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1. How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock

   Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost...

2. A practical guide to Amazon Nova Multimodal Embeddings

   In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications.

3. Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references

   Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand...

4. Democratizing business intelligence: BGL’s journey with Claude Agent SDK and Amazon Bedrock AgentCore

   BGL is a leading provider of self-managed superannuation fund (SMSF) administration solutions that help individuals manage the complex compliance and reporting of their own or a client’s retirement savings, serving over 12,700 businesses across 15 countries. In this blog post, we explore how BGL built its production-ready AI agent using Claude Agent SDK and Amazon Bedrock AgentCore.

5. Use Amazon Quick Suite custom action connectors to upload text files to Google Drive using OpenAPI specification

   In this post, we demonstrate how to build a secure file upload solution by integrating Google Drive with Amazon Quick Suite custom connectors using Amazon API Gateway and AWS Lambda.

6. AI agents in enterprises: Best practices with Amazon Bedrock AgentCore

   This post explores nine essential best practices for building enterprise AI agents using Amazon Bedrock AgentCore. Amazon Bedrock AgentCore is an agentic platform that provides the services you need to create, deploy, and manage AI agents at scale. In this post, we cover everything from initial scoping to organizational scaling, with practical guidance that you can apply immediately.

7. Agentic AI for healthcare data analysis with Amazon SageMaker Data Agent

   On November 21, 2025, Amazon SageMaker introduced a built-in data agent within Amazon SageMaker Unified Studio that transforms large-scale data analysis. In this post, we demonstrate, through a detailed case study of an epidemiologist conducting clinical cohort analysis, how SageMaker Data Agent can help reduce weeks of data preparation into days, and days of analysis development into...

8. How Clarus Care uses Amazon Bedrock to deliver conversational contact center interactions

   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...

9. Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI

   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...

10. Simplify ModelOps with Amazon SageMaker AI Projects using Amazon S3-based templates

    This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment.