This documentation supports the 20.08 version of BMC Helix Platform. 
To view an earlier version, select 20.02 from the Product version menu.

Leveraging conversation capabilities in your application

The goal of this use case is to enable a user to leverage conversation capabilities in an application by using the BMC Helix Platform chatbot framework.


In a support organization of a company, when a user wants to report an issue. To report an issue, usually a help desk agent needs to know how to use an application to create a ticket, to classify the issue, assign the issue to a support group, and so on. The support organization has a large number of issues that need to be reported daily and the help desk agents perform the issue reporting task. The process of reporting issues is manual, involves learning, requires resources, and can be error prone.

To overcome the challenges, the head of the organization decides to use an application that has conversation capability. A developer then builds a BMC Helix Platform application that uses the chatbot framework. The chatbot framework allows the application to include the built-in cognition required to report an issue based on a conversation with the end user. The users now directly interact with the application chatbot in natural language and the chatbot reports the issue on the behalf of the user. In this way, the organization saves resources and perform tasks accurately without any human intervention.

Roles involved in this use case

The following roles are involved in this use case:

  • Developer
  • Administrator


The chatbot framework provides the following capabilities to develop chatbot applications:

  • Integrates with chatbot collaboration service (such as Slack)
  • Provides open architecture to integrate with third-party collaboration services (such as Facebook Messenger)
  • Integrates collaboration service users with BMC Helix Platform users
  • Performs chat with users, updates response message and chat context with chat action results
  • Manages chat context
    • Persists chat context
    • Makes chat context available to all the nodes in a cluster (server group environment)
  • Performs efficiently in high load conditions by using horizontal scaling
    Event mechanism is used to integrate the chatbot provider and BMC Helix Platform so that each node in a cluster can handle chat events. When load increases, new nodes can be added to the cluster and the newly added nodes can immediately join a conversation even in an ongoing chat session.

Related topics

Leveraging cognitive capabilities in your application

Leveraging machine learning metrics to improve cognitive service data sets

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