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SHANGHAI ABLE DIGITAL SCIENCE&TECH CO., LTD.

上海卓越睿新數碼科技股份有限公司

(A joint stock company incorporated in the People's Republic of China with limited liability)

(Stock Code: 2687)

ANNOUNCEMENT OF ANNUAL RESULTS FOR THE YEAR ENDED 31 DECEMBER 2025

The board (the "Board") of directors (the "Directors") of Shanghai Able Digital Science&Tech Co., Ltd. (the "Company") is pleased to announce the audited consolidated results of the Company and its subsidiaries (collectively, the "Group" or "we") for the year ended 31 December 2025 (the "Reporting Period"), together with the comparative figures for the year ended 31 December 2024.

FINANCIAL HIGHLIGHTS

| Items | 2025
(RMB'000) | 2024
(RMB'000) | Year-on-year change
(%) |
| --- | --- | --- | --- |
| Revenue | 969,406 | 848,198 | 14.3% |
| Gross profit | 634,598 | 525,158 | 20.8% |
| Gross profit margin | 65.5% | 61.9% | 5.8% |
| Net profit | 130,224 | 105,071 | 23.9% |
| Adjusted net profit (Non-IFRS measures^{Note}) | 148,456 | 122,687 | 21.0% |

Note: For more details, please refer to the section headed "Non-IFRS Measures" in the Announcement of Annual Results.

MAJOR NEW DEVELOPMENTS IN 2025

The Company’s self-developed “Polymas” large model successfully completed the filing for generative artificial intelligence services with the Cyberspace Administration of Shanghai, demonstrating the Company’s strong technological expertise and compliant operations in the education AI field. This achievement effectively mitigates risks associated with non-compliant business operations and significantly enhances the Company’s brand credibility and recognition among partners. In the long run, it will facilitate the commercialization and monetization of large model-related products, ensure stable revenue growth, and further improve overall profitability.
The Company added four new algorithm filings, bringing the cumulative total to nine. These filings cover key technological domains including large language models, knowledge graph generation, text generation, dialogue synthesis, information retrieval, and digital humans. This achievement further solidifies the Company’s compliance foundation and technological competitiveness, laying a robust groundwork for business expansion and product application.
In the third batch of National First-Class Undergraduate Courses, an additional 309 courses were selected, bringing the cumulative total to 936. This further enriches the Company’s reserve of high-quality course resources, significantly strengthens the market pricing power of its course services, and supports the Company in expanding market coverage and deepening engagement with existing partner universities, in alignment with the core needs of higher education teaching reform.
The Company officially launched Polymas Agent, with more than 500 projects delivered to date. This initiative introduces a new model and ecosystem for the deep integration of artificial intelligence and educational curricula. It further enriches the Company’s product matrix and creates synergistic effects across technology, content, and services. The offering has been widely recognized by teachers and students and has established a solid foundation for large-scale promotion. Over the long term, it is expected to become a new core engine for the Company’s revenue growth and a sustained contributor to profitability.
Building on its professional expertise in digitalization of higher education, the Company has achieved notable success in translating teaching services into tangible outcomes, a total of 11 provincial teaching achievement awards have been publicly announced in 2025. Meanwhile, many teaching practice cases have been successfully included in the typical case library of the Ministry of Education, fully demonstrating the Company’s technical strength and practical value in the field of educational digitalization. Among them, the “AI Course for Botany Based on Knowledge Graph” of Huazhong Agricultural University was selected as one of the 50 typical teaching and research achievements for the pilot construction of virtual teaching and research office by the Ministry of Education. The construction of the “AI Knowledge Center” of Beihang University and several other cases have been selected as typical cases of the “Artificial Intelligence + Higher Education” application scenarios by the Ministry of Education. The acquisition of these achievements has verified the implementation effectiveness of the Company’s technology and services, and has also effectively consolidated its position as an industry benchmark, laying a solid foundation for subsequent business expansion.
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The “Xinghai” large model, jointly developed by the Company and Harbin Engineering University, was successfully launched on the Smart Education of China Higher Education. As China’s first proprietary large model dedicated to ship and ocean engineering education, it fills a gap in AI applications for education in this field. This development effectively expands the application boundaries of the Company’s large models and opens up new business growth opportunities. Leveraging the official endorsement of the national platform is expected to accelerate market promotion of the product.
The “Introduction to Artificial Intelligence” Smart MOOC, jointly developed by the Company and Zhejiang University, was selected as one of the first global core demonstration cases of Smart MOOCs and was showcased at the 2025 Global MOOC and Online Education Conference. This recognition significantly enhances the Company’s brand influence in MOOC development and supports the high-quality development of its business.
The “Suiming” Optical Intelligent Learning Cloud Platform, jointly developed by the Company and Tianjin University, won the First Prize in a competition organized by the China Association for Educational Technology and was the only project in the optical field to receive this honor. This achievement fully demonstrates the Company’s technological strengths in the field of optical intelligent learning and supports the Company in expanding into specialized optical education scenarios while deepening industry – university collaborative innovation.
The Company actively undertakes and fulfills its social responsibilities. In collaboration with the office of academic affairs, academic department and undergraduate school of more than 70 universities throughout the country including Beihang University and Beijing Institute of Technology, the Company jointly launched the first session of “Zhihuishu Cup” National Smart Course Innovation Competition. The competition attracted nearly 400 universities from 30 provinces, cities and autonomous regions to participate and collected more than 1,500 excellent smart course works. During the competition, multiple sessions of public welfare live streaming training were conducted relying on the “Teacher Talks” column. The competition builds smart course evaluation standards in a form of multi-party co-construction, deepens the understanding of the integration of AI and teaching, enhances teachers’ digital teaching capabilities, promotes cross-school and cross-regional teaching research, injecting strong impetus into the digital transformation of education and the improvement of talent cultivation quality.
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MANAGEMENT DISCUSSION AND ANALYSIS

BUSINESS REVIEW

In 2025, the Company focused on the integration of education technology and artificial intelligence, achieving high-quality growth in both customer scale and operating revenue. The total number of customers increased to 1,797, representing a year-on-year increase of 3.4%. Total revenue reached RMB969 million, representing a year-on-year increase of 14.3%. Average revenue per customer increased to RMB539 thousand, up 10.5% year-on-year, highlighting the effectiveness of the Company's customer value development strategy.

Product innovation continues to upgrade the Company's growth momentum, with both core and emerging businesses advancing in tandem. The number of knowledge graph projects increased by 122.4% year-on-year, making it the core growth engine of the digital educational content services segment. Meanwhile, the agent-based business seized the opportunities arising from the rapid development of AI technologies, achieving a breakthrough from zero to scale with more than 500 projects during the year, establishing an important new pillar for the Company's future performance growth.

The implementation of a tiered customer operation strategy has delivered tangible results, with the value contribution from top-tier customers continuing to increase. Revenue from Lighthouse customers (universities under the "Project 985", "Project 211", and "Double First-Class Initiative", as well as vocational colleges nominated under the "Double High Plan") reached RMB301 million, representing a year-on-year increase of 21.3%, with revenue per customer reaching RMB1.388 million, up 34.1% year-on-year. This demonstrates the Company's deep service capabilities and strong product recognition among leading institutions. The number of High-value Customers (customers with annual transaction amounts exceeding RMB1 million) increased to 281, representing a year-on-year increase of 16.6%, thereby establishing a solid foundation for the Company's long-term growth.

OUR SERVICES AND PRODUCTS

Our revenue is primarily generated from two categories of services and products: digital educational content services and products and digital teaching and learning environment services and products. The digitalization of higher education teaching includes both educational content digitization and teaching and learning environment digitalization, and we are well positioned to meet the diverse needs of higher education institutions in these areas.

