In-depth Report: Automotive Data Alliance Officially Established, LAUNCH TECH (02488) Helps Build New Infrastructure for Aftermarket Data Circulation

Title context: In-depth Report: Automotive Data Alliance Officially Established, LAUNCH TECH (02488) Helps Build New Infrastructure for Aftermarket Data Circulation Text: A meeting that could reshape the automotive aftermarket quietly concluded recently. On September 22, the "First Automotive Data Alliance Conference," themed "Empowering the Aftermarket with Digital Intelligence, Creating Win-Win Through Ecosystem Symbiosis," was successfully held in Yangshuo. The conference was hosted by the Automotive Data Alliance Special Working Group of the Shenzhen Blockchain Technology Application Association, co-organized by Shenzhen Mingrui Data Technology Co., Ltd., and supported as a core unit by Shenzhen LAUNCH TECH Co., Ltd. (02488), a company listed on the Hong Kong Stock Exchange. The conference brought together 25 leading enterprises across the industry chain, including M-Hero, Wanji, TecAlliance, F6, Chaboshi, Lemoncha, Digital Automotive Cloud, CATARC, and Shenzhen Longgang District Data Group Co., Ltd., spanning core tracks such as OEMs, diagnostic equipment, repair chains, parts data, used-car inspection, insurance risk control, and government data groupsencompassing virtually every key node in the automotive aftermarket data value chain. These 25 enterprises are the first founding members of the Automotive Data Alliance. As a representative of the association's chairman unit, Mr. Liu Yizhi, Chairman of LAUNCH TECH, welcomed industry chain colleagues from across the country in his speech and articulated the core proposition of the conference: break down silos through data interoperability and build the industry's future through alliance co-construction. He stated bluntly that there is only one "path to breakthrough" for the industry: collective development and open symbiosis. Mr. Liu Yizhi stated that the integration of automotive data and communication technology will realize its commercial value through a blockchain modelthis is precisely the original intention behind LAUNCH TECH and the association initiating the Automotive Data Alliance: break down silos through data interoperability; standardize circulation through co-built standards; safeguard applications through compliant evidence preservation; and unlock data value through AI capabilities. "Data is the fuel for AI, and the alliance is the gas station. Once everything starts here, there will be no more fighting alone." Mr. Liu Yizhi also extended an invitation to the entire industry: join the alliance, work together to build industry standards, share technological achievements, and jointly explore new AI tracks. The successful holding of this conference means more than "establishing an alliance"it simultaneously operationalizes three things in the elementization of automotive aftermarket data: using a white paper to set standards, using edge-side models to define products, and using blockchain plus trusted spaces to define profit sharing and compliance. It provides a replicable set of industry infrastructure for the automotive aftermarket as it moves from the work-order era into the model-subscription era, and opens a new stage in which the intrinsic value of automotive data enterprises is released at an accelerated pace. Automotive data accounts for about 5% of data resources, and its huge circulation value potential remains to be tapped Against the backdrop of China's accelerated efforts to build a nationally integrated data market, automotive data has become a scarce "high-value mineral deposit" among new production factors, and its value foundation lies in its enormous industrial scale. From a macro perspective, in 2023, the total output value of China's entire automotive industry chain was approximately RMB 11 trillion, surpassing real estate for the first time to become China's largest economic pillar, accounting for nearly 10% of GDP. Such a large industrial scale naturally breeds abundant data resources. This can be seen from the data released by CATARC at this conference: among all types of data nationwide, transportation data accounts for 8%, of which automotive data accounts for more than 70%, corresponding to automotive data accounting for about 5% of total social data. This 5% data share contains enormous potential circulation value. At the conference, LAUNCH TECH used its own development as an example to demonstrate that the potential of macro-level data circulation is being accelerated into reality through micro-level scenarios. As the global leader in automotive diagnostic equipment, LAUNCH TECH's hardware devices cover 234 countries and regions, with nearly 3.9 million active units, cumulative connections to more than 420 million vehicles, and 1.2 million diagnostic reports per day. This massive volume of diagnostic data has been deeply integrated into various