Score accounts against labeled examples

/deepline-research Identify evidence sources for the scoring or prioritization decision I describe. Separate observed facts, candidate proxies, and missing information. Check whether the evidence can be available at the decision date. Recommend a source plan and explain its coverage before collecting additional data. Do not invent scoring weights or treat source availability as proof of predictive value.

/deepline-scoring Build an account-scoring workflow with Deepline. Express the criteria explicitly and preserve the evidence behind each contribution. Separate unknown, false, and unavailable inputs. Compare a representative labeled sample and show disagreements rather than changing labels to match the score. Inspect source coverage and usage before a pilot. Return scores for review without changing CRM fields.

/deepline-scoring Compare the result with the acceptance rule we agreed. Show the evidence for accepted and rejected outcomes, unresolved questions, and recorded Deepline usage where work ran. If work is still running or a response is uncertain, inspect the original saved execution before proposing another request. Explain the next decision I need to make.

/deepline-gtm Implement the reviewed scoring or prioritization policy from this conversation. Preserve the agreed model or rule version, input definitions, evidence requirements, missing-data behavior, and output contract. Reuse collected evidence where it fits. Check the Play and propose a representative pilot before expanding the job or changing CRM records. Do not tune the scoring policy silently during implementation.