On March 26, 2026, BAIRONG-W (06608) delivered a financial report that signifies a pivotal shift for the AI industry from "technology validation" to "commercial realization." At a time when large AI models are moving beyond a focus on parameter counts to a competition based on practical applications, BAIRONG-W's report—characterized by stable revenue, high gross margins, and a strategic upgrade—sends a clear signal to the market: this AI-native company is transitioning from a "capability-building phase" into a "value-realization phase."

**Strategic Resilience and Proactive Adjustment Amid High Margins** Financially, BAIRONG-W demonstrated strong operational resilience in 2025. The company achieved annual revenue of RMB 2.92 billion, remaining largely flat compared to the previous year. Gross profit reached RMB 2.102 billion, with the gross margin maintaining a high level of 72%. Although net profit was under pressure due to significant strategic investments, the company still managed to maintain a positive operating profit against an industry backdrop of widespread cost compression. This data indicates that, despite external volatility, the company's core business foundation remains solid, and its high-margin model supporting long-term profitability remains intact.

A deeper analysis of the revenue structure reveals a profound strategic upgrade underway at BAIRONG-W. The substantial investments that seemingly weighed on current profits are, in fact, the "admission ticket" paid for this transformation. During the reporting period, MaaS (Model-as-a-Service) business revenue grew 9% year-on-year to RMB 1.019 billion, emerging as a new highlight in the revenue mix. Conversely, revenue from the traditional BaaS (Business-as-a-Service) segment declined 5% to RMB 1.901 billion. This "one up, one down" dynamic reflects not a decline in operational capability but an active shift in strategic focus. Against the backdrop of short-term impacts from financial regulatory policies on traditional loan facilitation services, BAIRONG-W is accelerating its transition towards higher-value AI Agent services applicable across broader scenarios, rather than clinging to its established domains.

More critically, the company's core customer retention rate reached a historic high of 98%. This figure strongly suggests that BAIRONG-W's relationship with its clients has evolved beyond simple "tool transactions" to become deeply embedded in their business processes, positioning it as an indispensable growth partner. In the current climate of macroeconomic fluctuations, this high level of stickiness signifies not only revenue certainty but also the enduring viability of its AI solutions through economic cycles.

**From "Single-Point Breakthrough" to "Cross-Industry Application"** The greatest challenge for an excellent AI product is not achieving excellence in a single scenario but enabling scalable replication across hundreds of industries. In 2025, BAIRONG-W's AI Agent technology demonstrated remarkable "cross-boundary" capabilities—breaking down industry barriers to achieve large-scale implementation across diverse scenarios. This validation of commercialization holds greater strategic value than short-term profit growth.

In the blue-collar recruitment sector, the case of Yupao Zhipin is highly representative. Blue-collar recruitment has traditionally been labor-intensive, characterized by high frequency, large volume, and inefficiency. Faced with hundreds of millions of job seekers and vast amounts of recruitment information, traditional manual outbound call teams are overwhelmed. BAIRONG-W integrated its VoiceAgent into the Yupao Zhipin platform, creating a tireless "silicon-based recruiter." This "silicon-based employee" operates with extreme precision: when massive enterprise data flows in, it first conducts preliminary screening to identify genuine hiring needs. It then uses the VoiceAgent for job-matching outbound calls. Unlike traditional robotic voices, this "employee" possesses ultra-low latency (below 30-40 milliseconds) and high-precision semantic recognition capabilities (up to 99%). During calls, it can converse fluently like a human, analyzing the job seeker's speech rate and emotion in real-time, and can even accurately detect negative intentions like "I don't want to be disturbed," automatically logging this to avoid follow-up contact. Only after confirming a clear cooperative intent and labeling the lead does it transfer the contact to a human team. This "silicon-based screening + human follow-up" model has enabled Yupao Zhipin to handle up to 100 million daily call interactions while significantly improving the responsiveness and satisfaction of its outbound services.

