Artificial Intelligence
Category: Amazon Bedrock Knowledge Bases
Use generative AI in Amazon Bedrock for enhanced recommendation generation in equipment maintenance
In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI.
Build real-time travel recommendations using AI agents on Amazon Bedrock
In this post, we show how to build a generative AI solution using Amazon Bedrock that creates bespoke holiday packages by combining customer profiles and preferences with real-time pricing data. We demonstrate how to use Amazon Bedrock Knowledge Bases for travel information, Amazon Bedrock Agents for real-time flight details, and Amazon OpenSearch Serverless for efficient package search and retrieval.
Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors
In this post, we demonstrate how to integrate Amazon S3 Vectors with Amazon Bedrock Knowledge Bases for RAG applications. You’ll learn a practical approach to scale your knowledge bases to handle millions of documents while maintaining retrieval quality and using S3 Vectors cost-effective storage.
Amazon Bedrock Knowledge Bases now supports Amazon OpenSearch Service Managed Cluster as vector store
Amazon Bedrock Knowledge Bases has extended its vector store options by enabling support for Amazon OpenSearch Service managed clusters, further strengthening its capabilities as a fully managed Retrieval Augmented Generation (RAG) solution. This enhancement builds on the core functionality of Amazon Bedrock Knowledge Bases , which is designed to seamlessly connect foundation models (FMs) with internal data sources. This post provides a comprehensive, step-by-step guide on integrating an Amazon Bedrock knowledge base with an OpenSearch Service managed cluster as its vector store.
How PayU built a secure enterprise AI assistant using Amazon Bedrock
PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. In this post, we explain how we equipped the PayU team with an enterprise AI solution and democratized AI access using Amazon Bedrock, without compromising on data residency requirements.
Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight
In this post, we dive into how we integrated Amazon Q in QuickSight to transform natural language requests like “Show me how many items were returned in the US over the past 6 months” into meaningful data visualizations. We demonstrate how combining Amazon Bedrock Agents with Amazon Q in QuickSight creates a comprehensive data assistant that delivers both SQL code and visual insights through a single, intuitive conversational interface—democratizing data access across the enterprise.
Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents
In this post, we focus on building a Text-to-SQL solution with Amazon Bedrock, a managed service for building generative AI applications. Specifically, we demonstrate the capabilities of Amazon Bedrock Agents. Part 2 explains how we extended the solution to provide business insights using Amazon Q in QuickSight, a business intelligence assistant that answers questions with auto-generated visualizations.
How Rocket streamlines the home buying experience with Amazon Bedrock Agents
Rocket AI Agent is more than a digital assistant. It’s a reimagined approach to client engagement, powered by agentic AI. By combining Amazon Bedrock Agents with Rocket’s proprietary data and backend systems, Rocket has created a smarter, more scalable, and more human experience available 24/7, without the wait. This post explores how Rocket brought that vision to life using Amazon Bedrock Agents, powering a new era of AI-driven support that is consistently available, deeply personalized, and built to take action.
Democratize data for timely decisions with text-to-SQL at Parcel Perform
The business team in Parcel Perform often needs access to data to answer questions related to merchants’ parcel deliveries, such as “Did we see a spike in delivery delays last week? If so, in which transit facilities were this observed, and what was the primary cause of the issue?” Previously, the data team had to manually form the query and run it to fetch the data. With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. In this post, we discuss how Parcel Perform incorporated generative AI, data storage, and data access through AWS services to make timely decisions.
Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data
In this post, we discuss how to make your Amazon Aurora PostgreSQL-Compatible Edition data available for natural language querying through Amazon Bedrock Knowledge Bases while maintaining data freshness.