Earnings Call: H2 2025

Mar 27, 2026

Summary

FY2025 revenue was CNY 2.92B, with positive reported and non-IFRS profit despite significant AI investment and policy pressure. MaaS grew 9%, while 2026 priorities center on converting AI-agent capabilities into scalable value.

Distinguished investors, analysts, and friends from the media, good day. I am Sandy Qin, Investor Relations Director of Bairong. I sincerely thank all investors joining us online for your continued attention to Bairong. The company released its 2025 annual results announcement after the market closed yesterday, March 26. In this results presentation, we will report and share the company's operating achievements for 2025, look ahead to 2026, and answer questions of interest. This results presentation consists of three parts. The first part features the CEO introducing business progress and outlook. The second part features the CSO explaining financial performance. The final part is the Q&A session. Participants online can submit questions in the live stream's question box or dial in by phone. Numbers for mainland China are 02362737123. For Hong Kong, 85230183602. For international callers, +862362737100. Chinese conference passcode is 595868414. English conference code is 291375421. Management will answer questions after the presentation. This presentation contains forward-looking statements reflecting the company's current beliefs and expectations about the future. These statements include words like anticipate, believe, intent, estimate, expect, and words with similar meanings. All statements in this presentation, other than historical facts, are forward-looking statements. These forward-looking statements reflect only the views of the company's management as of the date of this presentation, and are not guarantees of future performance. The company, any member of the group or any of their relevant affiliated parties or any of their respective Directors, Officers, employees, advisors, or representatives assumes no obligation and expressly disclaims any obligation or commitment to disseminate any updates or revisions to any forward-looking statements. Now I will introduce the company's management attending this results briefing. They are Mr. Zhang Shaofeng, Founder, Chairman of the Board, and Chief Executive Officer of Bairong. Mr. Kelson Chen, Executive Director and Chief Strategy Officer. Mr. Duan Ying, Co-Partner and Senior VP. Now, I invite the Founder, Chairman, and CEO of the company, Mr. Zhang Shaofeng, to introduce the 2025 business progress. Thank you, Sandy. Dear shareholders, investors, analysts, and friends from the media, good morning. Welcome to Bairong's 2025 annual results presentation. It is a great honor to report on Bairong's performance, progress, and growth, and to share with you some of our thoughts on long-term development in the agentic AI era. First, company introduction. Let me briefly introduce Bairong, the company. We are a leader in enterprise-grade AI agents. And we are the inventor of a Results as a Service business model. We were the first to product this concept globally. Through the Results as a Service or RaaS business model, we deliver silicon-based employees or agents to institutional clients. Our positioning is to design, develop, deploy, and train AI agents for enterprises. Relying on the Results Cloud for enterprises, we integrate our self-developed BR-LLM with the Baigong enterprise-grade AI agent operating system. We help clients build their own silicon-based employee systems. And we have role-based charging models. Or we could charge for services. In other words, one department can be outsourced to Bairong, and we will charge by results. And therefore, we either deliver by roles or staffing or by services. To date, we have served over 8,000 institutional clients with core customer retention rate in some key business areas reaching 98%. Representative clients include industries such as internet retail, telecommunications, education, and healthcare, with applications spanning scenarios like marketing, recommendation, operations, customer services, outbound calls, contract invoice, system parsing, credit granting, anti-fraud, claim settlement, and archive management, recruitment and training, among others. Next, performance overview. In 2025, the macro environment remained challenging. However, Bairong, leveraging solid business insights and customer foundations, continued to deepen its AI strategic transformation and achieved steady development. First, regarding revenue, our MaaS business, which is our ballast, has shown steady recovery with a year-over-year increase of 9%, surpassing the 5% growth we recorded in 2024. As you know, there were policy environment pressure, including Document No. 9. We were able to achieve 9% year-over-year growth, really demonstrating our advantages in this era. We were able to achieve increase in both the number of clients and average revenue per client. Furthermore, retention rate for MaaS key clients improved to 98%, a level that leads the enterprise service field. This reflects our clients' high recognition and continued reliance on our AI decision-making capabilities. Regarding the BaaS business or Business as a Service business, as I communicated with everyone at mid-year last year, policies like Document No. 9 and integrated filing and implementation, have varying degrees of impact on the revenue of both the financial scenario and insurance scenario. However, it is noteworthy that the total premium facilitated by the company last year increased by 20% year-over-year. First year premium, a very important indicator, facilitated increase by 25% year-over-year. Life insurance premium renewal rate remained stable above 90%, making us among the best in the industry. The pressure on revenue was mostly because of policy pressure from the regulatory environment. However, we are still improving in terms of our positioning in the market. Once the regulatory environment becomes more favorable, and as there is less competition, we will continue to grow. I know everyone is concerned about the group's overall revenue and profit performance last year, especially the significant pressure on profit. Let me try to elaborate on this. At the beginning of last year, the company signaled to the market that it would significantly enhance R&D and business investment for the full year, seizing market shares at all costs. This was noted in our annual report. If we add back investment, especially AI investment from last year, I think we can all demonstrate, we can show that the company's mature business maintains a stable profitability. So where were these AI agent or AI-related investments went to? First, talent reserves. Last year, we spent a significant amount of funds on recruiting AI talent to strengthen our algorithm, engineering, and product teams, building AI native organizational capabilities. If you followed our annual report, you will see that our R&D headcount increased by 383 year-over-year, with total R&D headcount now exceeding 1,100. This accounts for a substantial 64% of the company's total workforce. The R&D team is the long-term foundation for our growth in the AI space. The second part where we spent money was on proprietary data. We expanded the procurement and labeling of vertical data, enhanced the model training corpus, and consolidated our moat in data assets. Third, computing infrastructure. This involved expanding our IDC facilities, adapting to domestic GPUs, and achieving self-reliance in our training and inference platform to reduce long-term computing costs. Fourthly, we spent money on operational support. This covers expenses related to workplace expansion and personnel growth, ensuring the organization maintains high efficiency and synergy during a period of rapid scaling. In comparison, I think Bairong's cost effectiveness in preemptive AI investment is relatively high. Last year, our gross margin remained high at above 72%, and we maintained profitability while heavily investing in the future. This puts us at an excellent level among current AI agent companies out on the market. Moreover, these investments are both necessary and urgent for a company that has the ambition to become a leading firm in AI agents. Many people have read a report published by Citrini Research at the beginning of the year. About 