# AI Entity Extraction (beta)

AI Entity Extraction allows you to specify a predetermined set of values for a field in a [Collection Schema](https://docs.credal.ai/user-guide/platform/agent-builder/configuration/add-connectors/document-collections/collection-schemas), and have an **LLM automatically extract these values** as we crawl data.

> For example, if you had a list of customer documents in Google Drive, or sales call transcripts in SharePoint, you could specify `Customer Name` as an entity and a list of possible customers to try to extract during syncing. This gives you automatic data curation and tagging, and allows users to use [Smart Filtering (beta)](https://docs.credal.ai/user-guide/platform/agent-builder/configuration/add-connectors/document-collections/smart-filtering-beta) on these entities just by asking questions to your agent.

Contact our Credal team support@credal.ai for help setting up AI Entity Extraction.

## Related pages

- [Connectors](./platform-agents-configure-steps-connectors-overview.md)
- [Data Sources](./platform-agents-configure-steps-connectors-data.md)
- [Actions](./platform-agents-configure-steps-connectors-saas.md)
- [Document Collections](./platform-agents-configure-steps-connectors-document-collections.md)
- [Smart Filtering (beta)](./platform-integrations-metadata-and-smart-filtering-smart-filtering.md)
- [Collection Schemas](./platform-integrations-metadata-and-smart-filtering-collection-schemas.md)
- [Bring Your Own Custom Data Sources](./platform-integrations-bring-your-own-data.md)
- [Credal MCP Servers](./platform-agents-configure-steps-connectors-mcp-servers.md)

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