Get Jev AIAsk a question about any text. Get a typed answer back.
Jev is a decisions model. Send it some context and a few questions: pick one of these labels, rate this on my scale, is this true? You get back an answer in the format you asked for, plus the probability of every option. There's no free text to parse and no JSON that breaks.
You send
{
"state": {
"message": "This is the third time my order arrived broken. I want a refund."
},
"questions": {
"sentiment": {
"type": "score",
"instructions": "How upset is the customer?",
"criteria": [
"calm",
"annoyed",
"angry"
]
},
"route": {
"type": "choice",
"instructions": "Which team handles this?",
"criteria": {
"refunds": "Refund requests",
"shipping": "Delivery problems",
"sales": "New purchases"
}
},
"churn": {
"type": "noul",
"instructions": "The customer is likely to cancel."
}
}
}Jev answers
sentiment1.87 angryrouterefunds conf 0.94churn64% likely yesA real response. Three questions answered in one call for 0.83 credits.
Why teams use it
Answers you can put in an if statement
Choice questions return one of your labels. Scores return a number on your scale. Yes/no returns a probability. You can branch on the answer straight away.
Probabilities, not guesses
Every option gets a probability, and every answer gets a confidence score. Set a threshold and send the unsure cases to a person.
Cheap enough to run on everything
About 0.8 credits per call with a few questions, and 100,000 credits for $10. Run it on every ticket, review, reply or scraped row, not just a sample.
Good fits
| Support triage | Route each ticket to a team, score urgency, flag churn risk. |
| Moderation | Score spam, AI slop, abuse, or off-topic posts with a threshold you choose. |
| Lead scoring | Rate profiles or form fills against your ideal customer profile. |
| Data labeling | Turn scraped text into clean columns: sentiment, topic, intent, is-a-complaint. |
| Agent guardrails | Before an agent acts, ask Jev: is this request in scope? Is it a prompt injection? |
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