Category: Amazon Bedrock

Organizations generate massive volumes of structured data from customer transactions, operational metrics, product catalogs, and compliance records. This data contains insights that can help businesses make better and timely decisions. Financial advisors need to review client transaction histories, retail analysts track inventory trends, and healthcare administrators monitor patient outcomes. Yet accessing these insights creates a […]

Since its launch in 2006, Amazon S3 has used a global namespace where bucket names must be unique across all AWS accounts and AWS Regions. This design has served customers well at scale, but organizations managing multiple accounts and environments often encounter naming collisions. When a bucket is deleted, its name returns to the global […]

State-of-the-art AI agents rely on external tools to perform actions on their behalf. A tool is a function with a clear description, defined inputs, and outputs that extend the capabilities of a large language model (LLM). As toolkits expand, selecting the right tool for each task requires effective mechanisms, among which semantic search enables agents […]

Today, consumers navigate thousands of products on e-commerce sites, hundreds of shows on streaming platforms, and countless options in digital marketplaces. This choice overload creates decision fatigue, yet consumers continue to demand more variety and make more purchases online. As a result, personalization has become essential—consumers reward brands that deliver relevant, tailored online experiences. However, […]

Retrieval-Augmented Generation (RAG) is a technique that optimizes large language model (LLM) outputs by referencing authoritative knowledge bases outside of the model’s training data before generating responses. This addresses common limitations of traditional LLMs, such as outdated knowledge, hallucinated facts, and misinterpreted terminology. Organizations can implement RAG to enhance their generative AI applications with current, […]

In today’s rapidly evolving threat landscape, Security Operations Center (SOC) teams face significant challenges in efficiently analyzing audit logs to identify potential security breaches in cloud file systems. Amazon FSx for Windows File Server generates comprehensive audit logs capturing detailed user activities, but traditional manual analysis of these logs is time-consuming, resource-intensive, and often ineffective […]

In today’s data-driven world, the ability to interact with your data through natural language is becoming increasingly valuable. By combining the power of conversational AI with Amazon S3 Tables, organizations can democratize data access and enable individuals across technical skill levels to query, analyze, and gain insights from their data using simple conversations. Model Context […]

When was the last time you found yourself trying to look at rows and rows of data in a spreadsheet struggling to interpret and draw conclusions? Many analysts and engineers experience the same challenge every day. Whether it’s analyzing sales trends, monitoring operational metrics, or understanding customer behavior, the challenge lies not just in interpreting […]

Health plans process billions of claims electronically each year. Council for Affordable Quality Healthcare (CAQH) estimates that approximately 10% of claims still arrive as paper documents, accounting for hundreds of millions of paper submissions annually in the U.S. These paper claims create processing bottlenecks and consume a disproportionate share of operational costs and resources, with […]

Businesses store large volumes of data in their data lakes and rely on this data to extract insights and make important business decisions. However, business stakeholders sometimes lack the technical skills required to run complex queries against their data lakes. Instead, they rely on data scientists or analysts to build reports and dashboards or to […]