AWS Public Sector Blog

In this post, we explore how The Chronicle of Higher Education harnessed the power of generative AI and Amazon Bedrock to build Chron: a search assistant that unlocks the full value of The Chronicle’s rich archive for educators, administrators, and all higher-education professionals.

Recently, the AWS Large Language Model (LLM) League took center stage at the AWS Summit Washington, DC 2025, captivating attendees with its hands-on approach to fine-tuning large language models (LLMs). Read this post to learn more.

In this post, we walk through creating a Retrieval Augmented Generation (RAG)–powered chat assistant using Amazon SageMaker AI and Amazon Kendra to query donor data on AWS.

With over a decade of experience supporting more than 2,000 school districts across North America, Panorama Education had already built powerful tools for integrating academic, attendance, behavior, and life skills into one unified view of student progress. To unlock the value of that data securely, Panorama built Solara, a generative AI platform built on AWS. Solara is designed to help educators make sense of student data faster, design personalized student improvement plans, and reduce administrative burden while maintaining trust and data privacy at scale.

This significant growth in military unmanned aerial vehicles, which serve as a proxy for the broader autonomous systems market, underscores the urgent need for scalable, secure, and efficient software deployment solutions. As the number of autonomous platforms increases, so does the complexity of managing their software systems. In response to this growing challenge, Shield AI and AWS have collaborated and successfully demonstrated a scalable architecture and proof of concept (PoC) that addresses challenges often seen when deploying autonomy software and machine learning algorithms onto autonomous systems.

As more and more public sector organizations deploy generative AI workloads, we are increasingly asked what can be done to make sure that these workloads are resilient to failures. In this post, we discuss the key factors that mission-based organizations should consider so that their generative AI workloads are resilient to failures.

To help researchers reclaim their time and focus on discovery, Indiana University developed a secure, scalable, and cost-effective Automated Transcription Service (ATS) built on AWS. Since launching, the service has supported nearly 60 projects across 16 departments and has become a model for how universities can use cloud-native tools to affordably streamline research workflows and protect sensitive data.

Learning Upgrade worked with AWS to modernize its platform and ultimately migrated fully to the cloud. This strategic shift empowered Learning Upgrade to scale globally, respond faster to new opportunities, and better serve learners in dynamic, resource-constrained environments. Read this post to learn more.

In this post, we use AWS Glue, Amazon Athena, and Amazon QuickSight to build a dashboard that tracks costs associated with AWS and third-party providers. This single pane of glass allows for quick monitoring at the senior leadership level, with the ability to dig further into specific spending categories for tools and environments. Additionally, this data system makes further analytics possible.

By using generative AI, government agencies can analyze large amounts of data to identify patterns and insights, streamline manual processes, and deliver faster, personalized, more accessible services to citizens while maintaining the highest standards of accuracy and compliance. Read this post to learn more.