This post was co-written with Julio P. Roque Hexagon ALI.
Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding these advantages, we partnered with...
Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more context-specific...
In the era of AI and machine learning (ML), there is a growing emphasis on enhancing security— especially in IT contexts. In this post, we demonstrate how your organization can reduce the end-to-end burden of resolving regular challenges experienced by your IT support teams—from...
This post is co-written with Kilian Zimmerer and Daniel Ringler from Deutsche Bahn.
Every day, Deutsche Bahn (DB) moves over 6.6 million passengers across Germany, requiring precise time series forecasting for a wide range of purposes. However, building accurate forecasting models traditionally required significant expertise...
With Amazon Bedrock Evaluations, you can evaluate foundation models (FMs) and Retrieval Augmented Generation (RAG) systems, whether hosted on Amazon Bedrock or another model or RAG system hosted elsewhere, including Amazon Bedrock Knowledge Bases or multi-cloud and on-premises deployments. We recently announced the general...
The Amazon Bedrock multi-agent collaboration feature gives developers the flexibility to create and coordinate multiple AI agents, each specialized for specific tasks, to work together efficiently on complex business processes. This enables seamless handling of sophisticated workflows through agent cooperation. This post aims to...
