The client is a leading brand in the retail industry, managing millions of customer interactions daily. As a major player in the sector, the company recognizes the importance of quick customer support to improve customer satisfaction and operational efficiency.
They wanted to build a robust Retail AI chatbot that handles increasing customer queries effectively, reduces response times, and ensures customer support automation.
Increasing Customer Queries: With the growing number of customer interactions, the client needed AI for customer service to manage and resolve queries promptly without overburdening the customer support team.
Context-Aware Conversations: The system needed to understand and process customer inquiries with context-aware responses to ensure a seamless interaction.
Escalation Management: The AI model had to know when to escalate queries to human agents, particularly for more complex issues that required personalized attention.
Scalability: The chatbot solution needs to handle diverse queries across regions and languages to ensure services remain responsive and reliable at scale. Thus, the development of a multilingual chatbot was required.
Complexity of Knowledge Base: The chatbot needed access to customer-related knowledge, including product details, order histories, and troubleshooting guides, to share the information in real time.
We developed a Generative AI Chatbot using
Integrated advanced natural language processing (NLP) tools like Hugging Face and Langchain to improve the chatbot’s ability to understand and respond to varied customer queries.
The AI model was fine-tuned with custom intents and workflows tailored to the client’s specific needs to provide context-aware and personalized responses.
Integrated VectorDB (FAISS) into the chatbot for knowledge retrieval. It could efficiently store and search through large-scale knowledge repositories such as:
VectorDB enables fast retrieval of relevant information by converting text-based data into vector representations. It empowers the chatbot to provide highly accurate and contextually relevant responses from an expansive knowledge base.
The AI chatbot development with escalation workflows allows for the seamless transfer of more complex issues to human agents.
To ensure smooth operation and integration with existing systems, we used Flask as a lightweight framework in the NLP chatbot to ensure smooth interactions between front-end and back-end.
Implemented tracking and analytics to measure key performance indicators, such as response times, resolution rates, and customer satisfaction. It helps in the continuous improvement of the system based on real-time feedback and performance data.
75% Improvement in Customer Satisfaction
80% Reduction in Response Times
43% Faster Issue Resolution
28% Cost Savings due to Automation
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