AWS for Industries
From cloud to clinic: How AWS powers digital psychiatry at scale
In this post, we describe how the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School built mindLAMP, an open source digital psychiatry platform running on Amazon Web Services (AWS). Now deployed at 65 sites across 17 countries, mindLAMP enables continuous patient monitoring, digital phenotyping, and AI-augmented clinical workflows—all powered by serverless AWS infrastructure that a small academic group can operate without a dedicated site reliability engineering team.
How AWS Payment Cryptography Raises the Cryptographic Security Bar
In this blog, learn how on-prem HSMs can be replaced by AWS Payment Cryptography which is a fully managed, cloud-native payment cryptographic service designed to help customers meet PCI PIN Security, PCI P2PE, PCI DSS requirements and regional requirements such as Cartes Bancaires, France’s national card payment network and Australia Standard 2805 (AS2805).
Reduce SMT Defects with Agentic AI: Automating Polarity Validation on AWS
Surface Mount Technology (SMT) polarity programming defines how a polarity-sensitive component feeds from tape reel or tray to the pick-and-place machine, designed to help maintain correct orientation on the Printed Circuit Board Assembly (PCBA). Get it right, and boards run clean. Get it wrong by 180°, and downstream systems (Automated Optical Inspection (AOI), X-ray, In-Circuit […]
How Morningstar built a financial advisor AI assistant powered by Amazon Bedrock AgentCore
In this blog, learn how Morningstar designed the agentic orchestration, enforced guardrails, enabled comprehensive audit trails for a regulated environment, and deployed a production system that keeps the advisor in control while automating the research-to-action workflow.
Standing Up a Governed AWS Foundation in Days, Not Quarters: A Post-Merger Integration Pattern for Financial Services
In this post, you will learn how to sequence a post-merger cloud foundation so that governance is established before workloads land, and how pre-close design compresses the integration timeline from quarters to days.
Building AI-augmented B-pillar DFMEA on AWS: Architecture, multi-agent orchestration, and implementation
In this post, we deliver the implementation blueprint. We walk through the complete reference architecture built on Amazon Web Services (AWS), break down the layer service topology, detail the multi-agent orchestration pattern using Amazon Bedrock AgentCore and the Strands Agents SDK.
Introducing seven new features of the Agentic Shopping Assistant on AWS
Earlier this year, we announced the Agentic Shopping Assistant on AWS, a generative AI-powered solution that helps retailers give online shoppers the kind of expert guidance they would get from a knowledgeable in-store associate. The Agentic Shopping Assistant on AWS brings the expertise and insights behind Amazon’s successful Alexa for Shopping AI assistant to retail customers.
How financial institutions can operationalize AI-DLC quickly and effectively
In this post, you will learn how to build a governance harness in days, and what engineering and Governance, Risk and Control (GRC) teams gain from it.
Building AI Agents for Telecom Network Operations
This post shows how to close that gap with AI agents that reason about telecom alarms the way a senior engineer does using a portable, framework-agnostic skill that packages 3GPP standards, vendor equipment procedures, and event correlation patterns into structured domain knowledge. Teams adopt whichever agent framework fits their operating model – Strands Agents, LangChain/LangGraph, or Amazon Bedrock Agents, without rewriting the skill.
EUC 2.0: Why Uncontrolled copilot platforms are Financial Services’ Next Governance Challenge
In this post, we explain why uncontrolled AI agent development on managed copilot platforms is the next generation of EUC risk, why discovery is the prerequisite that most governance frameworks skip, and how a four-tier classification model can help financial institutions capture the productivity gains of citizen AI without repeating the remediation cycles of the past.









