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As your conversational AI initiatives evolve, developing Amazon Lex assistants becomes increasingly complex. Multiple developers working on the same shared Lex instance leads to configuration conflicts, overwritten changes, and slower iteration cycles. Scaling Amazon Lex development requires isolated environments, version control, and automated deployment pipelines. By adopting well-structured continuous integration and continuous delivery (CI/CD) practices, …

Drive organizational growth with Amazon Lex multi-developer CI/CD pipeline Read More »

Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain greater control over their deployments, optimize costs, and align with compliance requirements. However, this flexibility introduces a critical technical challenge: response format incompatibility with Strands agents. While these custom …

Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints Read More »

Organizations can face two critical challenges with conversational AI. First, users need answers where they work—in their CRM, support console, or analytics portal—not in separate tools. Second, implementing a secure embedded chat in their applications can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. Amazon Quick Suite embedded …

Embed Amazon Quick Suite chat agents in enterprise applications Read More »

Call center analytics play a crucial role in improving customer experience and operational efficiency. With foundation models (FMs), you can improve the quality and efficiency of call center operations and analytics. Organizations can use generative AI to assist human customer support agents and managers of contact center teams, so they can gain insights that are …

Unlock powerful call center analytics with Amazon Nova foundation models Read More »

This post is cowritten by Jeremy Jacobson and Rado Fulek from Ricoh. This post demonstrates how enterprises can overcome document processing scaling limits by combining generative AI, serverless architecture, and standardized frameworks. Ricoh engineered a repeatable, reusable framework using the AWS GenAI Intelligent Document Processing (IDP) Accelerator. This framework reduced customer onboarding time from weeks …

How Ricoh built a scalable intelligent document processing solution on AWS Read More »

Canvas in AI Mode is now available for everyone in the U.S. Plus, it can now help you draft documents or build interactive tools. ​ 

Learn more about Google DeepMind’s Project Genie and how to write prompts to create your own worlds. ​ 

Gemini 3.1 Flash-Lite is our fastest and most cost-efficient Gemini 3 series model yet. ​ 

In this first post in a two-part series, we examine how retailers can implement a virtual try-on to improve customer experience. In part 2, we will further explore real-world applications and benefits of this innovative technology. Every fourth piece of clothing bought online is returned to the retailer, feeding into America’s $890 billion returns problem …

Building a scalable virtual try-on solution using Amazon Nova on AWS: part 1 Read More »

This post was co-written with Davesh Maheshwari from Lendi Group and Samuel Casey from Mantel Group. Most Australians don’t know whether their home loan is still competitive. Rates shift, property values move, personal circumstances change—yet for the average homeowner, staying informed of these changes is difficult. It’s often their largest financial commitment, but it’s also …

How Lendi revamped the refinance journey for its customers using agentic AI in 16 weeks using Amazon Bedrock Read More »