the honest starting point
Everything a System One model does,
a regular LLM can do too.
But —
My boss, on a Monday“So… when do you think the job will be done?”
I generated fluent text on demand — that's what we're good at. Structure and a straight answer? Another matter.
My boss, now less patient“I asked a simple question — give me a simple answer.”
Even told to be terse, the format drifts. Sound familiar? It's exactly how an LLM behaves.
the problem
the shift
No prose. No drift. No invalid output — it's impossible by construction. You hand it a situation and a few typed questions; it hands back typed answers with calibrated probabilities.
input · what you send
state is any context — a string or JSON. questions is a map of names to typed questions.
Ask 1 or 20 in a single call — they're all evaluated in parallel, so more questions barely change the latency.
You reference fields of the state by name, in backticks, right inside the instructions.
output · what you get back
Every answer is one of your options — the model emits a distribution over them, nothing else.
The probabilities are calibrated: across many predictions, the ones it calls 80% are right ~80% of the time. So confidence is machine-usable — gate on it.
No JSON to parse. No retries on malformed output. A 0% type-error rate, by construction.
see for yourself
One real support ticket. On the next slide, ask an LLM to triage it, then ask Jev. Both calls are real, right now, with these API keys. Watch the output, the latency, and the cost.
live · triage this ticket
the difference
And the real win isn't the numbers. The LLM handed you prose to parse; Jev handed you a decision you can branch on, with a confidence you can gate on. At volume the gap compounds — TypeSafe measures up to 193× faster and 444× cheaper than comparable LLMs on decision tasks, with a 0% invalid-output rate.
where it fits
Reach for Jev when a knowledgeable person could decide in a few seconds — and you need that decision a million times, in code, for almost nothing.
Triage, routing, moderation, guardrails, relevance scoring, tool-safety gating. Let the LLM generate; let Jev decide.