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Pega Intelligent Virtual Assistant features

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Pega Intelligent Virtual Assistant™ (IVA) is a chatbot that provides you with several useful features, depending on your version of Pega Platform. After you create, configure, and build a chatbot, users can interact with your application by using a social messaging platform, such as Apple Business Chat, Facebook, SMS/MMS, Twitter, and WhatsApp, to instantly ask for help, obtain more information, or report problems. The comparison matrices in the sections below provide a comprehensive view of feature support across different Pega Platform versions.

To learn more about IVAs that work as chatbots, see Pega Intelligent Virtual Assistant overview.
To learn about new chatbot and email bot features in Pega Platform version 8.5, see the What's New in Conversational Channels video on Pega Community.

Chatbot types

The following table lists available chatbots by Pega Platform version.

Chatbot type 7.3.1 7.4 8.1 8.2 8.3 8.4 8.5
IVA for Alexa
IVA for Web Chatbot
IVA for Facebook (legacy channel)
IVA for Facebook (Unified Messaging channel)
IVA for SMS/MMS - Twilio (Unified Messaging channel)
IVA for Apple Business Chat (Unified Messaging channel)
IVA for Twitter (Unified Messaging channel)
IVA for WhatsApp (Unified Messaging channel)
In Pega Platform 7.4, the IVA for Web Chatbot is called IVA for WebChat.

Run time

The following table lists available run-time features by Pega Platform version.

Run-time feature 7.3.1 7.4 8.1 8.2 8.3 8.4 8.5
Rich user interface menu (IVA for Facebook and Web Chatbot).
Date picker control (IVA for Web Chatbot).
File attachments (IVA for Web Chatbot).
Time and date picker control (IVA for Web Chatbot).
Simple forms (IVA for Web Chatbot).
Framework for integration with Knowledge Management articles (IVA for Web Chatbot).
Menu of commands displayable as simple menu buttons or text replies.

Design time

The following table lists available design-time features by Pega Platform version.

Design-time feature 7.3.1 7.4 8.1 8.2 8.3 8.4 8.5
Add new commands in the preview console.
Edit questions in the preview console.
Test the chatbot using the preview console.
Use an entity-to-case property mapping definition.
Build a chatbot using the preview console.
Create a single channel to expose on multiple messaging platforms (Unified Messaging).

NLP analysis

The following table lists available NLP analysis features by Pega Platform version.

NLP analysis feature 7.3.1 7.4 8.1 8.2 8.3 8.4 8.5
Perform NLP analysis of each chat message from users.
Suggest case commands that are based on NLP in order to start a business case.
Highlight detected entities in the chat message in the preview console.
Supports decisioning capabilities in chat conversations.
Supports keyword-based entities.
Supports named entity recognition, including multiple entities in the text analytics model.
Supports the iNLP text analyzer and uses outcomes from multiple text analyzers.
Change the default analyzer in the advanced configuration and display the text analysis settings on the Behavior tab.
Configure the chatbot to detect small talk separately.

Training models

The following table lists available training model features by Pega Platform version.

Training models feature 7.3.1 7.4 8.1 8.2 8.3 8.4 8.5
Use the Training data tab to perform inline editing of records.
Send feedback about training data to the text analytics model in the IVA channel.
Use bulk Mark reviewed and Delete actions on the Training data tab.
Update the text analytics model with one click.
Edit and mark up the training data records on the Training data tab.
Create, update, and remove entities on the Training data tab.
Highlight entities in the preview console.
Create both entity and topic feedback records for text analytics models.
Click Save as to create a copy of out-of-the-box entities in the application ruleset.
Export both reviewed and unreviewed records together with entity models.
Import both reviewed and unreviewed records together with entity models.
Send feedback to the text analytics model when removing an entity.
Text analytics model versioning.
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