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Use cases

Improve your cold call connect rate.

Low connect rates usually start with the list, not the rep. Check every number before the session so dials go to people likely to pick up, and the rest go to email and LinkedIn.

US phone numbers onlyNo card required
Who has this problem
  • SDR and BDR teams working outbound lists
  • Agencies dialing on behalf of clients
  • Recruiters calling candidates and hiring managers
  • Founders doing their own outbound
The problem

What's going wrong.

Reps dial a fresh list top to bottom and spend most of the session on voicemail, no answer and wrong numbers. A handful of conversations come from a long list of dials.

The list is older than it looks

Numbers are reassigned, disconnected and replaced. A list exported months ago carries numbers that no longer reach the person.

Not every number is a direct line

Data tools return switchboards, desk lines and personal mobiles side by side, and they don't connect equally often.

Every contact gets the same treatment

Without a way to tell numbers apart, the best and worst get dialed in the same session, in whatever order the export came out.

How it helps

Check first, then dial.

Check before the session

Every contact comes back High chance, Low chance or Bad data, from five signals including whether the number belongs to the person.

Dial the right contacts

High chance and Low chance contacts go in the dial list. Bad data contacts stay out of it.

Keep everyone in the sequence

Bad data isn't a removal. Those contacts move to email and LinkedIn, so nobody is dropped.

The workflow

Step by step.

  1. 1

    Export your list

    From your data tool or CRM, with names and US phone numbers.

  2. 2

    Check it

    Upload the CSV, call the API, or ask your AI tool over MCP.

  3. 3

    Split by outcome

    Dial High chance and Low chance. Route Bad data to email and LinkedIn.

  4. 4

    Check again before the next session

    Lists change. Check close to the day you dial.

What changes

The day, after.

  • Sessions start with the contacts most likely to pick up.
  • Fewer dials go to disconnected and wrong numbers.
  • Contacts the phone won't reach still get a touch, by email and LinkedIn.
Example

A 120-contact list on a Monday morning

A rep exports 120 contacts from their data tool and checks the file before the session.

The checked file comes back with an outcome on every row. They filter to High chance and Low chance and load those into the dialer.

The Bad data rows go straight into an email and LinkedIn sequence instead of the dialer.

Example

The list, sorted by who will answer.

A checked calling list with High chance contacts marked for phone and Low chance and Bad data contacts marked for email and LinkedIn
An example list after checking: dial the High chance contacts first, and work the rest on other channels. Names and numbers are invented.
FAQ

Questions, answered.

Will checking make my reps dial less?

It moves dials, rather than cutting them: fewer go to numbers that won't connect, and more of the session goes to people likely to pick up.

What happens to contacts marked Bad data?

They stay in your outbound and move to email and LinkedIn. Bad data is a change of channel, not a removal.

Know who’ll answer
before you call.

Grade 250 contacts free. No card required, and US phone numbers only.

  • US phone numbers only
  • No card required
  • One credit per checked contact