10,000 words in this lengthy report, which projected that in 2 years time, or in 2028, global intelligence would scale to such an extent that agents would be widely adopted. The dawn of future is near, and the time waits no one. If we did not invest in AI, then we would have just watched, for example, the open floor development on the sideline during the Chinese New Year this year. I think for the new business we have high hopes for, I completely understand that investors' eagerness to see investments translate into financial returns. Here, though, I'd like to offer one way to look at it. In the early development stage of AI agent business, there's a significant amount of opportunities to scale up. For example, in particular, we're talking about project-based demand. In other words, we can send employees on-site to the client's place and to deliver projects through customization. This is certainly something that we can do to scale up revenue. However, we know that the last cycle of software boom in China did not end very well. If we accepted all such demand, revenue would show very attractive short-term returns. However, that would not be sustainable. We aim to build long-term sustainable RaaS cooperation with B2B clients. We will not sacrifice long-term sustainability for short-term profitability. If we went for short-term projects at the beginning of the company, we wouldn't have been where we are today. There are some listed companies in China who are doing well with those large projects. I think we are on a path where if we overcome short-term pressure, long term, we can become more sustainable and scalable. I think we look at more important leading indicators similar to what Jensen Huang tracks in his company. For example, we look at efficiency improvement achieved on the product side, number of high-quality clients acquired, and continuous output of silicon-based. In other words, we will not sacrifice our long-term sustainability for short-term profitability. On the product side, last year, we built a flagship silicon-based employee matrix covering customer experience, CX, and employee experience, EX. I think this categorization is also something that is quite common in the industry. One is for improving external service quality, and the other is for improving internal efficiency. The silicon-based marketing and service specialist which is BaiYing, is already used in over 1,000 scenarios in wealth management, telecom, communications and retail. BaiYing has achieved 90% cost savings and reducing annualized attrition from 70% to 0%. Human employees would leave, right? Attrition is high. But silicon-based employees do not leave their jobs unless you cut the power. Consultation conversion rate improved by 210%, and customer satisfaction increased from 16% to 55%. The silicon-based analyst, called BaiZhi. By connecting multiple AI agents, it helps professionals quickly achieve a closed loop process of listening, recording, summarizing, writing. Previously, an in-depth report would take maybe 10 people, 20 days, but now you only need a couple of days, 4 days, for example. There is also the silicon-based legal, business, finance, and tax expert, BaiXin. It is for employee experience that handles standardized tasks like data collection, case retrieval, document drafting, and process management. A lot of the services, for example, for overseas market expansion, location selection, among other things, that is able to help our annual client cost from CNY 3 million-CNY 5 million a year to CNY 1 million a year. The silicon-based recruitment specialist, BaiCai, driven by AI interviews and intelligence screening, shortens the recruitment conversion cycle from 28 days to 2 days, improves trial placement matching rate from 60% to 90%, and increases individual HR monthly recruitment efficiency from 5 people to 20 people. Additionally, internally, the company built the silicon-based employee home based on the Baigong AI agent operating system. This operating system manages over 200 types of internal silicon-based employee roles. Each role corresponds to the workload of multiple human employees who are carbon-based employees and can be frequently called by multiple users. For example, by finance, legal, HR, operations, among others. We are even achieving lead to cash for client development through the system. We have over 2,500 clients that are being served by our silicon-based employees, from onboarding to invoicing, to renewal, to, for example, purchase of more add-on modules, et cetera. We were able to cut the staffing on the team from 50 to only 5, plus 18 silicon-based employees. On the client side, last year, with silicon-based employees as the core, we achieved commercialization breakthroughs from zero to one across hundreds of industries, which validates the strong universality of AI agents across industry migration capabilities. Previously we were in, for example, banking sector for the most part, but now in the securities space. China Securities, for example, are using wealth AI silicon-based employees. We are also seeing banks that are purchasing our silicon-based AI service agents at CNY 5,000 a month, which help them cut cost. Then we also have a trust client that has been using our customer service AI agent. Then there is also an international investment bank that uses our telephone and WeChat session management AI agents. These agents are able to interact with their end client in very realistic scenarios. Already our services are paying for themselves for the end client. Also a state-owned joint stock bank became the first among the 6 major banks in China to sign an AI expert service framework agreement with Bairong. In telecommunications, Bairong deeply participated in the digital intelligent transformation of the telecoms industry, achieving systemic verification and scaled implementation in provinces such as Guangdong. In Guangdong, we were able to facilitate over 1,000 telecoms packages for a client in the industry. In healthcare, we are providing a mental health consulting AI agent for a renowned medical university. That university trains doctors, but the students there also need psychological consultation, and our AI agents can help them in that regard. We also have a chronic disease management agent for a medical technology company. We also are able to provide BaiCai, which offers recruitment services for blue collar recruitment processes for many large clients. For emerging car makers who are up and coming car making space, we are able to provide AI agents that help sales lead management. We are also able to provide AI contract support for a maternal and infant industry leader who is in the high-end postpartum care space. We also have a dual carbon intelligent AI agent that provides audit for environmental policy research institutes, which really verifies our ability to migrate from financial know-how to complex industrial scenarios. Actually, 90% of non-financial industries are not as complex as the requirements in the financial sector. So we have strong abilities of migration. I think for clients, their willingness to pay is high, especially versus traditional software, because AI agents can pay for themselves to a large degree. Also, I want to report our last year's R&D progress. We have increased investment to attract top AI talent, enhanced data and algorithm input, and solidified infrastructure foundation. We have achieved breakthroughs in technology and have built differentiated advantages. The company has obtained 573 patents and software copyrights. We set up a joint lab together with a leading university. We achieved in-depth integration from recruitment to engineering, R&D development among all the steps in this closed loop. In particular, we work with a leading university research institute on MoE application, especially those automated AI agents who can cover long processes and complex processes. At the application level, we have completed the key leap from usable to user-friendly. In speech model, we upgraded from the traditional three-stage architecture to an end-to-end large model. In other words, we do not go from voice to text to voice, but rather just go from voice-to-voice or speech-to-speech. This can greatly improve our service quality, and our model already supports 52 voice styles, including Cantonese, Shanghainese, and