Category: How-To

Learn how to build a no-code customer retention pipeline in Amazon Quick that detects at-risk customers from call transcripts and CSAT data, scores them by retention priority with a custom MCP Action, and generates personalized retention letters, reducing response time from days to minutes.

OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock. Learn how to select a model, run inference through the Responses API on the bedrock-mantle endpoint, reduce cost with prompt caching, connect the OpenAI Codex coding agent, and plan for quotas and scaling.

This post outlines the development of a cost-effective and scalable intelligent document processing pipeline on AWS, powered by Amazon Bedrock and its features. BDA is a managed service within Amazon Bedrock that automates the extraction of insights from documents. We demonstrate how BDA extracts and analyzes document content, while Strands Agent hosted on Amazon Bedrock AgentCore Runtime coordinate specialized processing tasks, and Amazon Bedrock Knowledge Base enable contextual understanding across multiple documents. By combining these capabilities within a unified architecture, organizations can transform their document processing workflows with minimal development effort.

In this post, we share how Amazon Bedrock’s granular cost attribution works and walk through example cost tracking scenarios.

In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach.

In this post, we discuss how Amazon Nova demonstrates capabilities in conversational analytics, call classification, and other use cases often relevant to contact center solutions. We examine these capabilities for both single-call and multi-call analytics use cases.

In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement, combined with custom AWS Lambda functions orchestrated by AWS Step Functions.

This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.

This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. We demonstrate how to implement an AI assistant web interface with AWS Amplify and explain the prompt engineering strategies adopted to generate the SQL queries. Finally, we present instructions to deploy the service in your own AWS account.

In this post, we walk through how to build the FoodSavr solution (fictitious name used for the purposes of this post) using Amazon Rekognition Custom Labels to detect the ingredients and generate personalized recipes using Anthropic’s Claude 3.0 on Amazon Bedrock. We demonstrate an end-to-end architecture where a user can upload an image of their fridge, and using the ingredients found there (detected by Amazon Rekognition), the solution will give them a list of recipes (generated by Amazon Bedrock). The architecture also recognizes missing ingredients and provides the user with a list of nearby grocery stores.