Category: Amazon Bedrock

In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities–when requested or for urgent cases–and an analytics pipeline for performance insights.

Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […]

In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you’ll have a working implementation of global cross-Region inference in af-south-1.

The agent-based approach we present is applicable to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To demonstrate these concepts in action, we walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to various content review needs by adjusting the agent configurations, tools, and verification sources.

In this post, we walk you through Pushpay’s journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS.

This blog post demonstrates how to build an intelligent contract management solution using Amazon Quick Suite as your primary contract management solution, augmented with Amazon Bedrock AgentCore for advanced multi-agent capabilities.

This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock.

Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner.

In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock.

In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process.