This project aims to improve the customer service of a call center by developing a chatbot that can handle frequently asked questions from clients. The Google dialogue flow technology and Python will be used to create the chatbot. The objective is to shorten the time clients spend talking to representatives and boost call centre productivity, which will improve customer happiness and boost the company’s profitability.

Business Challenge:
For a website of a call center due to huge number of client queries resulting in high engaging time which was a big problem where many clients had to wait for their turn. This resulted in poor customer support system.

Solution:
By developing a chatbot that would be capable of catering to frequently asked questions by the clients, the problem at hand could be resolved. Moreover, this would improve the efficiency of the call center.

AI Engine Results:
Chatbot engine will first try to capture the user’s intent from the query and respond accordingly.

Results:
By implementing this chatbot, the engaging time would decrease plus more clients would be dealt with in less time. Resulting in improved customer service.

Client Success:
The organization would become more profitable and this would result in an increase in their customer satisfaction.

Technology Stack:
Language: Python
Technologies: Google dialogue flow

Our AI-powered system generates personalized news suggestions using a probabilistic prediction model created by analyzing reader behavior, holding their attention for longer and saving time. As a result, website traffic rises by 5 to 8%, and the material is improved based on reader preferences. Deep learning and NLP models were used in the Python-based system, which was then saved on Apache Cassandra.

Business Challenge:
News websites aim to increase their readers and traffic on their websites. However, they fail at captivating their reader’s attention and interest for longer time span.

Solution:
A probabilistic prediction model can be generated by studying the liking, disliking and digital footprints data gathered from records of previous readers.

AI Engine Results:
By using the results of probabilistic prediction model the liking and disliking of the readers can be predicted and with the aid of those predictions recommendations can be made to readers according to their interest.

Results:
Readers interest can be captivated for longer time span. It will save their time in searching for news and there would be fewer chances for them to miss on news of their interest.

Client Success:
The web traffic over the website would increase by 5-8%. The client would have more systemized data depending on liking and disliking of readers, and this can aid in improving the content of the website.

Technology Stack:
Language: Python
Models: Deep Learning Models, Natural Language Processing
Storage: Apache Cassandra