Category: AWS Lambda

Government agencies handle vast volumes of bureaucratic documents daily, ranging from tax forms to medical records. This document-heavy workflow, often reliant on manual processing, can result in delays, errors, and increased operational inefficiencies, causing frustration among both employees and stakeholders. This post explores how intelligent document processing (IDP) solutions from Amazon Web Services (AWS) can modernize bureaucratic workflows, improve efficiency, and enhance service delivery within government agencies.

In this post, you will discover the key challenges public sector agencies face when it comes to real-time data access, and how Amazon Bedrock Agents and Powertools for AWS Lambda work together to address these challenges. The post also includes real-world use cases demonstrating practical applications in the public sector and technical implementation details and best practices.

AWS App Studio is a low-code development platform for users to build, deploy, and manage applications without extensive coding knowledge. It is designed to streamline the application development process, making it more accessible to a wider range of users, including business users, IT professionals, and startups that don’t have large specialized IT teams. This post guides you through the process of creating a user-friendly create, read, update, and delete (CRUD) application to efficiently manage birth certificate applications using AWS App Studio.

Today, generative artificial intelligence (AI) is at the core of many of the decisions and technologies businesses across the globe are implementing. The linchpin to ensuring that generative AI is effective depends on the foundation models (FMs) storing the data. To make sure that those FMs are pulling the correct data, Amazon Web Services (AWS) has developed a solution through Amazon Bedrock to generate SQL to answer user questions about the data. The solution was developed to support the Federal Institute of São Paulo (IFSP) and has optimized their decision-making.

In this post, we explore how AWS services, open source software, and bespoke source code can help address resource sharing enhancements requested by public sector customers using AWS Outposts. This solution aims to provide greater flexibility, control, and cost management for organizations leveraging Outposts in multi-account environments, specifically focusing on key AWS resources, including Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Block Store (Amazon EBS), and Amazon Simple Storage Service (Amazon S3) on Outposts.

In today’s fast-paced and competitive business landscape, companies are constantly seeking ways to enhance efficiency, streamline operations, and provide top-notch customer service. Artificial intelligence (AI) has emerged as a powerful tool to help companies achieve these goals while revolutionizing the way they interact with customers. In this post, you can read about the transformative potential of email-based virtual assistants—a standout among AI applications—powered by Amazon Web Services (AWS).

Seeking to make access to economic policy documents easier, the Mercatus Center — a university-based research center affiliated with George Mason University — created a generative artificial intelligence (AI) solution. Mercatus developed a platform, QuantGov, with assistance from Amazon Web Services (AWS) resources, including Amazon Bedrock, AWS Lambda, AWS API Gateway, and AWS RDS. Accessible through an interactive AI assistant, the platform provides scalable, accurate, and no-cost access to a treasure trove of data, helping policymakers craft informed public policy that will positively impact people’s lives.

In this post, we provide technical guidance to help satellite operators build, deploy, and analyze satcom waveforms on Amazon Web Services (AWS). Details are provided about the orchestration of multiple waveforms, including an example of one Amazon Field-Programmable Gate Array (FPGA) Image being swapped in for another. We analyze the FPGA utilization and metrics in Amazon CloudWatch and Amazon QuickSight, and validate network performance against system latency requirements. Finally, we recommend actions you can take to build an agile satcom strategy.

In this post, we showcase a Retrieval-Augmented Generation (RAG) application that can search and query across multiple Indian languages using the Cohere Embed – Multilingual model and Anthropic Claude 3 on Amazon Bedrock. This post focuses on Indian languages, but you can use the approach with other languages that are supported by the large language model.

Amazon Web Services (AWS) offers a variety of options for building chat-based assistants with generative artificial intelligence (AI) capabilities. The goal of this post is to present in simple words some of these options and what to keep in mind to decide which to use and how to get started.