Qianfeng Interview with Zhou Chao of Shujing Cloud: Cross-Border E-Commerce Tax Compliance Services Move from 'Wild Growth' to the 'Era of Reckoning'

【Ebrun Original】Recently, during the 2026 Qianfeng Award interview, Ebrun, Ebrun Think Tank, and Zhou Chao, co-founder of Shujing Cloud (Hangzhou) Intelligent Technology Co., Ltd., held an in-depth discussion around 'Modal Evolution Brought by AI and Going Global.'

Ebrun: This year's Qianfeng Award takes 'Modal Evolution Brought by AI and Going Global' as its core theme. In the context of cross-border e-commerce tax compliance services, how do you understand this round of industrial modal evolution jointly driven by AI and globalization? What is Shujing Cloud's positioning and business model?

Zhou Chao: Cross-border e-commerce is undergoing a transition from 'wild growth' to 'intensive cultivation.' In the past, the industry competed on products, traffic, and supply chain efficiency. But as platform data, customs data, fund data, and tax data are gradually connected, the gray space previously maintained by experience and manual work is shrinking. Overseas competition has extended to corporate governance capability.

We believe the essence of this round of modal evolution is: the infrastructuralization of professional service capabilities. As cross-border business enters the 'era of reckoning,' tax capabilities are moving from a back-office function to a core operating capability. Based on this, Shujing Cloud has been anchored since its founding not to a single-point tool, but to 'cross-border tax compliance infrastructure'—with self-developed compliance AI Agents as the core engine, positioning itself as an 'intelligent service provider for global enterprise cross-border compliance.' In terms of specific layout, we split our capabilities into three products: Jingguan (AI document assistant, solving 'how goods go out'), Jingshui (AI tax refund assistant, solving 'how taxes are refunded'), and Jingzhang (AI accounting assistant, solving 'how operations are calculated'). The underlying layer connects to the Single Window, electronic tax bureaus, Amazon API, bank interfaces, and logistics nodes, forming a complete tax and financial closed loop for cross-border enterprises.

Ebrun: At present, in which core business links is AI being implemented?

Zhou Chao: Three core links. Jingguan—AI documents: When goods are warehoused, the system archives and pre-classifies information such as product names, SKUs, declaration elements, HS codes, materials, net and gross weight, and brands. At shipment, it automatically extracts SKUs, box numbers, box specifications, and receiving addresses from FBA shipment box labels, then calls the product database declaration data to automatically complete document preparation, push orders, and declaration. Jingshui—AI tax refund: Before tax refund, the AI analysis system conducts tax detection on the upstream supply chain, cross-comparing contracts, goods, funds, and invoices to front-load risk identification. During tax refund, it continuously verifies business data through intelligent allocation, pre-refund order write-off, and sales order data collection; and automatically generates relevant ledgers from shipment and tax refund links. Jingzhang—AI accounting: Undertakes full-set accounting to truly align goods, invoices, payments, and accounts.

Ebrun: Could you share one of the most representative 'AI value chain' cases?

Zhou Chao: The most representative is the 'Jingshui intelligent tax refund—front-loaded risk control' link. The common pain point it solves is: cross-border e-commerce tax refund links are long and involve many parties (suppliers, platforms, customs, tax authorities, banks). The four dimensions of contracts, goods, funds, and invoices must correspond to each other. If any link goes wrong, at best the tax refund is delayed, and at worst a letter inquiry is triggered. In practice, before tax refund, AI is first used to conduct tax detection on upstream suppliers, and four-flow cross-comparison front-loads risk identification. During tax refund, continuous verification is achieved through intelligent allocation and pre-refund order write-off. The value created has several layers: for the enterprise itself, the tax refund cycle is controllable, and there is a basis for responding to letter inquiries; for upstream and downstream, suppliers' compliance levels are made visible, which in turn drives overall supply chain compliance; for the industry chain, as of June 2026, the number of nationally registered cross-border e-commerce merchants had reached 158,900. Under the trend of 'tax governance by data,' enterprises that complete data standardization in advance actually gain a first-mover advantage in operating efficiency.

Ebrun: You mentioned that a self-built cross-border compliance team (at least three to four people covering documents, taxation, cross-border finance, and overall architecture coordination) costs at least more than 300,000 yuan per year, and experience leaves with the person; Shujing Cloud uses a 'product plus service' approach to greatly dilute the cost. What does this cost structure mean for small and medium-sized sellers?

