Category: Customer Solutions

Amazon Finance Automation developed a large language model (LLM)-based question-answer chat assistant on Amazon Bedrock. This solution empowers analysts to rapidly retrieve answers to customer queries, generating prompt responses within the same communication thread. As a result, it drastically reduces the time required to address customer queries. In this post, we share how Amazon Finance Automation built this generative AI Q&A chat assistant using Amazon Bedrock.

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset.

In this post, SophosAI shares insights in using and evaluating an out-of-the-box LLM for the enhancement of a security operations center’s (SOC) productivity using Amazon Bedrock and Amazon SageMaker. We use Anthropic’s Claude 3 Sonnet on Amazon Bedrock to illustrate the use cases.

This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI. Rad AI has reshaped radiology reporting, developing solutions that streamline the most tedious and repetitive tasks, and saving radiologists’ time. Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product—Rad AI Impressions— has significantly reduced the […]

Commercial Real Estate Exchange, Inc. (Crexi), is a digital marketplace and platform designed to streamline commercial real estate transactions. In this post, we will review how Crexi achieved its business needs and developed a versatile and powerful framework for AI/ML pipeline creation and deployment. This customizable and scalable solution allows its ML models to be efficiently deployed and managed to meet diverse project requirements.

This post explores how 123RF used Amazon Bedrock, Anthropic’s Claude 3 Haiku, and a vector store to efficiently translate content metadata, significantly reduce costs, and improve their global content discovery capabilities.

we’re excited to share the journey of the VW—an innovator in the automotive industry and Europe’s largest car maker—to enhance knowledge management by using generative AI, Amazon Bedrock, and Amazon Kendra to devise a solution based on Retrieval Augmented Generation (RAG) that makes internal information more easily accessible by its users. This solution efficiently handles documents that include both text and images, significantly enhancing VW’s knowledge management capabilities within their production domain.

This post shows how Mark43 uses Amazon Q Business to create a secure, generative AI-powered assistant that drives operational efficiency and improves community service. We explain how they embedded Amazon Q Business web experience in their web application with low code, so they could focus on creating a rich AI experience for their customers.

LLM training has seen remarkable advances in recent years, with organizations pushing the boundaries of what’s possible in terms of model size, performance, and efficiency. In this post, we explore how FP8 optimization can significantly speed up large model training on Amazon SageMaker P5 instances.

In 2018, I sat in the audience at AWS re:Invent as Andy Jassy announced AWS DeepRacer—a fully autonomous 1/18th scale race car driven by reinforcement learning. At the time, I knew little about AI or machine learning (ML). As an engineer transitioning from legacy networks to cloud technologies, I had never considered myself a developer. […]