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新架构·新能力:智齿科技发布 AI Agent 驱动的新一代客户联络平台

龚作仁 2026-07-31 10:37
龚作仁 2026/07/31 10:37

邦小白快读

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本文核心是智齿科技发布了AI Agent驱动的新一代客户联络平台,带来了客户联络领域的底层变革,核心干货如下:

1. 核心逻辑发生变化:原来AI只是辅助人工提效的工具,现在大模型技术让AI变成能独立理解、独立决策、独立完成工作的智能劳动力,行业底层逻辑从“人操作软件”变成“智能体独立干活,人工负责调优”。

2. 推出了全新的三层架构:分为Agents智能体、Nexus统一底座、Experts专家团队三层,从架构层原生适配AI Agent,不是在旧系统上加装AI。

3. 给使用企业带来三大好处:不用为每个新渠道重新搭建系统,AI服务效果可以量化追踪持续优化,AI能在日常运营中持续学习进化,不会出现效果衰减。

本文介绍了大模型时代客户联络领域的新变革,对品牌商优化服务、提升用户体验有诸多参考价值,核心干货如下:

1. 用户需求和消费趋势变化:当前用户对客户服务的期待已经从“有人接待”升级为“问题当场解决”,企业也不再满足于AI只降本,要求AI能扛起业务结果,这要求品牌商升级自身的客户联络体系。

2. 新产品可匹配品牌多场景需求:新的AI智能体能覆盖售前咨询、售后服务、VIP服务、内部员工服务等多场景,可独立解决绝大多数重复性、流程性问题,满足用户当场解决问题的需求,同时降低品牌的人工服务成本。

3. 解决了品牌多渠道布局的痛点:品牌的客户触点分散在网页、APP、社媒、电商平台等多个渠道,新架构用统一底座接入所有渠道,能保证全渠道的服务标准和数据统一,避免用户换渠道后体验打折,有利于维护品牌口碑。

智齿科技推出的新一代AI驱动客户联络平台,给各类卖家带来了降本增效、提升用户体验的新机会,核心干货如下:

1. 当前市场需求已经发生变化:消费者对客服服务的要求从“能接通、有礼貌”升级为“问题当场解决”,率先完成服务体系升级的卖家能获得竞争优势,抓住用户偏好。

2. 解决了卖家多渠道布局的痛点:大多数卖家同时布局多个线上渠道,过去每开通一个新渠道就需要重新搭建一套客服系统,成本很高,新架构用统一底座接入所有渠道,全渠道数据和服务标准一致,减少了重复投入。

3. 机会与可学习点:AI已经从辅助工具变成独立劳动力,能帮卖家处理绝大多数重复性客服问题,还能形成闭环持续优化进化,效果可量化追踪,既能降低人力成本,又能提升客户满意度,是值得卖家关注和尝试的新方向。

本文介绍的AI Agent驱动的客户联络变革,给工厂推进数字化转型、挖掘新商业机会带来不少启示,核心干货如下:

1. 存在明确的商业新机会:大模型落地后客户联络行业发生范式革命,大量企业都有升级客户联络体系的需求,有相关能力的工厂可以切入To B服务领域,传统工厂也可对接这类AI服务商优化自身服务,提升竞争力,出海工厂的收益会更明显。

2. 数字化转型的启示:智齿科技是从底层架构开始适配AI技术,而非在旧系统上加装AI,这给工厂数字化转型提供了思路,修修补补的升级无法充分发挥AI价值,需要从底层架构适配新技术才能拿到技术红利。

3. 运营优化参考:工厂可以借鉴“智能体+人工专家”的架构,用AI处理重复性的订单咨询、售后对接等工作,真人负责后端调优和复杂问题,兼顾效率与服务质量。

本文披露了客户联络行业的最新发展动向,给To B服务商把握行业趋势、解决客户痛点提供了干货参考,核心干货如下:

1. 行业发展的核心新趋势:大模型技术推动客户联络行业发生范式革命,行业底层逻辑已经从“AI辅助人提效”转变为“AI智能体独立完成工作,真人专家负责调优升级”,AI的角色从辅助工具变成了独立劳动力,这是未来行业的核心发展方向。

2. 当前企业客户的核心痛点:传统客户联络体系存在三个核心问题,一是多渠道分散,每个新渠道都要重新搭建系统,成本高体验差;二是AI服务效果无法量化追踪,难以优化;三是AI用久了效果会衰减,无法满足用户“问题当场解决”的新需求,这些都是服务商可以切入的市场机会。

