Category: *Post Types

This post shows you how to implement intelligent notification filtering using Amazon Bedrock and its gen-AI capabilities. You’ll learn model selection strategies, cost optimization techniques, and architectural patterns for deploying gen-AI at IoT scale, based on Swann Communications deployment across millions of devices.

LinqAlpha is a Boston-based multi-agent AI system built specifically for institutional investors. The system supports and streamlines agentic workflows across company screening, primer generation, stock price catalyst mapping, and now, pressure-testing investment ideas through a new AI agent called Devil’s Advocate. In this post, we share how LinqAlpha uses Amazon Bedrock to build and scale Devil’s Advocate.

In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.

Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. Through its partnership with AWS, Iberdrola implemented different agentic architectures using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization.

In this post, we show how this integrated approach transforms enterprise LLM fine-tuning from a complex, resource-intensive challenge into a streamlined, scalable solution for achieving better model performance in domain-specific applications.

Working with the Generative AI Innovation Center, New Relic NOVA (New Relic Omnipresence Virtual Assistant) evolved from a knowledge assistant into a comprehensive productivity engine. We explore the technical architecture, development journey, and key lessons learned in building an enterprise-grade AI solution that delivers measurable productivity gains at scale.

In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration.

Today, we’re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples.

In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.

Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost savings while maintaining operational excellence. The document classification system, developed using the Generative AI Intelligent Document Processing (GenAI IDP) Accelerator, is designed to integrate seamlessly into existing workflows. It revolutionizes how employees interact with document management systems by reducing the time spent on manual classification tasks.