AWS for Industries
Category: Artificial Intelligence
Accelerating physical AI with AWS and NVIDIA: building production-ready applications with simulation and real-world learning
Defining physical AI beyond digital intelligence Physical AI represents a transformative evolution in artificial intelligence, extending beyond purely computational systems, to intelligent agents that perceive, reason, and interact directly with the physical world. Unlike traditional AI systems that process information in digital domains (such as chatbots or recommendation engines), physical AI embeds intelligence in systems […]
Event-Driven Digital Pathology: Governed Whole Slide Image Ingestion to Scalable Inference with Amazon SageMaker
This blog post will detail how Genmab, a leading biotech company, built an automated pipeline on AWS that handles whole-slide images from start to finish, cutting analysis time from hours to under 30 minutes per batch and reducing manual work by 80 percent. We will walk through how Genmab achieved this using AWS services and share the key lessons they learned along the way.
How Amazon Connect Health brings agentic AI to the point of care
In this post, we show how EHR companies, healthcare independent software vendors (ISVs), and tech-enabled provider organizations can use Amazon Connect Health to embed these capabilities into their existing workflows using a single, unified Software Development Kit (SDK).
From Prompt to Pipeline: AI-Powered Bioinformatics Workflow Development with Kiro and AWS HealthOmics
Learn more about Kiro — an agentic AI-powered IDE built by AWS that helps you go from prototype to production with spec-driven development — and how to improve Kiro’s bioinformatics workflow abilities, we built the AWS HealthOmics Kiro Power — a companion package that automatically configures the HealthOmics Model Context Protocol (MCP) server and provides Kiro with domain-specific steering guides.
How Amazon Devices Eliminated Credential Risk to Scale AI across Engineering Tools
Amazon Devices engineers needed AI assistance directly in their design tools to accelerate hardware development. But AI agents running locally on engineering workstations had no way to authenticate with AWS services without distributing credentials, creating unacceptable security risks. The Design Technologies team solved this challenge by implementing browser-based authentication that establishes user identity and enables […]
Build ChatGPT Apps with MCP Servers and AWS Infrastructure
In this post, we’ll unpack what ChatGPT Apps are, why they’ve become a strategic priority for retailers, and how they fit into a broader agentic commerce vision. We’ll also show you why AWS is the ideal back-end infrastructure.
The Luggage Lab: Accelerate product innovation with AWS generative AI services
Learn how AWS showcased a proven path to staying ahead of consumer expectations — from compressing innovation cycles to delivering the personalized experiences that drive loyalty at the 2026 National Retail Federation Big Show with the Luggage Lab.
AI-Powered Collections: How EXL Uses AI on AWS for Debt Recovery at Scale
Learn how to transform collections operations for banks and fintechs with AI and ML on AWS The Business Challenge Financial institutions face mounting pressure in collections operations. Traditional debt collection operates on a simple playbook: aggressive phone calls, generic messaging, and rule-based workflows. Yet customers have unique capacity to pay (income, assets, competing obligations), willingness […]
How Amazon Bedrock transforms Microsoft Teams conversations into actionable insights
Organizations around the world are growing their usage of Microsoft Teams every year. Currently Microsoft Teams has 320 million users across more than 1 million organizations. Microsoft Teams has multiple uses including file storage and user communication. Companies that are focused on industrial areas such as oil and gas will experience users discussing various topics […]
Building an End-to-End Physical AI Data Pipeline for Autonomous Vehicle 3.0 on AWS with NVIDIA
Autonomous Vehicles (AV) development has been maturing and is advancing through clear architectural changes: AV 1.0: classical modular stacks (perception → prediction → planning → control) with hand-engineered interfaces AV 2.0: multi-modal LLM end-to-end (E2E) learned stacks that reduce modularity and improve scaling with data AV 3.0: end-to-end reasoning VLA (Vision–Language–Action) systems that perceive, reason, […]







