Tag: Generative AI

Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding these advantages, we partnered with AWS to embark on a journey to develop HxGN Alix, an AI-powered digital worker using AWS generative AI services. This blog post explores the strategy, development, and implementation of HxGN Alix, demonstrating how a tailored AI solution can drive efficiency and enhance user satisfaction.

In this post, we showcase how Dr. Kori Ramajoo, Dr. Sonia Brownsett, Prof. David Copland, from QARC, and Scott Harding, a person living with aphasia, used AWS services to develop WordFinder, a mobile, cloud-based solution that helps individuals with aphasia increase their independence through the use of AWS generative AI technology.

In this post, we show how to create an application using Amazon Q Business with Jira integration that used a dataset containing a Trusted Advisor detailed report. This solution demonstrates how to use new generative AI services like Amazon Q Business to get data insights faster and make them actionable.

In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we explain in detail the fine-tuning process, hosting fine-tuned models with provisioned throughput, and using the fine-tuned Amazon Nova models for inference.

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We’ll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.

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.