Jev AI

What do you think of it all then?

Is Jev worth it and what are your use cases?

Need to test this out

Fast, low-cost AI turning text into structured decisions in ~0.62s.

Publishing a median latency number (~0.62s) is more honest than the usual "blazing fast" claim — respect for that. Is that measured at the decision boundary end-to-end, or model inference only? Curious how it holds up under bursty load.

The uncertainty-aware outputs are a thoughtful touch—probabilities and confidence scores are much more actionable for routing and evaluation than an opaque yes/no answer. The machine-readable format should also make it easier to plug decisions into existing workflows.

The uncertainty-aware responses are a smart touch — most decision APIs just return a label without any confidence signal. Does the API let you set custom thresholds for when to escalate to a human review?

Well, its quite famous now due to the fast latency number. I am yet to try it in any of my projects

Uncertainty-aware outputs are the useful part for routing and guardrail use cases. Are the confidence scores calibrated, meaning an output scored 0.8 is right about four times in five, and is there a way to inspect that calibration in the playground? A documented suggestion for where to put the abstain threshold per use case would make the API much easier to trust.

The ~0.62s median latency is impressive for structured outputs. I like that it returns probabilities and an uncertainty signal instead of just a label — that's what makes it usable for routing support tickets automatically. Question: how do you handle low-confidence cases? Is there a recommended threshold to send something to a human instead, or does the API return a "not sure" state you can act on directly? Congrats on the launch!

Congrats on the launch! best tool

For lead scoring the confidence number is only useful if it's calibrated. If Jev says 0.8, is it right about 80% of the time? Do you publish any calibration numbers, or is there a way to check it against my own labeled data before I wire it into routing?

Interesting approach to structured AI decisions. The combination of confidence scores, scoring, and machine-readable outputs looks particularly useful for automation and routing workflows.

Interesting approach to structured AI decisions. The combination of confidence scores, scoring, and machine-readable outputs looks particularly useful for automation and routing workflows.

super cool tool, wish I could found it early

Uncertainty-aware outputs are exactly what's missing from most AI classifier APIs, too many vendors return a bare label with no calibrated confidence, which makes downstream routing logic a guessing game. Do you expose the confidence thresholds as tunable parameters per decision type, and is there a streaming mode for long-running evaluations? At ~0.62s median latency this looks like a strong fit for webhook-driven pipelines like lead qualification and support triage, where you can't block the request path on an LLM call.

Best possible tool of this year in my opinion

I found Jev AI interesting because it turns natural-language inputs into structured decisions, scores, and probabilities. After reading tech launches like this, I often play Subway Surfers https://subwaysurferspc.io which keeps things fast and entertaining.

sounds really coo tho

Congrats on the launch!