AI-Powered Chatbots for E-Commerce and Healthcare Companies

Company Overview

Enhancing Customer Support with AI-Driven Chatbots!

Our client for this project is a reputed name in the North American healthcare market. This client faced issues managing user interactions and automating customer service procedures because he relied excessively on manual procedures, emails, and phone calls. Such a strategy did not provide customers with real-time information and personalized services.

Also, our client did not operate 24/7. Existing and potential customers demanded more from the client. Manual processes took up a huge chunk of resources and employee time. Even then, customers were dissatisfied due to longer waiting times and unsatisfactory service. We faced the challenge of developing a custom chatbot to suit diverse business needs.

Business Challenges

Accurate Responses

Earlier, the client had difficulty providing accurate responses to healthcare-related questions. It increased support time for customers, resulting in dissatisfaction. Our primary challenge was to train the language model on vast volumes of medical data to ensure accurate and relevant responses.
Relevant and Real-Time Information

The client found it challenging to integrate personalized responses with real-time information retrieval. So, we had to develop a chatbot that could provide dynamic yet customized responses. In addition to keeping up with updated subject matter information, the model had to adapt to individual user needs.
Scalability Without Compromise

The client demanded robust scalability and security features. At the same time, they didn’t want to sacrifice dependability or performance. Still, the present systems and procedures didn’t have scalability capabilities.

Business Solution

Retrieval-Augmented Generation (RAG)

We used retrieval-augmented generation to improve customer query resolution rates and reduce ambiguous responses. This technique involves combining large language models with real-time data retrieval. The benefits include accurate and timely responses plus reduced escalations.

Interactive Learning

Using Django effectively, we created a smart chatbot with a scalable backend. Next, we connected it to an advanced AI model to enable interactive learning. Besides providing improved responses over time, the chatbot can handle multiple users without impacting performance.

Contextual Understanding

Our newly designed chatbot also identifies keywords that match the user’s mood. At the same time, natural language processing capabilities help us understand the meaning of the sentences. Together, the sentiment analysis and contextual understanding provide accurate and empathetic responses.

Conclusion

It took five technical experts and 12 weeks to complete the project from start to finish. The client wanted an advanced chatbot to replace customer service executives during non-working hours. Furthermore, the chatbot had to provide accurate, real-time responses to specific questions. The road wasn’t smooth, but constant R&D and coordination with the client ensured we came out with flying colors.

After implementing the chatbot, we surpassed the client’s expectations. The client had set a target of 20% increase in user engagement. Our newly designed chatbot increased customer engagement by 30% and decreased response time by 40%. The chatbot can handle 200 conversations simultaneously, helping clients schedule appointments and answer specific product queries. Also, customer satisfaction improved drastically due to precise and tailored assistance.