103 lines
3.1 KiB
Markdown
103 lines
3.1 KiB
Markdown
# S-10 Slide Design V1
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## Status
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- Status: validation design.
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- Source page: P10.
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- Page title: 我们是谁：专家智能体的系统架构师与模具师.
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- Role: first content anchor validation.
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- Line: validation sample.
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- Write policy: append-only; overwrite allowed: no.
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## Design Purpose
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P10 是全套 PPT 的定位核心页。它不能只是漂亮地说“我们很专业”，而要让销售带走一个可复述的判断：
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> 我们帮助企业把人脑里的专家能力，变成组织可复用的智能能力。
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本页要验证这套视觉系统能否承载“专家能力工程化”这种硬判断，而不是只会做航线氛围。
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## Visual Concept
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画面是一个“专家能力工程化模具舱”。
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左侧是人脑里的专家能力：不是人物肖像，而是抽象的经验云团、隐性判断光点、业务语境片段、案例碎片和组织经验网络。中间是我们的角色：系统架构师与模具师，用精密模具、工程剖面、校准仪表和结构化管线表示。右侧是组织可复用的专家智能体能力：岗位卡、流程接入线、可校准智能体模块、客户业务流程节点。
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核心流程必须清楚：
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```text
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专家经验 → 抽取 → 封装 → 验证 → 校准 → 进入流程
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```
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画面要像深蓝黑 AI 航线作战图里的“核心加工舱 / 模具舱”，不是工厂广告，不是程序员写代码，也不是普通系统架构图。
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## Text / Typography Plan
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Title:
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> 我们是谁：专家智能体的系统架构师与模具师
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Core judgment:
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> 把人脑里的专家能力，变成组织可复用的智能能力
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Positioning label:
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> 专家能力工程化
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Pipeline labels:
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> 抽取 / 封装 / 验证 / 校准 / 进入流程
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Optional contrast labels:
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> 不是平台
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> 不是外包
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> 不是只写提示词
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Typography:
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- title at top, large but not crowding;
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- core judgment should be the strongest center text;
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- pipeline labels short and readable;
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- avoid too many long Chinese lines inside the diagram.
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## Layout
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Recommended composition:
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- Left third: abstract expert capability cloud and tacit knowledge nodes.
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- Center third: precision mold / architecture engine / calibration instrument.
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- Right third: reusable expert agent modules connected to business workflow.
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- Center line: cyan-gold pipeline from left to right.
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- Bottom or central label: `专家能力工程化`.
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Visual priority:
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1. core judgment;
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2. left-to-right engineering pipeline;
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3. title;
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4. mold / calibration visual;
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5. optional contrast tags.
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## Constraints / Avoid
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Must avoid:
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- concrete portraits of experts;
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- generic humanoid robots;
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- code wall / programmer worship;
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- platform logo wall;
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- ordinary SaaS dashboard;
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- treating us as a low-price outsourcing team;
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- adding claims not in the accepted skeleton;
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- stuffing the raw material into the page.
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## Validation Focus
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User should check:
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1. Does this page make our positioning clear enough?
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2. Does it visually explain extraction, encapsulation, validation, calibration, and workflow entry?
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3. Does it feel continuous with the previous deck while being harder and more analytical?
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