links of the industry chain, including R&D, repair, insurance, and used cars, becoming a vivid footnote to the transformation of automotive data from "resource" to "value." Specifically, on the R&D and quality side, based on the ECU-FDI fault density index, LAUNCH TECH conducts cross-statistics by vehicle series, vehicle age, mileage, region, and climate to derive detailed fault data for different vehicle series. This can feed back to OEMs, enabling them to use data to identify common faults in specific models or systems and improve product design and warranty strategies. On the repair and parts side, based on global diagnostic data, LAUNCH TECH forms a closed loop of "diagnostic report fault code repair solution parts SKU." Repair shops can stock parts directly according to diagnostic conclusions rather than relying on experience-based blind purchasing, which can directly reduce inventory costs for repair shops. On the insurance and anti-fraud side, LAUNCH TECH's automotive diagnostic and repair data can build a multi-dimensional verification and early-warning system. By relying on historical fault codes and repair records to identify deliberately staged accidents for insurance fraud, risk control shifts from post-event to pre-event; at the same time, it can integrate accident and modification data and connect to theft and robbery databases to verify a vehicle's true identity. On the used-car and finance side, LAUNCH TECH's data allows vehicle condition, mileage, accidents, modifications, and original-parts records to be directly anchored to residual value. The spatiotemporal trajectory of maintenance and repair supports dynamic vehicle locating and long-cycle traceback for financed vehicles, strengthening the defense line for post-loan asset preservation. Moreover, the monetization of data in the aforementioned automotive aftermarket scenarios is only one facet of the value of automotive data elements. As CATARC emphasized at the conference, cross-entity data integration can better support core scenarios such as intelligent connected vehicles and vehicle-road collaboration. The accelerated development of these scenarios will further release the value of data assets and drive the formation of new quality productive forces. In the autonomous driving field, perception and decision-making data from vehicle operation is becoming the core DRIVE for algorithm iteration; in the vehicle-road collaboration field, the integration of vehicle-side and roadside data enables the transportation system to move from "single-point intelligence" to "global optimization"; in the energy interconnection field, charging, discharging, and battery data of new energy vehicles are supporting vehicle-grid interaction and virtual power plant scheduling. From aftermarket repair, insurance, used cars, and finance to front-end autonomous driving training and vehicle-road collaborative scheduling, the application scenarios of automotive data already cover the entire industry chain, forming a complete value map. The circulation radius of data directly determines its value ceilingthe more data circulates, the more its value multiplies. This is a "rich mine" that urgently needs to be jointly developed by the entire industry. Industry pain points are prominent, making the establishment of an Automotive Data Alliance an inevitable trend Although automotive data resources contain enormous potential circulation value, over the past two decades, automotive aftermarket data has long lain dormant in enterprises' internal BI reports, playing a supporting role in "assisting decision-making." Repair shop work orders, diagnostic device fault codes, insurance claim records, used-car inspection reportsthese data fought their own battles and never truly entered cross-entity circulation, and the compounding value of data was locked inside firewalls. The aftereffects of this "data dormancy" have been sharply amplified during the industry's transformation period. As the automotive aftermarket bids farewell to traditional extensive operations and enters a new stage driven by data, empowered by AI, and characterized by ecosystem symbiosis, the industry's pain points have become increasingly obvious, namely severe data silos, inconsistent standards, fragmented applications, and a lack of fuel for AI models, which have increasingly constrained the industry's high-quality development. Especially under data silos, AI models trained by a single enterprise have weak generalization ability and are difficult to truly implement. If this dilemma of "data without circulation, models without fuel" cannot be solved within the window period of industry transformation, it will evolve from an internal constraint into a survival crisis for enterprises in the future. More urgently, the window period is narrowing. We are currently in a period of technological transformation driven by AI, with the wave of large models and edge-side AI sweeping through the automotive