Small Brick has launched its Series C financing of 150 million yuan, and its Series B investors include Yuanhe Hope Capital and other institutions.
AI runs through the closed loop of flexible employment transactions, Xiaozhuanqi is seeking Series C financing
For a chain catering enterprise, the shortage of 30 part-time workers in five new stores in Hangzhou next month is not a complex arithmetic problem. However, to fully and compliantly implement the recruitment, contract signing, shift arrangement, attendance checking, acceptance, payroll calculation, invoicing and tax declaration for these 30 people, it often requires the involvement of HR, finance, legal teams and multiple outsourcing service providers. Similar scenarios are spreading from the catering and retail sectors to manufacturing, logistics, customer service, supply chain and other links. Shanghai Xiaozhuanqi Network Technology Co., Ltd. defines such demands as "employment transactions" rather than "recruitment information", and tries to undertake them with a set of AI-native transaction infrastructure. At present, Xiaozhuanqi has launched its Series C financing, planning to raise 150 million RMB, which is expected to cover the key milestones in the next 12 to 18 months.
Both supply and demand sides are fragmented, and employment transactions lack a trusted underlying foundation
In China, the number of people in broad-sense flexible employment has exceeded 300 million, accounting for nearly 40% of the country's total employed population. Meanwhile, the number of individual independent business entities (OPC) in the form of individual industrial and commercial households has exceeded 16 million, accounting for about 27% of the total number of enterprises. Industry research cited by Xiaozhuanqi predicts that China's flexible employment market size will grow by about 15% year on year in 2026, and the number of flexible employees will increase by about 33% year on year. Behind the figures are structural changes: Facing the pressure of demand fluctuation, skill iteration and operational efficiency, enterprises are shifting from permanent headcount to project outsourcing, flexible employment and flexible talent. Individuals no longer only have one full-time job, but combine multiple forms such as full-time work, part-time work, project-based work and skill monetization at the same time.
The problem is that the intermediate layer connecting the two sides is still fragmented. Recruitment platforms are good at information matching but cannot deliver results. HR SaaS platforms excel at process management but do not have a two-way supply and demand network. Flexible employment platforms can complete the order dispatching and settlement of standardized orders, but it is difficult to cover complex performance across industries, regions and multiple forms. Global Employer of Record (EOR) and payroll service providers solve cross-border compliance issues, but lack the density of real-time local supply and demand. Xiaozhuanqi judges that what the market lacks is not another traffic entry, but a transaction infrastructure that can link opportunity discovery, combination, performance and settlement.
Changes in the regulatory environment have further widened this gap. Labor relationship identification is shifting from "focusing on contracts" to "focusing on actual management and performance facts", and tax regulation is also changing from "result declaration" to penetrating verification of multi-stream data including identity, income, contracts, services, capital, invoices and information. Different forms such as full-time employment, part-time employment and platform-based employment correspond to different rules for contracts, payroll tax, social security and security respectively. Manual judgment is not only difficult to scale, but also prone to errors. As a result, compliance has evolved from a legal work to an infrastructure capability that requires systematic support.
Split posts into tasks and skills, and train occupational intelligence with real performance results
Xiaozhuanqi's approach is to first reconstruct the way of understanding the supply side. The platform splits posts into task and skill units, builds a capability model around "task-skill-talent-result", and continuously corrects it with real performance records, delivery results and enterprise evaluations, forming an occupational intelligence base different from the static resume database. The so-called occupational intelligence includes three levels: skill-oriented capability map, individual-oriented career development and opportunity recommendation, and enterprise-oriented labor force insight and performance prediction. For workers, their education experience, training certificates, skill proficiency, post and task trajectories are deposited into a verifiable professional asset account. For enterprises, demands are split into matching skill combinations, and the system judges candidates, predicts performance and collaborates to handle exceptions accordingly.
The training data of this model comes from the transactions themselves. Corresponding to all links covered by Xiaozhuanqi, including application, communication, employment, contract signing, onboarding, task execution, acceptance, income and settlement, each transaction will generate new performance records, income records, evaluation credit and result verification, which feeds back to the model's understanding of tasks, verification of skills and prediction of performance. The company stated that the model evaluation focuses on real result conversion rather than recommendation click-through rate, and the matching rules must conform to the principles of fairness, inclusiveness and transparency. At present, Xiaozhuanqi's AI Agent has processed more than 3 million candidates in total, with the automatic processing rate of standard processes reaching 95%. The Agent is responsible for demand clarification, task splitting, talent combination and performance management, while humans are responsible for defining goals, rules and responsibility boundaries, and checking risk links.
