Use cases
One technical result can mean different things to Revenue Operations, SDR leadership, GTM engineering, and outbound agencies. Each team needs a clear acceptance rule before verified addresses move downstream.
RevOps defines state mapping, freshness windows, suppression precedence, deduplication, CRM field ownership, and exception queues. A valid result can update a verification field, but it should not erase source, checked time, or a previous opt-out.
Acceptance question: Can every changed field be explained and reversed?
Representatives need a concise result that distinguishes mailbox risk from account relevance. Catch-all and unknown states should not be silently treated as verified, and a human should approve the contact, message context, and regional requirements before sending.
Acceptance question: Can the rep explain why this person and address belong in this campaign?
Engineers inspect package permissions, scoped secrets, provider responses, rate limits, retries, schema changes, logging, retention, and deletion. Testing begins with masked known examples in a non-production environment.
Acceptance question: Does the workflow fail safely without exposing credentials or upgrading uncertainty?
Agencies separate client policy, verification evidence, suppression lists, sender authentication, and approval records. Client data must not leak between workspaces, and a fresh verification result must never override a global or client-specific exclusion.
Acceptance question: Can the agency prove which policy and reviewer approved each handoff?
| Requirement | RevOps | SDR leadership | GTM engineering | Agency |
|---|---|---|---|---|
| State definitions | Field contract | Queue guidance | Schema test | Client policy |
| Checked time | Stored timestamp | Visible in task | Expiry rule | Campaign window |
| Suppression | Hard precedence | Before send | Integration test | Workspace isolated |
| Human review | Exception owner | Named approver | Release gate | Client approval |
| Deliverability | Data hygiene | Sending limits | SPF/DKIM/DMARC | Per-client monitoring |
Bulk verification can reduce obvious address errors while increasing the number of records that require policy decisions. A single-source check is simpler to explain; a waterfall may resolve more candidates while adding cost, conflicts, and deletion work. Browser automation can be convenient but fragile; an API may be more repeatable but introduces credential, rate-limit, and schema dependencies. Select the method against the operational decision, not a universal accuracy claim.
Build a small cohort with known outcomes, ambiguous catch-all domains, prior suppressions, and stale records. Review every disagreement before expanding.
npx -y @okki-global/okki-go-taroball