How to create an NLU Model

To create a new model, proceed as follows:

  1. Navigate to NLU → NLU Models section.


  2. Click Create. The following dialog box opens:

3. Fill in the fields below:

  • Name: Model name

  • Group: Group of users who can access the model

  • Domain: Based on the selected domain, an out-of-the-box understanding of intents and entities will be available using Omilia’s pre-tuned xPacks. Regardless of the selected domain, you can add your own data to augment your model’s understanding using Machine Learning. Available domains are the following:  

    • The Universal domain offers out-of-the-box Entities understanding only. In case you want to also include intents, you can build your own fully custom intent understanding.

    • The Custom domain has no out-of-the-box understanding and you can fully customize it by adding your own data and uploading your own custom NLU Logic (which is your own NLU file developed on Conversation Studio).

    • Insurance

    • Telecommunication

    • Car Retail

    • Banking

    • COVID

    • Energy

  • Language: The NLU model’s Language. Out-of-the-box understanding is for the language you select.

  • Version: Software version. The default value is 2.7.2.

  • Training Set: Allows you to add your own custom training data to augment the model’s intent understanding using Machine Learning.

    Add a file with training data in TXT, CSV, or TSV. This is an optional field.

  • Description: Provide a short description of the NLU model. This field is optional.

4. Click Create to confirm. The model is created.

Model drill-down page

After having created a model, you are forwarded to the model drill-down page.




The model name.


The language selected for the model.


The model domain.


The model identification number.

To copy the ID to the clipboard, click .


The name of the user the model was created by.


The model status. The following statuses are possible:

  • Not Ready: The model is not ready and cannot be deployed. Add your own custom datatrain it and then deploy it.

  • Working: The model is currently being trained with your custom data.

  • Ready: The model is ready to be used. Go ahead, deploy it and test it!

  • Failed: The model training has failed.

Depending on the model domain, the drill-down page may look different:

  • If you have created a model with a specific domain, all the out-of-the-box intents, and example utterances, if available, will be visible there as shown below:


  • If you used a TXT, CSV, or TSV file to add your custom data (intents and utterances), they will also be visible on the drill-down page.

  • If you have created a custom domain model, no out-of-the-box understanding is available and you have to build it from scratch.