Category: HAQM Bedrock Agents

In this post, we explore how Noodoe uses AI and HAQM Bedrock to optimize EV charging operations. By integrating LLMs, Noodoe enhances station diagnostics, enables dynamic pricing, and delivers multilingual support. These innovations reduce downtime, maximize efficiency, and improve sustainability. Read on to discover how AI is transforming EV charging management.

Non-conversational applications offer unique advantages such as higher latency tolerance, batch processing, and caching, but their autonomous nature requires stronger guardrails and exhaustive quality assurance compared to conversational applications, which benefit from real-time user feedback and supervision. This post examines four diverse HAQM.com examples of such generative AI applications.

In this post, we walk through how to build a multi-agent investment research assistant using the multi-agent collaboration capability of HAQM Bedrock. Our solution demonstrates how a team of specialized AI agents can work together to analyze financial news, evaluate stock performance, optimize portfolio allocations, and deliver comprehensive investment insights—all orchestrated through a unified, natural language interface.

In this post, we present a solution to incorporate HAQM Bedrock Agents in your Slack workspace. We guide you through configuring a Slack workspace, deploying integration components in HAQM Web Services, and using this solution.

In this post, we introduce a multi-agent collaboration pipeline for processing unstructured insurance data using HAQM Bedrock, featuring specialized agents for classification, conversion, and metadata extraction. We demonstrate how this domain-aware approach transforms diverse data formats like claims documents, videos, and audio files into metadata-rich outputs that enable fraud detection, customer 360-degree views, and advanced analytics.

In this post, we explore how Onity Group, a financial services company specializing in mortgage servicing and origination, transformed their document processing capabilities using HAQM Bedrock and other AWS services. The solution helped Onity achieve a 50% reduction in document extraction costs while improving overall accuracy by 20% compared to their previous OCR and AI/ML solution.

AWS partnered with Vxceed to support their AI strategy, resulting in the development of LimoConnect Q, an innovative ground transportation management solution. Using AWS services including HAQM Bedrock and Lambda, Vxceed successfully built a secure, AI-powered solution that streamlines trip booking and document processing.

Generative AI tools have transformed how we work, create, and process information. At HAQM Web Services (AWS), security is our top priority. Therefore, HAQM Bedrock provides comprehensive security controls and best practices to help protect your applications and data. In this post, we explore the security measures and practical strategies provided by HAQM Bedrock Agents to safeguard your AI interactions against indirect prompt injections, making sure that your applications remain both secure and reliable.

This post explores a financial assistant system that specializes in three key tasks: portfolio creation, company research, and communication. This post aims to illustrate the use of multiple specialized agents within the HAQM Bedrock multi-agent collaboration capability, with particular emphasis on their application in financial analysis.

In this post, we introduce agentic automatic mortgage approval, a next-generation sample solution that uses autonomous AI agents powered by HAQM Bedrock Agents and HAQM Bedrock Data Automation. These agents orchestrate the entire mortgage approval process—intelligently verifying documents, assessing risk, and making data-driven decisions with minimal human intervention.