Your customer will love it when the assistant knows their needs
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.
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.
CHAL helps the assistant learn continuously based on
developer input. User conversations can be annotated &
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.
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.
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.
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.
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.