> For the complete documentation index, see [llms.txt](https://docs.rootcause.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rootcause.ai/start-here/readme.md).
# Quick Start Tutorial
Your First Analysis
This tutorial walks through all seven steps of the RootCause workflow using sample data. Download the two CSV files below - they represent customer demographics and subscription information for a fictional telecoms company.
{% file src="/files/oOa9U6C1e7nwpYruDlSZ" %}
{% file src="/files/KEiTi6YXSMtM3ycDnXWv" %}
***
## Step 1: Create a Workspace
Within your organisation create a **New workspace**. Give the workspace a name and click **Create**.
***
## Step 2: Connect Data
Go to **Sources** and click **Import data**. Select **Local File Upload**, drag both CSV files onto the drop zone or browse files, and click **Upload**. RootCause will process the files and begin building the ontology automatically.
Full details: [Connect Data](/user-guide/connecting-data.md)
***
## Step 3: Build Ontology
Go to **Ontology**. The RootCause.ai platform has already scanned the datasets that were uploaded in the previous step and built a semantic map. It automatically identified that `Customer Id` appears in both files and linked them together. Review the network to confirm the connections look right.
Full details: [Build Ontology](/user-guide/ontology-concepts.md)
***
## Step 4: Build Unified Dataset
Go to **Ontology** and look at the **Recommended Datasets** section in the right panel. Click the recommendation the **Create dataset** to build a joined view of both datasets. RootCause generates the join recipe automatically. You can inspect and edit it in the **Operations** editor.
Full details: [Build Unified Table](/user-guide/data-views.md)
***
## Step 5: Build Digital Twin
Within the dataset you want to observe, click **Build digital twin** on the top right of the page.
Scroll through sections (you don't need to make any adjustments to the setup) :
1. How do you want to build it?
2. Choose your data
3. Add domain knowledge
4. Review & run
In the builder's final **Review & run** step, click **Discover & train**.
This action runs causal discovery and trains the model. Training runs a sequence of stages and takes a few minutes. When it completes you'll see a model fit score.
Full details: [Build Digital Twin](/user-guide/creating-digital-twin.md)
***
## Step 6: Digital Twin
When the causal discovery completes, you will see the completed **Digital Twin**. Click any variable to view more information like explanations, prediction quality, distribution of the data, and causal subgraph.
***
## Step 7: Run Simulations
From your Digital Twin, click the **Simulations** on the right panel. Try typing a plain-language question into **Generate from Query**:
```
What happens to churn if we give away free tech support?
```
Then click **Generate Scenario**. Review the generated configuration and run it.
The simulation will take a few seconds to complete. Once completed you will see the impact of the **Intervention** simulation.
Full details: [Run Simulations](/user-guide/simulations.md)
***
## Step 8: Produce Reports
On the simulation result page that was generated on the previous step, click **Export as report** located on the top right bar. RootCause.ai produces a structured report with an Executive Summary and evidence-linked findings. Export to PDF to share it.
Full details: [Produce Reports](/user-guide/reports.md)
***
## What success looks like
If you followed all eight steps, you now have:
1. An ontology where `Customer Id` is linked across both files
2. A joined Data View built from the Recommended Datasets recommendation
3. A trained Digital Twin with a model fit score
4. A Digital Twin you can click into for explanations, prediction quality, data distribution, and causal subgraphs
5. A simulation run showing the impact of your intervention
6. An exported PDF report with an Executive Summary and evidence-linked findings
