This shift in the AI industry's narrative has come more thoroughly than the market anticipated.
In the past two years, almost all of the capital market's attention has been focused on the general model: volume parameter size, ranking, and multi-modal volume ability. It seems as if the larger the parameters, the higher the value ceiling.
But as big model capabilities are gradually being commercialized, the market's yardstick is quietly shifting — from “whether technology is brilliant or not,” to “stable implementation, and whether it can deliver measurable results.”
Among all AI implementations, voice interaction is the most easily underestimated scenario, yet closest to the core business of an enterprise. Meanwhile, a Hong Kong stock company has quietly completed the transition from a “fintech solution provider” to an “enterprise-level voice AI leader”: the number of AI customer service customers has increased 3.5 times in one year, the cumulative actual call volume has reached 2.6 billion, and its self-developed voice model has reached the top of the world's authoritative evaluation list.
This is Bairong Smart (06608). These profound fundamental changes are still not fully reflected in market pricing.
The overlooked 75% interactive blue ocean: the second growth curve is accelerating
A fact that has long been overlooked by AI narratives is that 45% of human communication behaviors in a sober state are used for listening and 30% for speech expression, for a total of 75% are phonetic interactions. Third-party research also confirmed that 76% of customers prefer voice over text when dealing with complex business, disputes, and high-risk issues; voice input is 3 times more efficient than typing.
Voice has never been “one more interactive portal”; it is the only main channel that users are willing to use in scenarios with the highest value, highest risk, and most likely to lose customers. Whoever can stably undertake business on this channel is in the position where the enterprise's AI expenditure is most intensive.
The questionnaire handed over by Bairong has already shown a clear growth outline.
In the past year, the number of the company's AICC (Intelligent Voice Customer Service) customers increased 3.5 times from the previous year's level to 140, covering 8 industries including logistics, airlines, brokerage, banks, and operators; in the first half of 2026, revenue from the new scenario was 11.26 million yuan, an increase of 195% over the previous year. On the technical side, the self-developed voice model ranked first in the world with 90.46 points in the VoiceBench Global Voice Model Comprehensive Capability Assessment, leading rivals such as NVIDIA and Ali; the cumulative actual call volume reached 2.6 billion, forming a closed loop of private data that cannot be replicated.
However, the market's perception of Bairong is still generally stuck in the old label of “digital financial services.” As we break the label and look at the business, its second growth curve has moved from “proof of concept” to the fast track of “large-scale replication.”
The real barrier: 2.6 billion real calls are a moat where parameters cannot be built
Enterprise-grade voice AI has never been as simple as “being able to understand people's words” to steadily take on real business. From listening clearly to words and understanding intentions, to multiple rounds of interaction and dealing with interruptions and language changes, to calling business systems, completing tasks in a closed loop, and writing back work orders. The first two hurdles are models, and the third level is systems and engineering. The bottom barrier is data sedimentation for real scenarios.
Bairong's answer is the VoxOne voice smart dock, with a three-tier architecture of “self-developed model + scenario product + open platform”. And the moat, which can't really be replicated, is hidden under a pedestal.
First, there is a closed loop of private data composed of 2.6 billion real conversations. The customer service scenario never requires generalized generic language, but real business conversations with demand identification, transaction results, and satisfaction tags. This data cannot be synthesized, cannot be publicly obtained, and has full user authorization. The big manufacturer's training data is larger, but it's generational data; Bairong's 2.6 billion calls are business data for vertical scenarios — this is a generational gap, not a scale gap. These data feed back model iterations through reward functions, forming a positive cycle of “landing - data - optimization - more landing”, which runs more and more steadily.
Second, the three self-developed models each carried out their duties, forming a combination barrier. Many people think that voice AI only relies on one big model; in fact, that's not the case. Bairong has built three sets of collaborative models: BR-Voice's large end-to-end multi-modal speech model is responsible for “hearing clearly”, with an end-to-end delay of about 500ms, supports full-duplex interruption, and the character error rate is as low as 2.30%; the BR-LLM-Proactive actively guided dialogue model is responsible for “questioning and meeting control”. The goal is not natural chatter, but rather to follow business processes, control compliance risks, and reduce illusion output; BR-Voice-Reasoner is responsible for “deep reasoning”, and it took over VoiceBench is number one in the world. No single parameter, only extreme optimization for enterprise customer service scenarios — this is exactly the most difficult shortcoming of the GM Big Model to make up for.
Third, engineering and cost barriers stemming from financial requirements. The real telephone network is sampled 8,000 times per second, and the information density is only half that of Internet voice, which is significantly more difficult to process; only by superimposing communication engineering capabilities such as high concurrent carrying, seamless manual transfer, recording quality inspection, and compliance control, can the AI Agent actually connect to the customer's existing system and operate stably. This set of capabilities has been refined over and over again in a scenario where the fault tolerance rate is extremely low, such as finance, and then when moving to other industries, it is almost a dimensional reduction attack. More importantly, the phone-specific small-parameter model brings extremely low inference costs, and the token output cost is significantly reduced compared to the general-purpose large model — this is the core premise of “charging according to results can also maintain gross profit.”
Misplaced competition: Voice AI that “delivers results” without competing with general-purpose big models
This is the most misjudged point in the market: Bairong and GM Voice's big model manufacturer have never replaced the competitive relationship.