other dialects, as well as foreign languages. Decision latency could be compressed from hours to milliseconds, less than actually 200 milliseconds. Understanding accuracy exceeded 98%. Emotion recognition exceeded 95%. Interruption recognition can reach 100%. Response speed or response time now is under 40 milliseconds. Also, in the silicon-based employee onboarding and application space, we have a lifecycle management system of AI agents. Similar to how we recruit human employees, we are able to reduce the development cycle for silicon-based employees from 2 months to 2 weeks. Particularly, we are able to allow for self-evaluation of silicon-based employees before deployment, but also allow them to self-iterate after deployment. This is very impressive. At the large model level, our large models underwent 6 iterations, covering scenarios like telecom package recommendation, wealth management, product marketing, credit card installation, recruiting, screening, as well as other scenarios that I mentioned. Not just large language models. Our vision models achieve breakthroughs in financial and medical document classification and complex structure extraction. It was a breakthrough from zero to one. This opened a new path for future commercialization. At the cost efficiency level, we understand that the cost profile is very important. We actually addressed the industry reality where cost determines the ceiling by restructuring our self-developed training and inference platform, where latency was reduced by a third, resource usage saved by 50%, and throughput increased by one and a half times. We are able to use a mix of different GPUs, including domestically developed chips, and utilizing the BR Vortex inference engine, integrating computing, intelligent scheduling, and multilevel caching. Computing resource utilization increased by 30%. Our hybrid scheduling throughput also increased threefold. In terms of awards and honors, Bairong's AI capabilities are moving from industry recognition to standard setting. We were selected for Morgan Stanley's China Top 60 AI Companies and received comprehensive coverage from IDC. From industry large models, AI agent development platforms, and deep application scenarios like finance, marketing, HR, legal, we received coverage from IDC. We were consecutively listed on KPMG's Double 50 list for two years. At the national level, Baigong platform and the BR-LLM large model passed filing with the Cyberspace Administration of China. We became a leader in the standard-setting body for intelligent AI ecosystem construction group under China Academy of Information and Communications Technology. We also obtained DevOps level 3 certification. At the industry level, we were named 2025 China's Financial AI Agent Service Excellent Provider, included in IDC's PeerScape Best Practice Cases for Banking and Insurance AI Agents. We also received recognition as a trusted data space resource partner and credit risk prevention and control industry benchmark. We won awards such as Best Digital Finance Award from Xiamen International Bank and the Annual AI Interaction Innovation Award from Caijing New Media. Next, on ESG. As a benchmark practitioner for new quality productive forces, Bairong received A rating in Wind ESG assessment last year. We strengthened industry-university research cooperation. We jointly developed an AI joint laboratory with people with disabilities through our Baigong AI agent operating system. We created a full-process intelligent solution for Chongqing Disabled Persons Employment Guidance Center. In its Zhiyou AI WeChat public platform, we embedded intelligent Q&A agents based on a large language model in Baigong, effectively solving problems like employment information asymmetry, scattered training resources, and low efficiency in job matching for the disabled population. We are building a smarter, more efficient, and more precise employment channel for approximately 600,000 people with disabilities in Chongqing. In addition, we also introduced AI agents into employment service support. At an employment service center in a city in west China, we established standard workflow of intelligent outreach, information collection, policy interpretation, and follow-up steps. The project now is fully operational, and it is a model that can be replicated elsewhere. The AI agents now can go from extensive or standardized application to more targeted application, and we are able to improve efficiency. Our system processes up to close to 1,000 follow-up data points daily, with a connection rate of close to 80%, far exceeding traditional outbound calls. Information collection is also more standardized and comprehensive, and this is able to provide a solid data foundation for subsequent services. We believe the best technology is not to be admired from afar, but to make people see, understand, and trust, to truly solve pain points in everyone's work and life. Next, I want to provide a short outlook for Bairong's future. 2026 will be a critical year for the company's strategy to transition from the capacity building period to a value recognition period. As underlying technologies mature and industry applications accelerate, we'll be driven by the dual engines of scaled profitability in mature scenarios and forward-looking positioning for innovative scenarios. In this way, we're able to grow sustainably, and we can also grow for the future. We serve over 8,500 institutional clients. We've accumulated a lot of experience. We're going to take advantage of the know-how and the vertical data points that we collected over the last decade to build a competitive moat, and we will achieve industry-leading positioning in the era of agentic AI. In the new year, our key focus areas will include the following. First, industry focus areas. Last year, with the issuance of Document No. 9, I think the potential risks in the financial and insurance sectors are now all very transparent. In the foreseeable future, the likelihood of the industry going through new policy adjustments is quite low. We have many, actually, opportunities that we can explore. For example, the AI agents that can, for example, manage mid to low interest rate credit marketing, wealth management, as well as non-performing asset or loan management. This is something where there's a big demand. Additionally, we think there are some scenarios where we think there are many opportunities. First, AI coding, that's where I think PMF got achieved in the first instance. The second, I think, is a contact center. It's only after skilled AI coding. AI agents can receive calls, or conduct outbound calls, send messages on WeChat and other channels. This is what we call CC or contact center scenario. Contact center scenarios are usually covered by carbon-based or human agents. If you ask an LLM today, you will see that there are about 30 million human agents who are working in China. You have a few million agents who are receiving inbound calls, a few million who are for non-performing loan management, and we also have a few million, to say the least, that are doing marketing. The cost per seat is about CNY 100,000 a year. This is a huge market. If we're able to capture just 10% of that market, that's already a trillion-dollar opportunity. We are eating into the wages there, because we're not just selling software. We also want to be in more specialized domains. For example, legal services or let's say consultation, such as services provided by, for example, McKinsey or Roland Berger. These contracts can be a few million yuan each. Not to mention financial and tax professional services. These services can be greatly empowered by AI, because the output essentially is various documents. AI systems today can develop high quality documents. We can provide AI native financial and tax consultation platforms. For especially Chinese companies expanding overseas. If you're a Chinese company, you engage with a multinational corporation for consultation. One of such contracts can easily set you back by a few million yuan. I think Chinese companies are going global. I believe that's going to be a main theme in the next at least 10 years. Bairong needs to tap into this trend, and we want to help Chinese companies expand overseas. Through AI agents, we hope to support them as they seek financial and tax services. This