Our client, a mid-market investment firm, provides robust and long-term investment options for investors with varied risk profiles. To better serve their clients and provide real-time support, the firm desired an AI-solution that would provide informative and personalized communications. Investment firms are always looking for ways to improve customer experience and satisfaction while reducing costs. In such a scenario, the need for a chatbot solution arises
The client faced several challenges, which warranted the need for a chatbot solution such as:
Our team used a deep learning model to develop & deploy a chatbot-based solution to understand and automatically reply to customer queries. The model was trained on a large dataset of customer queries and responses to understand the context of the queries and provide accurate responses. As the chatbot interacts with more customers, the model learns and improves its responses over time. The chatbot can also handle complex queries by understanding the intent behind them and providing relevant responses.
Some of the functions which the chatbot was programmed to handle included:
For developing the chatbot, Quantilus leveraged Rasa for the Chatbot Development Framework, Python, PyTorch for the Deep Learning Framework, NLTK for the Natural Language Processing Library, Django for the Backend Framework, PostgreSQL and AWS for the Cloud Platform.
Benefits of the chatbot solution include:
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