ELIF: An End-to-End Architecture for an Observable and Continuously Learning AI Assistant

Abstract

This paper details the end-to-end design and implementation of "ELIF," an enterprise-ready AI assistant for corporate knowledge management. The system is engineered to provide verifiable and accurate information to bank representatives by utilizing a Retrieval-Augmented Generation (RAG) framework. The knowledge base is built upon both static corporate documents (PDF, web content, JSON, Excel, PPTX) and dynamic user feedback. Its modular architecture, built on Python/Flask and orchestrated by LangGraph, ensures scalability and maintainability. A key engineering achievement is the closed-loop continuous learning pipeline. User feedback is not merely logged but is actively processed by a Large Language Model (LLM) to generate structured Q&A data. This data autonomously enriches a FAISS vector database, allowing the system to learn from interactions without manual intervention. The solution includes comprehensive user and admin interfaces built with React, offering features like performance analytics, chat history monitoring, and manual training triggers. Deployed via a REST API and integrated into Microsoft Teams, ELIF serves as a practical blueprint for building, deploying, and maintaining observable, self-improving AI systems in a corporate environment