AWS for Industries
Category: Uncategorized
GreenBridge.AI redefines renewable energy operations with agentic AI on AWS
Renewable energy operators face a growing challenge: Managing increasingly large, complex, high-value portfolios across solar, wind, and battery energy storage system (BESS) while meeting tighter compliance, grid, and market demands. Many operational workflows remain reactive, manual, and fragmented—limiting both performance and scalability. However, GreenBridge.AI is innovating renewable energy operations through AI agents for greater automated efficiency and scalability.
Build a voice-enabled Automotive and Manufacturing assistant using Amazon Nova Sonic and Amazon Bedrock AgentCore
In this post, we show you how Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands Agents come together in VEMA (Voice-Enabled Manufacturing Assistant)—an AI-powered voice assistant that lets workers speak naturally to get instant answers, hands-free, in English or Spanish, without ever leaving their workstation.
Managing AI agent sprawl across business units
This blog introduces a governance framework designed for organizations with multiple business units that need to enable fast agent development while maintaining enterprise-wide visibility, cost control, and safety.
How Autel Transformed Charging Station Management with AI Agents on AWS
This post shows how Autel used AWS cloud services and AI technologies to build an AI digital employee system that streamlines charging station operations. We explore the five specialized AI agents we created, the technical architecture powering them, and how these intelligent employees enable CPOs to manage their charging networks more efficiently with conversational interfaces rather than complex manual processes.
Scaling ML in production: how BBVA accelerated delivery with MLOps
This post describes how BBVA used pilots to identify reusable ML patterns, standardize operational workflows, and design extensible MLOps templates that accelerate ML delivery while maintaining governance and flexibility across teams and business domains.
Accelerate RISC-V Software Development Before Silicon: Virtual Prototyping with MachineWare’s SIM-V on AWS
Software engineering teams building for RISC-V architectures often wait months for hardware prototypes before they can start development and evaluations. This post shows how MachineWare’s SIM-V — an ultra-fast RISC-V Virtual Platform — runs on Amazon Web Services, Inc. (AWS), so software teams can develop, debug, and validate RISC-V software long before silicon is available. […]
Deploy diagnostic-quality imaging globally with MedDream and AWS HealthImaging
Learn how by combining MedDream’s FDA-cleared diagnostic viewer with AWS HealthImaging and automated AWS Cloud Development Kit (AWS CDK) deployment, you can deploy a complete, production-ready medical imaging solution in under an hour—achieving sub-second loading times and reducing storage costs by up to 40% compared to on-premises infrastructure.
Coins in Motion: Building agentic blockchain payments for in-vehicle experiences
Agentic blockchain-based payments are poised to transform in-vehicle driving experiences. As vehicles become increasingly connected and autonomous, they are evolving from passive transportation tools into active economic agents capable of conducting their own financial transactions [see HBR Article, 2021]. Imagine your car automatically paying for highway tolls, electric charging sessions, parking fees, or even purchasing […]
Edge-to-Cloud Architecture for Real-Time Surgical Intelligence with AWS and NVIDIA
Learn how to architect an end-to-end pipeline that processes surgical video at the edge for de-identification, instrument detection, and surgical phase recognition—while using the cloud for model training and fleet management.
Reimagining B-Pillar DFMEA: Why Ontology-Grounded AI Is the Future of Automotive Engineering
This two-part series explores how ontology-grounded agentic AI transforms Design Failure Mode and Effects Analysis (DFMEA) for safety-critical automotive components — from the strategic imperative driving adoption to the architectural patterns and implementation details. In this post, we focus on: How AI can help with DFMEA, how engineering ontologies enable AI to reason for failure mechanisms rather than pattern-match, and what engineering leaders should prioritize today.