The general model addresses “general speech ability”: recognition, synthesis, multi-language, and open domain dialogue, and pursues broad generalization; Bairong's solution is “enterprise-grade voice AI customer service”: identifying intentions in a real call, calling knowledge bases and business systems, completing tasks and writing back work orders in a closed loop according to standard procedures. The objective function of the two is completely different, and the track is not in one dimension at all.
Currently, players on the racetrack are roughly divided into three categories: Internet giants and general-purpose model manufacturers, traditional call centers and voice technology vendors, and vertical AI agent startups. Bairong's differentiated location is a “vertical moat” three-piece set: massive real private data + self-developed scenario-based voice model + pre-deployment team. In particular, deep into the customer site to open up the “last mile” engineering capability — no matter how strong the model is and can operate a computer, it is not equal to being able to directly implement the enterprise's business; it is only by opening up the entire process system adaptation and business connection that determines whether AI can actually enter the production process.
More importantly, Bairong directly upgraded the delivery logic to RaaS (Result as a Service), and directly included acceptance indicators in contract pricing: billing for effective tasks completed independently by AI, with no manual labor or closed-loop completion, and customer systems can be directly checked; billed according to the number of deployed silicon-based employees to agree on workload and service standards; and deeply tied to customer value according to the scale of the business contributed.
What the customer buys is not a software license, nor the number of calls, but real business results that can be tested and priced. The commercial value of this model is that revenue is directly linked to the customer's actual business value and is extremely sticky; once the customer completes the system switch, the cost of replacing suppliers is extremely high, and the partnership is naturally stable.
At present, this model has moved from finance to a broader industry.
The average daily import volume of the logistics industry is huge, the manual connection rate plummeted during peak periods, and the pain points are extremely rigid. In this field, Bairong has achieved full link scenario coverage. Benchmark customers have entered the production and implementation stage, and industry replication is progressing.
The direction of finance is also picking up speed. Zhitong Finance learned that the company has signed contracts with more than 10 institutions. The number of silicon-based employees at a leading brokerage firm increased from about 30 to about 300 at the beginning of the year. The scenario extends from basic customer service to value-added links such as financial management and investment education.
A triple leap in the market that has yet to be priced: the long-term value of two-wheel drive
Taken together, Bairong is undergoing three fundamental changes: the first is a shift in business boundaries. From focusing on finance and credit, it expanded to 8 industries, and achieved substantial breakthroughs in manpower intensive and rigid pain points in logistics, brokerage, etc., and the ceiling for growth was completely opened. The second is a shift in business models. From software/project-based billing to RaaS charging based on results, revenue quality and customer stickiness improved simultaneously, and upgraded from “selling tools” to “delivering value.” The third is a shift in technological identity. From an “AI application provider” to a model manufacturer with a self-developed voice model base and topped the international authoritative list, the technology moat far exceeded market expectations.
If you use a two-wheel drive framework to look at Bairong's long-term value, the logic will be more clear. The first round is the second growth curve for AICC's main business. The path is very clear: benchmarking first, peer replication. Voice understanding, compliance control, and engineering capabilities refined in the financial scenario can be directly transferred. Entering a new industry only requires supplementing the industry knowledge base and business rules. The standard scenario can complete the first round of verification in a few weeks, and marginal replication costs continue to decrease. Subsequent growth was driven by the triple resonance of an increase in the number of customers, an increase in the number of silicon-based employees in a single customer, and the upgrading of the billing model to RaaS. The second round is a collaborative engine for AI industry investment. On the one hand, it promotes human-robot collaboration through investment and “AI+BPO” to directly expand the implementation scenario of AI capabilities; on the other hand, it cooperates with CICC Capital to establish a 500 million yuan AI industry fund to invest in upstream and downstream AICC computing power, hardware, and technology companies to directly transform investment behavior into the main business scenario entry and technology supply. This “investment - collaboration - feeding back the main business” link is also currently not fully priced by the market.
From an investor's perspective, following up on the implementation of the company's value can be tracked from several core dimensions: looking at the revenue share and scale of the RaaS model at the commercial level to verify the degree of success of the payment model; looking at the customer development level, looking at the growth in total AICC customers and the increase in single customer value to observe the overall speed of replication across industries; the industry penetration level focuses on the replication pace of benchmark scenarios such as logistics, brokerage, etc., and the implementation progress of large-scale industries such as airlines and banks; at the same time, it is also possible to track the iteration of technology-side models and the synergy of industry-side investment.
epilogue
Looking back at this round of value migration in the AI industry, the rules are always clear: in the first stage, the market pays for the model's imagination; in the second stage, the market sets the definitive price for implementation.
Even the most powerful generic model must eventually enter a real business scenario; no matter how strong the ability to interact, it must eventually deliver measurable commercial results. As AI moves from “answering questions” to “delivering results,” the closer it is to the core business of the enterprise and the more able it is to pass through closed-loop value, the greater the long-term value.
Bairong Intelligence holds the data barriers built up by 2.6 billion real business conversations. It has the ability to implement 8 industries, and is a closed loop of complete delivery from dialogue to results. When the market finally turned its gaze from the hustle and bustle of GM's big model to the actual implementation and real growth of the vertical scene, it was probably just beginning to revalue Bairong Intelligence.