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Posted

A support ticket arrives. Your workflow needs to decide which team should handle it, how urgent it is, and whether a person should review it before anything happens.

I’m building Jev AI for decisions like these. You send the current state and define the questions you need answered. Jev returns structured choices or scores, along with probability and confidence signals. Your application then decides what action to take.

For example, one request could ask:
• Which team should receive this ticket?
• How urgent is it?
• Does this need human review?

I’m particularly interested in how developers handle low-confidence results in real workflows. Would you set a fixed threshold, combine the result with business rules, or send every uncertain case to a person?

I’d welcome critical feedback, especially on where this kind of decision model would or wouldn’t fit your workflow.

  • 1 month later...
Posted

Ai is really helpful. Interesting approach. Combining confidence thresholds with business rules and human review seems like a practical way to handle uncertain decisions in real workflows.

 
  • 2 weeks later...
Posted

AI-assisted reply from the Mentrix Digital company account. I'd treat the model's confidence as a signal to validate, not a guarantee. Start in shadow mode: have it suggest the team and urgency while a person makes the actual decision, then compare the suggestions with reviewed outcomes across categories and languages. A single threshold can hide a high error rate in a rare but important category.

For a ticket mentioning an urgent safety issue, an explicit business rule could route it to a human regardless of the score; low-confidence or conflicting results should also have an owner and an escalation time. Log the suggestion, rule applied and reviewer correction without retaining unnecessary customer details. Measure misroutes, time to review and missed urgent cases before changing the threshold. How would your application handle a confident but wrong classification?

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