> For the complete documentation index, see [llms.txt](https://boundaryai.gitbook.io/boundaryai-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boundaryai.gitbook.io/boundaryai-docs/getting-started/how-bai-analytics-works.md).

# How BAI Analytics works

BAI Analytics is the intelligence layer across all your customer feedback. Whatever channel feedback arrives on, it ends up in one place and is analysed the same way, so you can see what matters without stitching tools together.

Here is the model in four steps.

## 1. Bring feedback in from any source

Collect feedback however it reaches you:

* **Surveys** you build and share
* **Uploads** of existing spreadsheets and documents
* **Connectors** that pull tickets from your support and project tools
* **Social listening** that gathers reviews, social posts, and news from the open web
* **AI Visibility** that captures how AI assistants answer questions about your brand

## 2. Organise it into feedback groups

A **feedback group** is the container for one project, programme, or topic. Every source you add lands in a group, and BAI Analytics brings them all into the same model, so a Trustpilot review and an NPS comment sit side by side.

## 3. Analyse it automatically

As feedback arrives, BAI Analytics surfaces **themes** and **grouped themes** across every source, scores **sentiment**, raises the things you're **monitoring**, and lets you **segment** the results by who answered. You can also track how everything **changes over time**.

## 4. Share what you learn

Turn any group into a **report**: a shareable summary for stakeholders, on demand or on a schedule.

{% hint style="success" %}
New here? Start with the [Quickstart](/boundaryai-docs/getting-started/quickstart.md), then create your first [feedback group](/boundaryai-docs/feedback-groups/feedback-groups.md).
{% endhint %}