Zhou Chao: For small and medium-sized sellers, building an in-house team is both expensive and unstable—when people leave, experience goes with them. Shujing Cloud uses 'AI products plus professional teams' to put diagnosis, architecture, customs affairs, finance, taxation, and execution within the same system, so enterprises can obtain complete compliance capability at a cost far lower than building an in-house team. More importantly, compliance capability no longer depends on any one person's experience, but is embedded in systems and processes.

Ebrun: Regarding data competitiveness, how does Shujing Cloud understand 'data connectivity'?

Zhou Chao: Data is the foundation of our entire system. The system's underlying layer connects to the Single Window, electronic tax bureaus, Amazon API, bank interfaces, and logistics nodes, allowing data across the four dimensions of contract flow, goods flow, funds flow, and invoice flow to correspond and flow automatically within the system. It can be said that without data connectivity, there is no such thing as 'AI understanding business logic.' Our strategy is to 'deepen based on proprietary industry data and extend toward cross-entity collaboration.' Cross-border tax data itself has strong cross-entity attributes; platform data, customs data, bank data, and foreign exchange data are naturally dispersed among different entities. There are two core challenges: first, the interface standards and granularity of different data sources vary, requiring continuous data standardization and cleansing; second, under compliance requirements, data security and permission boundaries must be strictly controlled. The response is to embed data access and governance capabilities as underlying product capabilities, rather than customizing separately for each client.

Ebrun: Regarding overseas layout and capability iteration, what is Shujing Cloud's model?

Zhou Chao: The cross-border enterprises served by Shujing Cloud are spread across major global e-commerce markets, covering regions such as Europe, the Americas, and Southeast Asia where Chinese sellers are most concentrated. In terms of localization, the biggest feature of cross-border taxation is 'one country, one policy': different countries have different VAT filing cycles, and platform withholding and remittance also have their own applicable conditions. We break down these differentiated rules into the product system and front-load localization capability into the 'architecture design' stage—first conducting enterprise diagnosis, sorting out the four flows and risk lists, then formulating an architecture plan, and subsequently implementing customs affairs, finance, taxation, and business execution.

Strictly speaking, Shujing Cloud's overseas model belongs to a third type: it is not building localized supply chains with heavy assets, nor simply exporting brands, but rather taking the compliance capability of Chinese cross-border e-commerce enterprises overseas together with them in the form of 'digital infrastructure.' The core model is dual-wheel delivery of 'AI products plus professional teams': on one end are the three self-developed product systems Jingguan, Jingshui, and Jingzhang, which break down professional work highly dependent on personal experience into reusable and traceable standardized processes; on the other end are professional teams for export, documents, tax refunds, and full-set accounting, embedded in the product system to handle complex scenarios that the system cannot standardize.

Ebrun: How do you view the support of AI and digitalization for cross-border business?

Zhou Chao: We have gone through three stages, and we are now productizing the third stage. The first support is efficiency—turning manual experience into standard actions, most typically Jingguan's AI documents. The second support is risk control—turning after-the-fact remediation into before-the-fact identification. Jingshui's AI analysis system conducts tax detection on the upstream supply chain before tax refund, with four-flow cross-comparison to front-load risk identification. The third support, which is also the industry direction we judge—from 'recording business' to 'understanding business logic.' Cross-border tax software in the past recorded business that had already occurred; in the future it must understand why business occurs this way. This depends on comprehensive connectivity of underlying data. There are three specific lessons: compliance must be front-loaded and cannot wait until risk is exposed; professionalism must be productized, otherwise scaling is impossible; data connectivity is the prerequisite for all AI capabilities.

As an annual bellwether in the TO B field, the '2026 8th Ebrun Industrial Internet Annual Conference and First Industrial Agent Conference' will be held in Beijing on November 20, 2026. The theme is set as 'Modal Evolution Brought by AI and Going Global,' focusing on in-depth deliberation of frontier topics such as the implementation of industrial agents, value reconstruction of AI supply chains, data elements and the TOKEN economy, and the new ecosystem of industrial going global. The conference will gather academic authorities, benchmark central state-owned enterprises, industry leaders, investment institutions, and service ecosystem partners to pool core industry strength, break down the path of industrial digital and intelligent transformation, and share frontier practical experience. At the same time, the annual industrial internet report and benchmark cases will be released, and a decision-makers closed-door meeting and the 'Qianfeng Award' ceremony will be held.

This article was first published on the official website of Ebrun.