Digital Educational Content Services and Products

The Company has deep expertise in the field of digital educational content and provides flexible and diversified services and products tailored to the varied needs of higher education institutions. These offerings cover 12 disciplines and 92 majors recognized by the Ministry of Education and remain the core pillar of the Company's revenue. In 2025, revenue from this segment accounted for 85.8% of total revenue, maintaining a consistently high proportion. Starting from the digital transformation of online courses, the Company has continuously iterated its technologies and services and has successively launched innovative products such as virtual simulation and knowledge graphs, helping customers create interactive and personalized teaching experiences. The coordinated development of multiple product lines has delivered notable results.

Among them, the AI knowledge graph business, which serves as the core growth engine of the segment, delivered particularly strong performance. The Company began forward-looking R&D deployment in 2021 and achieved commercialization in 2023, leveraging AI technologies such as natural language processing and serving a wide range of academic disciplines. In 2025, the number of delivered knowledge graphs reached 10,386, representing a 122.4% year-on-year increase compared with 2024, while the number of customers increased from 732 to 1,125. The digital course business, supported by a comprehensive end-to-end quality control system, served 1,217 customers in 2025 and delivered more than 9,200 courses, maintaining stable scale. The virtual simulation business, leveraging virtual reality (VR) and augmented reality (AR) technologies, achieved simultaneous growth in both customer numbers and delivery volume in 2025, realizing improvements in both scale and efficiency.

Digital Teaching and Learning Environment Services and Products

The Company provides a full suite of digital teaching and learning environment services and products, including AI Cloud LMS (Learning Management System) and digital classroom construction, helping higher education institutions build integrated online - offline teaching management platforms. These solutions enable efficient integration of teaching resources, precise delivery of teaching content, and enhanced interaction between teachers and students. In 2025, this segment accounted for 14.2% of total revenue, forming strong synergies with the content services segment.

Among these offerings, the Company’s core AI innovation, Polymas Agent, deeply integrates natural language processing technology and is specifically designed to meet the needs of higher education institutions. It provides services such as personalized learning guidance and real-time Q&A, and seamlessly integrates with the Company’s self-developed AI Cloud LMS system, lowering the barrier to adoption. In 2025, this business achieved a breakthrough from zero to scale, with more than 500 implemented projects, and steadily expanded market penetration by leveraging the base of 676 subscribing customers of the Cloud LMS system. Meanwhile, the digital classroom construction business provides comprehensive one-stop services covering multiple types of teaching spaces. In 2025, it served 43 customers, representing a 26.5% year-on-year increase, with simultaneous improvements in both the service system and customer recognition.

TECHNOLOGICAL AND SERVICE ADVANTAGES

Self-developed AI Large Model Core Engine: Our platform is powered by proprietary AI large model technology and has established a core engine with scalable capabilities and controllable processes, building comprehensive strengths across the data layer, algorithm layer, and evaluation layer. In terms of training data, we possess extensive high-quality subject data resources, including tens of thousands of specialized and general education courses across all disciplines accumulated through our proprietary platforms, hundreds of millions of teaching questions, billions of teaching Q&A records, and years of accumulated teaching and learning behavioral data. These massive datasets provide a solid knowledge foundation for model training. In terms of the training process, we have achieved full-process observability and evaluability, breaking away from the traditional “black-box” model approach and establishing a professional training framework that ensures every generated output is traceable, thereby addressing the “hallucination” issue associated with large models. At the same time, we strictly enforce data quality verification to ensure data validity and 100% coverage. Through a version comparison mechanism, we select higher-quality versions based on dimensions such as quality and structural completeness, enabling visualization and verification of model iteration results and ensuring the stability and reliability of model outputs.

Exclusive Industry Data Asset Barriers: Data represents the core barrier in the era of large model applications. Through years of platform accumulation, we have developed proprietary industry data in the field of higher education teaching that cannot be obtained through public networks. These teaching datasets are characterized by high levels of specialization, privacy, diversity, and structured attributes. They not only record knowledge itself but also embody teaching logic and expert experience, forming a data moat that is difficult to replicate. By deeply internalizing these unique datasets, our models are able to understand real higher education teaching scenarios and provide solutions that comply with industry standards and possess practical value, thereby significantly surpassing models trained solely on general internet data in terms of professional relevance.

Multi-dimensional Scenario-based Application System: Our platform possesses a systematic AI capability combining general-purpose and specialized functionalities that can be flexibly allocated and deployed, establishing a multi-dimensional, multi-modal, and full-scenario intelligent large model application system. First, our evaluation framework aligns with industry-specific requirements and incorporates key dimensions such as security, accuracy, programming capability, creativity, reasoning capability, language ability, and instruction adherence, ensuring content safety and logical rigor in sensitive scenarios. Second, our system supports discipline-specific matching. It dynamically aligns model capabilities or knowledge bases with all primary academic disciplines, automatically adapting to professional terminology and reasoning patterns to avoid cross-disciplinary knowledge confusion. Finally, our platform supports adaptation across different teaching scenarios and stages. It can flexibly adjust model strategies for diverse contexts such as knowledge content teaching, teaching administration management, internship and practical training support, professional competency training, and teaching evaluation and supervision. For example, it emphasizes operational logic in practical training scenarios and scoring standards in evaluation scenarios. This scenario-based scheduling capability enables deep integration between technology and business, maximizing the effectiveness of models in real workflows.

End-to-end Super Intelligent Agent Interaction System: We have built a comprehensive AI solution covering all teaching scenarios by integrating AI technologies with higher education through a "course - platform - institution" framework. The core support lies in the end-to-end super intelligent agent interaction system we have developed. Based on our underlying model and data advantages, this system establishes a dual-layer architecture consisting of a "Course Super Agent" and an "Institution Super Agent." It not only engages in dialogue with teachers and students but also invokes platform tools to directly complete various tasks for teaching administrators, discipline leaders, frontline teachers, and students, forming a closed loop from knowledge services to task execution. At the interaction technology level, the system supports multimodal input and real-time video dialogue and integrates digital human technology to provide a human-like companion experience, enabling 24-hour on-demand responses. At the execution capability level, based on the MCP protocol, it can invoke professional tools and possesses complex task planning capabilities, enabling it to complete practical operations such as reservations, analysis, and inquiries, thereby enabling a human - machine collaborative working model. At the application scenario level, it provides comprehensive support across the entire teaching lifecycle, including but not limited to course and resource management, lesson preparation and preview tasks, classroom and live teaching, classroom teaching tool invocation, homework and exam grading, and discussions and Q&A related to teaching content. Through localized deployment, it also achieves deep integration with internal campus systems, enabling seamless cross-system integration while ensuring data privacy. Ultimately, this end-to-end system forms a complete digital teaching ecosystem.

Technology Implementation and Service Assurance: We have established hundreds of customer service and support centers across the country, forming a comprehensive and highly responsive on-campus service network. Relying on this high-density service network, we have accumulated extensive experience in large-scale technology deployment and operations and maintenance, while also establishing a full-chain service capability covering demand research, solution customization, technical deployment, and continuous iterative upgrades. From precise alignment of product functions and stable implementation of technological applications to subsequent optimization, iteration, and after-sales support, we are able to provide end-to-end professional assurance for customers across diverse scenarios. Through a standardized and refined service system, we ensure continuous optimization of the customer experience throughout the entire lifecycle, providing strong support for the efficient realization of technological value.

