Digital Network Assistant Chatbot for a Large Telecom Client

A large telecom client’s primary objective of this project is to help 10,000+ Network and Technology specialists across the globe ranging from circuit engineers to field operators who manage extremely complex provisioning, troubleshooting, and maintenance workflows under significant time pressure with a Digital Network Assistant.

The functional goals of this solution are: (1) Improved self-service capability, (2) Simplified information search, (3) Easy Troubleshooting. The solution must enable users through a conversational interface on Web, Mobile, and Instant messaging platforms, and be available 24x7.

The functional scope of work of this project encompassed:

Use the conversational interface to assist users in handsfree mode
Integrate with multiple AI / ML platforms such as Google’s Dialog Flow, Amazon’s AI, etc.
Enable creation of multiple Bots or multiple instances of the same Bot to serve different groups across the globe
Build an interface where business users without the technical background can create new intents and train the Bot with new skills

The technical scope includes:

Building a platform to centrally manage the data, intents, skills to avoid conflicts and redundancy
Building AWS infrastructure to manage and support Bot lifecycle
Building the components and action handlers to fetch the corresponding data and display the information through an interface
Building CI/CD pipeline to quickly develop, test, and deploy the new skills into production
Building a user interface to help business and operational users to develop and train the Bot with new skills with code-less development architecture

The operational scope includes:

Mining the skills of different groups and understanding the frequency of the usage
Prioritizing the skills for development and quantifying the benefits
Queuing the skills for development into Sprint Backlogs
Training the new users on how to leverage Chatbot in their day to day tasks

Our Solution:

A chatbot is a tool to retrieve information and generate human-like conversation. It is mainly a dialog system aimed to solve/serve a specific purpose. To accomplish this goal, it needs:

an interface (dialog or keyboard)
NLP service to interpret the command
action handlers to retrieve data
secure connections and authentication
data and intent storage
orchestration services
messaging platform integration

A Chatbot requires a robust framework to tie these components together and serve its purpose. The following figure illustrates the Chatbot framework:

Chatbot Framework

The solution was built on the following best practices:

Decouple the dependency between UI and NLP Backend – Build custom UI, use NLP via APIs for easy switching.
Save the intents and dialogs within the local database – Improves performance and independence from provider.
Design for multiple Bot Instances – To serve multiple business lines or geographies effectively.
Develop different services – For Training, Testing, and Production lifecycle management.