Category: Amazon Machine Learning

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.

This post documents a working system. Five KYC/KYB checks, each with its own agent architecture, prompt methodology, and failure modes. We built it, ran it, broke it, fixed it, and are publishing the full method so you can build your own.

Reducing unplanned downtime in automotive paint shops requires unifying siloed operational data into a contextualized fabric that enables AI-driven root cause analysis and anomaly detection. Today, when a paint shop line goes down, the industrial engineer must manually correlate data across Supervisory Control and Data Acquisition (SCADA) systems, Manufacturing Execution Systems (MES) platforms, downtime logs, […]

This post was co-written with Harsh Vardhan, Global Head of the Digital Innovation Hub, from Apollo Tyres Ltd. Every recipe change on a tyre extrusion line triggers a stabilization period—minutes of off-spec material that must be reworked or scrapped. For high-mix manufacturers running 30–35 setup changes per day, this adds up to 5–6% of total […]

This is part 3 of our three-part series on how Amazon Web Services (AWS) aligns with MPA Content Security Best Practices with a particular focus on media archives.

This guide walks one agent through all six pillars: a Parts Inventory Agent for an automotive manufacturing plant.

In this post, we demonstrate how a large North American Utility is currently running a multi-agent AI architecture on Amazon Bedrock AgentCore to compress alarm triage from hours to minutes, avoiding the manual, multi-system investigation that operators perform dozens of times per shift. We will walk through the industry problem, the multi-agent design, and a reference implementation on GitHub that you can deploy into your own environment.

As part of its ongoing commitment to operational excellence, Mary Kay sought to further accelerate the investigation and resolution of routine infrastructure events through AI-powered automation and intelligent orchestration by activating a capability most Amazon Web Services (AWS) Enterprise Support customers already pay for but never use: the AWS DevOps Agent.

In this post, we show how you can use AWS HealthLake to meet CMS-0057-F requirements while building a FHIR data solution that accelerates your broader digital transformation.

We are excited to announce resource matching for AWS HealthLake, now available in gated preview. Resource matching is a built-in capability that automatically identifies FHIR resources representing the same real-world entity and links them without modifying or merging the original data.