RISK FACTORS

Market competition and technological iteration risks: China's higher education teaching and learning digitalization market remains in a stage of continuous innovation and development. Although we have accurately captured industry transformation opportunities and focused on optimizing and upgrading digital teaching content and environments to meet the evolving needs of teachers and students, we still face multiple risks that may adversely affect our business, financial condition, and operating results. In order to maintain competitiveness, we must closely follow industry trends and technological developments. However, the digitalization needs of higher education institutions continue to evolve in terms of content, formats, and methodologies, posing challenges for us in maintaining product relevance and adapting to new technologies, which requires continuous investment of resources. Our services and products must be compatible with various networks, devices, and software and hardware platforms and therefore require ongoing modifications and upgrades. If we fail to develop and launch relevant optimization features in a timely manner, or if research and development and delivery costs increase, customer demand may decline and customer satisfaction may decrease, which may further adversely affect our business prospects.

Network security and data compliance risks: The business model and operation of the Company are highly dependent on the IT system, involving core links such as the intelligent teaching platform and AI large models, as well as a large quantity of personal information of teachers and students and teaching data, which poses a risk of network security. In the event of malicious attacks, data leakage and other security incidents, it may lead to system disruptions, data loss, impacts on the service continuity and damage to the user trust, and trigger compliance liabilities, causing adverse impacts on the Company's operating results.

Artificial intelligence regulation and industry reputation risks: The Chinese government has continuously strengthened the regulation of the artificial intelligence industry, and introduced multiple laws and regulations imposing strict requirements on AI technology research and development, data compliance, algorithm filing, etc. The Company's core business involves the application of large AI models. Failure to meet regulatory standards may result in risks including rectification, service suspension, and administrative penalties, which may restrict the business implementation and expansion.

FUTURE OUTLOOK

After years of technological development and industry accumulation, we have emerged as an AI company in the higher education sector, boasting the broadest coverage of academic disciplines, the deepest penetration into instructional scenarios, and the most comprehensive service value chain and a leading market share. Looking ahead, the Company will remain firmly focused on the core track of artificial intelligence and the digitalization of higher education. Supported by its self-developed large model technology and driven by knowledge graphs and intelligent agents as core growth engines, the Company will continue to deepen technological innovation and strengthen its commercial competitiveness. At the same time, it will proactively assume responsibility for advancing the digitalization of education, promoting the coordinated growth of both commercial and social value, continuously empowering the high-quality development of higher education, and striving to become a digital solutions service provider widely recognized by the industry and society.

At the technology and research and development level, the Company will continue to increase investment in the iterative development of its fully self-developed large model "Polymas", continuously optimizing the model's multimodal interaction capabilities, scenario-based reasoning, and industry adaptability. It will further advance the upgrading and multi-dimensional expansion of the knowledge graph technology architecture, accelerate algorithmic innovation, scenario development, and functional enhancement of the intelligent agent business, and continuously strengthen its technological moat. Meanwhile, the Company will promote the deep integration of core technologies including large models, knowledge graphs, and intelligent agents, building a more competitive intelligent application system that integrates both general and specialized capabilities. Through technological innovation, the Company aims to drive technological upgrades and model innovation in the education digitalization industry and provide replicable technical pathways and practical experience for industry development.

At the product and business level, the Company will build on the large-scale expansion of the knowledge graph business to further increase its core contribution to digital educational content services, continuously amplifying the profitability driven by high-margin businesses. It will also seize the development opportunities of the intelligent agent business by accelerating its implementation and large-scale expansion across multiple scenarios, including teaching, research, and academic administration, positioning it as a core new pillar of the Company's revenue growth. At the same time, based on the practical needs of higher education teaching, the Company will continue to refine product practicality and adaptability, providing strong support for universities to improve teaching efficiency and innovate talent cultivation models, enabling artificial intelligence technologies to be effectively applied in classrooms and serve both teachers and students.

At the market and customer level, the Company will continue to deepen its coverage and service upgrades for leading universities and key customers, further enhancing value per customer and strengthening partnership stickiness while leveraging the demonstration effect of benchmark customers. It will also intensify expansion and penetration among high-value customers, continuously increasing the base of high-value customers and raising their contribution to overall revenue, while steadily expanding the scale of key customers to achieve simultaneous growth in both the number of value customers and value per customer. Adhering to a long-term orientation and a customer-first philosophy, the Company will meet the digital transformation needs of various institutions through high-quality products and services, deepen customer engagement, and build a mutually beneficial and win-win cooperative ecosystem.

At the service and ecosystem level, the Company will leverage its nationwide, multidimensional on-campus service network to continuously enhance its full-chain service capabilities covering demand research, solution design, technology deployment, and iterative upgrades, ensuring efficient realization of technological value through standardized and refined service processes. At the same time, the Company will continue to deepen the "course - platform - institution" integrated model, promoting the deep integration of AI technologies across all teaching scenarios in higher education. It will further enhance the end-to-end super intelligent agent interaction system and build an open, collaborative, and symbiotic digital teaching ecosystem. The Company will also actively collaborate with industry partners and academic institutions to consolidate industry resources, jointly explore innovative directions and implementation pathways for the digitalization of education, and promote standardized and high-quality development of the higher education digitalization industry as a whole.

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At the financial and operational level, while maintaining stable revenue growth, the Company will leverage the scale effects and high-margin characteristics of its core businesses to continuously optimize its overall gross margin and achieve steady profit growth. It will strengthen operational efficiency management, optimize accounts receivable management, and ensure adequate liquidity to support a virtuous cycle of research and development investment, business expansion, and profit growth. At the same time, through stable operational development, the Company aims to create long-term and sustainable value returns for shareholders while generating more employment and cooperation opportunities for the industry and fulfilling its corporate social responsibilities.

Looking ahead, the Company will continue to take technological innovation as its core driving force, remain customer-oriented, and assume industry responsibility as its mission. It will continue to deepen its presence in the field of higher education digitalization, actively explore innovative applications of artificial intelligence technologies in educational scenarios, and strive to become a leading domestic and internationally influential digital solutions service provider for higher education. In doing so, the Company aims not only to consolidate the foundation for its own development but also to inject sustained momentum into the digital transformation of higher education, contributing to the cultivation of high-quality talent and the high-quality development of the education sector.

FINANCIAL REVIEW

During the Reporting Period, the Group achieved steady growth in operating results:

Revenue increased from RMB848.2 million in 2024 to RMB969.4 million in 2025, representing a year-on-year increase of 14.3%. Gross profit increased from RMB525.2 million in 2024 to RMB634.6 million in 2025, representing a year-on-year increase of 20.8%. Net profit increased from RMB105.1 million in 2024 to RMB130.2 million in 2025, representing a year-on-year increase of 23.9%.

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Revenue Breakdown

| Item | 2025
(RMB'000) | 2024
(RMB'000) | YoY Change
(%) |
| --- | --- | --- | --- |
| Digital Educational Content Services and Products | 831,272 | 709,964 | 17.1% |
| Digital Teaching and Learning Environment Services and Products | 137,555 | 137,620 | 0.0% |
| Others | 579 | 614 | -5.7% |
| Total | 969,406 | 848,198 | 14.3% |

In 2025, the Company’s revenue increased by RMB121.2 million compared with 2024, representing a year-on-year increase of 14.3%, demonstrating a stable growth trend. The key drivers of this revenue growth were mainly the digital educational content services and products business, driving continuous expansion in customer coverage and a sustained deepening of service engagement, as detailed below:

I. Product Dimensions

1) Digital Educational Content Services and Products: Empowered by AI, emerging as the core growth engine

In 2025, this business segment generated a revenue of RMB831.3 million, representing a year-on-year increase of 17.1% from 2024, acting as the absolute primary driver of the Company’s overall revenue growth. This growth was fundamentally fueled by the deep, scenario-based application of artificial intelligence.

