Stop wrestling with complex UI filters for transaction monitoring. Discover how to use Red Hat OpenShift AI to build an intelligent spending monitor that translates natural language into precise alert rules.
In this video, Software Engineer Theia Surette demonstrates the Spending Transaction Monitor AI quickstart. Learn how to leverage natural language processing (NLP) to turn vague requests like "alert me if I spend too much on shopping" into data-driven logic based on a user’s 30-day spending average. Theia walks through the dashboard, the transaction management system, and the real-time notification engine. You will also see how the system uses an LLM to recommend personalized rules and provide clear logic previews before a rule is finalized, ensuring transparency in your AI-driven financial applications.
Red Hat AI quickstarts are a catalog of ready-to-run, industry-specific use cases for your Red Hat AI environment. Each AI quickstart is simple to deploy, explore and extend. They give teams a fast, hands-on way to see how AI can run solutions on enterprise-ready, open source infrastructure.
🚀 Explore the spending monitor quickstart → https://github.com/rh-ai-quickstart/spending-transaction-monitor
✨ Explore other AI quickstarts → https://docs.redhat.com/en/learn/ai-quickstarts
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