AI-powered platforms verify contact data in two layers: a deterministic check (syntax, MX record, SMTP handshake) that confirms the address technically exists, and a probabilistic scoring layer that handles the cases SMTP can’t resolve, mostly catch-all domains, role-based inboxes, and spam traps. Cleaning runs the same pipeline against an existing database, flagging stale records and replacing them through enrichment against a live data graph.
The deterministic stack hasn’t changed much in a decade. Syntax check, MX record lookup, SMTP handshake. That pipeline catches typos, dead domains, and inboxes that don’t exist. The AI layer sits on top of it and addresses the cases SMTP can’t resolve: catch-all servers that accept mail to every address by default, disposable inboxes, role-based addresses (info@, sales@), and known spam traps. Most modern platforms assign a confidence score from 0 to 100 using signals like domain reputation, historical engagement, and pattern analysis across a wider data graph. Cleaning an existing CRM works the same way, run in reverse: every record gets re-verified, flagged, and either refreshed via enrichment or suppressed. According to Apollo’s 2026 data decay report, B2B contact data decays at roughly 2.1% per month, which is why a list verified six months ago and never re-cleaned can hit bounce rates above 5%. Re-verification before each campaign send (not just at enrichment) is what catches the drift.
Run new contacts through real-time verification at the point of entry so bad data never enters the CRM. For anything that can’t be automatically verified, use Quality Assurance processes to test data samples.
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