In Development

Banking LLM Assistant with RAG

Biznesni Rivojlantirish Banki

⚡A bank can't ship an assistant that hallucinates policy - every answer about products, fees, or procedures has to trace back to an authoritative document.

⚠️Internal knowledge was spread across many long documents; finding the right clause manually was slow for both customers and support staff.

⚙️Built a Retrieval-Augmented Generation pipeline - documents chunked and embedded into a vector store, relevant passages retrieved per query, and the LLM answers strictly from retrieved context.

🛡️Built an NLP intent classifier that auto-categorises customer requests and routes them, plus an LLM-backed chatbot / virtual-assistant layer on top.

🚀Result: a document-grounded assistant answering internal-policy and product questions, lowering the volume reaching human agents.

↳ LLM chatbot and internal-document Q&A grounded in retrieval - answers tied to the bank's own documents instead of model guesswork

LLMRAGLangChainHugging FaceNLPFastAPI