AWS Machine Learning Blog

Solution architecture diagram

Adobe enhances developer productivity using HAQM Bedrock Knowledge Bases

Adobe partnered with the AWS Generative AI Innovation Center, using HAQM Bedrock Knowledge Bases and the Vector Engine for HAQM OpenSearch Serverless. This solution dramatically improved their developer support system, resulting in a 20% increase in retrieval accuracy. In this post, we discuss the details of this solution and how Adobe enhances their developer productivity.

How Gardenia Technologies helps customers create ESG disclosure reports 75% faster using agentic generative AI on HAQM Bedrock

Gardenia Technologies, a data analytics company, partnered with the AWS Prototyping and Cloud Engineering (PACE) team to develop Report GenAI, a fully automated ESG reporting solution powered by the latest generative AI models on HAQM Bedrock. This post dives deep into the technology behind an agentic search solution using tooling with Retrieval Augmented Generation (RAG) and text-to-SQL capabilities to help customers reduce ESG reporting time by up to 75%. We demonstrate how AWS serverless technology, combined with agents in HAQM Bedrock, are used to build scalable and highly flexible agent-based document assistant applications.

NVIDIA Nemotron Super 49B and Nano 8B reasoning models now available in HAQM Bedrock Marketplace and HAQM SageMaker JumpStart

The Llama 3.3 Nemotron Super 49B V1 and Llama 3.1 Nemotron Nano 8B V1 are now available in HAQM Bedrock Marketplace and HAQM SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s newest reasoning models to build, experiment, and responsibly scale your generative AI ideas on AWS.

Solution Architecture

Automate customer support with HAQM Bedrock, LangGraph, and Mistral models

In this post, we demonstrate how to use HAQM Bedrock and LangGraph to build a personalized customer support experience for an ecommerce retailer. By integrating the Mistral Large 2 and Pixtral Large models, we guide you through automating key customer support workflows such as ticket categorization, order details extraction, damage assessment, and generating contextual responses.

Mental model for choosing HAQM Bedrock options for cost optimization

Effective cost optimization strategies for HAQM Bedrock

With the increasing adoption of HAQM Bedrock, optimizing costs is a must to help keep the expenses associated with deploying and running generative AI applications manageable and aligned with your organization’s budget. In this post, you’ll learn about strategic cost optimization techniques while using HAQM Bedrock.

How E.ON saves £10 million annually with AI diagnostics for smart meters powered by HAQM Textract

E.ON’s story highlights how a creative application of HAQM Textract, combined with custom image analysis and pulse counting, can solve a real-world challenge at scale. By diagnosing smart meter errors through brief smartphone videos, E.ON aims to lower costs, improve customer satisfaction, and enhance overall energy service reliability. In this post, we dive into how this solution works and the impact it’s making.

Building intelligent AI voice agents with Pipecat and HAQM Bedrock

Building intelligent AI voice agents with Pipecat and HAQM Bedrock – Part 1

In this series of posts, you will learn how to build intelligent AI voice agents using Pipecat, an open-source framework for voice and multimodal conversational AI agents, with foundation models on HAQM Bedrock. It includes high-level reference architectures, best practices and code samples to guide your implementation.

Stream multi-channel audio to HAQM Transcribe using the Web Audio API

In this post, we explore the implementation details of a web application that uses the browser’s Web Audio API and HAQM Transcribe streaming to enable real-time dual-channel transcription. By using the combination of AudioContext, ChannelMergerNode, and AudioWorklet, we were able to seamlessly process and encode the audio data from two microphones before sending it to HAQM Transcribe for transcription.