system is called Baintian. Baintian will help especially SMEs that are seeking to expand overseas. We will provide specialized financial and tax consultation to these companies. So far, we are the only one in this space, we do not have any peers to compete with. Also, the other thing is, we will make it open our core capabilities such as voice interaction and intelligent decision-making to empower industries. We think these capabilities will replace the traditional HMI. For example, smartphones use user interface and it is all graphic, but the future human machine interface will be voice-based. You can talk to AI directly, and you do not necessarily have to type or click or tap. We also develop with ecosystem partners such as software vendors, distributors, and system integrators to make it even more powerful. We will also leverage the strength of capital. We jointly establish AI industry funds with top-tier capital providers such as Hillhouse, Sequoia, and Falchion for co-investment. I hope you will see our exciting news very soon. We want people to know that Bairong is willing to leave profit on the table. We not only provide AI agents, we also provide capital support so that we can attract more traditional software companies to join us on the platform. We are happy to share profit with them, 30% or 50%, just for example. We have two leading AI companies in the U.S., OpenAI and Anthropic, they are also doing this dual-track approach. There is organic business expansion and also there is capital-backed expansion. The latter scenario would make it natural for the partners to use their AI tools for AI systems. Similarly, Bairong has invested in an AI BPO company, and we are already helping them to replace human employees with silicon-based employees to help them improve efficiency. We are also helping wealth management companies and other companies for empowerment. In the future, there is also a possibility that we might acquire such companies. I think expansion is on the business side, and that is based on organic growth and also business expansion, similar to Tencent's growth story more than a decade ago, so that in the future we can build an ecosystem that revolves around the Bairong ecosystem. This is going to, we believe, be critical for Bairong's success in the long term. The ecosystem takes time and efforts to build, but we are patient, and we are more than willing to be patient there. Similarly for NVIDIA, the CUDA ecosystem took them decades to build. But CUDA now is a stronger competitive moat, even than their chips. Well, as we all know, AI native startups in the U.S., OpenAI and Anthropic. OpenAI being the most well-known and followed by Anthropic as the runner-up. OpenAI focuses on consumer sector. It wants to replace Google. For Anthropic, it targets enterprise sector. They want to empower companies with higher productivity. OpenAI's momentum and valuation once far surpassed Anthropic's valuation and momentum. However, in the past 6 months, with Anthropic's tremendous success in the U.S. enterprise AI agent market with OpenClaw, it has been very successful for Anthropic, both in terms of revenue and market valuation. It has a trend of catching up with and even surpassing OpenAI. Many top-tier analytical institutions believe that the launch of OpenClaw marks the turning point where Anthropic surpasses OpenAI. The underlying reflection is a widespread industry view. Previous generation of innovation focuses more on the consumption and distribution side. This time, this current AI transformation brings about a greater leap in production and supply. Consequently, the industry is beginning to recognize that Anthropic's growth potential exceeds OpenAI because Anthropic helps the supply side, the production side. Historically, the most significant industrial revolutions, the steam engine revolution, the electrical revolution, and the information computer revolution, were all first and foremost revolutions on the production and supply side, which later benefited the consumption and distribution side. This is also why the current AGI revolution is considered comparable to the first three revolutions. While the internet and mobile internet revolutions have not yet reached that scale because they are more focused on the consumers and consumption side. The big revolutions need to start with the supply/production side. Anthropic has already proven that enterprise-grade agents have surpassed the critical inflection point from concept to implementation, from tools to productive forces, and Bairong is positioned to become the Chinese Anthropic. We have the confidence, the wherewithal, and the faith because from day one, we have been delivering results to companies instead of just tools. For a long time, our models were not recognized as the industry benchmark or as the industry mainstream, but we have never wavered in our direction. We have been deeply involved for years and have accumulated implementable results and experience in multiple industry scenarios. Of course, we are also clearly aware that the domestic enterprise service market has its unique rhythm, and we have been accumulating know-hows in that particular rhythm. Chinese customers tend to have longer decision-making chains, longer validation cycles, and a more cautious requirement for value delivery. The willingness to pay or the habit of paying is not as strong as that in the U.S., but we have already validated our model and proven that companies are willing to pay. It may take us longer to gain verification in terms of financial returns, but this does not change our long-term optimistic view of this direction. Bairong has never had a classic software-type business since its inception. We know that companies in China are not willing to pay for the tools. They only pay for the results. So all our revenue is priced based on results. This is baked in our gene. We want to deliver results, not just tools. Leveraging this inherent DNA of delivering results rather than tools, we officially and comprehensively launched RaaS to the market in 2025. This is the inaugural year of our RaaS business, and we started, I believe, a big campaign or reform in the industry around the world. Our AI strategy conference received 400,000 people's attention and repost, which the conference on December 18 last year was a major event. It might not be delivering a big difference in the short run. However, in just 3 to 5 years' time, it will create an insurmountable competitive gap. Previously, the ecosystem of tech companies in China and the U.S. differed greatly. China focused on consumer sectors, while the U.S. emphasized the enterprise sector. After Facebook, there was no major B2C companies. In the U.S., the strength lies in B2B enterprises. But in China, the B2B companies were struggling. The revenue models of Chinese and U.S. enterprise software companies varied significantly. The Chinese B2B players in the software sectors were loss-making significantly. This time around, with our silicon-based employees, agentic AI employees, if you look at the service model and products and revenue models, I believe there's going to be a historic convergence. This presents a tremendous historic opportunity for Chinese enterprise or Chinese tech companies to overcome past challenges. Although Bairong's scale may not be the largest, we look beyond the service industries we serve, such as internet, e-commerce, telecommunication, and finance. If we just look beyond those industries and carefully analyze our product forms, which is end-to-end result delivery, and our revenue model, charging based on role compensation or transaction sharing, you will understand that the business we conduct is essentially a pure-blooded agentic economy, a concept that is considered advanced even in the United States. From day one of the company's founding to the present, our revenue from traditional tool-based software models has remained virtually zero. What we provide is, in essence, a new quality labor force for the entire human labor market. As Zhou Cai, Chairman of Alibaba, recently pointed out at a Siemens RXD conference, the global white-collar labor market is worth nearly $50 trillion, and this value is expected to be restructured or enhanced by intelligent agents. Just 1 or 2 weeks ago, Alibaba launched their new model, UCO, which focuses