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When hundreds to thousands of users are onboarded to an enterprise AI platform, business leaders and platform owners need visibility into who is using the platform, whether users are satisfied with the answers they receive, and which capabilities are driving the most engagement. Without a centralized observability solution, this data is scattered across multiple AWS […]
When hundreds to thousands of users are onboarded to an enterprise AI platform, business leaders and platform owners need visibility into who is using the platform, whether users are satisfied with the answers they receive, and which capabilities are driving the most engagement. Without a centralized observability solution, this data is scattered across multiple AWS services and difficult to analyze at scale.
Amazon Quick is a generative AI-powered platform that brings together Spaces, Chat agents, Flows, Automate, Research, and Amazon Quick Sight business intelligence capabilities in one place. As organizations scale their Amazon Quick deployments, they need a reliable way to track adoption, measure satisfaction, monitor costs, and audit governance from a single pane of glass.
In this post, we show you how to deploy a solution that consolidates the Amazon Quick operational data from Amazon CloudWatch vended logs and AWS CloudTrail events into a secured data lake in Amazon Simple Storage Service (Amazon S3) that can be queried using Amazon Athena, a Quick Sight dashboard, and a Quick custom chat agent.
Amazon Quick publishes usage and interaction data through the vended logs to deliver chat conversations, user feedback, agent/research hours usage, and index storage usage in Amazon Quick. Amazon Quick is integrated with AWS CloudTrail, which provides a record of actions taken by a user, a role, or an AWS service in Amazon Quick.
The solution encrypts the data at rest using a customer managed AWS Key Management System (AWS KMS) key with automatic key rotation. The solution encrypts the Amazon CloudWatch Log Groups, Amazon Data Firehose delivery streams, AWS Lambda function environment variables, and Amazon S3 data lake. This provides a unified encryption strategy across the entire pipeline.
The deployment is organized into steps, each building on the previous one. You can stop after any step and have a working solution at that level. Settings like the AWS CLI profile, resource prefix, database name, and workgroup name are saved locally after each step, so subsequent steps auto-populate them.