RAG Based Chatbot to Boost Efficiency

The project involved developing a financial chatbot based on the Retrieval-Augmented Generation (RAG) system. The primary aim of the chatbot is to serve as a centralized knowledge base for employees, enabling them to quickly and effectively find answers to document-related questions. These documents include company policies, frequently asked questions, rules, handbooks, guides, technical walkthroughs, and more. By leveraging the RAG system, the chatbot integrates document retrieval with AI-based answer generation, ensuring accurate and contextually relevant responses.

This solution was designed to improve employee productivity by streamlining access to information, thus enabling them to cater to customers more efficiently. The chatbot ensures that the knowledge base is always up to date by syncing the latest document changes, thereby guaranteeing accurate responses to queries. The AI model used in the system detects relevant documents based on the query and analyzes their content to extract precise details required to generate answers. This functionality is of immense value to organizations seeking to improve knowledge accessibility, enhance employee performance, and deliver better customer service.

Client Name: Confidential

Industry: Finance

Location: United States

Python, FastAPI, PostgreSQL, SQLAlchemy, AWS EC2, , NumPy, Pandas, Matplotlib, Seaborn, FAISS, Nginx, Gunicorn, OpenAI