Virtual Assistant Features for Intelligent Banking
Context-aware, multi-turn conversations, with the help of integrated dynamic world models, packages, and contextual FAQ builders.
Automated Conversation Management
Develop context-aware conversations with minimal effort
It allows developers to delegate powerful, context-aware multi-turn conversation handling to the platform. With minimal coding required; developers can focus on coding the fulfillment logic of a user task.
ACM uses an Experience Model, rendering sophisticated task completion. Using in-built context-switching, memory, and dialog management; interfaces Automated Conversation Management uses a structure that groups – inputs, properties and activities required for fulfilling a user task.
ACM uses an Experience Model, rendering sophisticated task completion. Using in-built context-switching, memory, and dialog management; interfaces Automated Conversation Management uses a structure that groups – inputs, properties and activities required for fulfilling a user task.
Intelligent Assistance Management
The best customer assistance by symbiosis of Virtual Assistant and Human Assistant
Intelligent Assistance Management helps converge a human to a conversation and a virtual assistant based on the triggers during end-user conversations. The AI assistant can be sentiment and tone aware to route conversations to Customer Support Agents.
Continuous Human Assisted Learning
Inbuilt continuous learning loop for the assistant
CHAL helps the assistant learn continuously based on developer input. User conversations can be annotated and used for learning. Machine learning models help suggest new experiences to developers. Pseudo- Anonymizing and Annotating user conversations using both automated processes and Human-in-the-Loop.
Your customers will love it when the assistant knows their needs
Integrated Dynamic World Models
Combine the normally separate language, data, and execution models
IDM consists of data models, language models and execution models to power rich conversations. Data models help in input-query-ambiguity-resolution, inference of new knowledge and relationships. Language models with advanced NLU to enable developers to semantically define language at a granular level.
Packages
Containers which logically group - Data, Language and Execution models
Developers can create various assistants with speed and precision by logically grouping the models for reuse. Select or override existing models to enable AI assistant functionalities. Packages can be reused across assistants enabling developers to rapidly create and deploy AI assistants with rich functionality.
Contextual FAQ Builder
Build powerful contextual conversations
Empowers developers to create rich multimedia responses for language and channels. Quick and easy FAQ builder at its core to create and associate FAQs to build contextual conversations. Advanced FAQ builder enables developers to create and associate FAQs with IDM or custom tags to power contextual conversations.
Advanced Natural Language Understanding Engine
Semantic Language Models to lend context to data models
ANLU enables developers to achieve high accuracy, provide coreference resolution models, and provide Out of the box data parsers for a wide range of data. Developers can achieve high accuracy easily for complex and domain specific queries by using Semantic Language Models thus keeping data models in context of the user query. Provides coreference resolution models which enable developers to better understand and appropriately respond to user queries.
