Category: HAQM Bedrock

The collaboration between Clario and AWS demonstrated the potential of AWS AI and machine learning (AI/ML) services and generative AI models, such as Anthropic’s Claude, to streamline document generation processes in the life sciences industry and, specifically, for complicated clinical trial processes.

This post demonstrates how to integrate open-source multi-agent framework, LangGraph, with HAQM Bedrock. It explains how to use LangGraph and HAQM Bedrock to build powerful, interactive multi-agent applications that use graph-based orchestration.

This post demonstrates how enterprises can implement a scalable agentic text-to-SQL solution using HAQM Bedrock Agents, with advanced error-handling tools and automated schema discovery to enhance database query efficiency.

This post describes how the AWS Customer Channel Technology – Localization Team worked with TransPerfect to integrate HAQM Bedrock into the GlobalLink translation management system, a cloud-based solution designed to help organizations manage their multilingual content and translation workflows. Organizations use TransPerfect’s solution to rapidly create and deploy content at scale in multiple languages using AI.

The introduction of HAQM Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with HAQM Nova models as a baseline. We conducted a comprehensive comparison study between model customization and RAG using the latest HAQM Nova models, and share these valuable insights.

In this post, we demonstrate how to use HAQM Personalize and HAQM Bedrock to generate personalized outreach emails for individual users using a video-on-demand use case. This concept can be applied to other domains, such as compelling customer experiences for ecommerce and digital marketing use cases.

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using HAQM Bedrock and CrewAI. We demonstrate how to build a multi-agent system that can automatically summarize new regulations, assess their impact on operations, and provide prescriptive technical guidance. You’ll learn how to use HAQM Bedrock Knowledge Bases and HAQM Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

In this post, we demonstrate how to get started with the Pixtral Large model in HAQM Bedrock. The Pixtral Large multimodal model allows you to tackle a variety of use cases, such as document understanding, logical reasoning, handwriting recognition, image comparison, entity extraction, extracting structured data from scanned images, and caption generation.

In this post, we focus specifically on enabling end-users to approve actions and provide feedback using built-in HAQM Bedrock Agents features, specifically HITL patterns for providing safe and effective agent operations. We explore the patterns available using a Human Resources (HR) agent example that helps employees requesting time off.

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements. The multi-LLM approach enables organizations to effectively choose the right model for each task, adapt to different […]