industry. Whoever masters high-quality, multi-dimensional data will master the initiative in the next generation of competition. If industry leaders cling to the "data moat" mindset and are satisfied with the one-time profit from hardware sales, they will face the risk of being "boiled like a frog in warm water." The only way to break through is to actively innovate, open up cooperation, and integrate enterprise data into the industry foundation in order to achieve lasting success. This is precisely the core motivation for establishing the Automotive Data Alliance this time: through the alliance mechanism, data scattered across OEMs, diagnostic providers, repair chains, insurance, used cars, and other links will be aggregated and circulated in a compliant manner, providing sufficient "fuel" for AI models and driving the aftermarket from "fighting separately" to "ecosystem symbiosis." The alliance produces edge-side/local small-model capabilities and distributes revenue according to data contribution When the establishment of an Automotive Data Alliance becomes an inevitable trend in industrial development, a key question emerges: in what manner will the alliance be established, and in what form will it operate? As the global leader in automotive diagnostic equipment and the core supporting unit of this conference, LAUNCH TECH has already made considerable progress in data value mining and has provided a model answer to this question through its own practice. The core of this answer was fully presented at this conferenceby demonstrating its advantages in data scale, compliance layout, and AI analysis capabilities, LAUNCH TECH proved the feasibility of transforming automotive data from "raw material" into commercial value and explained in detail the operating model of the "Automotive Data Alliance." Relying on the hard power of cumulative connections to more than 420 million vehicles and 1.2 million diagnostic reports per day, supplemented by 250 million repair work orders in the United States and a compliant layout across three major global data centers, LAUNCH TECH defined the industry's "ECU-FDI fault density index" and released at this conference the "Automotive Electronic Control System Quality White Paper," completed by AI in three days, laying the foundation of trust for the establishment of the alliance. LAUNCH TECH proposed that the Automotive Data Alliance is an ecosystem collaboration organization based on technical trust, not a simple data exchange pool. In terms of member structure, it strictly limits participation to industry leaders, inviting only the top two in each segmented field. The 25 enterprises attending the first conference covered OEMs, insurance, parts platforms, and government data groups. At the data access level, it adopts "thematic data spaces" and blockchain technology to achieve "usable but invisible, controllable and measurable," abandoning direct exchange of raw data. At the level of rights confirmation and profit sharing, smart contracts automatically execute and distribute revenue according to data contribution. In the face of the domestic legal framework prohibiting data trading, the alliance processes data into compliant products such as diagnostic reports and risk warnings to ensure that monetization paths are legal. In terms of organizational mechanism, starting with the release of the white paper, it regularly shares PPTs and attendee lists, establishes a senior-level community, and promotes specific cooperation through "throwing out a brick to attract jade plus in-depth discussion." It is worth noting that the alliance's core output is not raw data, but edge-side/local small-model capabilities. Edge-side models can generate diagnostic results locally within 15-20 seconds without networking, balancing speed and privacy, and evolve iteratively through continuous data. The blockchain profit-sharing system automatically records data sources in model calls, ensuring that all parties receive returns according to their contributions, allowing OEMs, repair shops, parts suppliers, and others to profit from "sleeping data" and break free from the silo dilemma. As Mr. Jiang Shiwen, Senior Vice President of LAUNCH TECH, summarized: "Everyone has a treasure map in their hands, but if you don't share it, you will never find the treasure." The establishment of the Automotive Data Alliance marks the industry's move from fighting separately to ecosystem symbiosis. This is not only an upgrade of the business model of China's automotive aftermarket, but also a key step for the industry to seize the initiative in global competition in the AI era. In the future, the alliance will further address potential challenges such as rights confirmation and traceability, privacy protection, antitrust concerns, and enterprise participation incentives. With the help of federated learning, transparent rules, and already implemented cross-entity collaboration cases, it will continue