This support ticket classifier built with JEV categorizes tickets, assesses urgency, and determines if human intervention is needed. It has achieved 100% accuracy in initial tests and operates at a lower cost.
added by @ifahimreza
i built a tiny support ticket classifier with JEV that initially used GPT, then tested the same flow with JEV using a few real customer messages.
JEV decides the category, urgency, and whether the ticket needs a human or can be handled automatically.
so far, it has been 100% accurate in my small test and costs much less to run.
View similar
Ranked from stored criteria vectors. No live classification on this page.
The Jev adapter classifies prompts before they reach the LLM, reducing token consumption and costs. It provides classification and confidence scores, marking prompts for review when confidence is near the threshold.
Developed a small expense categorizer for UPI payment notes using TypeSafe AI's Jev model. The focus was on the testing process rather than just the model implementation.
This Chrome extension listens to YouTube audio and detects sponsor segments to skip them in real-time. It is a prototype project that costs approximately $0.005 per video and is open-source.