Notably, the knowledge graph products and services within this segment, empowered by both large models and AI agent technologies, have been deeply tailored to higher education teaching scenarios, thereby achieving scaled expansion, generating revenue of RMB573.5 million in 2025, representing a 68.5% year-on-year increase compared with 2024, and the proportion to the total revenue was increased to 59.2% from 40.1% in 2024. Leveraging its core technological barriers and strong adaptability to educational scenarios, the business achieved large-scale deployment and became a key driver of overall revenue growth.

2) Digital Teaching and Learning Environment Services and Products: Focusing on the optimization of existing operations, with the business scale remaining stable

In 2025, this business segment recorded a revenue of RMB137.6 million, remaining relatively stable compared to 2024. This was primarily because the Company's strategic resources were concentrated on the expansion of the teaching content business in 2025. Consequently, for this business segment, the primary focus was on maintaining the existing customer base and upgrading the service experience, thereby keeping the overall revenue scale steady this year.

Notably, AI Agents services and products within this business, positioned as the core innovative product of this segment, successfully completed the delivery of over 500 projects throughout the year through iterative algorithm optimization and deep engagement in vertical scenarios. By achieving deep synergy with the AI Cloud LMS, they precisely match the digital transformation needs of higher education institutions, thereby emerging as a crucial pillar for optimizing our business structure and driving future growth.

II. Customer Dimensions

1) Deepening collaboration with lighthouse customers (Note 1) highlights the Company's leading position in the field of AI applications and its capability in real-world scenario implementation

Lighthouse customers serve as important benchmarks for the Company's revenue growth. In 2025, the average revenue per lighthouse client increased from RMB1.035 million in 2024 to RMB1.388 million, representing a year-on-year growth of 34.1%. This consistently validates the effectiveness and scalability of the Company's service model for top-tier customers, and fully demonstrates the substantial value of the Company's services and products, as well as its core competitiveness.

2) Expansion of the high-value customers (Note 2) base solidifies the core foundation for revenue growth and reinforces the value drivers behind it

High-value customers are a critical driver of the Company's revenue growth. In 2025, the number of high-value customers increased from 241 to 281, representing a year-on-year growth of 16.6%. This further reinforces the core foundation of the Company's revenue growth and underscores its industry penetration and ability to unlock client value.

Note 1: Lighthouse customers refer to universities under the "Project 985", "Project 211" and "Double First-Class" initiatives, as well as higher vocational colleges nominated under the "Double High Plan".

Note 2: High-value customers refer to customers generating revenue of RMB1 million or more.

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Cost of Sales

Our cost of sales increased by 3.7% from RMB323.0 million in 2024 to RMB334.8 million in 2025, primarily due to the expansion of our business, which led to an increase in the number of personnel responsible for production and delivery and their performance-based compensation, as well as increased procurement of goods.

Gross Profit and Gross Profit Margin

Item	2025 Gross Profit (RMB'000)	2025 Gross Profit Margin (%)	2024 Gross Profit (RMB'000)	2024 Gross Profit Margin (%)
Digital Educational Content Services and Products	526,600	63.3	434,766	61.2
Digital Teaching and Learning Environment Services and Products	107,634	78.2	89,969	65.4
Others	364	62.9	423	68.9
Total	634,598	65.5	525,158	61.9

Our overall gross profit increased by 20.8% from RMB525.2 million in 2024 to RMB634.6 million in 2025, and our overall gross profit margin increased from 61.9% in 2024 to 65.5% in 2025. The increase in gross profit margin was primarily attributable to the higher proportion of high-margin businesses, such as knowledge graphs and intelligent agents, in our revenue in 2025. Among these, the knowledge graph business saw its revenue contribution increase from 40.1% in 2024 to 59.2% in 2025, becoming the core engine driving the improvement in overall profitability. Going forward, we will continue to optimize the technological architecture of knowledge graphs and the commercialization pathways of other artificial intelligence-related services and products, further amplifying their positive impact on the Company's gross profit and gross profit margin.

Distribution and Selling Expenses

Our distribution and selling expenses increased by 13.5% from RMB215.7 million in 2024 to RMB244.9 million in 2025, primarily due to the Company's continued expansion of its sales team and ongoing efforts to deepen market penetration, which led to corresponding increases in staff compensation and business development-related expenses.

General and Administrative Expenses

Our general and administrative expenses increased by 15.5% from RMB68.6 million in 2024 to RMB79.2 million in 2025, primarily due to an increase in personnel and the corresponding rise in administrative expenses.

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Research and Development Expenses

Our research and development expenses increased by 48.9% from RMB126.9 million in 2024 to RMB189.0 million in 2025. The significant increase in R&D expenses was mainly due to the allocation of resources to strategic development areas and the expansion of the R&D team, which resulted in higher employee-related expenses. The key areas of investment included:

1) Advancement of core technologies, focusing on the iterative upgrade of the fully self-developed large model "Polymas";
2) Precise optimization of knowledge graphs and innovation in multimodal interaction algorithms, continuously strengthening the Company's technological moat and enhancing its core competitiveness;
3) Breakthroughs in emerging businesses, with increased R&D investment in the intelligent agent business, particularly in key areas such as algorithm iteration, multi-scenario adaptation, and functional testing, along with related human resources and costs, to cultivate future profit growth drivers.
4) The Company will continue to focus on technological innovation as its primary development direction, strategically allocate R&D resources, and promote the deep integration of technological achievements with educational scenarios, thereby injecting strong momentum into the Company's long-term development.

Net Profit

Our net profit increased by 23.9% from RMB105.1 million in 2024 to RMB130.2 million in 2025, primarily due to the following factors:

1) Expansion of high-margin core businesses driving overall gross margin improvement

The knowledge graph business continued to scale rapidly, generating RMB573.5 million in revenue in 2025, representing a 68.5% year-on-year increase. The increasing proportion of high-margin product revenue further improved the overall gross margin and amplified profit elasticity.

2) Improved cost-side efficiency, achieving cost reduction and efficiency enhancement

By leveraging AI technologies to optimize service processes, the Company reduced unit service costs. At the same time, the scale effect of core businesses improved the efficiency of fixed cost allocation, directly expanding the profit margin.

3) Conversion of technological R&D into profit drivers and accelerated commercialization

In 2025, R&D investment focused on key areas such as large model iteration and intelligent agent product optimization. Although R&D expenses increased, technological achievements were rapidly converted into scalable products and services, forming a virtuous cycle of “R&D investment – technological breakthroughs – profitability enhancement.”

4) Increased proportion of high-value customers amplifying profit elasticity

The number of customers with annual transaction values exceeding RMB1 million increased from 241 as of 31 December 2024 to 281 as of 31 December 2025, with their revenue contribution rising to 62.9%. High-value customers typically demonstrate high renewal rates, stable average transaction values, and relatively low service costs, and the increase in their proportion further optimized the Company’s profit structure.

Adjusted Net Profit (Non-IFRS Measures)

To supplement our consolidated financial statements which are presented in accordance with IFRSs, we also use the adjusted net profit (a non-IFRS measure) as an additional financial measure, which is not required by, or presented in accordance with, IFRSs. We believe that such non-IFRS measure facilitates comparisons of operating performance from period to period and company to company by eliminating potential impacts of certain items.

We define the adjusted net profit (a non-IFRS measure) as the net profit adjusted by adding back the following items: (i) share-based payment expenses included in the cost of sales, administrative expenses, research and development expenses, and selling and distribution expenses, which relate to share awards granted by us to participants of share incentive schemes, and are non-cash expenses (“Share-based payment”); (ii) listing expenses.

