Bairong Intelligent Releases Voice AI Brand "Bairong Shengdong" and Voice Foundation VoxOne

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Recently, Bairong Intelligent released its unified Voice AI brand "Bairong Shengdong" and its unified voice intelligence foundation "VoxOne", and simultaneously launched three product lines: Baifu silicon-based customer service, Baiying intelligent marketing, and Baiyu voice interaction open platform. VoxOne is positioned as an "enterprise-grade, production-ready voice intelligence foundation", relying on its self-developed BR-Voice series models to provide voice capabilities for scenarios such as intelligent contact (AICC) and intelligent terminals.

Conversational AI is now ushering in broad market space. MarketsandMarkets predicts that the global conversational AI market will grow from USD 17.05 billion in 2025 to USD 49.8 billion in 2031, with a compound annual growth rate of 19.6%, among which enterprise services are an important application direction.

Behind the market expansion, the industry's criteria for evaluating AI have also become more focused—understanding needs and advancing tasks have become the core of product evaluation. The "production-ready" emphasis of VoxOne is precisely in line with this industry trend. As agents begin to take on specific work, voice is expected to become an important entry point for collaboration between users and enterprise agents, and its value will also extend along the dialogue chain to business execution and result delivery.

Crossing the threshold of business systems

In Bairong Intelligent's product system, VoxOne provides a unified voice intelligence foundation, while Baifu, Baiying, and Baiyu respectively target enterprise customer service, intelligent marketing, and the voice interaction open ecosystem. Around the implementation of enterprise-grade Voice Agents, Baigong supports the construction, testing, and operation of agents, while the Results Cloud provides support for operation management and result delivery, driving the connection of voice capabilities with enterprise knowledge, tools, and business processes.

The entire set of capabilities above is meant to solve exactly how voice agents can move from a "reception entry point" to an "execution unit within job processes". Taking customer service as an example, an agent can identify user issues, query service status, verify information, and feed back results. When an issue exceeds authority or involves complex judgment, it is then handed over to a human. In marketing scenarios, it can also handle external communication, record needs, and follow up subsequently, effectively advancing business.

Bairong Intelligent calls enterprise-grade agents that can enter job roles, call systems, and take on tasks "silicon-based employees". This positioning refines the application of AI to specific job roles: interacting around work goals and testing technical capabilities through task execution. The competition in enterprise-grade Voice AI thus extends to the collaborative capabilities among models, tools, systems, and processes.

Real conversations test more than recognition

In the field of voice interaction, recognition accuracy, synthesized voice timbre, and response speed are already basic requirements. What matters more are the "discrete" needs that arise in real business scenarios. For example, users may have accents, their answers may contain repeated pauses, or they may even change their statements midway. In such cases, the system needs to judge and understand the user's true demands.

Previously, a logistics customer service conversation disclosed by Bairong Intelligent presented a typical scene of such non-standard communication. An elderly person contacted logistics customer service because several pairs of socks were missing from her shipment. She could not immediately say clearly which platform the order came from, and even contradicted her own information. The voice agent sorted out the correct clues through multiple rounds of communication and guided her to the corresponding after-sales channel.

The difficulty in this conversation lies in the fact that the user's needs did not appear as complete, clear instructions. The system needed to follow the user's expression to confirm information step by step and then provide the next step of guidance. This can be said to be a challenge to the real service level of voice agents.

Accordingly, the voice technology architecture has also been raised to a higher level of requirements. The traditional cascaded approach of "automatic speech recognition (ASR)—large model processing (LLM)—text-to-speech (TTS)" connects different capability modules in series. Processing and information transfer between modules may increase latency and may also make it difficult to fully preserve paralinguistic information such as tone and pauses. In scenarios requiring real-time interruption and multi-turn communication, the system faces a greater test and needs to understand and respond promptly on the basis of hearing clearly.

Bairong Intelligent's BR-Voice adopts an end-to-end voice interaction approach and works with the Vortex streaming inference engine to achieve "listening, parsing, and outputting simultaneously". Its end-to-end response latency is within 500 milliseconds, overall response speed is more than three times faster than traditional solutions, and inference cost is reduced by 30%.

These capabilities jointly point to the practical requirements in enterprise services: when users supplement information, the conversation can continue; when needs change, the system can adjust; when communication lasts for a long time, the task can still proceed stably. That is, when voice agents enter the production environment, every round of interaction can precisely serve need understanding and task completion.

Using "delivering results" as the measure

In the past, voice services often used metrics such as connection rate, response speed, and call duration to measure operational performance. As agents begin to take on tasks, enterprises also need to pay attention to whether user issues are effectively handled, whether repeated communication is reduced, whether processes meet requirements, and whether human intervention can be taken over in a timely manner when needed. This is the core standard pointed to by VoxOne's "production-ready": allowing voice interaction to enter business processes and testing the value of AI through task completion and actual service results.

The products released by Bairong Intelligent this time are priced on a RaaS (Results as a Service) basis. Charges can be based on successfully completed work orders or the number of valid calls, the number of actually deployed silicon-based employees, or revenue sharing based on the scale of business facilitated.

The industry also has high expectations for this direction. Gartner predicts that by 2029, Agentic AI is expected to autonomously resolve 80% of common customer service issues without human intervention and bring about a 30% reduction in operating costs. This forecast shows the development space for agents to move from assisted responses to autonomously handling business.

Bairong Intelligent's interim report shows that in the first half of 2026, the company's AICC business revenue was RMB 166 million, a year-on-year increase of 52%; revenue from new scenarios such as logistics, securities firms, banks, telecom operators, and auto insurance increased by 195% year on year.

From a broader industrial perspective, the entry of Voice AI into enterprise service processes will drive the value of voice technology to extend from interaction experience to production efficiency. For Bairong Intelligent, as related capabilities continue to be applied to high-frequency scenarios such as customer service and marketing, the value of enterprise-grade AI will be gradually realized through need understanding, process advancement, and result delivery, becoming a sustainably operating productivity force in the enterprise service system, while also driving the company's own business to expand into more industries, forming a positive cycle in which technological iteration, scenario replication, and business growth reinforce one another. news_keyword_pub,stock, iframe below main text iframe below main text General statement General statement QR code at end start QR code at end start

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