Operator

In the current era of deep integration of digital office and smart life, people have to frequently switch between dozens of tools every day – writing reports with document tools, using tabular software for data statistics, using design tools to draw pictures, and using communication software to synchronize progress…… This state of “tool fragmentation” not only leads to information scattered everywhere and inefficient collaboration, but also makes people fall into the internal friction of “using tools for the sake of using tools”. The AI-native operating system “Operator” created by the cutting-edge technology team breaks the boundaries of traditional tools with the innovative positioning of “unified AI hub”, integrates scattered functions and information into a “one-stop task processing platform”, and redefines the underlying logic of human-machine collaboration.

1. Break down tool silos and build an AI hub for the “Internet of Everything”

While traditional operating systems are essentially “connectors between hardware and software”, operators have evolved into “intelligent hubs for tools, data, and user needs”. It completely solves the problem of “tool fragmentation” through two core capabilities:

(1) “Seamless call and integration” of full-scenario tools

Operator has a built-in open tool ecosystem, which is connected to more than 200 mainstream tools such as document processing (such as Notion and Feishu Docs), data computing (such as Excel, Python), design creation (such as Figma, Midjourney), communication collaboration (such as WeChat, Slack), etc., and allows users to add their own private tools. More importantly, it allows different tools to “work together” rather than “fight separately”. For example, suppose a user needs to create a “quarterly sales analysis report”. In that case, he only needs to issue instructions in the operator. The system will automatically execute it: call the browser to capture industry market data, use Python scripts to clean and visualize the data, synchronize the generated charts to Word documents, and then send the first draft to team members through WeChat, and finally collect comments and automatically update the report – the whole process does not require users to manually open any tool, and all operations are completed by the operator in a unified manner.

(2) “Intelligent aggregation and management” of cross-platform data

Operators can transform data scattered across different tools (e.g., customer needs in WeChat chat history, sales data in Excel, project plans in documents) through AI-powered data aggregation capabilities. Users can quickly search through natural language, such as asking “What is the corresponding sales order amount of product customization requirements mentioned by customer A last month?”, and the operator will automatically associate WeChat chat records, sales Excel sheets and project documents to extract key information and generate answers. At the same time, the system supports classified management of data according to dimensions such as “project, customer, and time” to ensure that information is “available as needed” and avoid work delays caused by data scattering.

2. Three core competencies to “simplify complex tasks”

Operator is not the “stacker” of the tool, but the ability to “actively serve” the tool through AI, and its three core functions truly realize the “task-centric” efficient processing mode:

(1) Natural language-driven “automatic task dismantling”

Users do not need to learn complex instructions, just describe requirements in everyday language, and the operator can automatically disassemble the task and plan the execution path. For example, if an employee of the marketing department proposes to “plan an online promotion campaign for a new product, with the goal of acquiring 3 potential customers within 500 days, with a budget of 1,5 yuan”, the system will break down it into five links: “target group positioning→ promotion channel screening→ content creation→ delivery execution→ and data tracking”, and each link will automatically match the corresponding tool: use big data analysis tools to target “25-35-year-old working people”, filter “Xiaohongshu, Douyin” through the delivery platform data as the core channel, and call Midjourney Generate promotional posters, set budgets and targeting with ad delivery tools, and finally sync delivery data to dashboards in real time – users only need to wait for results without intermediary processes.

(2) Context-aware “intelligent collaboration and reminders”

Operators can perceive user work scenarios in real time and proactively provide adapted services and reminders. For example, if a user is designing a product prototype with Figma, and the system detects that the “login page” in the design draft lacks a “verification code module”, it will automatically pop up a prompt and recommend similar design solutions from the team’s historical projects. When the user mentions “the project review meeting will be held at 3 pm on Friday” in the document, the operator will automatically sync it to the calendar, send a reminder 10 minutes in advance, and package the project documents required for the meeting and the list of questions to be reviewed to the participants. If a task (such as “Send a contract to a customer”) is overdue, it will be reminded through multiple channels (desktop pop-up, WeChat, SMS) and provide a quick action of “quickly call email to send”.