After adding back the share-based payment and listing expenses for the year, our adjusted net profit (a non-IFRS measure) for 2024 and 2025 amounted to RMB122.7 million and RMB148.5 million respectively. The table below reconciles adjusted net profit to net profit, the most directly comparable financial measure calculated and presented in accordance with International Financial Reporting Standards, for the reporting year.

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| Item | 2025
(RMB'000) | 2024
(RMB'000) | YoY Change
(%) |
| --- | --- | --- | --- |
| Net profit | 130,224 | 105,071 | 23.9% |
| Add: | | | |
| Share-based payment | 3,578 | 3,252 | |
| Listing expenses | 14,654 | 14,364 | |
| Adjusted net profit (Non-IFRS Measures) | 148,456 | 122,687 | 21.0% |

We believe that the adjusted net profit (a non-IFRS measure) provides useful information to investors and others to assist them in understanding and evaluating our consolidated results of operations in the same manner as they help our management. However, our presentation of adjusted net profit (a non-IFRS measure) may not be comparable to similarly titled measures presented by other companies. The use of the non-IFRS measure has limitations as an analytical tool, and should not be considered in isolation from, or as substitute for analysis of, our results of operations or financial conditions as reported under IFRSs.

Liquidity and Financial Resources

As of 31 December 2025, our liquidity amounted to RMB414.7 million (including cash and cash equivalents and restricted cash), representing an increase of RMB179.8 million compared with RMB234.9 million as of 31 December 2024.

As of 31 December 2025, our borrowings amounted to RMB92.9 million, primarily consisting of short-term, guaranteed and unguaranteed bank borrowings obtained mainly to fund our working capital, representing an increase of RMB36.7 million compared with RMB56.2 million as of 31 December 2024.

We believe that, taking into account the Company's business development and expansion plans, our current level of liquidity is sufficient to support the funding required for our day-to-day operations.

Trade Receivables and Retention Money Receivables

Our trade receivables and retention money receivables increased by 54.8% from RMB345.5 million as of 31 December 2024 to RMB534.7 million as of 31 December 2025, primarily due to the growth of our business. Affected by the industry characteristics of the higher education sector, where project acceptance procedures and payment settlement cycles are relatively long, there is a periodic timing difference between the pace of payment collection and business growth, which has led to a corresponding increase in the scale of receivables as the business continues to develop.

Capital Structure

The Company’s capital structure management focuses on ensuring funding for business development, optimizing the capital structure, controlling financial risks, and improving capital utilization efficiency. The Company coordinates its funding sources through diversified channels, including shareholder capital contributions, accumulated cash flows from operating activities, and bank borrowings, balancing capital liquidity and stability to provide solid financial support for the continuous development of its principal businesses and the implementation of its core strategies.

In 2025, the Company’s funding sources primarily consisted of net cash inflows generated from operating activities, supplemented by an appropriate level of bank borrowings. The structure of funding sources remained stable and sustainable. As of 31 December 2024 and 31 December 2025, the Company’s cash and cash equivalents amounted to RMB230.2 million and RMB407.8 million, respectively, while its bank borrowings amounted to RMB56.2 million and RMB92.9 million, respectively. The gearing ratio (calculated by using total debt (including lease liabilities) divided by total equity and multiplied by 100%) was 16.4% and 10.8%, respectively. The Company maintains sufficient liquidity to cover short-term debt repayment needs as well as operating and investment funding requirements for the foreseeable future, and its liquidity risk remains controllable. Meanwhile, the Company’s financial leverage level remains within a reasonable range, reflecting a relatively strong risk resilience.

During the Reporting Period, the Company completed its initial public offering, raising net proceeds of approximately HK$431.1 million, which further increased the scale of total equity and significantly strengthened the Company’s capital base, providing adequate financial support for research and development investment and market expansion.

Going forward, the Company will continue to dynamically optimize its capital structure based on its business development strategy, changes in the market environment, and actual operational needs. On the one hand, it will continue to strengthen the management of operating cash flows and enhance internal capital accumulation to consolidate its capital foundation. On the other hand, it will prudently coordinate external financing channels and maintain an appropriate level of financial leverage to balance funding costs and capital returns. At the same time, the Company will strengthen regular monitoring and analysis of its capital structure to ensure alignment with its stage of development and business layout, thereby supporting long-term and stable operations.

18 -

19 -

Foreign Exchange Risk

The Company’s functional currency is RMB. During the Reporting Period, the Company’s operations were primarily conducted within the PRC. The Company has not currently established a foreign exchange hedging policy. However, management will continue to monitor foreign exchange-related risks and will consider implementing hedging arrangements for significant foreign exchange exposures when necessary.

Charges on Assets

As of 31 December 2025, the Group had no charges on assets.

Significant Investments

As of 31 December 2025, the Group did not hold any significant investments.

Future Plans for Significant Investments and Capital Assets

As of 31 December 2025, the Group did not have any plans for significant external investments or capital assets.

Material Acquisitions and Disposals of Subsidiaries, Associates and Joint Ventures

From the Listing Date to the end of the Reporting Period, the Group had no material acquisitions or disposals of subsidiaries, associates or joint ventures.

Contingent Liabilities

As of 31 December 2025, the Group had no contingent liabilities.

Employees, Training and Remuneration Policies

The Company places strong emphasis on employee protection, talent development, and long-term incentives. In accordance with applicable laws and regulations in the PRC, the Company provides statutory welfare benefits to its employees, including pension insurance, maternity insurance, unemployment insurance, work-related injury insurance, medical insurance, and housing provident fund contributions. Based on job responsibilities, business needs, and operational conditions, the Company implements corresponding working hour and leave arrangements and continuously improves related management mechanisms to support business development and enhance employee experience.

The Company continuously optimizes its compensation and benefits management system. Based on factors such as job value, individual capability, performance, market benchmarks, and the Company's operating conditions, it has established a compensation system that is competitive in the market while maintaining internal fairness. In line with its operational objectives, employee contributions, and business development needs, the Company continues to refine its remuneration and incentive mechanisms to attract, retain, and motivate talent. The Company has also implemented employee incentive plans and plans to continue granting share-based incentives to employees in the future to encourage their contributions to the Company's growth and development. The Company also conducts ongoing training related to safety, compliance, and job responsibilities and adheres to lawful, compliant, fair, and equitable employment practices. As of 31 December 2025, the Company had a total of 2,728 employees.

Environmental Policies and Performance

The Company is committed to achieving environmental sustainability and integrating it into the daily operations of the Group. In addition to complying with all relevant environmental regulations and laws, management has consistently encouraged practices such as water conservation, recycling of water, energy, and materials, which are incorporated into performance evaluations.

Compliance with Relevant Laws and Regulations

For the year ended 31 December 2025, the Company did not record any material non-compliance with laws and regulations in relation to its operations.

OTHER INFORMATION

Purchase, Sale, or Redemption of the Company's Listed Securities

As of the date of this announcement, the Company's share capital consists of 66,666,700 ordinary shares, including 4,713,900 domestic unlisted shares and 61,952,800 H Shares. The number of H Shares issued in the Global Offering was 61,952,800, representing 92.93% of the total issued share capital of the Company upon Listing.

Save for the Global Offering, neither the Company nor any of its subsidiaries has purchased, sold or redeemed any of the Company's listed securities during the period from the Listing Date to the date of this announcement.

20 -

Use of Proceeds from the Global Offering

H-shares of the Company were listed on the Main Board of the Stock Exchange of Hong Kong Limited (the "Stock Exchange") on 8 December 2025 (the "Listing Date"). After deducting the underwriting fees and related expenses, the net proceeds received from the Global Offering amounted to approximately HK$431.1 million. The Company will utilise these net proceeds for the purposes specified in the section headed "Future Plans and Use of Proceeds" in the prospectus dated 28 November 2025 (the "Prospectus").