3. 可落地的参考解决方案:智齿科技提出的“Agents+Nexus+Experts”三层架构是成熟的落地方案,前端智能体对接场景解决问题,中端统一底座承载全渠道接入,后端专家团队负责持续调优,三者互相支撑能很好解决当前客户的核心痛点。

本文通过智齿科技的转型,反映了企业对客户联络平台的最新需求,也给同类平台的发展提供了方向参考,核心干货如下:

1. 企业对客户联络平台的核心新需求:当前企业不再满足于AI只做辅助提效,要求AI能独立完成流程性工作,实现用户问题当场解决,同时要求支持多渠道统一接入,AI效果可量化持续优化,AI能自动迭代进化,这些需求是平台产品升级的方向。

2. 平台产品升级可借鉴的做法:智齿科技没有选择在原有旧系统上加装AI功能,而是从底层架构开始原生适配AI Agent,这种路径能充分发挥AI的能力,比修修补补的升级效果更好;同时“前端智能体+中端统一底座+后端专家支撑”的架构,能覆盖企业全链路需求,模式可复制性强。

3. 需要规避的发展风向:目前很多厂商只是给传统客服系统加装AI功能,没有做底层架构升级,无法真正满足企业需求,也无法充分发挥大模型AI的能力,这种发展路径风险较高,同类平台需要规避这类错误路径。

本文披露了大模型时代客户联络行业的最新产业动向,对产业研究有较高的参考价值,核心干货如下:

1. 产业发展新动向:大模型技术落地后,客户联络行业发生了范式级别的革命,AI的角色从辅助人力的工具转变为能独立完成工作的智能劳动力,行业底层逻辑从“人操作软件”转变为“智能体独立工作,人负责调优升级”。头部厂商智齿科技从一体化客户联络SaaS公司正式转型为AI Agent驱动的新一代客户联络平台,代表了整个行业的发展方向。

2. 新的商业模式研究方向:智齿科技推出的“Agents+Nexus+Experts”三层架构,形成了区别于传统SaaS的新商业模式,传统SaaS是交付系统后就离场,新模式是前端输出场景化智能体服务,中端输出统一底座能力,后端专家持续参与运营调优,更适配AI持续进化的特性,是值得深入研究的新商业模式。

3. 产业研究新问题:AI成为独立劳动力后,对企业的服务流程、组织架构、人力结构都会带来深远改变,原生AI架构下智能体的运营迭代机制,也是产业研究的新方向。

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Quick Summary

This article highlights Zhichida Tech's launch of a new generation AI Agent-powered customer engagement platform, which drives fundamental changes to the customer engagement industry. Key takeaways are as follows:

1. Shift in core industry logic: Previously, AI was only a tool to augment human efficiency. Now, large model technology has transformed AI into an intelligent workforce that can understand, make decisions and complete tasks independently. The industry's core logic has shifted from "humans operating software" to "agents work independently, humans handle optimization".

2. A new three-layer native architecture: The platform adopts a three-layer architecture consisting of Agents (intelligent agents), Nexus (unified infrastructure) and Experts (professional team), which is natively built for AI Agents, rather than just adding AI modules to legacy systems.

3. Three core benefits for client enterprises: There is no need to rebuild systems for each new channel; AI service performance can be quantified, tracked and continuously optimized; and AI can continuously learn and evolve through daily operations without performance degradation.

This article outlines new transformations in the customer engagement sector in the large model era, with actionable insights for brands looking to optimize service and improve user experience. Key takeaways are as follows:

1. Shifts in user expectations and consumer trends: Users now expect "instant problem resolution" instead of just "having an agent available", while enterprises are no longer satisfied with AI only cutting costs—they require AI to deliver tangible business outcomes. This pushes brands to upgrade their customer engagement systems.

2. The new platform supports multi-scenario brand needs: The new AI intelligent agents can cover pre-sales consultation, after-sales service, VIP support, internal employee services and more, independently resolving the vast majority of repetitive, process-driven problems to meet user demand for instant resolution while reducing brands' labor costs for customer service.

3. It solves the core pain point of multi-channel brand operations: Brand customer touchpoints are scattered across websites, apps, social media, e-commerce platforms and other channels. The new platform's unified infrastructure connects all channels to maintain consistent service standards and unified data across channels, eliminating inconsistent experience when users switch channels and helping protect brand reputation.

