Zhuiyi: AI as a working team

The unit of work becomes a team of agents

During the visit to Zhuiyi Technology in Shenzhen on October 19, 2026, the presentation placed an organization of agents at the center of its proposition. It used the image of a team of specialists: different functions, coordinated work and a shared business objective. Customer engagement, employee assistance, management analysis and operation of the AI system itself were presented as parts of that organization.

This framing changes the conversation about AI adoption. When a task crosses channels, records and people, the quality of a response is only part of the work. The context received, permitted decisions, handoffs between owners and tracking of the outcome also matter. The visit brought these questions into the everyday operation of a business.

Zhuiyi has its roots in natural language processing and conversational AI. Its company timeline records its founding in March 2016. That history helps frame the presentation: a business built around language and customer service that now describes its offering in terms of agents and processes.

Specialization requires knowledge of business rules

The presentation compared general-purpose tools for composing applications with agents prepared for tasks within a particular industry. Zhuiyi argued for the latter approach in settings such as finance and government, where service delivery depends on rules, domain knowledge and responsibility for what is executed.

The distinction is useful for a technology buyer. A flexible tool leaves more configuration and integration decisions with the customer. A specialized solution incorporates prior choices that also need examination: which situations it covers, how it receives updates and when it asks for help. Specialization may reduce implementation effort, but its suitability depends on each customer’s actual process.

The autonomy scale shown during the talk was part of the company’s explanatory framework. Its central point was a progression from fixed tasks to coordination around goals. That framework helps explain the proposition presented; it does not establish a universal classification of agents on the market.

Four functions for organizing the work

AIForce was described as a platform that coordinates four groups of agents across the customer relationship. The division helps clarify who benefits from each function and who needs to be accountable for its outcome.

The functions were presented as complementary. A conversation can create a record, feed an analysis and reveal a training need. The proposed architecture seeks to connect these stages. Its value needs to be observed across the complete journey, including cases handed over to a person.

The customer journey needs continuity between tasks

In customer service, the presentation described agents that interact with customers and assistants that support employees during calls. Tasks mentioned included finding product and procedure information, preparing a summary and updating the customer record. Each occupies a different place in the workflow.

A company evaluating this approach can start with the path of a real case. What information arrives with the first interaction? Where is an exception recognized? Who may correct the record or authorize the next step? A defined workflow makes these decisions visible and makes it possible to check whether automation preserves service continuity.

This also provides a way to define a pilot. Rather than grouping tasks simply because they use language, a company can choose a process with a beginning, an end, an owner and an indicator. The test can then compare ways of doing the same work and make it clearer where coordination between agents contributes.

A difficult customer can enter the test before deployment

One of the most interesting passages described a simulation environment with three roles. One agent represents a difficult customer, another performs the task and an observer evaluates the interaction and suggests improvements. The proposal is to practice challenging situations before placing that scenario in front of real customers.

This arrangement can turn a vague intention, such as better service, into observable situations. The simulated customer might interrupt, provide incomplete information or introduce an exception. The executor needs to respond within the service objective. The observer should assess what happened against criteria defined in advance.

Simulation provides material for review and training. Its results still need to be compared with actual operations, where behavior, data and consequences vary. For the buyer, practical questions include which scenarios are tested, how errors are recorded and what needs to improve before the system goes live.

AI operations also need accountable owners

The fourth group of agents presented addresses system evolution and operations. Configuring new scenarios, monitoring performance and issuing risk alerts appeared alongside an improvement cycle. This design recognizes that a service needs continued management after implementation.

Control becomes concrete when tied to specific decisions. Who may change an instruction? Which information is available to each task? Who reviews an incorrect answer? Who can stop an execution? These are governance questions for assessing a solution, rather than capabilities that can be assumed from the word agent alone.

The destination of a case that AI cannot resolve also needs to be defined. A handoff to an employee should carry enough context to prevent the customer from starting over. Human work comes to include reviewing exceptions and the design of the service itself, with clear accountability.

