Category: Customer Solutions

In this post, we share how DPG Media is introducing AI-powered processes using Amazon Bedrock into its video publication pipelines. This solution is helping accelerate audio metadata extraction, create a more engaging user experience, and save time.

In this post, we highlight how the AWS Generative AI Innovation Center collaborated with SailPoint Technologies to build a generative AI-based coding assistant that uses Anthropic’s Claude Sonnet on Amazon Bedrock to help accelerate the development of software as a service (SaaS) connectors.

In this post, we demonstrate how Intact’s Call Quality solution used Amazon Transcribe and other AWS services to improve critical KPIs with AI-powered contact center call auditing and analytics.

In this post, we present an Amazon Bedrock powered virtual assistant that can transcribe presentation audio and examine it for language use, grammatical errors, filler words, and repetition of words and sentences to provide recommendations as well as suggest a curated version of the speech to elevate the presentation.

In this post, we describe how using the capabilities of Amazon SageMaker has accelerated Indeed’s AI research, development velocity, flexibility, and overall value in our pursuit of using Indeed’s unique and vast data to leverage advanced LLMs.

In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock.

In this post, we dive into the Rufus inference deployment using AWS chips and how this enabled one of the most demanding events of the year—Amazon Prime Day.

In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.

In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. This solution establishes MLOps practices to standardize model development, streamline ML model deployment, and provide consistent monitoring.

In this post, we learn how Visier was able to boost their model output by 10 times, accelerate innovation cycles, and unlock new opportunities using Amazon SageMaker.