In the fiercely competitive automotive industry, BAIRONG-W's Agent plays the role of a "super salesperson." Facing price wars between new automakers and traditional OEMs, improving lead conversion rates has become a critical survival issue. A leading automotive dealer group introduced BAIRONG's "silicon-based customer service specialist," which operates in far more complex scenarios than standard customer service. The system first builds a customer profile based on the car owner's historical behavior. Upon identifying a potential need, the "silicon-based employee" proactively initiates voice communication. It can handle simple appointments and, during the conversation, assess customer intent in real-time. For instance, when a customer inquires about model specifications or preferential policies, leveraging a comprehensive car insurance knowledge base and large model reasoning capabilities, it can provide accurate answers instantly, even handling interruptions and follow-up questions. Once a clear purchase intention is identified, the system immediately pushes the high-value lead to a human sales consultant. This process helped the dealer group increase its customer reach rate to 96% and boost its sales lead conversion rate to 45%, effectively transforming "dormant data" into tangible revenue.

BAIRONG-W's breakthrough in the healthcare sector is even more notable. Addressing the shortage of professional psychological counselors, BAIRONG-W developed a mental health intelligent agent that offers private, responsive initial intervention and emotional companionship, effectively alleviating societal psychological pressure. Utilizing the powerful interpretive capabilities of large models, the AI can quickly analyze complex genetic testing reports and chronic disease data, providing decision support for doctors and personalized health management plans for patients. These applications not only demonstrate technological advancement but also reflect the humanistic aspect of technology. They prove BAIRONG-W's secure and compliant handling of highly sensitive data, successfully unlocking the potential of the trillion-yuan healthcare market.

Furthermore, in the telecommunications sector, BAIRONG-W achieved systematic deployment in key provinces like Guangdong, Shanghai, and Sichuan, with "silicon-based employees" deeply integrated into core processes for network operations and marketing. In emerging scenarios, the company successfully implemented the industry's first AI contract review and generation system at Aidigong, significantly shortening business cycles. These successful, fragmented applications collectively form a picture of "Hundred-Worker Intelligent Agents" capable of rapid adaptation and immediate effectiveness.

**Building an Enterprise-Grade Intelligent Agent Infrastructure** The capability leap demonstrated by BAIRONG-W in 2025 is reminiscent of the global AI benchmark firm, Anthropic. The market perceives Anthropic's true value not in creating a viral application, but in validating a key logic: the ultimate contest in enterprise AI will not be determined by narrative power or parameter scale, but by the ability to deeply integrate models, products, and platforms to build a trustworthy, sustainably operational system capability. BAIRONG-W is currently following this very path.

BAIRONG-W does not simply package general-purpose models into various scenario-specific applications. Instead, it focuses on different job roles and real business processes, continuously embedding industry expert experience into the silicon-based employees and their underlying models. This drives the evolution of AI from "models that can answer questions" into a fully deployable, stable, continuously optimizable, and massively deployable enterprise-grade production system.

The construction of this system capability is ultimately reflected in three key dimensions: the ability to define standards, win public benchmarks, and support large-scale deployment. * In defining standards, BAIRONG-W has upgraded its VoiceAgent to an end-to-end large model architecture, achieving sub-200ms response times and over 98% comprehension accuracy. It also introduced the CoDeTT evaluation benchmark, elevating the assessment of voice interaction from "whether the action is correct" to "whether the decision intent is reasonable." * Regarding winning public benchmarks, BAIRONG-W's proposed CLAR method reduced the proper noun recognition error rate by approximately 80% compared to the baseline. In OCR and multimodal document understanding, its performance in some vertical tasks has surpassed that of Claude Opus 4.5. * For large-scale deployment, its smaller 2B-parameter model, compared to general-purpose large models, achieved a 42%–53% reduction in error rates, while inference costs are only a fraction. Combined with a 30% improvement in computational efficiency from the BR Vortex architecture, the development cycle for a single silicon-based employee has been compressed from 2 months to 2 weeks.