on silicon-based employees. This concept was first proposed by Bairong around the world. We perceive this strongly validates that Bairong's strategic path, what we receive is no longer tool software fees, but labor compensation. Fundamentally, we're the same as Anthropic. We specialize in training and dispatching silicon-based employees. We're an AI-native company, and we're going to be part and parcel of this major groundbreaking historic shift. We'll contribute a role model and a benchmark for Chinese tech companies. What type of silicon-based employees are most promising? Programmers, contact centers, CRM systems, customer agents, and also unstructured. The agents that handle unstructured data, consulting, writing reports. Third would be orchestrating complex processes and even dynamically generating workflows. OpenClaw has been such a big hit because they're able to handle complex processes without any human interference or human intervention. These are the most promising real-world cases. But personal OpenClaw cannot be used for corporate purposes because there is a lot of authorization and security issues. For Bairong, we are the first one in China to launch a large-scale, high-capacity, validated AI agent for enterprise purposes. According to iResearch report, the AI agent market size is expected to expand continuously, projected to grow from CNY 57.4 billion in 2023 to CNY 3.3 trillion in 2028. This fully demonstrates the immense market potential for enterprise-grade AI agents as they are genuine tools for unprecedentedly vast market opportunity. This window is swift, and within two years, it will become clear who possesses champion qualities. Just as Zhang Yiming enabled ByteDance to seize opportunity in personalized information distribution during the mobile internet era, ultimately establishing it as dominant force in revenue among Chinese tech companies. Bairong will go all out to seize this critical transition period. We aim to establish a leadership position in the silicon-based economy wave and create long-term sustainable value returns for our shareholders. The core management team and I have unwavering confidence in this since the company's listing. I, as the founder, have not sold a single share, which fully demonstrates our steadfast conviction in the company's business model, technological path, and future prospects. The above is my brief summary of Bairong's 2025 annual performance and outlook for the future. Thank you for taking time to attend our annual results exchange. Thank you. Thank you, Chairman Zhang, for your insightful presentation. Now I am going to give the floor to Mr. Kelson Chen, Executive Director and CSO, to introduce 2025 annual performance and financial highlights. Thank you, Sandy. Dear analysts, shareholders and investors, and friends from media, I will now introduce Bairong's 2025 annual performance. In 2025, the company proactively positioned itself ahead of the historic opportunity of the generative AI while increasing strategic investment, achieved steady operational performance and financial results. Full-year revenue reached CNY 2.92 billion, basically flat compared to the same period last year. Gross profit reached CNY 2.102 billion with gross margin remaining stable at 72%, mainly benefiting from the scalable RaaS business, which is further realizing economies of scale. Operating profit was CNY 60.38 million, net profit CNY 73.87 million, with operating margin and net margin 3% respectively. Under non-IFRS net profit reached CNY 179 million with a net margin of 6%. EBITDA reached CNY 244 million with an EBITDA margin of 8%. In a year full of challenges, Bairong successfully stabilized revenue and maintained continuous profitability, demonstrating the resilience of its business model and its market competitiveness. In 2025, BaaS business revenue declined by 5% year-over-year to CNY 1.901 billion, accounting for 65% of total revenue. Within the BaaS business, financial industry cloud revenue decreased slightly by 3% to CNY 1.371 billion, accounting for 72% of BaaS and 47% of the total revenue. Insurance industry cloud continued to respond to regulatory changes, with revenue decreasing by 10% to CNY 530 million, accounting for 28% of BaaS and 18% of total revenue. Our balanced business, MaaS business, achieved revenue growth of 9% year-on-year to CNY 1.019 billion, accounting for 35% of total revenue. The revenue structure shows a pattern of BaaS as the mainstay and MaaS as the stabilizer. Our MaaS business assists institutions in making intelligent decisions through various silicon-based employees across different roles. In 2025, within the CNY 1.019 billion MaaS revenue, core customers, those contributing annual revenue above CNY 300K, accounted for 78% of revenue. The number of core customers increased by 12 year-on-year to 223 customers. The average revenue contribution per core customer increased by 6% to CNY 3.59 million. The MaaS business maintained steady growth and continued to provide sustained cash flow to the company. Our BaaS business is based on generative AI technology and leverages self-developed silicon-based marketing service specialists to assist institutions in intelligent marketing and intelligent operations, significantly improving asset operation efficiency in wealth management, insurance, and internet technology industries. BaaS financial industry cloud mainly adopts a business outsourcing AI BPO cooperation model, charging based on the business results or scale achieved, and does not charge any fees before assisting institutional clients in generating any revenue. In 2025, as a leading player in the implementation of AIGC application, our BaaS financial industry cloud revenue decreased slightly by 3% year-on-year to CNY 1.371 billion, mainly due to the impact of the policy environment. Our insurance industry cloud provides comprehensive customer insights and accurately recommends insurance products through decision-based AI and conducts high-value policyholder operation through a silicon-carbon integration approach. In 2025, revenue from our BaaS insurance industry cloud business was CNY 530 million. From the revenue structure perspective, new policy contribution revenue was CNY 450 million, a decrease of 8% compared to CNY 487 million in the previous year. The life insurance premium persistency rate exceeded 90%, ranking at the forefront of the industry. Renewal contribution was CNY 80 million, a decrease of 19% compared to CNY 99 million in the same period last year. Total premium reached CNY 6.52 billion, up 20% compared to CNY 5.442 billion last year. This reflects our resilience in value delivery and momentum of our business stabilizing and rebounding. In 2025, our R&D expenses reached CNY 637 million, an increase of 25% compared to CNY 509 million in the last year, accounting for 22% of total revenue and up five percentage points year-on-year. The incremental investment mainly focused on this construction of AI-native capabilities, including expansion of talent, large scale procurement of data assets, independent R&D of infrastructure and operational expenditure. Sales and marketing expenses reached CNY 1.142 billion, accounting for 39% of total revenue, flat from last year. General and administrative expenses was CNY 328 million, accounting for 11% of total revenue, flat from last year. We will continue to maintain investment in core technologies and computing power to ensure long-term competitiveness while focusing on improving marketing efficiency and controlling management expenses, and while ensuring growth, further enhancing profitability and return on capital. As for cash and assets, in 2025 our cash and cash equivalents and similar financial assets amounted to CNY 3.377 billion, down by 8% compared to CNY 3.657 billion at the end of last year, mainly due to Bairong improving capital utilization efficiency. The CNY 3.377 billion includes CNY 728.26 million in cash and cash equivalents, CNY 1.76 billion in large denomination certificates of our deposits, as well as current financial assets measured at fair value through profit and loss. In addition, the company is confident in its long-term future and attaches importance to shareholder returns. In 2025, the company repurchased a total of 9.6 million Class B shares from the open market, totaling HKD 90 million. As one of the