KOSDAQ lists on the Hong Kong Stock Exchange, targeting the automotive aftermarket digital intelligence upgrade opportunity

Kaisa Auto’s data-driven and intelligent platform for the automotive aftermarket submitted a listing application to the Hong Kong Stock Exchange on Thursday (April 2), with CICC serving as the sole sponsor. In terms of operating and financial performance, the company’s business conditions have continued to improve. In 2025, it achieved a transaction volume (GMV) of approximately RMB 7.6 billion and total revenue of approximately RMB 930 million, representing a year-on-year growth of 25.3%, demonstrating solid business expansion capabilities. Its overall gross margin has consistently remained at a relatively high level in the industry; in 2025, it reached 28.3%, reflecting the company’s efficient cost management capabilities and a high-quality product and service structure, laying a solid foundation for long-term sustainable development.

As China’s vehicle ownership continues to expand, the penetration rate of new energy vehicles rises, and car owners’ requirements for transparent repairs, genuine spare parts, and high-efficiency services keep increasing, the automotive aftermarket is moving from a traditional and fragmented circulation model toward a new stage centered on data, standards, and platform collaboration. Kaisa’s entry point is precisely the construction of data-driven and intelligent infrastructure in this round of industrial upgrading.

A large aftermarket scale, but long-standing fragmented pain points

The prospectus shows that in 2025, the total size of China’s automotive aftermarket is about RMB 1.6 trillion, and it is expected to grow to about RMB 2.2 trillion by 2030, with a compound annual growth rate of about 7.4% from 2025 to 2030.

Compared with the upstream OEM market, the automotive aftermarket features a long supply chain, many participants, a vast number of SKUs, and highly dispersed demand. From OEMs and auto parts manufacturers, to auto parts distributors, to auto service outlets and end car owners, the entire circulation chain involves numerous links, and there have long been issues such as information asymmetry, low inventory and replenishment efficiency, successive markups, and inconsistent service quality. Especially in an environment where multiple brands and multiple vehicle models coexist, repair scenarios often show a typical long-tail characteristic, making it difficult for traditional supply chains to efficiently support demand using standardized approaches.

This also means that, although the automotive aftermarket is large in scale, in the past it has always lacked a data-driven and intelligent platform that can truly connect upstream and downstream. Whoever can aggregate fragmented demand and standardize goods and services will have the opportunity to build deeper infrastructure capabilities in this track.

New energy and intelligent technologies are expanding demand, opening room for industry upgrading

From industry trends, the penetration rate of new energy vehicles and intelligent cars is steadily increasing, bringing new structural opportunities to the aftermarket. On one hand, new energy vehicles set higher requirements for data-driven and intelligent collaboration and professional identification in repair, maintenance, and parts services. On the other hand, cars are evolving from purely transportation tools into intelligent terminals and mobile living spaces, and demand generated around repair, maintenance, upgrades, modifications, and the driving and ownership experience is also becoming more diverse.

More importantly, as vehicles become increasingly intelligent, after-sales services are no longer just “fixing cars” themselves; they encompass detailed processes such as fault diagnosis, parts identification, fulfillment tracking, service certification, warranty management, and reaching out to car owners. This means that in the future, the key to competition in the aftermarket will not only be who sells more spare parts, but who can organize the supply chain more efficiently, manage service processes more transparently, and connect car owners’ needs more accurately.

Clear policy direction—data-driven, intelligent, and standardized approaches become the main line

Policy support also provides backing for the industry. In the 2026 government work report and the “15th Five-Year Plan” outline, it is clearly proposed to advance the construction of a Digital China in depth, continuously increase support for the data-driven and intelligent transformation of SMEs, and promote large-scale, commercialized applications of artificial intelligence in key industries; at the same time, focusing on the digital transformation of the automobile industry, standardized development of the automotive aftermarket, and data-driven and intelligent upgrading of supply chains, it supports the building of standardized, traceable industrial service systems. Kaisa’s business direction is highly aligned with these policy directions, including publicly disclosing automotive repair technical information, tracing the circulation of spare parts, managing repair files, transparent repairs in insurance claims, and co-building industry standards and a credit system. The company also mentioned that its related setup involves standards for spare parts data and a network for tracing parts circulation.

Against a backdrop of increasingly strict market supervision and car owners’ rising requirements for post-sales transparency, the automotive aftermarket is shifting from a past emphasis on transaction matching toward a new stage that takes into account quality assurance, after-sales tracking, and the construction of a credit system, clearly defining a data-driven, intelligent, standardized, and traceable direction for upgrading across the entire industry.

The key lies in building data-driven and intelligent infrastructure

Kaisa’s strategic advancement is also related to the industry operating and technical background accumulated by management over the long term. Mr. Jiang Yongxing, founder, chairman and chief executive officer of the company, is primarily responsible for overseeing the group’s overall strategic planning, business operations and management, coordinating the company’s development direction and core business implementation. Before founding Kaisa, Mr. Jiang had worked at Huawei for many years and possesses extensive experience in areas including technical R&D, marketing, and business operations. For Kaisa, Mr. Jiang’s management background combining technology, product, and industry operations perspectives also provides support for pushing forward the construction of a data-driven and intelligent platform for the automotive aftermarket.

