Lead scoring and signal discovery

/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 Prepare a Deepline lead-fit review for these examples. Translate my criteria into explicit rules and inspect the sources that can support them. Separate company fit, observed engagement, and inferred intent. Preserve evidence and missing values for every score. Compare a small labeled sample before expanding. Return proposals without automatically changing lifecycle stages or activating outreach.

/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.