The big voice model is turning “hearing and speaking clearly” into a common ability. As indicators such as recognition accuracy, synthetic anthropomorphism, and response time gradually leveled off, the industry's consensus became more and more clear: Voice AI's first-half volume model, second-half delivery.
The Zhitong Finance App learned that on October 27, 2026, Bairong Intelligence (06608) held a “Bairong Sound” brand and product launch in Shanghai to launch the Voice AI product system with VoxOne as a unified base. This press conference was far more than one new product — it was more like a living sample of the industry entering the second half: when models are available to everyone, what is the real moat?
The truth about enterprise-level implementation: easy to hear, difficult to accomplish
The real challenge was never in the model itself, but in the real business scene. Speech and text are two completely different sets of logic: a sentence carries semantics and emotions at the same time, and pauses, tone, and rhythm all convey subtext; traditional calls are narrowband sampling, with far less acoustic information than Internet audio, and the ability to be lost when applied directly; more importantly, voice interaction is continuous and dynamic — users can interrupt, change their language, bring emotions, and even request manual changes at any time.
Bairong summarized this into three levels: understanding needs, communicating naturally, and starting a business. The first two levels tested the model's performance; the third level was the real watershed. If the interaction in the consumer-grade scenario fails, it is probably a customer complaint if there is a problem with the enterprise communication scenario. This is also the core reason for the repeated disagreement of the General Solution in the enterprise scenario.
VoxOne dock + RaaS model: delivery is the core barrier
Bairong's solution is to consolidate capabilities to a unified base and make delivery into a standardized system. At the base level, VoxOne positions itself as “Voice AI that can be used in enterprise-level production”, equipped with a self-developed dual-model matrix. The end-to-end response delay is controlled within 500 ms, and the error rate for public evaluation words is as low as 2.30%. The more core barrier is data — the company has accumulated 2.6 billion copies of real business conversations, which cannot be synthesized or publicly available, and is the scarcest fuel for iterating on vertical models.
On the product side, three lines extend above the base: Baiying's silicon-based customer service covers the needs of large, medium, and medium customers. Baiying focuses on intelligent outbound marketing, and Baiying opens interactive capabilities to the terminal ecosystem. The entire product line uses RaaS “results as a service” pricing: profits are distributed based on successful work orders, deployment scale, or business transformation, and fees are directly tied to delivery results.
What really opens the gap is the ability to land. Bairong has set up a dedicated team of cutting-edge deployment engineers to go deep into the customer site to open up systems, sort out processes, and transform manual work into an AI workflow, also known as the “first 99 kilometers” — only if the basic tasks of system docking, process adaptation, and compliance control are solid, the last mile AI capability is meaningful.
In the first half of 2026, the company's AICC revenue was 166 million yuan, up 52% year on year. Among them, revenue from new scenarios such as logistics and brokerage increased 195% year over year. In the logistics scenario, the daily processing capacity of silicon-based customer service climbed from less than 1,000 to 15,000 at the beginning of the launch, and the number of silicon-based employees at leading brokerage firms expanded from 30 to 300 at the beginning of the year — customers actively used real money to expand capacity, which is the hardest proof of implementation ability.
What needs to be clarified is that Bai Rong has never competed head-on with GM's big model: the general model solves the “ability to hear and hear”, and Bairong solves “the enterprise scenario to get things done”. The two are a division of labor rather than a replacement. Instead of selling AI tools to customers, it takes over the task process from the original manual seat, so that humans can focus on complex services that require more temperature.
From product to industry: the long-term game of AI roll-up
Competition in the second half is bound not to stop at the product level; it will eventually lead to industrial integration. In North America, AI roll-up has long been a trend: first to acquire customers and workflows through capital integration, then to use AI to enable processes to improve quality and efficiency, and ultimately rely on cost and quality advantages to deliver value. The underlying logic is straightforward: America's annual employment compensation is tens of times larger than software spending. What makes money is never the software itself, but the efficiency dividends after embedding AI into actual operation.
Bai Rong has quietly rolled out two steps of chess. The first step is AI+BPO collaboration, working together to promote human-robot collaboration services, hand over standardized tasks to AI, manually undertake complex matters, and directly amplify the implementation scenarios of AI capabilities. The second step is to lay out industrial funds and establish a 500 million yuan AI industry fund with CICC Capital to focus on upstream and downstream computing power, hardware and technology companies. Unlike ordinary financial investments, the anchor of this fund has always revolved around the main business. It does not want short-term returns, but upstream assets that can be incorporated into its own delivery chain.
Zhang Shaofeng, founder and CEO of Bairong Intelligence, once proposed the path of “verify first, then copy, then magnify”. This press conference is a complete presentation of this path. At present, the logistics and brokerage scenarios have run through replicable models. Next, the replication speed of large-scale racetracks such as airlines, banks, and operators will directly determine the final outcome of this round of cards. However, most of the market's perception of Bairong is still limited to the impression of traditional fintech, and the second curve of rapid growth has not yet been fully priced.
When Voice AI's playing field shifts from model parameters to industrial implementation, players who really understand the scenario, can deliver, and can integrate will eventually get tickets to the second half.