The table below shows the plans and actual usage of net proceeds from the Global Offering as of the end of the Reporting Period:

Intended uses of proceeds	Percentage of total net proceeds (%)	Allocation of net proceeds (HK$ in millions)	Net proceeds utilised (Reporting Period) (HK$ in millions)	Net proceeds unutilised (as of 31 December 2025) (HK$ in millions)	Expected timeline for using unutilised proceeds
Research and Development	36.7	158.3	–	158.3	By the end of 2030
• Recruitment and cultivation of R&D personnel	29.0	125.1	–	125.1	By the end of 2030
• Improvement of R&D infrastructure	7.7	33.2	–	33.2	By the end of 2030
Enhancement of our customer service and support capabilities	31.8	137.2	–	137.2	By the end of 2030
• Employee recruitment	26.0	112.2	–	112.2	By the end of 2030
• Infrastructure	5.8	25	–	25	By the end of 2030
Establishment of knowledge graph construction centers	21.4	92.4	–	92.4	By the end of 2030
• Employee recruitment	19.0	82	–	82	By the end of 2030
• Infrastructure	2.4	10.4	–	10.4	By the end of 2030
Working capital and general corporate purposes	10.0	43.2	–	43.2	By the end of 2030
Total	100	431.1	–	431.1	/

The Group has not utilised any listing proceeds from the Listing Date to the date of this announcement and will continue to utilise the remaining net proceeds progressively in accordance with the intended usage as set out in the Prospectus. The expected timeline for utilising the net proceeds from the Global Offering is based on the best estimates of future market conditions made by the Company and is subject to changes in accordance with our actual business operations.

Dividend

The Board of Directors resolved not to recommend the payment of any final dividend for the year ended 31 December 2025.

Compliance with the Corporate Governance Code

The Company has adopted the code provisions of the Corporate Governance Code (the “CG Code”) as set out in Part 2 of Appendix C1 to the Rules Governing the Listing of Securities on the Stock Exchange (the “Listing Rules”) as its own code of corporate governance. The Board has reviewed the Company’s corporate governance practices and is satisfied that, save for the deviation from code provisions B.3.5 and C.2.1, the Company has complied with all code provisions set out in the CG Code throughout the Reporting Period.

The roles of the chairman and chief executive should be separate and should not be performed by the same individual under code provision C.2.1 of the CG Code. Mr. Wang Hui is the Chairman and chief executive officer of the Company, who is also one of the founders of the Company and possesses extensive industry experience. The Board believes that, as Mr. Wang Hui has been leading the Group’s strategic planning and business development, this arrangement enables the Group to efficiently formulate effective plans and implement business decisions and strategies under strong and consistent leadership, which is beneficial to the overall business management and development of the Group.

According to code provision B.3.5 of the CG Code, an issuer should appoint at least one director of a different gender to the nomination committee. At present, the nomination committee of the Company (“Nomination Committee”) has not yet appointed any director of a different gender. The Board will review the composition of the Nomination Committee and, where appropriate, consider following this arrangements.

To maintain a high standard of corporate governance, the Board will continuously review and monitor the Company’s corporate governance practices.

22 -

23 -

Model Code for Securities Transactions by Directors and Supervisors

The Company has formulated a code of conduct regarding securities transactions for its directors, supervisors, senior management and relevant employees (the "Code of Conduct"), which is no less stringent than the Model Code for securities transactions by Directors of listed issuers set out in Appendix C3 to the Listing Rules (the "Model Code"). Upon inquiry, all Directors and supervisors, confirmed that they have strictly complied with the Model Code and the Code of Conduct from the Listing Date to the end of the Reporting Period. The Group also has prepared written guidelines for relevant employees regarding securities transactions, which is no less stringent than the Model Code. The Company has not found any relevant employees violating the written guidelines.

Audit Committee Reviews the Annual Results

The Audit Committee of the Board (the "Audit Committee") consists of Mr. YAU Ka Chi, Professor LIU Ningrong, and Professor MA Xufei, all of whom are independent non-executive directors. Mr. YAU Ka Chi serves as Chairman of the Audit Committee and possesses the appropriate professional qualifications as required under Rules 3.10(2) and 3.21 of the Listing Rules.

The Audit Committee has reviewed the audited consolidated financial statements of the Group for the year ended 31 December 2025 and did not raise any objections to the accounting policies and practices adopted by the Company.

Scope of Work of the Auditor

The figures presented in the Group's consolidated statement of comprehensive income, consolidated statement of financial position, consolidated statement of changes in equity, and the related notes thereto for the year ended 31 December 2025, as set out in this announcement, have been agreed by the Group's auditor, PricewaterhouseCoopers, to the amounts set out in the audited consolidated financial statements of the Group for the year as considered and approved by the Board of Directors on 27 March 2026. The work performed by PricewaterhouseCoopers in this respect did not constitute an assurance engagement in accordance with Hong Kong Standards on Auditing, Hong Kong Standards on Review Engagements or Hong Kong Standards on Assurance Engagements issued by the Hong Kong Institute of Certified Public Accountants and consequently no assurance has been expressed by PricewaterhouseCoopers on this results announcement.

Events after the Reporting Period

As of the date of this announcement, the Group had no material events subsequent to the Reporting Period.

CONSOLIDATED STATEMENT OF COMPREHENSIVE INCOME
FOR THE YEAR ENDED 31 DECEMBER 2025

	Note	Year ended 31 December
		2025 RMB'000	2024 RMB'000
Revenue	3	969,406	848,198
Cost of sales	4	(334,808)	(323,040)
Gross profit		634,598	525,158
Distribution and selling expenses	4	(244,858)	(215,721)
General and administrative expenses	4	(79,240)	(68,622)
Research and development expenses	4	(189,040)	(126,923)
Net impairment losses on financial assets		(7,752)	(14,024)
Other income		13,291	8,619
Other (losses)/gains – net		(732)	241
Operating profit		126,267	108,728
Finance income		195	635
Finance costs		(4,397)	(2,765)
Finance costs – net		(4,202)	(2,130)
Profit before income tax		122,065	106,598
Income tax credit/(expense)	5	8,159	(1,527)
Profit for the year		130,224	105,071
Profit and total comprehensive income, attributable to owners of the Company		130,224	105,071
Earnings per share attributable to the owners of the Company (in RMB)
Basic and diluted earnings per share	6	2.15	1.75

– 24 –

CONSOLIDATED STATEMENT OF FINANCIAL POSITION
AS AT 31 DECEMBER 2025

	Note	As at 31 December
		2025 RMB'000	2024 RMB'000
ASSETS
Non-current assets
Property, plant and equipment	7	9,228	15,024
Right-of-use assets		18,266	24,632
Deferred income tax assets		39,455	29,185
Retention money receivables	8	11,411	7,612
		78,360	76,453
Current assets
Inventories		49,472	27,873
Trade receivables and retention money receivables	8	523,326	337,916
Other current assets		83,914	67,345
Financial assets at fair value through profit or loss		203,313	48,028
Restricted cash	9	6,910	4,721
Cash and cash equivalents	9	407,831	230,172
		1,274,766	716,055
Total assets		1,353,126	792,508
EQUITY
Share capital		66,667	60,000
Reserves		622,442	231,346
Retained earnings		335,532	207,523
Total equity		1,024,641	498,869

