# S-10 Slide Design V1

## Status

- Status: validation design.
- Source page: P10.
- Page title: 我们是谁：专家智能体的系统架构师与模具师.
- Role: first content anchor validation.
- Line: validation sample.
- Write policy: append-only; overwrite allowed: no.

## Design Purpose

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：

> 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。

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。

## Visual Concept

画面是一个“专家能力工程化模具舱”。

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。

核心流程必须清楚：

```text
专家经验 → 抽取 → 封装 → 验证 → 校准 → 进入流程
```

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。

## Text / Typography Plan

Title:

> 我们是谁：专家智能体的系统架构师与模具师

Core judgment:

> 把人脑里的专家能力，变成组织可复用的智能能力

Positioning label:

> 专家能力工程化

Pipeline labels:

> 抽取 / 封装 / 验证 / 校准 / 进入流程

Optional contrast labels:

> 不是平台
> 不是外包
> 不是只写提示词

Typography:

- title at top, large but not crowding;
- core judgment should be the strongest center text;
- pipeline labels short and readable;
- avoid too many long Chinese lines inside the diagram.

## Layout

Recommended composition:

- Left third: abstract expert capability cloud and tacit knowledge nodes.
- Center third: precision mold / architecture engine / calibration instrument.
- Right third: reusable expert agent modules connected to business workflow.
- Center line: cyan-gold pipeline from left to right.
- Bottom or central label: `专家能力工程化`.

Visual priority:

1. core judgment;
2. left-to-right engineering pipeline;
3. title;
4. mold / calibration visual;
5. optional contrast tags.

## Constraints / Avoid

Must avoid:

- concrete portraits of experts;
- generic humanoid robots;
- code wall / programmer worship;
- platform logo wall;
- ordinary SaaS dashboard;
- treating us as a low-price outsourcing team;
- adding claims not in the accepted skeleton;
- stuffing the raw material into the page.

## Validation Focus

User should check:

1. Does this page make our positioning clear enough?
2. Does it visually explain extraction, encapsulation, validation, calibration, and workflow entry?
3. Does it feel continuous with the previous deck while being harder and more analytical?