Zhichida Tech's new AI-powered customer engagement platform creates new opportunities for all types of sellers to cut costs, boost efficiency and improve customer experience. Key takeaways are as follows:

1. Shifting market demand: Consumers now expect "instant problem resolution" from customer service, instead of just "reachable and polite agents". Sellers that upgrade their service systems early will gain a competitive edge and capitalize on shifting user preferences.

2. It solves the pain point of multi-channel operations: Most sellers operate across multiple online channels, and previously had to rebuild an entire customer service system for each new channel, incurring high costs. The new platform's unified infrastructure connects all channels with consistent service standards and unified data, eliminating redundant investment.

3. New opportunities and actionable insights: AI has evolved from an auxiliary tool to an independent workforce, capable of handling the vast majority of repetitive customer service tasks, forming a closed loop for continuous optimization, and delivering quantifiable performance. It both cuts labor costs and improves customer satisfaction, making it a new direction worthy of sellers' attention and adoption.

The AI Agent-driven transformation of customer engagement introduced in this article offers useful insights for factories advancing digital transformation and exploring new business opportunities. Key takeaways are as follows:

1. Clear new business opportunities: Large model adoption has triggered a paradigm shift in the customer engagement industry, with a huge number of enterprises seeking to upgrade their customer engagement systems. Factories with relevant capabilities can enter the B2B service space, while traditional factories can partner with AI service providers to optimize their own services and boost competitiveness. Export-focused factories will see particularly notable benefits.

2. Insights for digital transformation: Zhichida Tech built its AI adaptation from the underlying architecture, rather than adding AI to legacy systems. This provides a key lesson for factory digital transformation: piecemeal upgrades cannot unlock AI's full value, and native adaptation of new technologies at the underlying architecture level is required to capture technology dividends.

3. Reference for operational optimization: Factories can adopt the "intelligent agent + human expert" architecture: AI handles repetitive work such as order inquiries and after-sales coordination, while human experts handle backend optimization and complex issues, balancing efficiency and service quality.

This article outlines the latest industry trends in customer engagement, providing actionable insights for B2B service providers to capture industry trends and solve client pain points. Key takeaways are as follows:

1. Core new industry trend: Large model technology has driven a paradigm shift in the customer engagement industry. The core industry logic has shifted from "AI augments human efficiency" to "AI agents complete work independently, human experts handle optimization and upgrading". AI has transitioned from an auxiliary tool to an independent workforce, and this is the core direction for future industry development.

2. Core pain points of enterprise clients: Traditional customer engagement systems face three key problems: first, fragmented multi-channel operations that require rebuilding systems for each new channel, leading to high costs and poor experience; second, AI service performance cannot be quantified or tracked, making optimization difficult; third, AI performance degrades over time, which cannot meet user demand for instant problem resolution. All of these are untapped market opportunities for service providers.

3. A actionable reference solution: Zhichida Tech's three-layer "Agents + Nexus + Experts" architecture is a mature, proven solution: front-end intelligent agents connect to use cases and resolve problems, a mid-tier unified infrastructure supports full-channel access, and a back-end expert team handles continuous optimization. The three layers work together to effectively solve enterprises' core current pain points.

This article uses Zhichida Tech's transformation to illustrate the latest enterprise demand for customer engagement platforms, and provides directional guidance for peer platforms. Key takeaways are as follows:

1. New core enterprise demand for customer engagement platforms: Enterprises are no longer satisfied with AI only augmenting efficiency; they require AI to independently complete process-driven work, deliver instant resolution to user problems, support unified multi-channel access, enable quantifiable and continuous optimization of AI performance, and support automatic AI iteration and evolution. These demands define the direction for platform product upgrades.

2. Actionable references for platform product upgrades: Instead of adding AI modules to legacy systems, Zhichida Tech built native AI Agent adaptation from the underlying architecture. This path unlocks AI's full potential and delivers far better results than piecemeal upgrades. Meanwhile, the "front-end agent + mid-tier unified infrastructure + back-end expert support" architecture covers enterprises' end-to-end needs, and the model is highly replicable.

3. Pitfalls to avoid: Many current vendors only add AI modules to traditional customer service systems without upgrading the underlying architecture. This approach cannot truly meet enterprise demand or unlock large model AI's full potential, and carries high development risk. Peer platforms should avoid this incorrect development path.

