Customer Solutions

In this post, we show you how Kyndryl integrated Amazon Q Business with ServiceNow in a few simple steps. You will learn how to configure Amazon Q Business and ServiceNow, how to create a generative AI plugin for your ServiceNow incidents, and how to test and interact with ServiceNow using the Amazon Q Business web experience. This post will help you enhance your ServiceNow experience with Amazon Q Business and enjoy the benefits of a generative AI–powered interface.

The Education and Training Quality Authority (BQA) plays a critical role in improving the quality of education and training services in the Kingdom Bahrain. BQA reviews the performance of all education and training institutions, including schools, universities, and vocational institutes, thereby promoting the professional advancement of the nation’s human capital. In this post, we explore how BQA used the power of Amazon Bedrock, Amazon SageMaker JumpStart, and other AWS services to streamline the overall reporting workflow.

This post shows how MuleSoft introduced a generative AI-powered assistant using Amazon Q Business to enhance their internal Cloud Central dashboard. This individualized portal shows assets owned, costs and usage, and well-architected recommendations to over 100 engineers.

In this post, we explore how Deep Instinct’s generative AI-powered malware analysis tool, DIANNA, uses Amazon Bedrock to revolutionize cybersecurity by providing rapid, in-depth analysis of known and unknown threats, enhancing the capabilities of AWS System and Organization Controls (SOC) teams and addressing key challenges in the evolving threat landscape.

Generative AI applications should be developed with adequate controls for steering the behavior of FMs. Responsible AI considerations such as privacy, security, safety, controllability, fairness, explainability, transparency and governance help ensure that AI systems are trustworthy. In this post, we demonstrate how to use the AWS generative AI best practices framework on AWS Audit Manager to evaluate this insurance claim agent from a responsible AI lens.

In this blog post, we explore a client services agent assistant application developed by the London Stock Exchange Group (LSEG) using Amazon Q Business. We will discuss how Amazon Q Business saved time in generating answers, including summarizing documents, retrieving answers to complex Member enquiries, and combining information from different data sources (while providing in-text citations to the data sources used for each answer).

This blog post with accompanying code presents a solution to experiment with real-time machine translation using foundation models (FMs) available in Amazon Bedrock. It can help collect more data on the value of LLMs for your content translation use cases.

In this post, we show you how Parameta used Amazon Bedrock Flows to transform their manual client email processing into an automated, intelligent workflow that reduced resolution times from weeks to days while maintaining high accuracy and operational control.