Muhammad Hamza
AI Engineer | LLM AI Agents & RAG Automated Support Systems
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Retrieval-Augmented Generation (RAG) Conversational Agent
Built using Python and FastAPI, the backend leverages RESTful APIs and asynchronous processing to ensure high-speed data handling and seamless system performance. The architecture is optimized for scalability, security, and maintainability, making it suitable for high-volume processing environments.
A dedicated test UI was developed to validate system functionality, improve usability, and ensure a smooth user experience. The interface is structured with staff convenience in mind, enabling intuitive navigation, simplified data access, and efficient task management — ultimately improving productivity and reducing operational complexity.
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RAG-Based AI Assistant for Automotive Customer Service
Developed an intelligent AI-powered chatbot for an automotive company to automate customer support and sales inquiries. The system handles car model details, pricing, availability, financing options, service bookings, and test drive scheduling. Integrated with a knowledge base using RAG to provide accurate, real-time responses from company data. Designed to improve customer engagement, reduce response time, and increase lead conversion through 24/7 automated support.
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