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Merge pull request #45 from StrategyLogic:release/pro-demo
Add Pro Demo Links to Documentation
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README.md

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👉 [Explore Omen on Streamlit Cloud](https://omen-demo.streamlit.app/)
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👉 [Explore Omen Pro on Streamlit Cloud](https://omen-pro.streamlit.app/)
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---
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## 🚀 Quick Start
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If you are:
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If you are a:
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- Data Scientist
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- AI Researcher
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pip install -e .
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```
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### 🌰 View Demo
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### 🌰 Run Demo
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If you want to quickly see Omen in action, a visualized sample case and its results are available in the `demo` directory. Run:
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Then open `http://localhost:8501` in your browser to explore the full strategic reasoning flow.
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### 🎵 Run Built-in Case
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#### End-to-End Flow
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![End-to-End Flow](docs/assets/images/streamlit-strategic-reason-flow.png)
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#### More details
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You can click on each panel on the page to inspect the full chain of outputs from source document to situation artifact, scenario artifact, simulation result, and explanation artifact.
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![Scenario Planning](docs/assets/images/streamlit-scenario-planning.png)
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---
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## 🎵 Run Built-in Case
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If you want to run a complete **Analyze - Simulate - Explain** workflow, we have prepared a built-in case simulating SAP's acquisition of Reltio in March 2026.
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The case document is `cases/situations/sap_reltio_acquisition.md`, and it can be run end-to-end with the following commands:
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#### Step 1. Analyze
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### Step 1. Analyze
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Omen's Analyze module combines strategy methodology and the data pipeline, allowing you to generate strategic insights and machine input artifacts from the source document with a single command.
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##### Situation Analysis
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#### Situation Analysis
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```bash
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# analyze the built-in case and pack it as "sap" alias
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This step generates the Situation Artifact and creates a package named `sap` for consistent use in subsequent steps.
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##### Scenario Planning
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#### Scenario Planning
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Omen `v0.1.9` provides deterministic A/B/C scenario planning capabilities.
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This step generates the scenario pack artifact under `data/scenarios/sap/` for simulation.
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#### Step 2. Simulate
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### Step 2. Simulate
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Omen's simulation engine can reason across different scenarios. Use the scenario pack generated in the previous step to run simulation:
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This step generates reasoning traces and writes the deterministic result to `output/sap/result.json`.
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#### Step 3. Explain
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### Step 3. Explain
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Omen's explanation module interprets simulation outcomes and traces back key decision points and risk items (known unknowns) from the situation artifact to generate decision-ready insights and recommendations:
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### Launch UI
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Omen also provides a Streamlit-based UI application for visualizing the full strategic reasoning flow.
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Run the Streamlit application for visualizing the full strategic reasoning flow.
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```bash
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streamlit run app/scenario_planning.py
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```
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#### End-to-End Flow
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![End-to-End Flow](docs/assets/images/streamlit-strategic-reason-flow.png)
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#### More details
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You can click on each panel on the page to inspect the full chain of outputs from source document to situation artifact, scenario artifact, simulation result, and explanation artifact.
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![Scenario Planning](docs/assets/images/streamlit-scenario-planning.png)
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## 👥 Target Audience
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Omen is built for the following roles:
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* Technology Strategy Teams
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* Product & Platform Leads
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* AI Infrastructure Researchers
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* Open Source Ecosystem Observers
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* Investors & Industry Analysts
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Then open `http://localhost:8501` in your browser to explore the results.
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## 🎬 Showcase
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### Strategic Actor Analyze
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We have built-in samples of five strategic actors:
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* 👤 [Elon Musk](cases/actors/elon-musk.md)
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* 👤 [Jeff Bezos](cases/actors/jeff-bezos.md)
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* 👤 [Steve Jobs](cases/actors/steve-jobs.md)
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* 👤 [Jack Ma](cases/actors/jack-ma.md)
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* 👤 [Chen Jiaxing (me)](cases/actors/chen-jiaxing.md)
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Run the following command to build strategic actors and gain insights into their profiles, behavior patterns, and influence relationship graphs:
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```bash
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streamlit run app/strategic_actor.py
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```
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### Strategic Reasoning Cases
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* 🧩 [Acquisition: SAP vs Reltio](cases/situations/sap_reltio_acquisition.md)

