AWS has launched an AI-powered knowledge management system designed to help organizations capture, maintain, and deliver institutional knowledge across various industries. The solution leverages AWS services to create a customizable, scalable platform that enables knowledge workers to access procedures and policies through natural language queries, while subject matter experts can upload documentation to preserve their expertise. The system supports voice interaction, text-based queries, and desktop browser access, adapting to the specific operational contexts of each industry. This solution aims to address the challenges of managing tribal knowledge that often disappears when key personnel leave, creating knowledge gaps that impact efficiency and innovation. Source: awsml
The system uses AWS services to build a scalable, configurable knowledge management solution that organizations can adapt to their needs. At its core, the architecture combines advanced AI capabilities with robust cloud infrastructure to deliver an intuitive, responsive knowledge delivery system. The foundation of the solution is a browser-based interface that supports both text and voice interactions, making knowledge accessible through natural conversation. This interface connects to a configurable avatar system that can work with any AI avatar solution, providing organizations with the flexibility to choose or change their preferred avatar technology. The knowledge-processing core uses Amazon Bedrock Knowledge Bases for managed Retrieval Augmented Generation (RAG), with institutional knowledge stored in Amazon S3 as the data source. Amazon Bedrock handles chunking, embedding, and retrieval, grounding each answer in the organization’s own documents. Source: awsml
The solution is designed to preserve institutional knowledge by allowing organizations to upload documents into Amazon S3, which are then ingested and made queryable through an automated sync process. Content management requires no technical expertise, as knowledge owners simply upload existing documents in supported formats such as Word, PDF, plain text, Markdown, or JSON. The system also includes a built-in DynamoDB cache that reuses previous answers for repeated questions, reducing variable AI inference costs. In testing, cache hit rates of 50–70 percent were achievable for workloads dominated by repeated questions. Source: awsml