to break through and drive industrial data from single-point applications to cross-domain integration. Promoting the digital transformation of member enterprises and accelerating the revaluation of data asset value The deeper significance of the data alliance is not merely to give enterprises "one more data channel," but to recalculate data from a cost item in internal reports into an asset that can circulate, be billed, and compound, thereby changing corporate valuation logic. Taking LAUNCH TECH as a sample, its growth path of data value revaluation is already clearly reflected in the company's performance. In the past, LAUNCH TECH mainly sold diagnostic equipment, recognizing revenue upon hardware delivery, but the equipment lifecycle is usually 3-5 years, with a long repurchase cycle. Relying solely on selling equipment made it difficult to form sustained cash flow, but the company's current business structure has clearly changed. In the first half of 2026, LAUNCH TECH's software business revenue was approximately RMB 120 million, a year-on-year increase of 67.9%, accounting for 11% of total revenue; data business revenue was approximately RMB 15.4 million, a year-on-year increase of 93.6%, the fastest growth among all businesses; AI service revenue such as remote diagnostics was approximately RMB 16.1 million, a year-on-year increase of 45.7%. The difference between these businesses and hardware is that software subscriptions, diagnostic model calls, and data interfaces are all paid periodically. After equipment is sold once, it can continue to generate revenue from upgrades, queries, reports, and agent calls. Customers use them frequently, and enterprise revenue is predictable. The alliance's core output products shifting to edge-side/local small models plus subscription licensing is an important measure to upgrade one-time sales into long-term services. What supports LAUNCH TECH's revenue structure in accelerating its tilt toward software, data, and AI services is the completeness of the company's data source structure and dimensions. LAUNCH TECH's diagnostic data runs through the entire vehicle lifecyclefrom intake fault codes, repair solutions, and parts replacement to subsequent recalls and used-car residual valueforming a complete data chain. At the same time, it covers different brands and new energy vehicle models, enabling comparison of differences among systems; it also accumulates work orders, climate, and road condition samples from different regions at home and abroad; combined with time dimensions such as 1.2 million diagnostic reports per day and nearly 3.9 million active devices, the model can continuously iterate and optimize by vehicle age, mileage, region, and season. The ECU-FDI fault density index and the "Automotive Electronic Control System Quality White Paper" released by LAUNCH TECH at this conference essentially distill such complex data into reusable quality indicators, transforming LAUNCH TECH from "selling equipment with reports attached" into "selling diagnostic capabilities." After the establishment of the data alliance, it will rewrite the traditional ledger in which enterprises "use data only internally." In the past, data was reflected in financial statements more as storage and maintenance costs; under the alliance's blockchain smart contract system, multi-dimensional data contributed by enterprises, such as diagnostics, work orders, claims, used cars, and parts, will automatically generate profit sharing based on call frequency and model improvement effects, directly transforming into model call rights, subscription revenue-sharing rights, and even joint intellectual property rights for industry white papers. Data is no longer a sleeping cost item, but an asset that can continuously generate returns. Once this profit-sharing model is operational, it will trigger a self-reinforcing growth flywheel: the larger the enterprise's data volume and the more complete its dimensions, the higher the accuracy of the trained edge-side model, the more times it is called by repair shops, insurance, used cars, and parts platforms, and the more exponentially its marginal returns rise. Data then changes from a "burden" into a "money printer." For enterprises in the alliance, this will bring a deep reconstruction of commercial valuation logic. The data assets of alliance member enterprises will be revalued from "numbers in self-use reports" to "licensable, tradable model assets." On this basis, as the weight of software, AI, and data businesses in financial statements continues to rise, market valuation logic will completely break free from the traditional hardware PE framework and leap to a composite model of "device entry + subscription revenue + data barriers." Once the compliant profit-sharing mechanism is implemented, the proportion of AI output of leading enterprises will rise rapidly. A wave of data-driven value revaluation will arrive at an accelerated pace.