This article discloses the latest industry developments in the customer engagement sector in the large model era, offering high reference value for industrial research. Key takeaways are as follows:

1. New industrial development trends: Following large model commercialization, the customer engagement industry has undergone a paradigm shift. AI's role has transformed from an auxiliary tool to an independent intelligent workforce capable of completing work on its own, and the industry's core logic has shifted from "humans operating software" to "agents work independently, humans handle optimization and upgrading". Leading vendor Zhichida Tech, formerly an integrated customer engagement SaaS company, has officially transformed into an AI Agent-powered next-generation customer engagement platform, representing the overall development direction of the industry.

2. A new direction for business model research: Zhichida's three-layer "Agents + Nexus + Experts" architecture forms a new business model distinct from traditional SaaS. Where traditional SaaS exits after delivering the system, the new model delivers front-end scenario-based agent services, mid-tier unified infrastructure capabilities, and continuous operational optimization from back-end experts, which is better aligned with AI's characteristic of continuous evolution. This is a new business model worthy of in-depth research.

3. New research questions for the industry: After AI becomes an independent workforce, it will bring profound changes to enterprises' service processes, organizational structures and workforce composition. The operation and iteration mechanism of intelligent agents under a native AI architecture is also a new direction for industrial research.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

过去十年,客户联络行业进步的方式,是把“系统”越做越全——在线客服、呼叫中心、工单……一个个环节被数字化,但真正在服务、真正在解决问题的,始终是系统背后的人。大模型的出现,让这件事有了另一种可能:AI不再只是提高人效的工具,而是可以独立理解、独立决策、把事情真正办完的智能体。

7月 30日,智齿科技以旗下AI Agent产品的一次全面升级,又一次站上这个拐点。新版Agents将围绕搭建、执行、运营、分析四大能力全面焕新,从“一个会回答问题的机器人”,进化为“一套会自己进化的智能体运营体系”。与这次产品升级同步,智齿科技将整体产品架构正式升级为“Agents、Nexus、Experts”三大系列。

不同于把AI加装进传统客服系统的厂商,智齿科技从架构层就为Agent而生:智能体独立接待与主动触达,统一底座承载全部通道,真人专家在背后持续调优。三件事共同指向一个事实——智齿科技正在从一家“一体化客户联络SaaS公司”,转型为“AI Agent驱动的新一代客户联络平台”。

Agents·Nexus·Experts:三重架构、互为支撑

先看这次最直观的变化。新版Agents完成了一次从“回答问题”到“办成事情”的跃迁:一句话就能搭建、诊断、调优一个Agent,运营门槛从“专家级”降到“对话级”;知识、技能、工具、记忆统一沉淀为可复用的企业资产,让智能体不仅能理解问题,更能连接系统、把事情真正办完;上线不再是终点,构建→评测→调优→观测的闭环,让它每天都在学着变得更好……

这次升级看得见的是Agent本身,看不见的,是与之同步启用的一整套全新架构——Agents、Nexus、Experts。

站在客户面前的,是Agents。从售前咨询、售后服务,到VIP专属服务、企业内部的员工服务台,不同场景对应不同的智能体,独立接待、主动触达,把绝大多数重复性、流程性的问题解决在第一线。它们不是等着被提问的机器人,而是能自己判断“下一步该做什么”、并把事情真正办成的数字员工。

让所有Agent真正跑起来的,是Nexus。网页、App、邮件、电话、社媒、IM、电商平台——过去,每接入一个新渠道,往往意味着重新搭建一套系统;现在,全部统一接入同一套底座,智齿的每一个智能体,都运行在Nexus之上,数据、上下文、服务标准在所有渠道保持一致。

站在Agent身后的,是Experts。从方案设计、AI部署,到训练调优、日常运营,专家团队全程在场——不是把系统交付给客户后就此退场,而是持续观察、持续调优,让每一个智能体在真实业务中越用越准、越用越可靠。

三者环环相扣,缺一不可:没有Nexus,Agents只能困在单一渠道里;没有Experts,Agents就不会进化的那么快;而没有Agents,Nexus和 Experts也就没有真正要服务的对象。

对客户而言,这意味着三件事同时发生:不必再为每一个新渠道重新搭建一套系统;不必再靠感觉判断AI好不好用,效果第一次可以被量化、被追踪、被持续优化;也不必担心AI用久了会衰减——它会在日复一日的运营中持续学习,越用越懂业务。

