Auto Apply

Add-on module required
You will only have access to the features described in this article if you have purchased the Classification Intelligence add-on module with your Records365 subscription.

Role required
To manage the Auto-Apply feature you need to be assigned to either the Application Administrator or Records Manager role in Records365.

Introduction

Once you have successfully completed a training run in Records365, you will be able to view the record categories that Records365 has been trained on. A health indicator is shown for each record category that represents Records365’s ability to correctly identify content of that category. With this information in hand, you will be able to determine which record categories are suitable for automatically applying suggestions.

Managing auto-apply

You can turn auto-apply ON/OFF for one or more record categories by performing the following steps:

  1. In the left-hand navigation menu, click on the Intelligence section and select Training
  2. Select the record categories that you want to turn Auto Apply on or off for
  3. Click the Auto Apply button
  4. On the sidebar, select ON or OFF
  5. Click Save

Please consider the following when using Auto Apply:

  • When a record category is suggested for newly discovered content or updates to existing content then it will be automatically applied to the record if Auto Apply is turned on for the suggested record category
  • When a suggested category is automatically applied to a record, it will no longer appear on the Manage tab of the Intelligence page

Managing auto-applied records

There are new fields available for records that have been automatically classified by Records365 Classification Intelligence. These new fields are shown on the record details page when drilling down into individual records.

  • Classification Type: Will be set to “Automatically Classified” when a suggestion has been automatically applied
  • Prediction Health: Shows a health indicator for the suggestion made by the Classification Intelligence engine
  • Experiment ID: Shows the identifer of the model that made the suggestion