25 -

26 -

	Note	As at 31 December
		2025
RMB'000	2024
RMB'000
LIABILITIES
Non-current liabilities
Lease liabilities		5,752	8,157
Current liabilities
Trade payables		11,294	11,084
Other payables and accruals		103,936	87,126
Borrowings		92,937	56,240
Lease liabilities		11,497	17,593
Contract liabilities	3	103,069	113,439
		322,733	285,482
Total liabilities		328,485	293,639
Total equity and liabilities		1,353,126	792,508
Net Current Assets		952,033	430,573

CONSOLIDATED STATEMENT OF CHANGES IN EQUITY

FOR THE YEAR ENDED 31 DECEMBER 2025

	Attributable to owners of the Company
	Share capital
RMB'000	Reserves
RMB'000	Retained earnings
RMB'000	Total equity
RMB'000
Balance at 1 January 2024	60,000	216,569	113,977	390,546
Comprehensive income
Profit for the year	–	–	105,071	105,071
	60,000	216,569	219,048	495,617
Transactions with equity holders:
Share-based payment	–	3,252	–	3,252
Appropriation to statutory reserve	–	11,525	(11,525)	–
	–	14,777	(11,525)	3,252
Balance at 31 December 2024	60,000	231,346	207,523	498,869
	Share capital
RMB'000	Reserves
RMB'000	Retained earnings
RMB'000	Total equity
RMB'000
Balance at 1 January 2025	60,000	231,346	207,523	498,869
Comprehensive income
Profit for the year	–	–	130,224	130,224
	–	–	130,224	130,224
Transactions with equity holders:
Issuance of ordinary shares upon global offering	6,667	385,303	–	391,970
Share-based payment	–	3,578	–	3,578
Appropriation to statutory reserve	–	2,215	(2,215)	–
	6,667	391,096	(2,215)	395,548
Balance at 31 December 2025	66,667	622,442	335,532	1,024,641

– 27 –

NOTES TO THE CONSOLIDATED FINANCIAL STATEMENT FOR THE YEAR ENDED 31 DECEMBER 2025

GENERAL INFORMATION

Shanghai Able Digital Science&Tech Co., Ltd. (the “Company”) was incorporated in the People’s Republic of China (the “PRC”) on 7 April 2008 as a limited liability company under the Company Law of the PRC. The address of the Company’s registered office is Room 901–904 Building 1, No. 1188 North Qinzhou Road, Xuhui District, Shanghai, PRC.

The Company and its subsidiaries (together, “the Group”) are principally engaged in the provision of services and products relating to: (i) digital educational content services and products; and (ii) digital teaching and learning environment services and products in the PRC.

Mr. Wang Hui and his wife, Ms. Ge Xin, are the ultimate controlling shareholders of the Company as at the date of this report.

The Company has been successfully listed on the Main Board of the Stock Exchange of Hong Kong Limited since 8 December 2025.

These consolidated financial statements were approved for issue by the Board of Directors on 27 March 2026.

BASIS OF PREPARATION

(i) Compliance with IFRS Accounting Standards and Hong Kong Companies Ordinance Cap. 622

The consolidated financial statements of the Group have been prepared in accordance with IFRS Accounting Standards as issued by the International Accounting Standards Board and disclosure requirements of the Hong Kong Companies Ordinance Cap. 622.

(ii) Historical cost convention

The consolidated financial statements have been prepared on a historical cost basis, except for the certain financial assets that are measured at fair value.

(iii) New and amended standards adopted by the group

The group has applied the following standards, amendments and interpretation for the first time for its annual reporting period commencing 1 January 2025:

Standards and amendments	Effective for accounting periods beginning on or after
IAS 21 (Amendment) ‘Lack of exchangeability’	1 January 2025
Amendments to Illustrative Examples on IFRS 7, IAS 1, IAS 8, IAS 36 and IAS 37 – Disclosures about Uncertainties in the Financial Statements	1 January 2025

28 -

(iv) New and amended standards and interpretations not yet adopted

Certain new accounting standards and amendments to accounting standards have been published that are not mandatory for 31 December 2025 reporting periods and have not been early adopted by the group. The Group’s assessment of the impact of these new standards and amendments is set out below.

Standards and amendments	Effective for annual periods beginning on or after
IFRS 9 (Amendment) and IFRS 7 (Amendment) ‘Amendments to the classification and measurement of financial instruments’	1 January 2026
IFRS 9 (Amendment) and IFRS 7 (Amendment) ‘Contracts referencing nature-dependent electricity’	1 January 2026
Annual Improvements to IFRS Accounting Standards – Volume 11	1 January 2026
IFRS 18 ‘Presentation and disclosure in financial statements’	1 January 2027
IFRS 19 ‘Subsidiaries without public accountability: disclosures’	1 January 2027
IAS 21 (Amendments) Translation to a Hyperinflationary Presentation Currency	1 January 2027
IFRS 10 (Amendment) and IAS 28 (Amendment) ‘Transfer of assets between an investor and its associate or joint venture’	To be determined

REVENUE AND SEGMENT INFORMATION

(a) Revenue during the reporting period

	Year ended 31 December
	2025
RMB’000	2024
RMB’000
Digital educational content services and products	831,272	709,964
Digital teaching and learning environment services and products	137,555	137,620
Others	579	614
	969,406	848,198

Disaggregation of revenue from contracts with customers by the timing of revenue recognition is as follows:

	Year ended 31 December
	2025
RMB’000	2024
RMB’000
At a point in time	936,891	805,258
Over time	32,515	42,940
	969,406	848,198

(b) Contract liabilities

The Group recognized the following contract liabilities related to contracts with customers:

	As at 31 December
	2025	2024
	RMB'000	RMB'000
Current contract liabilities	103,069	113,439

Revenue recognized in relation to contract liabilities

The following table shows how much of the Group’s revenue recognized during the reporting periods relates to carried-forward contract liabilities.

	Year ended 31 December
	2025	2024
	RMB'000	RMB'000
Revenue recognized that was included in the contract liabilities balance at the beginning of the year	90,314	103,092

Contract liabilities of the Group mainly arise from the advance payments made by customers while the underlying products or services are yet to be provided.

The following table shows unsatisfied performance obligations of the Group as at 31 December 2025 and 2024:

	As at 31 December
	2025	2024
	RMB'000	RMB'000
Aggregate amount of unsatisfied performance obligations	392,417	360,712

Management expects that 85.3% (31 December 2024: 85.1%) of the transaction price allocated to the unsatisfied contracts as at 31 December 2025 will be recognized as revenue within one year. The remaining 14.7% (31 December 2024: 14.9%) will be recognized over one year.

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31 -

4. EXPENSES BY NATURE

The total of cost of sales, distribution and selling expenses, general and administrative expensed, research and development expenses are analysed as follows:

	Year ended 31 December
	2025	2024
	RMB'000	RMB'000
Changes in inventories of work in progress	(10,158)	(11,131)
Purchased goods used	8,496	23,769
Employee benefit expenses	650,269	534,504
Travel expenses	33,154	31,095
Depreciation of property, plant and equipment	9,600	11,663
Depreciation of right-of-use assets	20,577	19,101
Network service fees	26,123	18,744
Digital content editing fees	23,449	21,432
Marketing expenses	19,823	19,663
Listing expenses	14,654	14,364
Legal, consulting and other service fees	6,300	8,449
Short-term leases	1,006	1,870
Auditor’s remuneration
- Audit services	2,050	8
Others	42,603	40,775
	847,946	734,306

5. INCOME TAX (CREDIT)/EXPENSE

	Year ended 31 December
	2025	2024
	RMB'000	RMB'000
Current income tax expense	2,111	1
Deferred income tax (credit)/expense	(10,270)	1,526
	(8,159)	1,527

Income tax on profits assessable has been calculated at the rates of tax prevailing in the jurisdictions in which relevant entities operate.

