712
LinkedIn leads scored
7,120
Typed scores (10 criteria + 1 safety check)
8.3s
Total time, 140ms median per lead
$0.045
Total cost, 1.07M tokens
How a single lead flows through it
1
Lead + draft
Profile text and one or more message drafts, no formatting needed.
2
JEV scores it
Typed questions only, choice, score or noul, never free text.
3
Verdict + probability
A calibrated confidence comes back with every answer.
4 · Confidence gate
Interactive demo
Choosing the best message, live.
Runs entirely in your browser against 96 generated demo leads (illustrated avatars, fictional names, no real people), to illustrate the same mechanism as the 712-lead run above.
0
Leads read
0
Scores typed
0
Elapsed
0
Cumulative cost
Leads
Profile in progress
Waiting for scoring to start…
Hook
CTA / format
Buying signal
Best message selected
Part 1
Why a model that writes nothing beats an LLM at sorting.
The problem: you ask ChatGPT to "rate this message out of 10." It says 7, then 8 next time for the same message, with three paragraphs of justification nobody reads. Three seconds per call, a cent a lead, and no guarantee that 7 means anything consistent.
A generative LLM is built for open-ended text. Asking it to sort is like paying a novelist to check boxes. JEV (TypeSafe AI, released September 15, 2026) is what's called a "System One model": it reads a block of text and answers only typed questions, choice (one option out of up to 255), score (a rating on 2 to 10 levels), or noul (a yes/no probability between 0 and 1). It returns the answer AND the full probability distribution behind it. Since it can only ever output A, B or C, there is nothing to hallucinate.
The last row matters: JEV is less accurate than a big model, 24x cheaper and 10x faster. It's a sorting model, not an oracle. Use it with a confidence threshold and a human-review queue underneath, never blind.
Part 2
20 use cases where JEV replaces your LLM.
Each row is a bounded decision you're either overpaying for or doing by hand. Start with #3, #4 and #5, they touch money and have ground truth to check against: did the lead reply, yes or no.
The 15-criteria grid.
Every message gets these 15 answers in a single call. Names and types below, the exact instructions and selection thresholds are in the kit.
Part 3
The prompt, the API request, and the backtest script.
This is where the actual paste-and-run material lives, the full 15-question request against docs.typesafe.ai's format, the exact selection-rule thresholds, the best-of-5 draft picker, and the Node backtest script to validate reply_odds against your own sent DMs before trusting it. It's below in the kit, not on the page.
API key: console.typesafe.ai (early access, waitlist) · Model host: Vercel AI Gateway · Router alternative: Requesty, model typesafe/jev-latest
Full scoring prompt
All 15 typed questions, ready to paste against the endpoint
Selection thresholds
The exact confidence-gated rule behind SELECTED = YES
Best-of-5 picker
One request to rank multiple drafts for the same lead
Backtest script
Node, zero dependencies, computes your own AUC
Free access
Get the full JEV kit by email.
The scoring prompt, the 11-criteria grid, the complete API request, and the backtest script, all above, sent to your inbox as one reference doc.
The actual gain
Stop losing deals to slow triage.
This isn't a novelty. Wired into your CRM, it changes three things that directly affect revenue: how fast a hot lead gets touched, how many go stale in a queue, and whether the message that reaches them actually fits them.
Minutes, not hours
Time to first touch on a hot lead, once scoring gates the send queue instead of a human sorting it by hand
Zero cold leads worked first
In-market leads are pulled to the front automatically, the ones most likely to convert this week, not whichever came in most recently
No off-target message sent
The mismatch check catches a message that doesn’t fit its lead before it goes out, the kind of miss that quietly kills reply rate