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Sales & SDR

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.

The problem

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.

How it works with Decide
  1. 1

    One choice per reply

    Over your categories; route removal requests and questions immediately.

  2. 2

    Four scores per lead

    industry fit, company size, stated pain, buying signal — each 0–3, weighted in code.

  3. 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
Something you can check

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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