cultivated on this foundation consists of solutions tailored to specific industries, extending beyond Weimob's core retail and e-commerce sectors to include apparel retail, sports footwear, consumer manufacturing, food and beverages, commercial chains, and beauty and personal care. This application layer also offers six types of agents with measurable outcomes, including AI-powered customer service, AI training simulations, omnichannel management and control, AIGC, AI-driven channel operations, and investment and site selection for expansion.
The true value of this AI foundation lies in its "build once, reuse continuously" nature. Clients do not need to construct new technical architectures for every emerging scenario or repeatedly integrate data across different agents. As business grows, new roles can simply emerge from the existing foundation; when industry dynamics shift, only the corresponding knowledge packages and metric definitions need updating. This means every Xingcheng deployment is an asset accumulation for the client. As the client base expands and know-how deepens, the foundation's capacity broadens, which is the key to scaling FDE delivery and accelerating industrial adoption.
The creation of Xingcheng stems from a deep understanding of the challenges facing enterprise AI implementation. Currently, companies are encountering unprecedented "final mile" hurdles in deploying AI. According to Gartner, only 17% of enterprises will successfully implement AI agents by 2026. This indicates that significant barriers, such as system integration and business process adaptation, still separate AI from enterprise adoption. In response, the FDE model, or Forward Deployed Engineer, has emerged. Internationally, Palantir, a pioneer of the FDE approach, has leveraged the combination of its AI platform and on-site FDE teams to achieve revenue growth, with its market value once exceeding several hundred billion dollars, making it a benchmark for AI deployment. Domestically, AI application service providers like Weimob are intensifying their efforts in the FDE space, aiming to upgrade AI implementation from a labor-intensive process into a replicable engineering capability. Guolian Minsheng Securities has noted that the core value of FDE lies in translating model capabilities into business results that can be integrated into enterprise production systems. FDE bridges the value chain from traditional software services, through AI project implementation, to productized accumulation, potentially becoming a key driver in the revaluation of the computer sector in this cycle.
An AI-Native Matrix Takes Shape, Accelerating Commercialization
As the FDE track heats up, the competitive landscape is rapidly differentiating. Compared to traditional system integrators and large model vendor teams, the third category of service providers, represented by Weimob, holds distinct advantages. They possess frontline product and engineering capabilities, over a decade of accumulated industry knowledge from working at client sites, and the ability to feed every delivery back into a consolidated technology foundation. For instance, Xingcheng's competitiveness stems from the combination of three asset types: product, service, and know-how. AI-native product capabilities ensure reusability, the FDE service model ensures practicality, and the continuous feedback of accumulated know-how strengthens the foundation with each use.
Weimob's confidence is rooted in its deep expertise across retail, e-commerce, and local services gleaned from over ten years of operation. It is also one of the few companies that simultaneously possesses the three essential elements for enterprise AI deployment: field experience, know-how assets, and AI product engineering capability. This rare combination is precisely what enterprise AI implementation demands and is the fundamental reason Xingcheng can be rapidly replicated across multiple industries.
The launch of Xingcheng also marks a further consolidation of Weimob's AI-native application matrix. In 2026, under its core "AI First" strategy, Weimob has introduced a series of products, including the omnichannel intelligent operations hub "Xingshu," the AI-powered full-chain smart customer acquisition tool "Xingtuo," and the AI creative automation platform "Xingchuang." Now, Xingcheng fills the gap in enterprise AI implementation and management, completing Weimob's AI blueprint with a full closed loop of product, service, and delivery. Xingshu reshapes omnichannel operations for brand e-commerce, Xingtuo expands the customer base, Xingchuang enhances content production efficiency, and Xingcheng takes AI capabilities directly into the field for enterprises across various industries, moving AI from the tool layer to the operations layer. These four products each serve distinct functions while working in concert, signaling Weimob's accelerated evolution from a SaaS tool provider to an AI-native operating system.
From a business model perspective, Xingcheng advances Weimob's transition from selling tools to selling outcomes. The measurable delivery model of Xingcheng provides a basis for performance-based pricing. Each role delivered and every improvement in operational indicators constitutes quantifiable value. As AI upgrades from an add-on module of SaaS to a core business engine, Weimob's AI revenue structure is expected to shift further toward outcome-based income. Financial data has begun to validate this path. In 2025, Weimob disclosed its first annual AI-related revenue of RMB 116 million. In the first half of 2026, AI application business revenue surpassed RMB 133 million, a year-over-year increase of 89.2%, already exceeding the full-year total for 2025. AI-related revenue now accounts for 13% of SaaS subscription solutions revenue, showing that AI is growing from a supplementary tool into a core engine for SaaS business growth. A market consensus is forming: SaaS is reshaping its value proposition through AI, and the ability to scale enterprise AI implementation will be a critical variable in this round of revaluation.
Conclusion
As FDE transitions from an overseas concept to a mainstream paradigm for enterprise AI adoption in China, Weimob's Xingcheng is transforming AI capabilities into assets that are deliverable, priceable, and replicable. From tool to engine, from subscription to outcomes, and from software service provider to AI application service provider, Weimob is using Xingcheng as a lever to capture the benefits of scaled enterprise AI implementation. This represents Weimob's latest answer in the current AI revaluation cycle, unfolding a new growth narrative centered on deployment capability.