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Extract Data with On-demand and Batch Pipelines Dynamically
Many companies have large volumes of paper or electronic documents that contain untapped business intelligence. With the advancement of generative AI, various large language models can be used to accurately extract relevant data from these documents. This post demonstrates an intelligent document processing pipeline that consists of both on-demand inference and batch inference options on …
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Evaluate AI agents systematically with Agent-EvalKit
Teams building AI agents typically evaluate them the way they evaluate any other software: by checking whether the output matches expectations. But agents that autonomously choose tools and sequence operations across multiple sources produce behavior that output-level testing cannot fully characterize. An agent might deliver a well-structured, actionable response while hallucinating, fabricating facts because its …
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Spot trends faster, sort smarter: Unlocking Sparklines and Custom Sort in Amazon Quick
Amazon Quick Sight, the business intelligence capability of Amazon Quick, delivers a unified BI experience, from modern interactive dashboards and natural language querying to pixel-perfect reports, machine learning insights, and embedded analytics at scale. Amazon Quick brings together AI-powered agents for business insights, research, and automation in one integrated experience, helping teams work smarter and …
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Optimize blueprint extraction accuracy in Amazon Bedrock Data Automation
Extracting structured data from unstructured documents such as invoices, contracts, tax forms, and enrollment applications is a common automation goal for organizations. Achieving high extraction precision remains a key challenge. Accuracy degrades when documents diverge from expected templates, formats vary across vendors, or scan quality is poor. With Amazon Bedrock Data Automation (BDA), you can …
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How frontier teams are reinventing AI-native development
Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x. Six engineers. Seventy-six days. A project scoped for 30 developers over 12 to 18 months, delivered within a quarter. That is not hypothetical. It’s what happened when …
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Stop hand-tuning kernels: How Neuron Agentic Development accelerates AWS Trainium optimizations
As frontier AI models grow in scale and complexity, developers face a common challenge across every hardware platform: how do you extract the maximum performance and efficiency from the silicon their models run on. Whether delivering real-time experiences for world models, supporting deeper reasoning in agentic workflows, or reducing inference costs at scale, the gap …
Build an AI-Powered Equipment Repair Assistant Using Amazon Bedrock AgentCore
Managing equipment repairs for heavy farm machinery often requires technicians to diagnose issues without the right parts, leading to multiple site visits, extended downtime, and substantial financial losses, especially during harvest season. In this post, you build an AI-powered equipment repair assistant using Amazon Bedrock AgentCore that helps farmers and field technicians diagnose equipment problems, …
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Scale Robot Reinforcement Learning with NVIDIA Isaac Lab on Amazon SageMaker AI
Physical AI is moving from research into production. Robots are increasingly trained in high-fidelity simulation before being deployed to factories, warehouses, and logistics centers, because training in the real world is slow, expensive, and often unsafe, while GPU-accelerated simulation can compress months of learning into hours. This shifts the challenge to compute. Reinforcement learning (RL) …
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Hands-free first notice of loss: Using Strands Agents and Amazon Bedrock AgentCore Browser Tool for intelligent claims intake
Turning multimodal first notice of loss (FNOL) evidence into tagged, decision-ready intake so adjusters start with context instead of raw artifacts. Manual FNOL processing consumes significant expert time on repetitive tasks because unstructured, multimodal evidence must be interpreted through portals designed for human interaction. Photos captured in the field, walkaround videos, scanned documents, and dictated …