Make AI lead scoring reviewable with explicit rules
An unexplained score of 87 is hard to act on. Lead scoring becomes more useful when each criterion has a definition, a source, and a clear treatment for unknown values.
Build a rubric from observable facts
For a developer tool, relevant criteria might include whether a company builds software, whether the contact owns a relevant function, and whether the stated problem matches the product. A model can classify written evidence against those criteria. It should not guess budget, buying intent, or private company plans merely because a record lacks information.
- 1Define each criterion and the evidence needed…Define each criterion and the evidence needed to satisfy it.
- 2Represent unknown separately from a negative answerRepresent unknown separately from a negative answer.
- 3Compute the combined score from the criterion…Compute the combined score from the criterion values in code.
Try it on a small example
- Define each criterion and the evidence needed to satisfy it.
- Represent unknown separately from a negative answer.
- Compute the combined score from the criterion values in code.
What to verify
Review false positives before using scores to trigger outreach. A high-scoring record with weak evidence can waste more time than an unscored lead. Baleybots pipelines can structure classification and transformation steps, but the rubric remains your policy. Keep outreach as a separate action with its own approval and recipient checks. A score should help prioritize research, not imply that a prospect has expressed interest.
Product details checked against the Pipeline architecture on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.