这就是智齿眼中“好服务”该有的样子——问题解决在最前线,标准统一在底座里,专业沉淀在人身上。而这套架构之所以在此刻成型,背后是一个更大的判断。

为什么是现在:一个时代级的判断

每一天,全球有数十亿次客户联络正在发生——一次深夜的退款咨询,一通排队许久才接通的电话,一条发出去后石沉大海的消息……每一次的背后,都是一个等待被回应的人。

过去十年,行业习惯用“系统”去接住他们:在线客服、呼叫中心、工单,也包括早期基于规则和关键词匹配的AI。但那时,无论系统还是AI,扮演的都是“赋能”的角色——帮人更快、更省力,真正理解问题、做出判断、完成服务的,始终是人。

直到大模型出现,这件事第一次被改写。AI不再只是被动等待指令的工具,而是能听懂、能思考、能动手——自己理解客户意图、自己判断该走哪一步、自己把事情办成。用一句话概括:AI不再是工具,而是劳动力。客户联络的底层逻辑,正在从“人操作软件”,走向“智能体独立工作,人让它更聪明”。

这不只是一次技术升级,更是一次期待的升级。客户不再满足于“有人接待”,而是希望“问题当场解决”;企业也不再满足于“降低人工成本”,而是希望AI真正扛起业务结果——退款办成了、工单闭环了、客户满意了,而不只是一次礼貌的回复。谁能先把AI从“工具”变成真正能干活的“劳动力”,谁就能先一步接住这种期待。

工具升级了无数次,而这一次,是一场范式的革命。智齿科技,选择站上这个拐点,重新出发。

十余年积累才有今天的重新出发

这次重新出发,智齿并不是从零开始。

从2014年成立开始,智齿科技就走在这条路上:早期,第一款产品“在线客服机器人”用AI处理重复性问题;此后陆续整合呼叫中心、工单系统,形成“智能全客服”;2018年起,推出外呼、留资等营销侧产品,实现服务与营销的一体化;2019年起,围绕私域运营与BPO业务扩张,逐步形成“服务+营销”“公域+私域”“软件+BPO”三维一体化的客户联络解决方案,并于2021年正式发布“一体化客户联络中心”战略。此后几年,智齿同步推进国际化与智能化,是国内首批将大模型应用于客户联络场景的企业,也是首批出海并快速成长为“出海新势力领航者”的企业。

这十余年里,智齿科技已经为全球上万家企业提供了服务,覆盖智能制造、零售电商、互动娱乐、金融保险、生活服务等行业。从出海全球的库洛游戏,到智能制造领域的realme,再到直播电商的东方甄选,每一次顺畅的售后对话、每一次准确的订单查询,背后可能都有智齿Agents的身影——这些沉淀,既是智齿重新出发的底气,也是智齿Agents真正落地的最好起点。

我们相信,未来每一家企业都会拥有一支永不疲倦、越用越聪明的智能体队伍;而每一个联系TA们的人,依然能感到自己被认真对待。

智齿科技联合创始人兼CEO徐懿表示:“过去十多年,我们帮客户把系统越搭越全,但AI真正开始独立‘干活’,是从大模型这一刻才开始的。这次升级,我们不是给产品加一个功能,而是重新定义智齿是什么——我们从架构层就为Agent而生,而不是把AI装进旧系统里。AI负责效率,专家负责温度,这不是取舍,是我们对‘好服务’的全部回答。”

从创立那天起,智齿的愿景就没有变过——让每一家企业都享受智慧服务带来的改变。今天,这个愿景有了新的注脚:智齿科技是AI Agent驱动的新一代客户联络平台,让AI真正走进企业的每一条服务流程、每一次客户沟通,把愿景变成每一天都在发生的事实。让每一次客户联络,都有AI的效率和专家的温度。

注:文/龚作仁,文章来源:Laborer,本文为作者独立观点,不代表亿邦动力立场。

文章来源:Laborer

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FAQ回顾

AI Agent驱动的客户联络平台有什么优势?

该类平台采用Agents、Nexus、Experts三重架构,Agents可独立接待处理流程性问题,Nexus统一多渠道底座保障服务标准一致,Experts团队持续调优让智能体越用越准,可量化追踪效果,无需为新渠道重复搭建系统。

智齿科技新一代客户联络平台覆盖哪些行业?

智齿科技已为全球上万家企业提供客户联络相关服务,覆盖智能制造、零售电商、互动娱乐、金融保险、生活服务等行业,客户包括库洛游戏、realme、东方甄选等不同领域企业。

AI Agent在客户联络场景能实现什么功能?

在客户联络场景中,AI Agent可独立理解客户意图、判断处理流程、主动完成服务,解决绝大多数重复性、流程性问题,还可形成搭建、评测、调优、观测的闭环持续进化,直接承担业务结果。

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