Category: Analytics

Israel’s Ministry of Finance wanted to strengthen the way it supports tax revenue forecasting. The Ministry worked with Amazon Web Services (AWS) Professional Services Israel to develop an automated solution that evaluates several modeling approaches, uses wider data sources, and helps economists analyze forecasting faster.

In this post, we explain what IL5 requires, how AWS GovCloud (US) and SAP NS2 meet those requirements together, and how defense organizations can get started.

This post explores how an agentic AI architecture on Amazon Web Services (AWS) modernizes the procurement lifecycle from solicitation to proposal evaluation while maintaining compliance with government regulations including United States of America FAR (Federal Acquisition Regulation),United States of America DFARS (Defense Federal Acquisition Regulation), and Canadian procurement frameworks (Public Service Procurement Canada (PSPC) /Shared Services Canada (SSC).

In this post, we walk through three extended capabilities of Amazon S3: Amazon S3 Metadata, Amazon S3 Vectors, and Amazon S3 Tables and explore how each maps to real challenges government agencies face today.

This post presents two complementary patterns for implementing per-user token guardrails on Amazon Bedrock from Amazon Web Services (AWS).

This post introduces the Geospatial Power Pack, a Kiro power package that turns Kiro into a unified, AI-assisted geospatial workspace. Kiro is an agentic development environment created by Amazon Web Services (AWS). It helps developers and teams turn prompts into executable specs, validate code correctness to find bugs that unit tests miss, and build across large codebases with parallel agents that learn from every session.

The Amazon Web Servies (AWS) Open Data Sponsorship Program makes high-value, cloud-optimized datasets freely available for analysis, helping researchers and developers worldwide access critical data without cost barriers.

For the more than 2,800 administrators across approximately 1,200 participating institutions in the US, that means access to near daily provided to them through Amazon Quick Sight on Amazon Web Services (AWS) by the eduroam team at Internet2. However, this wasn’t always the case. This post walks through how the eduroam team modernized their data reporting pipeline, the architecture behind it, and the impact it’s had on institutional engagement.

Every major agent framework has a security model for this. Amazon Web Services (AWS), Microsoft, and Google each ship agent solutions with authentication and credential management built in. Amazon Bedrock Agents, for example, enforces AWS Identity and Access Management (IAM)-based authorization on which AWS Lambda functions, Amazon Simple Storage Service (Amazon S3) buckets, and Amazon Bedrock Knowledge Bases an agent might invoke.

Credit unions exist to serve members, not to run data centers. Every hour our team spends on infrastructure is an hour not spent on the question a member actually cares about: “Is my money safe, is my experience easy, and is my credit union on my side?” Moving to a serverless, managed-service foundation on Amazon Web Services (AWS) means our engineers spend their time on membership modeling, fraud analytics, and AI augmentation instead of capacity planning.