few AI companies in China that has identified a real deployment scenario for Agentic AI and achieved profitability. In 2025, the company maintained a stable revenue scale and sustained profitability. In the future, Bairong will seize strategic opportunity and continue to empower thousands of industries and business with AI. Thank you. Thank you, Carson, for your wonderful remarks. We will now move on to Q&A. If you are dialing in on the phone, feel free to press star and then 1. Press star first, followed by number 1. For those joining online, please leave your question in the form of text in the Q&A chat box. I will read out questions later. Please remember to state your name and affiliation while asking questions. We will begin the Q&A session now. Zhongtai Securities, Jiang Dan, you have the floor. Good morning, Mr. Zhang and management. This is Jiang Dan from Zhongtai Securities. Thank you for giving me the opportunity to ask this question. We see that Bairong's Baigong AI agent platform is developing very well, which reminds me of the currently popular enterprise-level AI agent giant, Anthropic, and the open source autonomous AI agent platform, OpenClaw. They are very similar, not only capable of answering questions, but also completing tasks, making decisions, and taking action with minimal user intervention. I believe that in the future, the technical barriers to building AI agents will become increasingly lower, and the deployment will become more convenient. Against this backdrop, can Bairong's vision of achieving scaled commercialization through AI agents be realized as expected? In which aspects does the company's differentiated competitive advantage lie? Thank you, Jiang Dan from Zhongtai Securities. I will give the floor to Mr. Zhang Shaofeng, CEO and Chairman of the company. Thank you for this great question. This is definitely pertinent to the core of our business. Like you said, be it open or closed source models, they are becoming more and more agentic AI capabilities. The company's understanding and recognition of agentic AI has been improving. OpenClaw from the Spring Festival this year is kind of like when DeepSeek was launched last year. It was very inspiring and exciting. I received a lot of questions from many companies, friends. They say, "Why don't I just download my own OpenClaw and use it directly?" Well, if you remember when DeepSeek came out, many people thought you can just download it and use it for yourself. That's what you thought. However, if you look at what happened last year, how many companies truly used DeepSeek by themselves by just downloading it? What you need is an agentic AI at a corporate level. Corporate agentic AI is much more demanding than an individual consumer-grade AI. They have to be very responsive, at the same time cost-effective. OpenClaw is fundamentally an open source agentic AI framework. Last week there was a problem, Meta, internally, because they were too aggressive. They used the OpenClaw framework and there was a lot of security bugs resulting in breaches of information and leakage of confidential information. So it's very hard to just download it and use it directly as a company because there are many things to take into consideration. How well do you understand your security environment? How can you manage the tools? At Bairong, we have a home for silicon-based employees, and that's a platform for managing your silicon-based agentic AI employees. We started in early 2020, and then there was no OpenClaw back then. I think OpenClaw is kind of accelerating people's understanding of the industry. It does not fundamentally change our strengths. It does not compromise our own capabilities. We have always specialized in corporate level agentic AI. We have done a lot of different OpenClaw kind of applications, and we have been promoting OpenClaw type of business to the whole industries. I think OpenClaw and the presence and popularity of it is a good sign. The things we have been talking to our customers will be proven right as now they have the context of OpenClaw because our ideas are now being validated in the mainstream channels. A lot of the big companies, they provided generic general purpose AI agents or AI models that can do a lot of things, including writing poetry, et cetera. They have all launched their Alibaba and TikTok, they have their own version of OpenClaw. Be it Tencent or DingTalk or Microsoft, they have targeted B2B sector with the general purpose AI. It's all work-related scenarios. Those work-related scenarios, be it, when it comes to medical, telecommunication, or insurance, it does not matter. It's general purpose. It could be B2B or B2C. It does not specify the specific use cases. For Bairong, we have the industry expertise and know-how. We integrate our tools into the process of our company's operation, which our agents are given a lot of authorization. I think it's a very good sign because it will enable our silicon-based employees to more easily connect. It's more of a collaborative symbiotic relationship. Our legal agent called BaiXin, they will organically integrate with DingTalk and Feishu Lark. It's going to be embedded in the workspace agentic AI or AI agents. The type of agents we develop are not the same as their AI agents. Another important factor is about access information. Which type of employee can access what information? For large tech companies, if something can be addressed by Commons works software or office software, then that's basically an upgrade of Microsoft Office. For Bairong, be it open source or closed source, or be it AI agents, it's not a direct competitive relationship. We have never thought about making inroads into their markets. Will they try to take our markets? I have two thoughts. Anthropic founder received an interview where he said AI is large as the potential lighting application. When people ask why you don't want to specialize in this industry specific application, Anthropic founder said, "We don't have enough resources. It's not possible for us to have enough high quality private domain data. That's why we only want to focus on the model level." OpenClaw is not going to fundamentally change that judgment, which we have believed in for the last two years. For Bairong, for general purpose AI agents and also corporate AI agents, we need to build Baigong, we have looked further in-house. Our Baigong is kind of a builder of corporate level OpenClaw. We will continue to use We are going to open up areas where we don't have full expertise of, we are going to do an IVV, independent agent developer. Basically, they will come in and develop their own specialized agents, and then we're going to do revenue share. Just like Tencent it was traffic plus resource allocation. We will do corporate infrastructure empowerment plus technological empowerment. In recent years, we are seeing more traditional ISV using Baigong to develop their own agents. That is my quick response. Thank you so much. We will give the floor to the next question. From Guotai Junan Securities, Mr. Yang Ling. Thank you for the management. This is Yang Ling from Guotai Junan Securities. I have a couple questions. Just now, we are very much agreeing on Mr. Chairman's judgment of the industry. My question has to do with our silicon-based employee strategy. What is our revenue expectation for silicon-based employee? Also, you mentioned different AI agents. What are the strengths of our silicon-based employees? As more companies provide AI agents, how will that affect our business going forward? Thirdly, AI cloud price increase. It has a direct impact on our cost. Will that affect the profit margin of our silicon-based employees? Thank you. Thank you, Mr. Yang Ling. Let me just quickly repeat your question. First, you want to understand the progress of our silicon-based employee, and I will give the floor to Mr. Zhang Shaofeng. Mr. Yang, when we talked about this overview, I said Bairong is kind of unique. We did not propose silicon-based employee today. It is a slogan we have been chanting since day one. We have always been delivering end-to-end solutions. Back then, there was no silicon-based employee concept. But once you break down Bairong's idea, like our MaaS or rather BaaS, H2 business for us. Basically H1, our MaaS is growing business, a mature business, and BaaS is more