Scenario delivery decides the second half of Voice AI; Bairong Intelligent (06608) secures its position to build long-term value.

Title context: Scenario delivery decides the second half of Voice AI; Bairong Intelligent (06608) secures its position to build long-term value. Text: Voice large models are turning "hearing clearly and speaking naturally" into a general-purpose capability. As metrics such as recognition accuracy, synthesis human-likeness, and response latency gradually converge, industry consensus is becoming increasingly clear: the first half of Voice AI was about competing on models, while the second half is about competing on delivery. On October 27, 2026, Bairong Intelligent (06608) held the "Bairong Voice" brand and product launch event in Shanghai, unveiling a Voice AI product system built on the unified VoxOne foundation. This launch was far more than a product update it was more like a vivid sample of the industry entering its second half: when models are available to everyone, what exactly is the real moat? The truth of enterprise-level deployment: hearing clearly is easy, getting things done is hard The real difficulty has never been the model itself, but the real business frontline. Voice and text are two completely different logics: a single sentence carries both semantics and emotion, with pauses, tone, and rhythm all conveying subtext; traditional calls use narrowband sampling, with far less acoustic information than internet audio, so directly applying general-purpose models leads to capability degradation; more critically, voice interaction is continuously dynamic users interrupt, change their words, carry emotion, and may even demand a transfer to a human agent at any time. Bairong summarizes this into three hurdles: understanding needs, communicating naturally, and getting business done. The first two test model performance, while the third is the true watershed. In consumer-grade scenarios, an interaction failure is no big deal just try again. In enterprise contact scenarios, a problem may mean a customer complaint. This is also the core reason why general-purpose solutions repeatedly fail to adapt in enterprise scenarios. VoxOne foundation + RaaS model: delivery is the core barrier Bairong's solution is to converge capabilities onto a unified foundation and turn delivery into a standardized system. At the foundation level, VoxOne is positioned as "enterprise-grade production-ready Voice AI," equipped with a self-developed dual-model matrix, with end-to-end response latency controlled within 500 milliseconds and a word error rate as low as 2.30% in public evaluations. The more core barrier is data the company has accumulated 2.6 billion real business conversations, which cannot be synthesized or publicly obtained, making them the scarcest fuel for vertical model iteration. On the product side, three lines extend from the foundation: Baifu silicon-based customer service covers the needs of large, medium, and small customers; Baiying focuses on intelligent outbound marketing; and Baiyu opens interaction capabilities to the terminal ecosystem. All product lines adopt RaaS "Result-as-a-Service" pricing: charging by successful work orders, deployment scale, or business conversion revenue sharing, with fees directly tied to delivery results. What truly sets them apart is deployment capability. Bairong has configured a dedicated team of forward-deployed engineers to go deep into customer sites to connect systems and streamline processes, transforming manual operations into AI workflows the so-called "first 99 kilometers." Only when foundational work such as system integration, process adaptation, and compliance control is done solidly does the last mile of AI capability become meaningful. In the first half of 2026, the company's AICC revenue reached RMB166 million, up 52% year on year, with revenue from new scenarios such as logistics and securities firms surging 195% year on year. In the logistics scenario, the daily processing volume of silicon-based customer service climbed from fewer than 1,000 calls at launch to 15,000 calls, while the scale of silicon-based employees at a leading securities firm expanded from 30 at the beginning of the year to 300 customers actively expanding with real money is the hardest proof of deployment capability. It should be made clear that Bairong has never competed head-on with general-purpose large models: general-purpose models solve "general hearing and speaking capabilities," while Bairong solves "getting things done in enterprise scenarios" the two are a division of labor, not a replacement. It does not sell AI tools to customers; rather, it takes over the task flows originally handled by human agents, allowing humans to focus on complex services that require more warmth. From product to industry: the long-term game of AI Roll-up Competition in the second half is destined not to remain at the product level, but ultimately to move toward industrial integration. In North America, AI Roll-up has long been a trend: first acquire customers and workflows through capital integration, then use AI to empower processes for quality and efficiency improvement, and ultimately realize value through cost and quality advantages. The underlying logic is straightforward: annual employment compensation in the United States is dozens of times software spending, and what makes money has never been software itself, but the efficiency dividend from embedding AI into real operations. Bairong has already quietly laid out two moves. The first is AI+BPO collaboration, working with Zhixing Heli to promote human-machine collaborative services, handing standardized tasks to AI while humans take on complex matters, directly amplifying the deployment scenarios of AI capabilities. The second is an industrial fund layout, jointly establishing a RMB500 million AI industrial fund with CICC Capital, focusing on computing power, hardware, and technology companies upstream and downstream of AICC. Unlike ordinary financial investment, this fund is always anchored to the main business; what it seeks is not short-term returns, but upstream assets that can be plugged into its own delivery chain. Zhang Shaofeng, founder and CEO of Bairong Intelligent, once proposed the path of "validate first, then replicate, then scale," and this launch event was the complete presentation of that path. At present, the logistics and securities firm scenarios have already run through replicable templates. Next, the replication speed in large-volume tracks such as airlines, banks, and telecom operators will directly determine the final quality of this round of positioning. Yet the market's perception of Bairong mostly remains stuck in the impression of traditional fintech, and has not yet fully priced this high-growth second curve. When the Voice AI arena shifts from model parameters to industrial deployment, the players who truly understand scenarios, can deliver, and know how to integrate will ultimately obtain the ticket to the second half.