Artificial Intelligence

Category: Amazon Bedrock

payu solution architecture

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 AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock

Sonatus partnered with the AWS Generative AI Innovation Center to develop a natural language interface to generate data collection and automation policies using generative AI. This innovation aims to reduce the policy generation process from days to minutes while making it accessible to both engineers and non-experts alike. In this post, we explore how we built this system using Sonatus’s Collector AI and Amazon Bedrock. We discuss the background, challenges, and high-level solution architecture.

Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation

This post presents an end-to-end IDP application powered by Amazon Bedrock Data Automation and other AWS services. It provides a reusable AWS infrastructure as code (IaC) that deploys an IDP pipeline and provides an intuitive UI for transforming documents into structured tables at scale. The application only requires the user to provide the input documents (such as contracts or emails) and a list of attributes to be extracted. It then performs IDP with generative AI.

Amazon QuickSight dashboard displaying sales analytics with multiple visualizations including a text summary showing 99 unique customers with $2,752,804 total sales revenue, a horizontal bar chart of total sales by customer name with Anthem at the top, summary metrics showing $2,752,804 sales and 99 customers, a scatter plot chart showing total sales quantity and profit by customer color-coded by company, and a detailed customer data table with order information including dates, contacts, names, regions and countries.

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.

Architecture diagram of the solution

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.

Long-running execution flows now supported in Amazon Bedrock Flows in public preview

We announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows.

End-to-end architecture diagram of voice-enabled AI agent orchestrated by Pipecat, featuring real-time processing and AWS services

Building intelligent AI voice agents with Pipecat and Amazon Bedrock – Part 2

In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for voice and multimodal conversational AI agents to build applications with human-like conversational AI. You learned about common use cases of voice agents and the cascaded models approach, where you orchestrate several components to build your voice AI agent. In this post (Part 2), you explore how to use speech-to-speech foundation model, Amazon Nova Sonic, and the benefits of using a unified model.

Uphold ethical standards in fashion using multimodal toxicity detection with Amazon Bedrock Guardrails

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models

In this post, we share some of the new innovations in SageMaker AI that can accelerate how you build and train AI models. These innovations include new observability capabilities in SageMaker HyperPod, the ability to deploy JumpStart models on HyperPod, remote connections to SageMaker AI from local development environments, and fully managed MLflow 3.0.

Use K8sGPT and Amazon Bedrock for simplified Kubernetes cluster maintenance

This post demonstrates the best practices to run K8sGPT in AWS with Amazon Bedrock in two modes: K8sGPT CLI and K8sGPT Operator. It showcases how the solution can help SREs simplify Kubernetes cluster management through continuous monitoring and operational intelligence.