In terms of its business model, Kaisa is not a traditional single auto-parts e-commerce business in the narrow sense; instead, it establishes an extensible data-driven and intelligent infrastructure around the automotive aftermarket. The prospectus shows that the company has already formed a service network consisting of “one data-driven and intelligent foundation, two industry standards, and three platform products,” and has built a full-chain ecosystem under the “F2B2b2C” business model. Specifically, its platform products include “Kaisa Select,” an auto parts procurement platform for the supply side; “Kaisa Auto Parts,” a one-stop transaction platform for automotive service outlets; and “Kaisa Certification,” a data-driven and intelligent certification system for store service scenarios for terminal services, while extending to smart store management systems, logistics, advertising, and other data-driven and intelligent value-added services.

As of December 31, 2025, Kaisa’s platform had onboarded 10 OEMs, 75 auto parts manufacturers, more than 12,000 auto parts distributors, and more than 375,000 automotive service outlets, with a total SKU volume of over 48 million. Leveraging its efficient supply-chain integration capabilities and extensive channel coverage, the company continues to promote data-driven and intelligent upgrading in the automotive aftermarket.

The key to this model is that the company is not only doing one transaction node; rather, it embeds capabilities in goods, services, fulfillment, and data throughout the industrial chain.

For upstream auto parts manufacturers, Kaisa’s platform can help them reach the end market more efficiently, and at the same time, by relying on real transaction data accumulated on the platform, accurately predict the quantity, frequency, and regional distribution of auto parts demand, enabling manufacturers to plan production more precisely, improve delivery capability, and shorten product improvement cycles.

For distributors, Kaisa’s platform provides them with diversified procurement channels covering multiple categories of products, such as original equipment manufacturer (OEM) parts, branded parts, and factory parts. It also enables fast and accurate matching of auto parts through intelligent search tools, while ensuring timely collection of payments to optimize capital turnover efficiency. In addition, through AI sales agents, Kaisa analyzes sales and inventory data to help distributors scientifically plan procurement plans, effectively improving overall operational efficiency and inventory turnover rates.

For automotive service outlets, Kaisa’s platform not only provides high-quality auto parts products and comprehensive warranty services, but also helps outlets improve repair efficiency and lower technical barriers through tools such as AI procurement agents and AI Repair Masters. At the same time, relying on data-driven and intelligent operation tools, outlets can achieve standardized upgrades in user acquisition, retention, and management, comprehensively improving operational efficiency and user experience.

For car owners, Kaisa’s platform provides genuine spare parts assurance, ensuring that the entire repair process is transparent end to end, and builds a trustworthy service system that makes the car owners’ driving and ownership experience safer and more convenient.

Standardization, data-driven intelligence, and AI capabilities

When looking at product competitiveness, Kaisa’s advantage is not just selling inventory—it is data-driven intelligence for the parts of the aftermarket that are most difficult to standardize and most difficult to track.

First is its ability to standardize goods and services. In the current aftermarket, there are problems such as it being hard to tell fakes from genuine items, the goods not matching what is stated, and inconsistent services. The company continues to advance two sets of industry standards for goods and services, with the goal of transforming vague descriptions of spare parts quality, compatibility, warranty commitments, and service capabilities into identifiable and verifiable standardized information, thereby improving key foundational capabilities.

Second is supply-chain collaboration and fulfillment capabilities. The company’s materials state that it has already carried out data-driven and intelligent collaboration with multiple spare-parts factories. Through the linkage of demand forecasting models, ERP systems, and warehousing and distribution systems, it improves delivery efficiency and supply-chain turnover capability. This means the platform’s value is not only in front-end matching of transactions, but also in converting fragmented, low-frequency, long-tail demand into manageable supply plans.

Third is the capability to precipitate AI and data. In this highly vertical professional scenario, data such as VINs, work orders, repair cases, parts matching, and fault handling have very high barriers. The materials show that Kaisa is combining relevant data assets with AI tools and extending them to scenarios such as intelligent parts trading, work order recognition, and repair solution assistance. This also gives the platform the possibility of extending from a transaction platform to industry intelligent tools.

From a transaction platform to an industrial network—what’s worth noting is the platform’s depth

Worth noting is that since its founding, Kaisa has received support from multiple investment institutions, including Sequoia, Shunwei, Sourcecode Capital, Fosun, Huaye Tiancheng, H Capital, and the Greater Bay Area Common Home, as well as Bosch, an industrial capital investor, and several rounds of investments.

For the capital markets, Kaisa’s investment value is not only in the aftermarket itself as an industry track, but also in whether the company can prove that it has platform-ization, networking, and infrastructure-building capabilities. In other words, market attention may not focus solely on the scale of a single business segment, but on whether it can build a role in the industrial chain with higher stickiness: one that can connect upstream brands and factories to achieve precise supply-chain matching, while also reaching downstream auto service outlets and car owners to form a full-chain industrial collaboration ecosystem.

In the process of China’s auto industry shifting from manufacturing advantages to service and data advantages, the data-driven and intelligent upgrading of the automotive aftermarket has become the next direction worth watching. For Kaisa, if it can turn the data-driven and intelligent system it builds into a mature framework that can be replicated at scale, its future value positioning in the automotive aftermarket may not stop at being a single platform company, but instead come closer to being an industry-level participant in data-driven and intelligent infrastructure.

Building on successful domestic, proven practices, Kaisa is accelerating its overseas expansion and layout. With its own advanced supply-chain strength and expertise in data-driven and intelligent technologies, it is constructing a global after-sales service network that is broad in coverage and responds quickly, supporting the upgrading of the global automotive aftermarket.

Contact: Hong Kong Economic Journal (HKET) Advertising Department, Listed Companies Team | annteam@hket.com