Knowledge Assistant
The Knowledge Assistant explores how generative AI can improve access to organizational knowledge, procedures, job aids, and support resources. This prototype demonstrates how a conversational interface can help employees locate information more efficiently, reduce time spent searching across multiple systems, and support workforce enablement through centralized knowledge access.
The demonstration includes a simulated knowledge assistant capable of answering common operational questions and illustrating how future Retrieval-Augmented Generation (RAG) solutions could support enterprise knowledge management.
Overview
The Knowledge Assistant explores how generative AI can improve access to organizational knowledge, job aids, and support resources.
Problem
Employees often spend significant time searching for information distributed across multiple systems and documents.
Solution
The assistant provides a conversational interface that enables users to quickly locate relevant information and resources.
Key Features
Knowledge retrieval
Natural language interaction
Document search and summarization
Support for common questions
Centralized information access
Technology
Python • Generative AI • Retrieval-Augmented Generation (RAG)
Future Opportunities
Potential future enhancements include enterprise document repositories, workflow support, and role-based guidance.
Watch Prototype Demo
Watch a short walk through of the Knowledge Assistant described above. This video demonstrates the working Streamlit prototype, including conversational knowledge retrieval, common support questions, and simulated responses based on organizational guidance content.
The demonstration illustrates how generative AI can improve access to information, reduce support effort, and support workforce enablement through a centralized conversational experience.
View Code on GitHub
This prototype was developed in GitHub using Python and Streamlit to simulate an AI-enabled knowledge assistant experience. Explore the repository to review the source code, project documentation, and implementation details. Future enhancements include Retrieval-Augmented Generation (RAG), document ingestion, enterprise knowledge repositories, and source-cited responses.