On the compliance side, Xiaozhuanqi sets a unified business ID for each transaction, connects the contract flow, service flow, capital flow, invoice flow and information flow, and accesses the national tax data interface to realize direct reporting, so that transactions change from "paper compliance" to verifiable, restorable and auditable. The system configures compliance verification, payroll accounting and tax declaration processes for nine major categories of employment forms respectively, and AI four-dimensional compliance review is embedded in the execution along the process. The company said that this design is intended to enable the complex multi-form employment that used to rely on a large number of service personnel to expand at low marginal cost.
Cross-ecosystem reach and compliance infrastructure build competitive barriers, Series C funds will be invested in three types of construction
The supply and demand network is the premise for this infrastructure to operate. On the individual side, Xiaozhuanqi has in-depth co-construction with Alipay Employment to form a trusted identity and exclusive cross-end communication entry, with more than 700,000 monthly active users. In the "Nearby Employment" scenarios of WeChat and Tencent Maps, it covers 87 cities through LBS plus social methods. Offline, it has built a "15-minute employment circle" through government-enterprise co-built employment service stations, with more than 10,000 points covering more than 200 cities, providing more than 1.45 million posts in total, and more than 2.3 million people have submitted resumes through face scanning. On the enterprise side, Xiaozhuanqi has exclusive co-construction with Meituan Shop Manager to cover all its merchants, and directly connects to the employment demands of millions of supply chain enterprises through 1688, while the Employment Through Train connects with collaborative platforms such as DingTalk and Feishu for scheduling. The company disclosed that its network has reached about 200 million flexible employees and about 10 million enterprises, with more than 1 million enterprises served in total.
In terms of commercialization, Xiaozhuanqi's revenue comes from multiple structures including transaction and result service fees, enterprise API services and individual Agent subscriptions. The company's financial forecast shows that its revenue scale will grow from about 103 million yuan to about 6 billion yuan in four years, the overall gross margin will increase from 14% to 32%, EBITDA will turn positive in 2027, reaching an estimated 56.7 million yuan in 2027 and 240 million yuan in 2028, and it plans to launch IPO application in 2028, with dual-track priority for Hong Kong Stock Exchange and Sci-Tech Innovation Board. The revenue scale in 2026 is expected to reach more than 500 million yuan. It should be noted that the above data are forecasts made by the company based on current business progress and have not yet been audited.
The team composition matches the cross-sectoral field where Xiaozhuanqi is located. Rong Haixu, Founder and CEO, is a serial entrepreneur with background in both internet and human resources, and has more than 10 years of industry experience. Ai Bo, Partner and CFO, is also a serial entrepreneur, responsible for strategic planning and capital operation. Li Yuequn, Head of AI, is a master of pattern recognition from the Chinese Academy of Sciences, who once served as an algorithm expert at Tencent and Alibaba, and led the implementation of federated learning for Tencent Cloud intelligent recommendation platform and large model recommendation system. Zhang Junliang, VP of Products, once led the transformation of Alipay's transaction system from PC to open ecosystem. Du Jiang, VP of Operations, once served as Operation Director of Alibaba and Alipay, and participated in the construction of trillion-level transactions and 100-million-level user ecosystem. Independent directors He Yongming once served as Vice President of Ant Group and CTO of Alipay, and Ye Jun is Vice President of Alibaba Group and former CEO of DingTalk. In addition, Zhang Chenggang, Associate Professor of Capital University of Economics and Business and Director of China New Employment Form Research Center, and Li Ning, Professor of the Department of Leadership and Organization Management of Tsinghua University, serve as special consultants respectively.
In terms of financing, Xiaozhuanqi has completed four rounds of financing before: the 2019 Angel Round was invested by Fangha Venture Capital and Pangu Venture Capital; the 2020 Pre-A Round was invested by Pangu Venture Capital, New Horizon Venture Capital and Pru Capital; the 2022 Series A Round was participated by Fangha Venture Capital, Huiqiu Investment, New Horizon Venture Capital, Fanchuang Capital, Pangu Venture Capital, etc.; the 2025 Series B financing reached hundreds of millions of yuan, with investors including Yuanhe Houwang, Baoshan State-owned Investment, Xinsongnan, Suzhou Industrial Fund, Pangu Venture Capital, New Horizon Venture Capital and Huiqiu Investment. The 150 million yuan raised in this round will be mainly used in three aspects: expanding transaction density in core industries, key cities and leading customers, improving regional supply and demand density, post filling rate, enterprise repurchase rate and performance quality; training the occupational intelligence model with tasks, skills and real performance results, deploying enterprise and individual Agents, and improving the efficiency of communication and collaboration, performance management and exception handling; upgrading the contract, performance, capital, invoice, payroll tax, risk control and data systems to support large-scale transactions of cross-regional, multi-industry and multi-form employment.