bankai-rag-platform

BankAI RAG Platform is a research project for building a banking focused Retrieval-Augmented Generation system. The platform is designed around a simple problem: Banking organizations have large volumes of policies, manuals, financial reports, regulatory material, product documentation, research, and operational knowledge. Finding the correct information quickly and generating an answer that remains grounded in the source material is difficult. BankAI explores how modern LLMs, semantic search, document retrieval, embeddings, and data-engineering pipelines can be combined to create a domain-specific information-access layer for banking. The project goals include building a reusable banking-domain RAG architecture, ingesting and organizing financial and banking knowledge, converting documents into searchable representations, retrieving relevant information for a user query, providing LLM-generated answers grounded in retrieved context, preserving document/source metadata for traceability, establishing an evaluation framework for retrieval and generation quality, creating a foundation for enterprise-grade banking AI, and exploring security, governance, auditability, and model-risk controls. The objective is not simply to generate fluent text. The objective is to make the generated response useful, traceable, and grounded in retrieved evidence.