Our conversational Artificial Intelligence with a personality uses the best of Natural Language Processing (NLP) techniques to address specific customer queries and take the conversation forward.
Our chatbot framework offers the following:
create decision-tree based conversations with an intelligent and interesting personality
use your own bank of questions and answers to steer your customers to quick, accurate answers
NLP and Learning
leverage NLP techniques with intent learning, entity extraction and context recognition
SO HOW DOES THIS WORK?
Tree-based Chat Flow
At the beginning of a user interaction, the chatbot ascertains the context and subject of the conversation.
Does your user want to apply for leave?
This dictates the next step in a conversation or decision tree that uses pre-determined questions and answers to chat with the user.
If intent is the action required, then entities are the variables needed to fulfil the action. Our proprietary algorithms detect and extract domain-specific entities from the user's message.
If the user has said, "I need to take leave on the 26th of this month," the entities are: leave, 26th.
Our FAQ engine uses basic NLP processing techniques to extract that a user is posting a query about a certain FAQ topic. It applies Information Retrieval techniques to retrieve the most relevant answer from the knowledge base.
A critical step in an AI-based conversation is the identification of the core action or Intent of the user's statements. We use machine learning and NLP techniques to identify the intent; essentially, a classification problem.
For our user, applying for a leave is the intent behind their query.
Natural Language to Query Generator
This module analyzes the intent and converts the identified entities into actionable commands for the system to understand and consequently, execute. And thus, complete the process.