Artificial Intelligence

Tag: Generative AI

Build character consistent storyboards using Amazon Nova in Amazon Bedrock – Part 1

The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer […]

Accelerate enterprise AI implementations with Amazon Q Business

Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can deploy Amazon Q Business to transform your enterprise productivity. The key to success lies in starting small, proving value quickly, and scaling systematically across your organization.

Enhance AI agents using predictive ML models with Amazon SageMaker AI and Model Context Protocol (MCP)

In this post, we demonstrate how to enhance AI agents’ capabilities by integrating predictive ML models using Amazon SageMaker AI and the MCP. By using the open source Strands Agents SDK and the flexible deployment options of SageMaker AI, developers can create sophisticated AI applications that combine conversational AI with powerful predictive analytics capabilities.

Architecture Diagram

How Infosys built a generative AI solution to process oil and gas drilling data with 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.

Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs

In this post, we demonstrate the implementation of a practical RAG chat-based assistant using a comprehensive stack of modern technologies. The solution uses NVIDIA NIMs for both LLM inference and text embedding services, with the NIM Operator handling their deployment and management. The architecture incorporates Amazon OpenSearch Serverless to store and query high-dimensional vector embeddings for similarity search.

How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights

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.

Pioneering AI workflows at scale: A deep dive into Asana AI Studio and Amazon Q index collaboration

Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.

Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server

In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).

Structured outputs with Amazon Nova: A guide for builders

We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%. In this post, we explore how you can use Amazon Nova FMs for structured output use cases.

Architecture

AI agents unifying structured and unstructured data: Transforming support analytics and beyond with Amazon Q Plugins

Learn how to enhance Amazon Q with custom plugins to combine semantic search capabilities with precise analytics for AWS Support data. This solution enables more accurate answers to analytical questions by integrating structured data querying with RAG architecture, allowing teams to transform raw support cases and health events into actionable insights. Discover how this enhanced architecture delivers exact numerical analysis while maintaining natural language interactions for improved operational decision-making.