For experience specialists and developers to build and train AI assistants for multiple channels and languages.
Import existing packages from our package library for ready intelligence and domain understanding.
Easily add trigger phrases or training data by using our advanced text editor with Intellisense.
Create using experience models to build and manage complex conversational flows to provide rich actionable responses to user queries.
Knowledge graphs are used for efficient data representation, retrieval, power inference, reasoning and recommendations.
Easily build and visualize using our intuitive user interface.ai that helps build assistants fast.
Define contextual FAQs with respect to entities, experiences, packages and Custom tags.
Code snippets that can be reused that are part of the packages such as business rules and validations that can be coded through the UI.
Real-time data, visualizations and actionable insights for enterprises to continuously measure, analyze, and refine the assistant.
To make data-driven decisions for improving the performance of the AI assistant.
For a deeper understanding of the customer behavior, popular intents, channel-specific usage and other business metrics.
Get insights into training data performance and take targeted actions with Live tips on how to improve bot performance.
Learning models for clustering unmatched or unhandled queries based on closely matched experience to power efficient and faster training.
To suggest new experiences to developers and append AI assistant functionality based on the user interest.
After refining, run the regression test suite to ensure correctness and introduce new improved metrics.
To help connect to human agents for further assistance beyond the AI assistant.
The Assistant can route conversations to Customer Support Agents.
Allows developers to configure a Tagger in a live conversation and assist with query understanding.
After configuration, route to the right escalation channel based on various parameters and historical data.