> For the complete documentation index, see [llms.txt](https://docs.toucanai.cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.toucanai.cloud/build/analyze-your-database-with-ai/ai-analysis-overview.md).

# AI analysis overview

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**Target Audience**: Non technical users
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### TL;DR

Toucan.ai uses AI analysis to enrich connected databases with semantic metadata, which is required for accurate chart generation and security configuration.

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### When to use this

Use this page to understand how metadata affects the platform's AI reasoning and when to initiate the analysis process.

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### Purpose of AI Analysis

The Analyze feature adds a semantic layer to raw data. This process is a required step for the following workflows:

* **Natural Language Queries**: Enriches tables and columns so the AI can interpret user prompts.
* **Dashboard Generation**: Improves the selection of relevant charts based on data types.
* **Security Preparation**: Prepares column structures for Row-Level Security (RLS) mapping by inferring data types, which determines the available comparison operators
* **User Clarity**: Replaces raw database names with human-readable display names.

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### Core Functionality

When the Analyze feature is triggered, Toucan.ai performs the following automated actions on a selected schema:

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**Selection Phase**

Before automated actions begin, the platform triggers a selection modal. This allows users to target specific subsets of data rather than analyzing the entire connection, optimizing processing time and relevance
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| Component   | Automated Action                                                                                |
| ----------- | ----------------------------------------------------------------------------------------------- |
| **Tables**  | Generates a description summarizing the table's content (e.g., "Provides regional sales data"). |
| **Columns** | Infers a semantic field type: Category, Text, Date, or Metric.                                  |
| **Naming**  | Creates a display name for use in the UI.                                                       |
| **Context** | Generates a short explanation for what each specific column represents.                         |

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**Note**: All AI-generated metadata remains editable. Manual refinement is required for ambiguous columns to ensure visualization accuracy.
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**Refresh schema:** If your database structure changes (e.g., added tables or modified columns), use the Refresh Schema button. This triggers a re-scan of your metadata, ensuring Toucan-AI reflects the most current architecture and maintains query accuracy.
{% endhint %}

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### Usage Requirements

* **Timing**: Initiate analysis immediately after connecting a new database or whenever the underlying schema changes.
* **Prerequisite**: Analysis must be completed before configuring RLS or creating AI-powered dashboards.
* **Persona**: This feature is designed for Product Managers and Data Analysts preparing data for end-users.

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### Limitations

* **Sample-Based**: Inference is based on schema structure and sampled values; it is not an exhaustive data audit.
* **Scope**: Multiple schemas and tables can be selected for analysis within a single operation via the selection modal.
* **Manual Review**: Toucan.ai assumes users will validate generated metadata before production use.


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