Waterfall Data Enrichment.
Multi-provider enrichment that finds emails a single source misses — roughly 80%+ match versus 40-50% single-source. The AI employee cascades each record across providers, verifies every address found, and projects bounce under 2% before the list ships.
How the AI employee does it
(from the Research Analyst playbook)Cascade across providers — each record tries multiple enrichment sources in sequence until it matches
Fill contextual fields — agentic web research answers questions like 'do they sell enterprise?'
Verify every email — each address checked before delivery, targeting under 2% projected bounce
Keep source-of-record — provenance logged per row for compliance
Tools on the job
(official APIs)Apollo.ioNeverBounceGoogle Sheets
Full stack on the Research Analyst knowledge base.
Measured by
(the number that matters)Email match rate ~80%+ via waterfall (vs 40-50% single-source), with verified-email bounce under 2%
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 waterfall data enrichment as part of the Research Analyst seat — flat from $499/agent/mo, one agent per dedicated machine. Build the staffer.