Category: Technical How-to

In this post, we show you how to build generative AI applications on Amazon Web Services (AWS) using the capabilities of Amazon Bedrock, highlighting how Amazon Bedrock can be used at each step of your generative AI journey. This guide is valuable for both experienced AI engineers and newcomers to the generative AI space, helping you use Amazon Bedrock to its fullest potential.

In this post, we demonstrate how we combined Amazon Nova, Amazon Rekognition, and Amazon Polly to automate the creation of accessible audio descriptions for video content. This approach can significantly reduce the time and cost required to make videos accessible for visually disabled audiences.

The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performance in Japanese language tasks, outperforming GPT-4o-mini and other leading models. This technical report details the training infrastructure, optimizations, and best practices developed during the project.

Learn how Articul8 is redefining enterprise generative AI with domain-specific models that outperform general-purpose LLMs in real-world applications. In our latest blog post, we dive into how Amazon SageMaker HyperPod accelerated the development of Articul8’s industry-leading semiconductor model—achieving 2X higher accuracy that top open source models while slashing deployment time by 4X.

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 Amazon 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 Amazon Bedrock, are used to build scalable and highly flexible agent-based document assistant applications.

In this post, we demonstrate how to use Amazon 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.

In this post, we demonstrate how Amazon Bedrock Guardrails helps block harmful and undesirable multimodal content. Using a healthcare insurance call center scenario, we walk through the process of configuring and testing various guardrails.

E.ON’s story highlights how a creative application of Amazon 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.

In this post, we explore the implementation details of a web application that uses the browser’s Web Audio API and Amazon 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 Amazon Transcribe for transcription.

In this post, we demonstrate how to use the Open AI Whisper foundation model (FM) Whisper Large V3 Turbo, available in Amazon Bedrock Marketplace, which offers access to over 140 models through a dedicated offering, to produce near real-time transcription. These transcriptions are then processed by Amazon Bedrock for summarization and redaction of sensitive information.