Scalability and Sustainability in Chatbot and Mobile Application Development

Abstract

A primary concern in the development of mobile applications is ensuring that the apps can manage a rising user base without degrading the user experience or performance. Therefore, the application’s architecture needs to be able to handle several requests per minute. Considering the scalability aspect, we developed a multilingual Chatbot using RASA as the Natural Language Processing (NLP) library, flutter as the cross-platform application development framework, and Django framework for doing server-side manipulations. Our application showcases the best architectural and security practices in application development to make the model scalable over time and keep it free from any security threats. The entire work is divided into two main components: a mobile application for the chatbot and a desktop website where users can enter data in several languages to train a deep learning-based model for intent detection. The developed chatbot, designed to be a university bot, will automatically identify the user’s preferred language and provide responses in that language. We aimed to make the application user-friendly and as safe as possible.