# EnateAI for Email EnateAI offers a number of different AI integrations to support how you can more easily deal with incoming emails, turning unstructured data into structured data, fast and adding extra insights into the information you can get from your mails. {% embed url="" %} The configuration of the EnateAI adapters is super simple and is just a click of a button to enable. EnateAI for Email contains the following integrations: * [Email Classification](https://docs.enate.net/whats-new/2023.3/2023.3-changes-overview/new-integrations-enateai-for-email#email-classification) - analyzes incoming emails which result in the creation of a new Ticket and automatically classifies and categorizes the Ticket, saving agents having to do this manually. * [Email Data Extraction](https://docs.enate.net/whats-new/2023.3/2023.3-changes-overview/new-integrations-enateai-for-email#email-data-extraction) - auto-populates important information from incoming emails which are creating new work items into data fields in the custom cards of your Tickets and Cases, saving agents from having to do this manually. * [Thank You Email Evaluation](https://docs.enate.net/whats-new/2023.3/2023.3-changes-overview/new-integrations-enateai-for-email#thank-you-email-evaluation) - automatically detects whether incoming emails to a Resolved work item are just simple 'thank you emails', and if so then have them automatically moved back to a state of 'resolved' without agent users having to manually perform such repetitive checks. * [Sentiment Analysis](https://docs.enate.net/whats-new/2023.3/2023.3-changes-overview/new-integrations-enateai-for-email#sentiment-analysis) - analyzes the content of every incoming email, determines their sentiment - e.g. if they're positive or negative and displays this information to your users to they can determine the sentiment of an email at a glance. You've also got the flexibility to control which of these Integrations run on which Mailboxes - keeping all your options open as to where you want AI to support your agents. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.enate.net/enate-help/enateai/enateai/enateai-for-email.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.