Tag: Generative AI

In this post, we demonstrate how to elevate business productivity by leveraging Amazon Q to provide insights that enable research, data analysis, and report potential fraud cases within Amazon Connect.

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

In this post, we explore how AI agents can streamline compliance and fulfill regulatory requirements for financial institutions using Amazon 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 Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents with CrewAI to create a comprehensive, automated compliance solution.

In this post, we explore how to use Amazon Bedrock for synthetic data generation, considering these challenges alongside the potential benefits to develop effective strategies for various applications across multiple industries, including AI and machine learning (ML).

OMRON Corporation is a leading technology provider in industrial automation, healthcare, and electronic components. In their Shaping the Future 2030 (SF2030) strategic plan, OMRON aims to address diverse social issues, drive sustainable business growth, transform business models and capabilities, and accelerate digital transformation. At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. This post explores how OMRON Europe is using Amazon Web Services (AWS) to build its advanced ODAP and its progress toward harnessing the power of generative AI.

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test data creation for multiclass classification supervised learning use cases. We dive deep into this process on how to use XML tags to structure the prompt and guide Amazon Bedrock in generating a balanced label dataset with high accuracy. We also showcase a real-world example for predicting the root cause category for support cases. This use case, solvable through ML, can enable support teams to better understand customer needs and optimize response strategies.

Discover how to build a GenAI powered virtual IT troubleshooting assistant using Amazon Q Business. This innovative solution integrates with popular ITSM tools like ServiceNow, Atlassian Jira, and Confluence to streamline information retrieval and enhance collaboration across your organization. By harnessing the power of generative AI, this assistant can significantly boost operational efficiency and provide 24/7 support tailored to individual needs. Learn how to set up, configure, and leverage this solution to transform your enterprise information management.

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on Amazon Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

Healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. LLMs lack the ability to seamlessly access and synthesize data from these diverse and distributed sources. This limits their potential to provide comprehensive and well-informed insights for healthcare applications. In this blog post, we will explore how Mistral LLM on Amazon Bedrock can address these challenges and enable the development of intelligent healthcare agents with LLM function calling capabilities, while maintaining robust data security and privacy through Amazon Bedrock Guardrails.