growing business. H3 is the incubation business growing fast. None of these businesses are tools per se. We have always been delivering results or have been delivering silicon-based employees from day one, for example, risk control and then approval and review. We help financial institutions use our tools to review applications from financial customers who can handle tens of thousands of applications. In the past, it was people handling just paper with a pen. Bairong is the first one to reform the industry. We have done the math. If you converted the cost to carbon-based, to human employees, they can handle probably dozens of cases per day. We have tens of thousands or hundreds of thousands of risk control agentic AI agents. So saving cost significantly. Also for our BaaS business, if you compare with traditional banks, when they issue credit cards, they have to outsource business to underground BD companies. They basically hand out pamphlets in office buildings, and they get CNY 200 to CNY 300 as commission, regardless of how the division of labor is organized. If you look at Bairong's peak performance, at its peak, we had 200,000 silicon-based employees using AI cold calls and WeChat chats for the financial use case. There is another third scenario, which is for telecom companies. AI is helping carriers and telecom players to renew services for customers, selling packages to customers. For example, if you are going to another province, you need to turn on roaming services. This kind of service has expanded quickly in scale to hundreds of thousands. We are seeing the hope, the light at the other end of the tunnel. Some areas are growing relatively slower, some are growing really fast at dozens of fold per year. Also for the birth care, like post-birth mother care centers, there has also been a lot of use cases. Now second question about AI agents. What is the competitive edge? Mr. Yang, you have the floor. Yes, that's my question. You are correct. The competitive edge of our own AI agents. While the large tech companies, they don't want to develop too many corporate level applications. I think you're well aware. The products they launched today are kind of like upgraded version of DingTalk, upgraded version of corporate WeChat or Feishu Lark. It's more for office scenarios. We don't want to compete with those kind of apps. Fundamentally, what we provide in corporate service or corporate agents, we are not really seeing any major corporate customers. Like using 100,000, 200,000 silicon-based employees with billions of revenue. Usually, if you look at companies with revenue in that scale, it's more traditional software companies, and the products they deliver are tools. They charge a project-based fee and then sell a license. Unlike our model, we only charge based on the workload completed and the number of positions filled by silicon-based employees. So it's definitely not comparable. We are the largest in this sector. We have always been dedicated to building silicon-based employees. So we have deep insights and know-hows. We know there's a lot of pitfalls. If you are just a traditional software company, you want to branch off into this business, you have to walk through all the pitfalls that we experienced already. For example, if you use Doubao for a dialogue interaction and then use that directly. But the result is not going to be as good as our performance. Because for Doubao, in addition to AI models, they lack infrastructure. For example, soft exchange of telecommunication. We noticed that when we do the voice agent, the simultaneous handling capacity is not enough. There is another generic layer called communication soft exchange. Companies typically do not have it. They don't know what's lacking, and they don't want to sell it or offer it to you. Even though ByteDance has it. So the large tech companies, they don't know what are the pain points of the customers. For small and medium-sized companies, they are not AI native. The first generation was decision-making AI. The second generation is generative AI. Previously, it was all insourced and internalized, but now we're able to output it. Many companies don't even realize that is what's the key point. That's to your second question. As for third question, price increase or rather the cloud cost increase, how would that affect our margin for silicon-based employees? Well, definitely that will lead to some price or cost increase. But for the cloud platforms to raise price, it could be a double-edged sword because many of our silicon-based employees' underlying models are developed by ourselves. We're not using any third-party models. So compared to other suppliers, we are at an advantage because they rely more on the generic cloud providers. Also, we sometimes use general purpose AI because users tend to ask a few very strange questions, which is considered a corner case. That's where we allocate the assignment to general models. But overall, it's a good sign because we have in-house capabilities for R&D. People are bullish on Google because they have their own TPU and it's not affected by NVIDIA. I hope that answers your question. Very clear. Thank you. That's all the questions I have. Thank you. Thank you so much, Mr. Yang Yi from Haitong International. Next question. Next, we are going to give the floor to Li Chengru from Guoyuan International. Thank you, management. I have a question about performance. We achieved double-digit growth in the first half of last year, and in the second half there was a decline. What is the reason for the decline? Second question. We mentioned AI investment in Document 9. Could you please provide a breakdown of this investment? Have we done any stress tests? Why that affected our gross margin so significantly, how will Document 9 affect our business going forward? Also revenue and profitability guidance for 2026. Thank you, Chengru. I will give the floor to Mr. Duan. Thank you for the question. Document 9 officially took effect in October 2025. As we all know, there were some policy shifts last November, which was known as Document No. 9. The shock caused by the policy in the financial industry is mainly because some of our clients took some products offline, and it takes time for their healthier, better new products to be launched. It takes time for them to get ready. This time frame, in the second half of 2025, it was a transitional period. That is why it affected our silicon-based employees' marketing scale, and that is why it hit our revenue, especially in the second half of the year. At the same time, the company's intensity of preemptive AI investment did not weaken compared to the first half. We aim to better seize the golden window in the next two years to rapidly accumulate first-mover advantages and expand the client scenarios. As CEO and CSO mentioned, if we add back these preemptive AI investments, it shows that the company's mature business line, MaaS and BaaS, still maintain stable profitability. Investors who have followed Bairong for a while will note that in 2020, affected by policy uncertainty, our revenue declined year-over-year for the first time by 10%. In 2020, our revenue declined by 10%. Back then, BaaS was experiencing an even greater decline. But in 2021, our revenue rebounded strongly with a year-over-year growth rate as high as 43%. This demonstrates, on the one hand, the abundant cash flow in our industry, and on the other, Bairong's high organizational resilience, capable of enduring cycles and supporting long-term business development. In 2026, high-risk business will be largely cleared, and we will experience a brief period of adjustments. However, if you focus on the various low-risk businesses I mentioned earlier, such as AI agents for mid to low interest rate credit marketing, wealth management, and high performing asset disposal in counter-cyclical growth scenarios, it is still possible to achieve considerable rapid growth. We are standing at a brand new starting point. Here is another way to look at it. Imagine us as the emerging AI agent disruptor, but backed by over RMB 3 billion in cash. That combination of innovation and financial strength creates substantial runway for growth. That is my quick response to your question. Thank you. Thank you, Mr. Duan. Thank you, Li Chengru, analyst from Guoyuan International's Tech sector. Next