Classify every reply and score every lead — in code, with probabilities you can weight.
Reply triage is a closed set of categories; lead fit is four ordinal scores you weight in code. Both are what a System One model does natively, and neither needs a paragraph of generated reasoning.
In development — the benchmark numbers on this page are measured; the Decide-1 model is in training.
Replies are sorted by hand
Or by an LLM that invents a fifth category.
Scores without numbers
“Strong fit” from a chat model is not weightable.
Nobody trusts the score
Because nothing measures whether a 0.8 was right eight times in ten.
- 1
One choice per reply
Over your categories; route removal requests and questions immediately.
- 2
Four scores per lead
industry fit, company size, stated pain, buying signal — each 0–3, weighted in code.
- 3
Calibrate on outcomes
Closed-won is the label. A temperature per question type keeps the scores honest.
r = client.system_one(state=reply, questions={
"category": {"type": "choice", "instructions": "How did they reply?",
"criteria": {"interested": "...", "not_interested": "...", "wrong_person": "...",
"out_of_office": "...", "remove": "...", "question": "..."}},
"buying_signal": {"type": "score", "instructions": "Buying intent", "criteria": ["none","curious","evaluating","ready"]},
})- Choice — pick one option, a probability on each
- Score — a position on your ordered rubric
Pattern from the maximem.ai decision-seams map (sales / SDR section). Calibration matters most here: the benchmark’s open encoder went from ECE 0.194 to 0.020 with one fitted temperature per type on 1,000 held-out judgments.
Numbers about Jev are its authors' or the cited source's; numbers from the benchmark are ours, on one harness, with the raw JSON published. Full table.
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Questions
- Can I add a category later?
- Yes — the options are in the request, not in the weights.