  Since the advent of chatbots in the commercial sector, they have been widely
employed in the customer service department. Typically, these commercial
chatbots are retrieval-based, so they are unable to respond to queries absent
in the provided dataset. On the contrary, generative chatbots try to create the
most appropriate response, but are mostly unable to create a smooth flow in the
customer-bot dialog. Since the client has few options left for continuing after
receiving a response, the dialog becomes short. Through our work, we try to
maximize the intelligence of a simple conversational agent so it can answer
unseen queries, and generate follow-up questions or remarks. We have built a
chatbot for a jewelry shop that finds the underlying objective of the
customer's query by finding similarity of the input to patterns in the corpus.
Our system features an audio input interface for clients, so they may speak to
it in natural language. After converting the audio to text, we trained the
model to extract the intent of the query, to find an appropriate response and
to speak to the client in a natural human voice. To gauge the system's
performance, we used performance metrics such as Recall, Precision and F1
score.