question, please. From Guotou Investment. Yang Yuran, please. Am I audible? Yes, you are audible. Sounds great. Dear management, this is Yang Yuran from SDIC International. My question is about our MaaS business. It has some barriers in terms of technology and other barriers, but when we develop new business, we still need corporate data to train the model. Against that backdrop, we might be slightly different from general purpose AI, or we are starting at the same starting line. But for the big tech companies, do they have any advantages in terms of cost and timeframe? If a lot of small companies use general purpose large models, will that create competition for us? Will that put us at a disadvantage? Thank you. Thank you, Yang Yuran. I will give the floor to Mr. Zhang Shaofeng. Well, just now I touched upon something relevant to this question. I think it is a key question. If we do not think it through, then it might have caused problems. Even for a company like Anthropic, their CEO said the biggest usage case is in application, not in platform. So thank you for the question, for me to elaborate on this issue. General purpose AI model. They are more about AGI or ASI. So those models need to know everything, even writing poetry and answering questions. But if you think about it, how many customers are truly aiming for that kind of uni or omnipotent AI agents? They do not want your corporate AI to answer how to book a restaurant or give you a guide to a tourist destination. So it is still restricted to a relatively small scope of work. So if you use general purpose AI, if you look at the number of parameters, it is typically trillion in size in terms of number of parameters. Even though there is an MoE structure, it is not going to be as lean and efficient as our dedicated industry-specific AI. So for our vertical industry AI, I want to limit it to just 10 billion parameters. Our smallest model is only 1 billion in parameter. The advantage of that is it is much cheaper. Just now, somebody asked a question about cost. Because if you do not upgrade the chips, the cost is going to be correlated with the squared total of your parameters. And for DeepSeek, yes, DeepSeek did very well, but there was a lot of hallucination. However, the tolerance for hallucination in a corporate use case is much smaller compared to B2C products. So from a general pattern perspective, that is the first initial response I want to make. We do not want to use pure general purpose AI, but if there is a good specific vertical model, that will be much more efficient. Second, a lot of the knowledge is not available on the public website. It is private domain knowledge. Let me give you a quick example. If you use DeepSeek, if you use Qwen, if you use Doubao and OpenAI, and if you ask for payment collection, it is really hard to achieve that. It is almost impossible. The models, if you say, "I am unemployed, I do not want to pay," then the AI model will just say, "Okay, you take a rest, you find a job." They are not able to collect payment for those who defaulted because they do not have any data points to learn from this. I am taking payment collection as just a simple example. Do they have data? It cannot even buy data because it has to do with user privacy. So it is called private domain data, dedicated specialized data. Without that use case, you are not able to train the model. Bairong's advantage is that we have a specialized model that is smaller in size, faster in response, lower in cost with lower hallucination. At the same time, we have accumulated a lot of different use cases with ever-growing pool of private domain data, and even if we have the same number of parameters, we are going to deliver better effects. For small companies, can they do it themselves? DeepSeek was launched last year, but how many companies have managed to build their own agents based on DeepSeek? While the requirements are getting higher, it is becoming more demanding. The overall level of requirement has been elevated. The technology might have improved this year. However, your competitors are posing a higher bar, so the deliverable will be much more demanding compared to last year. If you are able to deliver what DeepSeek is able to give it to you, it does not matter because the competitors will be using something far better than DeepSeek. So it forces you to use more advanced technologies because the competition you face. Just now I said about AI agents, the cloud. Can you just develop a corporate cloud yourself? It is very hard. Even Meta has struggles. Thank you, Mr. Zhang Shaofeng, and thank you, Yang Yuran. I will pass through the next question. Bloomberg, Robin. Hello, everyone. Thank you, management. This is Robin from Bloomberg. You mentioned that this year there will be a lot of upfront investment in AI agents. Could you help provide a detailed breakdown of this investment, including the specific composition and proportion of various expenditures such as R&D expenses, computing procurement, and talent recruitment? What is the ratio between expensing and capitalizing of AI-related investments? Are AI-related labor cost in 2025 or 2026 one-time investments, or part of that a continuous plan? What is the labor cost budget and hiring expansion plan for 2026? Thank you, Robin from Bloomberg Intelligence. This is Kelson. I will answer your question. Last year, we added a lot of upfront AI expenditure. The total R&D expense is CNY 637 million, a year-on-year increase of 25%. The first half expense was CNY 302 million, increase of 33%. Second half of the year, CNY 335 million, up 18%. If we take a broader view and include other AI-related cash expenditure, you will see that it continues to grow throughout the year. We are not just But if you take into consideration other AI-related cash expenditures such as servers and IDC data centers, as well as investment in ecosystem development and incubation, then the overall increase in investment for new business is even larger. Our largest R&D investment is in talent. In 2025, investment in AI R&D talent alone increased by more than CNY 100 million, up 24%. However, our return on talent investment is very high. As the Chairman mentioned earlier, 2026 will be a key year for the company's AI strategy to move from the capability building phase to the value realization phase. Therefore, we will continue to emphasize optimization and upgrading of AI talent. Relatively speaking, our investment in talent is highly worthwhile. Everyone can calculate Bairong's productivity. Our revenue per employee was already close to CNY 2 million in 2023, exceeded CNY 2 million in 2024. While our average salary over the past few years has been announced at CNY 500k to 600k, the return on talent investment is as high as 3 times. The return on R&D investment is also very high. Currently, most mainstream AI companies have not yet reached the stage of focusing on profitability. Bairong is one of the few companies that is truly doing AI and has real profits, and sustained real profits. Since we have decided to do AI and aim to become the Google or Amazon of agentic AI, we must have the determination to invest. Some companies in Hong Kong have suffered a loss of more than HKD 6.2 billion over the past 3 years, and some AI companies reported losses of more than HKD 3.6 billion in the first 3 quarters of last year. Anthropic requires $50 billion for a single data center, and Xiaomi plans to invest CNY 200 billion in chips, AI, and operating systems over the next 5 years. In comparison, Bairong's investment is highly cost-effective because we are more practical, and more vertical, and more results-oriented, which enables us to convert ahead of others. Thank you for your question. Thank you, Kelson. Thank you, Robin. In the interest of time, this concludes our Q&A section. Thank you all for participation, and thank you management for your responses, and thank you all for participating in our results conference for Bairong's 2025 annual performance. If you have any questions, please visit the company's IR website at ir.brgroup.com or email ir@brgroup.com. Also, you are welcome to scan the QR code to follow Bairong's digital IR assistant, Bairong. Bairong can interact with you directly and regularly release company business updates and progress. We look forward to our next meeting. Thank you.