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Implement a backup strategy for Amazon Quick Sight BI assets

Implement a backup strategy for Amazon Quick Sight BI assets

Amazon Quick Sight is a core feature within Amazon Quick — an agentic, AI-powered digital workspace designed to maximize end-user productivity— that provides AI-powered BI capabilities through natural language queries, interactive dashboards, and embedded analytics from trusted enterprise data sources. Amazon Quick Sight assets such as dashboards, analyses, datasets, and data sources can be backed up using the

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Pair Nova 2 Lite with Claude for cost-optimized document processing

Pair Nova 2 Lite with Claude for cost-optimized document processing

A scanned yearbook page contains 176 printed names, 4 portrait photographs, and zero machine-readable structure linking them. To digitize this page, you need reliable photo detection with bounding boxes and accurate name extraction. You also need a way to determine which name belongs to which face based on page layout. In this post, we show

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Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS

Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS

At PAR Technology Corporation, we build technology for the restaurant industry, supporting over 300 restaurant businesses, from independent operators to large, multi-brand franchise groups. Across this diverse customer base, we help organizations make better decisions by unlocking the value of their data. When we set out to build a natural language text-to-SQL agent for self-serve

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Build an agentic AI healthcare claims pipeline with Amazon Bedrock and AWS HealthLake

Build an agentic AI healthcare claims pipeline with Amazon Bedrock and AWS HealthLake

Manually processing paper-based forms remains a significant cost in the healthcare industry. Despite advancements in data extraction of scanned documents and images, human oversight is usually still needed. Entry error by the individual creating the form or lower-confidence extractions from the digitization still must be remediated. In this post, we show you how to build

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Debugging production agents with Amazon Bedrock AgentCore Observability

Debugging production agents with Amazon Bedrock AgentCore Observability

Production artificial intelligence (AI) agents can fail silently. They may return plausible but incorrect answers, enter infinite reasoning loops, or select the wrong tools without triggering error alerts. These failures make debugging production agent behavior difficult because standard logs and metrics do not capture how decisions are made. Amazon Bedrock AgentCore Observability addresses these debugging

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How Cara pioneers domain-specific AI for enterprise insurance brokerages with AWS

How Cara pioneers domain-specific AI for enterprise insurance brokerages with AWS

Insurance is an $8 trillion global industry burdened by manual workflows and a growing talent shortage. Cara delivers an AI-native solution on AWS that automates back-office processes for insurance brokerages. Insurance agents routinely spend hours on repetitive tasks. These include completing applications, analyzing policy coverages, re-keying data across systems, and relaying information between clients and

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Production-grade AI agents for financial compliance: Lessons from Stripe

Production-grade AI agents for financial compliance: Lessons from Stripe

This post is co-written by Christopher Phillippi and Chrissie Cui from Stripe. Stripe processes $1.4 trillion in annual payment volume across 50 countries, requiring compliance teams to review thousands of transactions daily. This post explores how Stripe built a production-grade AI agent system on AWS using Amazon Bedrock that reduced review handling time by 26

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