Category: Industries

In this post, we explore how Tyson Foods collaborated with the AWS Generative AI Innovation Center to revolutionize their customer interaction through an intuitive AI assistant integrated into their website. The AI assistant was built using Amazon Bedrock,

We built an advanced RAG solution using Amazon Bedrock leveraging Infosys Topaz™ AI capabilities, tailored for the oil and gas sector. This solution excels in handling multimodal data sources, seamlessly processing text, diagrams, and numerical data while maintaining context and relationships between different data elements. In this post, we provide insights on the solution and walk you through different approaches and architecture patterns explored, like different chunking, multi-vector retrieval, and hybrid search during the development.

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.

In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks.

In this post, we demonstrate how to create a powerful research assistant for drug discovery using Strands Agents and Amazon Bedrock. This AI assistant can search multiple scientific databases simultaneously using the Model Context Protocol (MCP), synthesize its findings, and generate comprehensive reports on drug targets, disease mechanisms, and therapeutic areas.

In this post, we demonstrate how Kyruus Health uses AWS services to build Guide. We show how Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) from leading AI companies and Amazon through a single API, and Amazon OpenSearch Service, a managed search and analytics service, work together to understand everyday language about health concerns and connect members with the right providers.

In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI.

In this post, we show how to create an AI-powered call center agent for a fictional company called AnyTelco. The agent, named Telly, can handle customer inquiries about plans and services while accessing real-time customer data using custom tools implemented with the Model Context Protocol (MCP) framework.

Sonatus partnered with the AWS Generative AI Innovation Center to develop a natural language interface to generate data collection and automation policies using generative AI. This innovation aims to reduce the policy generation process from days to minutes while making it accessible to both engineers and non-experts alike. In this post, we explore how we built this system using Sonatus’s Collector AI and Amazon Bedrock. We discuss the background, challenges, and high-level solution architecture.