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Optimize blueprint extraction accuracy in Amazon Bedrock Data Automation

Favorite 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

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Shared by AWS Machine Learning June 12, 2026

From PDFs to insights: Architecting an intelligent document processing pipeline with AWS generative AI services

Favorite Organizations process millions of documents daily, from insurance claims and invoices to legal contracts and medical records. While traditional optical character recognition (OCR) solutions extract text, they can’t understand context, relationships, or meaning embedded within complex documents. This limitation creates bottlenecks that require manual intervention, increasing processing time and

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Shared by AWS Machine Learning June 12, 2026

Building Supercharger: How Rocket Close optimized title operations with agentic AI

Favorite Rocket Close is a Detroit-based title agency and appraisal management company within Rocket Companies that provides title insurance, property valuation, and settlement services. As demand for mortgages and loans grew, title operations became a bottleneck in the homebuying process. Time-intensive, state-specific title examinations, combined with manual research and fragmented

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Shared by AWS Machine Learning June 12, 2026

Spot trends faster, sort smarter: Unlocking Sparklines and Custom Sort in Amazon Quick

Favorite 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

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Shared by AWS Machine Learning June 11, 2026

Evaluate AI agents systematically with Agent-EvalKit

Favorite 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

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Shared by AWS Machine Learning June 11, 2026

Extract Data with On-demand and Batch Pipelines Dynamically

Favorite 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

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Shared by AWS Machine Learning June 11, 2026