Voice and digital humans broaden the service interface

Speech recognition, speech synthesis and digital humans were part of the portfolio presented. The talk described taking these interfaces to websites, applications and physical terminals. In its public Call materials, the company explains the use of language processing, speech recognition and synthesis in automated calls.

Voice brings its own operational requirements. Interruptions, silence, noise and response time can change the experience even when the answer’s content is appropriate. A pilot needs to cover these situations, along with escalation to a person and a record of what was agreed.

The presentation also mentioned features related to tone and emotion. Such signals require careful evaluation: an inference about a conversation is not certain knowledge of someone’s state. Adoption criteria should be tied to observable service behavior and error handling. The visit presented capabilities and proposals; it did not independently measure the accuracy or cost of these interfaces.

Research, models and products occupy different layers

Bowen was presented as a language foundation oriented toward enterprise use. Its official product page positions it in customer service and marketing. The discussion emphasized industry fit, control and efficient use of computing capacity.

A public research contribution helps explain the company’s history. The RoFormer paper, originally submitted in April 2021, introduces Rotary Position Embedding, or RoPE. The authors identify their affiliation as Zhuiyi Technology in Shenzhen. In simple terms, the technique represents information about token position within the attention mechanism of Transformers.

That research history is distinct from assessing a product being purchased. In an enterprise operation, the model, domain knowledge, integrations and ongoing management need to work together. The technical contribution provides context; service outcomes still depend on the task, implementation and conditions of use.

Regulated industries make predictability more concrete

Finance, government, energy and transportation appeared among the fields discussed. Customer service, follow-ups and public services were used to explain the importance of combining domain knowledge with consistent execution. The public service portfolio describes this combination of interaction, assistance and analysis.

In these settings, evaluation needs to cover what happens when an answer is uncertain or an action may affect the customer. Availability, information access, service records and human review become part of service design. Local data protection and industry requirements need verification within the specific project.

A question about banking operations in the Americas arose during the discussion, but the regional scope remained open. The international references presented did not clarify which countries in the Americas had banking operations served by the company.

Local delivery is part of the product

When discussing expansion, the company separated its AI core from adaptation and customer delivery in each market. It described two forms of collaboration: providing capabilities through APIs for partners with their own applications, and offering a more complete product to partners that have customers and need a solution they can configure and operate.

This division helps explain the work of localization. Language, industry rules, channels, support and integration with existing systems all enter the implementation. The AI engine needs an organization capable of supporting the customer. The presentation discussed collaboration opportunities without establishing an agreement with NewHack or a confirmed Brazilian operation.

In a separate institutional session, the group also heard about an economic and commercial services center connecting China with Portuguese- and Spanish-speaking countries. The presentation covered professional services, business support and local coordination. This was a distinct presentation about the infrastructure for bringing markets together.

The question about jobs put the transformation in perspective

During the questions, a visitor asked whether deploying agents replaces employees or improves their productivity. The discussion moved on to the future of work. The response linked service improvement with cost reduction, acknowledged staff reduction or reorganization in some operations and mentioned the possibility of redeploying people who know the processes.

The discussion reveals a tension worth keeping visible. Automation can increase service capacity while changing the amount and nature of the work required. Redeployment requires decisions, preparation and concrete opportunities. It does not happen automatically because a new tool has been installed.

The closing discussion highlighted shared responsibility: suppliers need to study the process, and customers also need to invest in understanding and changing it. Buying technology is one step in that change. Redesigning roles, training and supervision remains work for the organization.

An agenda for deciding how to use agents

The visit offers a practical agenda for business owners and managers evaluating AI agents. The first step is to choose a process worth improving and understand how it operates today. From there, the conversation with a supplier can move from general features toward verifiable decisions.

The idea of a team of agents becomes more useful when these relationships are clear. Technology, processes and people become part of the same implementation decision. That is the visit’s most concrete contribution for anyone who needs to turn an AI proposition into work that can be monitored and improved.