Clean a list before launch
Verify every email on Friday’s list, dedupe against HubSpot, remove customers, and return the send-ready CSV with the risky rows separate.
It verifies emails and phones in bulk, deduplicates against your CRM, suppresses customers, competitors and do-not-contact records, scores fit against your ICP, and hands back a list your sender domain and your reps can trust, with the risky rows split out where you can see them.
Emails checked for deliverability and phones for validity across the whole list, with the result and the date stored on the row.
Rows matched against existing CRM records on identity keys, then customers, open deals, competitors and do-not-contact lists held out.
Every row scored against the ICP you describe, so the sequence starts with the best fit and the long tail waits its turn.
Deliverable, risky and unknown rows reported separately with counts computed exactly, so the send decision is made on numbers.
Verify every email on Friday’s list, dedupe against HubSpot, remove customers, and return the send-ready CSV with the risky rows separate.
Every contact created in the last 90 days: verify, flag duplicates, and propose merges as an approval batch.
Score this week’s 300 signups against our ICP, verify their work emails, and route the top 30 to the right owners.
What comes back: A send-ready list with the counts that justify it: verified, deduplicated, suppressed and scored, with the risky rows named.
Each run reserves budget before it executes and settles after - the cap is enforced by the ledger, never advisory.
Numbers come from tool output only, and every finding cites the capability that surfaced it.
Anything that spends, publishes or reaches out routes through your approval policies before it moves.
The console for teams, the CLI in your terminal, REST with an API key, MCP inside your assistant - the same agent on the same meter.
Every workspace ships with all seven agents and the full data stack behind them. The first mission runs minutes after signup.