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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 […]

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

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

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,

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)

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

Incident triage is time-sensitive because site reliability engineers (SREs) and support engineers often need to collect evidence, assess user impact, and create follow-up work across separate tools. With Amazon Quick and New Relic, you can coordinate those investigation and handoff steps in a single conversational workflow. This post shows engineering teams how to apply that

With access to the latest generative AI models and high-performance accelerated compute in high global demand, AWS customers need tools to take advantage of model availability and capacity across multiple AWS Regions, while still meeting their security and privacy requirements. cross-Region Inference (CRIS) on Amazon Bedrock meets these needs by automatically routing requests across multiple

There’s a habit going around. Walking from one meeting to the next with the laptop cradled half-open. Sitting through a 1:1 with the lid propped just enough to keep the screen alive. Riding home while holding your laptop because it must stay running. Anywhere except closed on a desk, because closed on a desk is

The science of optimal decisions — and how leading organizations are applying it. Every enterprise faces decisions that are too complex for intuition or manual decision-making alone. Which delivery routes minimize cost while meeting next-day promises? How should hundreds of robots sequence movements across a factory floor without collision? How do you staff a 24/7