More importantly, these capabilities have been consistently validated by external institutions: BAIRONG-W was included by IDC Market Glance across six dimensions, selected for the IDC PeerScape reports on Banking and Insurance Asset Management, and participated in relevant research and standard-setting processes by the Ministry of Industry and Information Technology (MIIT) and the China Academy of Information and Communications Technology (CAICT).

BAIRONG-W's true barrier has never been the breadth of scenario coverage, but its proven possession of a rare systemic capability—the ability to define industry standards, consistently outperform in public benchmarks, and ultimately implement all this as a scalable enterprise-grade intelligent agent infrastructure.

**RaaS Model Ushers in a New Era of "Delivering Results"** BAIRONG-W's strategic moves in 2025 also reflect a profound evolution in its business model. The company is progressively transitioning from traditional SaaS (Software-as-a-Service) to RaaS (Result-as-a-Service). The essence of this shift is an upgrade of the provider's role from "tool vendor" to "business partner," marking the formal entry of China's industrial AI into a new era of "pay-for-performance."

Under the traditional SaaS model, vendors deliver accounts and functionalities; after purchase, clients must figure out how to improve efficiency on their own, and the final results are often difficult to quantify. BAIRONG-W's RaaS model, however, directly anchors payment to the client's business outcomes—whether it's the sales lead conversion rate, the number of days reduced in the recruitment cycle, or the accuracy rate of contract review—all become the basis for billing. This "built-in pricing and sharing mechanism" creates a deep community of interest between BAIRONG-W and its clients: BAIRONG-W earns revenue only when the client achieves tangible "results."

Supporting this model is its new "Results Cloud" platform. Through a three-layer architecture of "BaiJi" (computing power and model foundation), "BaiGong" (intelligent agent operating system), and "BaiHui" (application store), the platform packages complex AI capabilities into standardized "silicon-based employees." These digital laborers are no longer simple chatbots but are role-based entities with perception, decision-making, and execution capabilities. They can be managed and evaluated like human employees, and even achieve "evolution through operation" via "reflective learning." This innovation of making AI capabilities "role-based, piece-rate based, and compensable" fundamentally addresses the pain points enterprises face when applying AI: difficulty in implementation and slow time-to-value.

**From "Endogenous Growth" to "Ecosystem Prosperity"** In 2025, deemed the "first year of value realization" for the AI industry, BAIRONG-W is not solely relying on endogenous growth but is also accelerating the construction of an AI industry ecosystem through capital empowerment. The company, in partnership with top-tier capital firms like Hillhouse, Sequoia, and Gaocheng, established an AI industry fund focused on investing in and incubating AI-native enterprises. It has already invested in ecosystem partners such as marketing/customer service BPOs and wealth management software, forming a dual-engine growth model of "internal growth + external expansion."

This capital strategy not only brings new growth avenues for BAIRONG-W but, more importantly, is building an industry ecosystem centered around "silicon-based employees." Within this ecosystem, BAIRONG-W provides the underlying AI Infrastructure and AgentOS, while ecosystem partners develop AI applications for specific scenarios on top of it, collectively driving the paradigm shift of AI from a "tool" to a "productive force."

**Outlook** Looking forward from the vantage point of 2025, BAIRONG-W's development path is becoming increasingly clear. In the short term, as the adjustment of its loan facilitation business concludes and the substantial prior AI investments begin to yield marginal benefits, the company's profit side is expected to see significant restorative growth in 2026. The previous strategic "squat" was to prepare for a stronger "jump."

In the long term, BAIRONG-W's growth ceiling has been substantially raised. Expanding from the singular vertical of finance into telecommunications, healthcare, retail, manufacturing, government services, and hundreds of other industries, its potential market size has multiplied, even by tens of times. The rich know-how, high-quality industry data flywheel, and vast "silicon-based employee" cluster accumulated during this cross-industry deployment process constitute a moat that is difficult to replicate.

For investors, what they see now may not be a trough in profits, but potentially the optimal entry point on the eve of the explosion of a trillion-yuan AI Agent market.