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Red Hat: Building a Transaction Monitor with OpenShift AI

Red Hat
06/29/2026
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Monitor AI Quickstart. This quickstart turns natural language into fully customized transaction alert rules. It then compares incoming transactions against those rules and alerts the user if a rule they created was triggered. Existing solutions suffer from cumbersome interfaces and restrictive alert rule categories, whereas this quickstart leverages the power of AI to make the process seamless for the user. To get started, we'll sign in with a test user and be brought to the homepage of our application. This quickstart has three pages. There's the dashboard, where you can view stats about your transactions, as well as your most recent alerts and transactions. There's the transactions page, where you can view a full list of all of your transactions with further details, as well as searching and sorting through them. And there's the alert rules page, where you can manage and add new alert rules. While we're here, let's add a new alert rule. For example, alert me if I spend more than usual on shopping. For most apps, this would be much too vague, but for this one, the AI starts working to craft a reasonable alert rule based off the user's transaction history. And once it's done, it'll show us a preview of the rule so we can see what it's going to do before it does it. For this one, it will notify us if a transaction is more than 20% higher than our average shopping transaction over the past 30 days. We'll go ahead and press create rule to add it to our list of active rules. Next, let's head over to our transactions page and take a look at our existing shopping transactions. Most of them are pretty low, but we do have a few bigger ones. So in order to make sure we get above the average, we're going to pick 2000 as our number. After that, we can hit add transaction. Once it's finished, we'll receive a notification in the top right, letting us know that one of our alert rules has been triggered. We can go check back in the alert rules page, and we see that yes, the alert rule that we set has been triggered and the notification has been sent via email. For our last example, let's take a look at our recommended rules. The recommended rules combine generally popular rules with user transaction data to generate alert rules that will be most useful to each user. If we open the weekly spending alert rule, we can see that in addition to the rule text itself, it gives an explanation as to why this particular rule would be helpful for this user. We can go ahead and add it by clicking the add rule button. One final time, we will make our way to our transactions page. And this time, because it's just weekly spending, all we need to do is add any transaction that will take us over $700. And in this case, that's basically any transaction. So once we fill in these boxes and press add transaction, we receive a notification in the top right. We can go to our alert rules page like before. We can see it in the box. I hope this was helpful. And if you have any questions, feel free to reach out to myself or one of the other maintainers. Thanks.

TL;DR

  • The Spending Transaction Monitor AI Quickstart converts natural language requests like "alert me if I spend too much on shopping" into precise, data-driven alert rules based on individual transaction history.
  • The application features three core interfaces: a dashboard for statistics and recent activity, a transactions page for detailed history management, and an alert rules page for creating and managing custom monitoring rules.
  • AI-powered recommended rules combine popular alert patterns with user-specific spending data to suggest personalized monitoring configurations, with transparent logic previews before rule activation.

Summary

This demonstration showcases the Spending Transaction Monitor AI Quickstart, a Red Hat OpenShift AI application that transforms natural language requests into intelligent transaction alert rules. Software Engineer Theia Surette walks through the complete workflow, demonstrating how users can create personalized spending alerts using conversational language rather than complex UI filters. The system analyzes transaction history to generate data-driven alert logic, such as triggering notifications when spending exceeds 20% above a user's 30-day average in specific categories. The quickstart includes a dashboard for viewing transaction statistics and recent alerts, a transactions page for detailed history management, and an alert rules page for creating and managing custom rules. The application also features AI-powered recommended rules that combine popular alert patterns with individual user spending data to suggest the most relevant monitoring configurations. Each rule provides transparent logic previews before activation, ensuring users understand exactly how their alerts will function. This quickstart exemplifies Red Hat's approach to making enterprise AI accessible through ready-to-deploy, industry-specific use cases that teams can quickly explore and extend on open source infrastructure.

Chapters

0:00 - Introduction and Overview
0:34 - Application Interface Tour
1:00 - Creating Natural Language Alert Rules
2:21 - AI-Powered Recommended Rules

Key Quotes

0:10 "This quickstart turns natural language into fully customized transaction alert rules."
0:22 "Existing solutions suffer from cumbersome interfaces and restrictive alert rule categories, whereas this quickstart leverages the power of AI to make the process seamless for the user."
1:24 "For this one, it will notify us if a transaction is more than 20% higher than our average shopping transaction over the past 30 days."

FAQ

How does the Spending Transaction Monitor determine what constitutes "spending too much" from a natural language request?

The system analyzes the user's transaction history over the past 30 days to calculate category-specific spending averages. When a user makes a vague request like "alert me if I spend too much on shopping," the AI generates a rule based on statistical analysis, such as triggering alerts when a transaction exceeds 20% above the calculated average for that category.

What are Red Hat AI quickstarts and how do they help teams implement AI solutions?

Red Hat AI quickstarts are a catalog of ready-to-deploy, industry-specific AI use cases designed for Red Hat OpenShift AI environments. They provide teams with hands-on examples that are simple to deploy, explore, and extend, demonstrating how AI can run on enterprise-ready open source infrastructure without requiring teams to build solutions from scratch.


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