The Intents Self Service tool in ASAPP’s AI Console provides a streamlined interface for managing intent classification. This automated, self-serve UI allows customers to:
Upload, create and modify intent labels without support team intervention
Manage intent label hierarchies during onboarding or as business needs evolve
Consolidate or create more granular intents
Deploy changes to production within minutes
The tool leverages GenAI (LLMs) to enable:
Zero-day customer onboarding
Self-service intent management through an intuitive frontend
This service is built on a front-end interface, no separate API configuration is required from the customers.Import Flow of Intents
Upload a csv/text file with the intent details, Refer to the provided links in the guidelines to familiarize yourself with the required file format and the necessary information for intents.
Select the desired file and upload the file
Review selected file before deploying the intents
Review and verify your uploaded intents
Adding a new Intent to the hierarchy
Review the existing Intent hierarchy and click ‘New Intent’ from the ‘Add’ button top right
Add intent details such as the intent label, parent intent, and description. Refer to sample file in case any further clarifications
Click on create intent to add the intent to the hierarchy
What file formats are supported for uploading intents label hierarchy?ASAPP’s Intent Self-Serve tooling supports CSV and Excel file formats for uploading intents.
Can I edit or update my intents hierarchy after uploading?Yes, the tooling functionality allows you to edit or update your intents at any time after uploading them, ensuring you can refine and improve your intent classification as needed.
Do I need to have technical expertise to use the Intent Self-Serve tooling?No, intent front-end tooling is designed to be user-friendly, with no API configuration required. The intent labels can be easily uploaded, created, and managed without needing any technical assistance.
ASAPP’s native Salesforce plugin now includes AutoSummary integration. This enables Salesforce administrators to quickly install and configure AutoSummary within their Lightning environment.The low-code plugin allows administrators to deploy AutoSummary in hours without complex integration work. Once enabled, the system automatically generates and saves conversation summaries to Salesforce records, eliminating the need for manual note-taking by agents.
AutoSummary works seamlessly alongside ASAPP’s AutoCompose for Salesforce.
How It Works video
Watch the video below for an overview on AutoSummary for Salesforce:
ASAPP introduces two feeds to retrieve data for free-text summaries generated by AutoSummary and edited versions of summaries submitted by agents as feedback.These two feeds enable administrators to retrieve AutoSummary data using the File Exporter API:
Free-text feed: Retrieves data from free-text summaries generated by AutoSummary.
This feed has one record per free-text summary produced and can have multiple summaries per conversation.
ASAPP introduces the AutoSummary Sandbox, a testing environment in AI-Console that allows administrators to validate and experiment with summary generation before deploying to production. The Sandbox supports both voice and messaging conversations, letting users simulate interactions or upload existing transcripts to preview how AutoSummary will perform.
AutoSummary's intent and free-text summary generated in the Sandbox.
The Sandbox starts with baseline contact center models and can be upgraded to use your custom-trained models once deployed. This allows teams to preview summary formatting and validate outputs throughout their implementation journey.
Free-text summaries are always available, while intent and structured data require additional configuration.
How It Works video
Watch the following video walkthrough to learn how to use the AutoSummary Sandbox:
AutoSummary now supports model retraining using agent feedback. The feedback endpoint receives free-text paragraph summaries submitted by agents, and uses the difference between the automatically generated summary and the final submission to improve the model over time.