Use Case

Dialogue Management System

With growing competitive landscape for businesses, it is essential for business to provide their customers with the best support and service but this often comes at a cost. It’s very difficult to provide customers with 24/7 support and service without compromising the customer experience. Customers often face long waiting times and poor engagement which ultimately impacts the operational efficiency of the business and reduces profitability. 

  • Increase in sales because of 24/7 availability
  • Increase in cost savings resulting from automation and small customer service teams
  • Increase in customer experience resulting from personalisation


With modern AI technology we can build Dialogue Management Systems or domain oriented chatbots. These chatbots can integrate with websites and apps and offer customers 24/7 support and service. This modern technology has huge operational advantage as it can allow us to build automated systems that can handle basic queries and issues, redirect the relevant queries to the right personnel and handle sales and support.

The Process

We have built our own proprietary NLP engine that allows us to train Dialogue Management systems for different domains such as ticket reservation system, hotel booking etc. The system is trained by conversing with a real human or a user simulator. The chatbot learns to understand it’s objectives and learns to ask questions that ultimately leads to its goal such as booking a ticket, ordering food etc. 

A real person from the business can interact with the dialogue agent and the dialogue agent can learn to perform different goal oriented tasks such as sales assistant, ticket booking, order handling etc. Once these agents are trained these can be deployed as widgets on websites, apps, facebook messenger bots etc.

Technical Bit

Our goal oriented dialogue system has 3 major components Natural Language Understanding, Natural Language Generation and Dialogue Management. The NLU component is responsible for parsing raw noisy text and extracting semantic frame from it. This is used by the dialogue management for learning and tracking. The Natural Language generation takes the output of the dialogue management system and generates response text to be understandable by humans. 

We use Reinforcement Learning for training AI conversational agents. Our system uses online learning algorithms which mean that they learn as they interact with users. The more they interact with users the better they get over time. 


Businesses have seen tremendous efficiency in sales assistant, customer support, order handling with chat bot deployment.

This report shows how businesses in the US alone can save up to $23 billion by automating 30% of the work done by contact centre staff.

The systems have generated better customer satisfaction and experience by saving users from long waiting times, handling basic queries without human assistance and increasing the quality of engagement. Businesses have seen increase in profitability and efficiency because of lower costs of employing humans.