Businesses lose valuable time answering repetitive customer questions. I built an AI-powered customer support agent that provides instant, accurate responses by searching company documentation instead of relying solely on a language model.

The assistant uses Retrieval-Augmented Generation (RAG) to retrieve relevant information from PDFs, FAQs, user manuals, and internal knowledge bases before generating responses. Every answer includes source citations, allowing users to verify the information with confidence.

✅ AI-powered customer support ✅ Knowledge Base & PDF ingestion ✅ RAG-based semantic search ✅ Conversation memory ✅ Source citations for every response ✅ Enterprise admin dashboard ✅ Responsive web interface for desktop and mobile

Python

Django

OpenAI

LangGraph

PostgreSQL

RAG (Retrieval-Augmented Generation)

• Reduced repetitive support requests by providing instant AI responses • Improved customer experience with 24/7 availability • Increased trust through citation-backed answers • Centralized company knowledge into a searchable AI assistant • Scalable architecture for enterprise support teams

Building AI applications isn't just about integrating an LLM—it's about designing reliable systems that deliver accurate, explainable, and production-ready results.

If you're looking to build an AI-powered customer support platform, internal knowledge assistant, or enterprise RAG application, I'd be happy to discuss your project.

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