(3) Personalized “intelligent habit adaptation”

Operators continuously optimize the service experience by learning from users’ working habits. For example, if a user is accustomed to checking the previous day’s sales data at 9 a.m. every day, the system will automatically generate a data briefing and push it during that period. If the user is recognized to commonly use “Excel+Python” to process data, it will actively recommend suitable data analysis templates when users upload tables. If users prefer a “concise report”, the document will automatically omit redundant content and highlight the core conclusions. This “the more you use it, the more you understand you”, makes the tool truly an “assistant that fits your personal habits”.

3. Adapt to all scenarios, from personal office to enterprise collaboration

无论是个人处理日常工作，还是企业推进复杂项目，Operator 都能精准适配，成为提升效率的 “核心引擎”：

（一）个人办公：告别 “多工具切换” 的内耗

对职场人而言，Operator 是 “效率加速器”。行政人员用它一键完成 “会议纪要生成→待办事项分配→日程同步”；新媒体创作者通过它实现 “热点话题抓取→文案撰写→图片生成→多平台发布” 的全流程自动化；财务人员借助它快速完成 “发票识别→凭证录入→报表生成”，大幅减少重复性工作。例如，自由职业者接到 “为客户撰写一篇产品软文并制作配图” 的订单，在 Operator 中下达指令后，系统会自动调用百度指数分析关键词热度，用 GPT-4 生成 3 版文案，通过 Midjourney 生成适配图片，最后由用户选择最优版本交付，整个过程耗时仅需 1 小时，比传统方式节省 80% 时间。

（二）团队协作：打破 “信息壁垒” 的障碍

对团队而言，Operator 是 “协作粘合剂”。在项目推进中，成员可在统一平台实时共享数据、同步进度：研发人员上传代码更新后，系统自动通知测试人员进行测试；测试发现问题时，一键生成 bug 报告并关联至任务管理工具；项目经理通过仪表盘，能实时查看 “研发进度、测试通过率、客户反馈” 等全维度数据，无需逐一询问成员。例如，一个远程协作的产品团队，通过 Operator 实现：设计师在 Figma 完成原型设计后，系统自动同步至研发工具并标注开发要点；研发完成后，自动触发测试工具进行自动化测试；测试通过后，推送上线通知至运营团队，整个流程无缝衔接，避免因信息不同步导致的协作低效。

(3) Enterprise management: Achieve “data-driven” decision-making

For enterprises, Operator is a “management efficiency improvement tool”. The HR department used it to automate the entire process of “resume screening→ interview arrangement→ and onboarding process”, shortening the recruitment cycle from 15 days to 5 days. Through it, the sales team integrates “customer communication records, order data, and after-sales feedback” to generate customer portraits and demand analysis to help precision marketing. The management system aggregates data from various departments and generates multi-dimensional reports such as “production, sales, and cost” to provide real-time data support for strategic decision-making. For example, after a manufacturing enterprise introduces Operator, the system automatically correlates the equipment data of the production workshop, the raw material cost data of the purchasing department and the order data of the sales department to generate the “production cost – order delivery cycle” analysis model to help management find the problem of “production delays caused by the long procurement cycle of certain raw materials”, adjust the procurement strategy in time, and improve the order delivery efficiency by 30%.

4. Future: From “Tool Hub” to “Smart Work Partner”

The emergence of Operator marks the leap from “hardware-driven” to “AI-driven” operating systems. In the future, it will further deepen two major directions: on the one hand, through the iteration of AI large models, improve the accuracy of task disassembly and the smoothness of tool collaboration, such as supporting more complex cross-industry tasks (such as “combining market data and production capacity to formulate annual product development plans”); On the other hand, it will build an “enterprise-level private AI hub” to meet the needs of enterprises for data security and customized tools, and help enterprises achieve “intelligent transformation of the whole process”.

From “switching for tools” to “letting tools proactively serve needs,” Operators are reshaping people’s relationships with digital tools. It not only solves the pain point of “tool fragmentation” at present, but also lays the foundation for an “efficient, intelligent, and collaborative” work model in the future. Whether it’s an individual seeking efficiency or an enterprise eager for digital transformation, Operator will become a “key variable” that breaks down tool barriers and unlocks productivity, making “simplifying complex tasks and automating simple tasks” the norm.