Category: Customer Solutions

This post presents how AWS and PwC are developing new reasoning checks that combine deep industry expertise with Automated Reasoning checks in Amazon Bedrock Guardrails to support innovation.

In this post, Amazon shares how they developed a multi-node inference solution for Rufus, their generative AI shopping assistant, using Amazon Trainium chips and vLLM to serve large language models at scale. The solution combines a leader/follower orchestration model, hybrid parallelism strategies, and a multi-node inference unit abstraction layer built on Amazon ECS to deploy models across multiple nodes while maintaining high performance and reliability.

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

In this post, we discuss how DTDC and ShellKode used Amazon Bedrock to build DIVA 2.0, a generative AI-powered logistics agent.

Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.

In this post, we show how to process multi-page documents with a human review loop using Amazon Bedrock Data Automation and Amazon SageMaker AI.

In this post, we explore how Handmade.com, a leading hand-crafts marketplace, modernized their product description handling by implementing an AI-driven pipeline using Amazon Bedrock and Amazon OpenSearch Service. The solution combines Anthropic’s Claude 3.7 Sonnet LLM for generating descriptions, Amazon Titan Text Embeddings V2 for vector embedding, and semantic search capabilities to automate and enhance product descriptions across their catalog of over 60,000 items.

In this post, we examine a solution adopted by Nippon Life India Asset Management Limited that improves the accuracy of the response over a regular (naive) RAG approach by rewriting the user queries and aggregating and reranking the responses. The proposed solution uses enhanced RAG methods such as reranking to improve the overall accuracy

In this post, we explore how Crypto.com used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.

In this post, we demonstrate how to build an intelligent eDiscovery solution using Amazon Bedrock Agents for real-time document analysis. We show how to deploy specialized agents for document classification, contract analysis, email review, and legal document processing, all working together through a multi-agent architecture. We walk through the implementation details, deployment steps, and best practices to create an extensible foundation that organizations can adapt to their specific eDiscovery requirements.