README.zh.md

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希望快速体验 Omen 而勿须本地安装,请访问我们部署在 Streamlit Cloud 的演示应用。点击下面的链接,直接探索战略推演流程:
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👉 [在线体验 Omen](https://omen-demo.streamlit.app)
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👉 [开源版本 omen-demo.streamlit.app](https://omen-demo.streamlit.app)
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👉 [商业版本 omen-pro.streamlit.app](https://omen-pro.streamlit.app)
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## 🚀 快速上手
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## 🚀 快速开始
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如果你是:
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pip install -e .
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```
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### 🌰 看例子
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### 🌰 演示
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如果你希望快速查看 Omen 的运行效果,`demo` 目录中提供了可视化案例及其结果。运行:
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在浏览器中打开 `http://localhost:8501`,即可查看完整的战略推演流程。
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### 🎵 跑流程
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**战略推演全流程视图**
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![全流程视图](docs/assets/images/streamlit-strategic-reason-flow.png)
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#### 更多细节
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你可以点击页面上各个面板,查看从源文档到情势工件、情景工件、推演结果,再到解释工件的完整链路产出。
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![Scenario Planning](docs/assets/images/streamlit-scenario-planning.png)
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---
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## 🎵 开始运行
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如果你希望亲自跑一遍完整的**分析——模拟——解释**流程,请使用我们准备的内置案例,用于模拟 2026 年 3 月 SAP 收购 Reltio 的情境。
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案例文档位于 `cases/situations/sap_reltio_acquisition.md`,可以通过以下步骤端到端运行。
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#### 第一步:分析
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### 第一步:分析
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Omen 分析模块融合战略方法与数据工程管道,你仅需一行命令,即可从源文档中自动生成战略洞察与机器可消费的工件。
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##### 情势分析
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#### 情势分析
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# 分析内置案例,并打包为 "sap" 别名
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omen analyze situation --doc sap_reltio_acquisition --pack-id sap
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此步骤会生成情势工件(Situation Artifact),并创建别名为 `sap` 的包,供后续步骤一致使用。
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##### 情景规划
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#### 情景规划
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Omen 当前版本提供了确定性的 A/B/C 情景规划能力:
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此步骤会在 `data/scenarios/sap/` 下生成用于模拟的情景包工件(Scenario Pack Artifact)。
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#### 第二步:模拟
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### 第二步:模拟
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Omen 提供的模拟引擎可针对不同的情景进行推演。下面使用上一步生成的情景包工件,运行模拟:
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此步骤将生成推演轨迹以及结果文件 `output/sap/result.json`
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#### 第三步:解释
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### 第三步:解释
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Omen 解释模块会对模拟结果进行解读,并回溯情势工件中的关键决策点、风险项(已知的未知),生成面向决策者的洞察与建议:
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### 启动 UI 应用
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Omen 还提供了一个基于 Streamlit 的 UI 应用,用于可视化完整的战略推演流程。
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Omen 提供了基于 Streamlit 的应用,用于可视化完整的战略推演流程。
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```bash
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**战略推演全流程视图**
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![全流程视图](docs/assets/images/streamlit-strategic-reason-flow.png)
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![Scenario Planning](docs/assets/images/streamlit-scenario-planning.png)
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## 👥 适用人群
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Omen 专为以下角色打造:
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* 技术战略团队
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* 产品与平台负责人
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然后在浏览器中打开 `http://localhost:8501`,即可探索结果。
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## 🎬 案例展示
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### 战略主体分析
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我们内置了五位战略主体的样本:
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* 👤 [Elon Musk](cases/actors/elon-musk.md)
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运行以下命令,构建战略主体并洞察其画像、行为模式及影响关系图谱:
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* 🧩 [SAP 收购 Reltio:主数据迷雾](cases/situations/sap_reltio_acquisition.md)

specs

Submodule specs updated from b549c9d to 0716f57

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