ICP Lead Scoring.
Every account scored on a weighted 0-100 rubric with A/B/C tiers and a 'why this account, why now' note per row. The AI employee proposes rubric recalibrations but never changes the scoring logic without your sign-off — silent drift corrupts every downstream list.
How the AI employee does it
(from the Research Analyst playbook)Build the rubric — your dream customers and bad fits become a weighted 0-100 scoring model, approved before use
Score and tier — every account graded into A/B/C tiers
Annotate with evidence — a why-this-account-why-now note with source URLs on each row
Propose, never drift — rubric changes are suggested to you, never applied silently
Tools on the job
(official APIs)Apollo.ioGoogle SheetsHubSpot
Full stack on the Research Analyst knowledge base.
Measured by
(the number that matters)Tier-A accounts surfaced per week that convert downstream, versus the ~18 hrs/week manual research baseline
Every action logged and reviewable — how trust works here.
Related tasks
(the staff work together)Put this on someone's desk
(alpha)Rae (Ext. 108) handles icp lead scoring as part of the Research Analyst seat — flat from $499/agent/mo, one agent per dedicated machine. Build the staffer.