32 -

PRC corporate income tax ("PRC CIT")

Pursuant to the PRC tax laws and regulations, the Company and its subsidiaries in the PRC are subject to PRC corporate income tax at the applicable tax rate of 25% on their taxable profits during the Track Record Period, except as disclosed below.

The Company was qualified as a High and New Technology Enterprise ("HNTE") in 2019 and was therefore entitled to a preferential corporate income tax rate of 15% for three years commencing 2019. The Company renewed its HNTE status in every three years intervals, in 2022 and 2025, and therefore continues to be entitled to the preferential corporate income tax rate of 15% in 2025 for a three year term. In addition, Shanghai Zhuoyue Ruixin Network Technology Co., Ltd., a subsidiary of the Group, was recognized as an HNTE in 2024 for a three-year term.

In accordance with the policies promulgated by the State Taxation Administration of the PRC, enterprises engaged in research and development activities may claim an additional tax deduction of 75% of the eligible research and development expenses ("super deduction") in determining their taxable profits for the year commencing 2018. With effect from 1 October 2022, the additional deduction ratio had been increased to 100%.

As of 31 December 2025, the Group's subsidiaries, Shanghai Tingri Technology Co., Ltd., Xinjiang Zhihui Tongfu Technology Co., Ltd. and Shanghai Wenjing Education Technology Co., Ltd., retained their status as Small and Micro-Profit Enterprises.

Pursuant to the Announcement on Policies for Reducing and Exempting Value-Added Tax for Small-Scale VAT Payers issued by the State Taxation Administration of the People's Republic of China (Caishui [2019] No. 13, Caishui [2022] No. 13 and Caishui [2023] No. 6), the corporate income tax of Small and Micro-Profit Enterprises is calculated as follows:

(i) for the portion of annual taxable income not exceeding RMB1,000,000, 25% of such income is included in taxable income, and corporate income tax is paid at a preferential tax rate of 20%; and

(ii) for the portion of annual taxable income exceeding RMB1,000,000 but not exceeding RMB3,000,000, 50% of such income is included in taxable income, and corporate income tax is paid at a tax rate of 20%.

Such preferential tax treatment applied throughout the Track Record Period.

The difference between the actual income tax expense charged to the consolidated statement of comprehensive income or loss and the amounts which would result from applying the enacted tax rates to profit before income tax can be reconciled as follows:

	Year ended 31 December
	2025 RMB'000	2024 RMB'000
Profit before income tax	122,065	106,598
Income tax expenses calculated at applicable tax rates	30,517	26,650
Tax effects of preferential tax rate	(11,390)	(9,239)
Super deduction of research and development expenses	(27,431)	(17,097)
Expenses not deductible for tax purposes	2,963	5,212
Previously unrecognized tax losses and deductible temporary differences recognized as deferred tax assets	(2,632)	(1,411)
Others	(186)	(2,588)
	(8,159)	1,527

6. EARNINGS PER SHARE

Basic earnings per share for the years ended 31 December 2024 and 2025 are calculated by dividing the profit attributable to the Company's shareholders by the weighted average number of ordinary shares in issue during the year.

The Company did not have any potential ordinary shares outstanding during the reporting period. Diluted earnings per share is equal to basic earnings per share.

	Year ended 31 December
	2025	2024
Profit attributable to owners of the Company (RMB'000)	130,224	105,071
Weighted average number of ordinary shares in issue	60,438,358	60,000,000
Basic and diluted earnings per share (expressed in RMB per share)	2.15	1.75

7. PROPERTY, PLANT AND EQUIPMENT

	Year ended 31 December
	2025 RMB'000	2024 RMB'000
Opening net book amount	15,024	16,943
Additions	3,804	9,744
Depreciation charge	(9,600)	(11,663)
Closing net book amount	9,228	15,024

TRADE RECEIVABLES AND RETENTION MONEY RECEIVABLES

	As at 31 December
	2025	2024
	RMB'000	RMB'000
Trade receivables	566,008	371,076
Retention money receivables	12,007	8,564
	578,015	379,640
Less: provision for impairment	(43,278)	(34,112)
	534,737	345,528

As at 31 December 2025 and 2024, the aging analysis of the trade receivables and retention money receivables based on date of revenue recognition is as follows:

	As at 31 December
	2025	2024
	RMB'000	RMB'000
Within 6 months	335,647	242,118
6 months–1 year	70,740	43,073
1–2 years	108,175	61,619
2–3 years	37,581	20,820
Above 3 years	25,872	12,010
Total	578,015	379,640

Trade receivables and retention money receivables are amounts due from customers for goods sold or services performed in the ordinary course of business. They are generally due for settlement within 1 year and therefore classified as current except for non-current retention money which are due for settlement after one year. Trade receivables and retention money receivables are recognized initially at the amount of consideration that is unconditional unless they contain significant financing components, when they are recognized at fair value. The Group holds the trade receivables and retention money receivables with the objective of collecting the contractual cash flows and therefore measures them subsequently at amortized cost using the effective interest method.

The loss allowance as at 31 December 2025 and 2024 was determined as follows for trade receivables and retention money receivables:

As at 31 December 2025	Within 6 months RMB'000	6 months-1 year RMB'000	1-2 years RMB'000	2-3 years RMB'000	Above 3 years RMB'000	Total RMB'000
Expected loss rate	1.14%	4.70%	9.89%	23.95%	63.46%
Gross amount	335,647	70,740	108,175	37,581	25,872	578,015
Loss allowance	(3,834)	(3,324)	(10,702)	(9,000)	(16,418)	(43,278)
As at 31 December 2024	Within 6 months RMB'000	6 months-1 year RMB'000	1-2 years RMB'000	2-3 years RMB'000	Above 3 years RMB'000	Total RMB'000
Expected loss rate	1.35%	4.39%	14.51%	45.29%	88.14%
Gross amount	242,118	43,073	61,619	20,820	12,010	379,640
Loss allowance	(3,264)	(1,889)	(8,943)	(9,430)	(10,586)	(34,112)

9. CASH AND CASH EQUIVALENTS AND RESTRICTED CASH

	As at 31 December
	2025	2024
	RMB'000	RMB'000
Restricted cash	6,910	4,721
Cash at banks and in hand	407,831	230,172
	414,741	234,893

As at 31 December 2025 and 2024, the Group's restricted cash mainly was deposits at bank for letters of guarantee.

Cash and cash equivalents are denominated in the following currencies:

	As at 31 December
	2025	2024
	RMB'000	RMB'000
RMB	227,663	234,893
HKD	187,078	-
	414,741	234,893

10. DIVIDENDS

The board of directors do not recommend the declaration of dividend by the Company for the year ended 31 December 2025 (2024: nil).

36 -

Publication of Annual Results and Annual Report

The annual results announcement is published on the website of the Stock Exchange (www.hkexnews.hk) and the website of the Company (www.able-elec.com). The Company's annual report for the year ended 31 December 2025, containing all information required by the Listing Rules, will be dispatched to shareholders and made available on the above websites in due course.

By order of the Board

Shanghai Able Digital Science&Tech Co., Ltd.

Mr. WANG Hui

Chairman of the Board and executive Director

Hong Kong, 27 March 2026

As at the date of this announcement, the Board comprises (i) Mr. WANG Hui, Mr. XI Puzhao and Ms. WANG Xin as executive directors; (ii) Ms. GE Xin, Mr. JIN Xingshen and Ms. WANG Ying as non-executive directors; and (iii) Mr. YAU Ka Chi, Prof. LIU Ningrong and Prof. MA Xufei as independent non-executive directors.