AWS Public Sector Blog
Category: Amazon Kendra
Anduril unleashes the power of RAG with enterprise search chatbot Alfred on AWS
Anduril Industries, a defense technology company, has launched Alfred, an internal enterprise search chatbot powered by cutting-edge Retrieval-Augmented Generation (RAG) architecture. By using Amazon Web Services (AWS) services, such as Amazon SageMaker, Amazon Kendra, and Amazon DynamoDB, on the secure AWS GovCloud (US) Regions, Anduril has built a robust and scalable data infrastructure that can support Alfred’s growing knowledge needs.
Use modular architecture for flexible and extensible RAG-based generative AI solutions
In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different Retrieval-Augmented Generation (RAG)-based generative AI resources in a flexible way. This solution provides several benefits, along with faster time-to-market and shorter development cycles.
4 ways conversational AI and Amazon Lex help the public sector transform customer engagement
Public sector organizations want to innovate the way they engage their communities to make information easier to access, improve user experience, expand their reach by supporting multiple communication channels, and improve efficiency and scalability through intelligent automation. Conversational artificial intelligence (AI) and chatbots can be used to transform the customer experience, enhance engagement, improve services, and help scale more simply.
AWS launches machine learning enabled search capabilities for COVID-19 dataset
As the world grapples with COVID-19, researchers and scientists are united in an effort to understand the disease and find ways to detect and treat infections as quickly as possible. Today, Amazon Web Services (AWS) launched CORD-19 Search, a new search website powered by machine learning that can help researchers quickly and easily search tens of thousands of research papers and documents using natural language questions.