Category: AWS Lambda

This post introduces Claude Tool use in Amazon Bedrock which uses the power of large language models (LLMs) to perform dynamic, adaptable entity recognition without extensive setup or training.

In this post, we demonstrate how to build a Slack integration using AWS Cloud Development Kit (AWS CDK). You will learn how to deploy the infrastructure with three specialized AWS Lambda functions, configure event subscriptions properly to handle Slack’s security requirements, and implement conversation management patterns that work for many agent use cases.

In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach.

Organizations find it challenging to implement a secure embedded chat in their applications and can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. In this post, we show you how to solve this with a one-click deployment solution to embed the chat agents using the Quick Suite Embedding SDK in enterprise portals.

In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight.

In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.

In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities–when requested or for urgent cases–and an analytics pipeline for performance insights.

In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents.

In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences.

In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care.