Use case
ContactCTL for lead list building
Start from your best customer, not from a database dump. ContactCTL expands one seed domain into similar companies at 0.35 credits per row, finds the right people with scoped search, and resolves their work emails — charged only when found.
01 — The problem
Bought lists are everyone’s lists
Static B2B databases sell the same rows to you and to every competitor who can pay. The result is leads that were cold before you exported them, plus a pile of contacts that never matched your ICP in the first place.
The opposite extreme — scraping — produces junk with a compliance bill attached. ContactCTL deliberately does neither: no scraping, no LinkedIn automation. It expands from seeds you already trust and keeps every step bounded and priced.
The workflow is three commands: lookalike to get companies that resemble a known winner, search people to pull the right titles at each account, find to resolve a verified work email for the people you actually want to reach.
02 — The workflow
How it runs
Seed, expand, resolve
One known-good customer domain in, a scored list of similar companies out. Then scope down to people, and only pay full enrichment for the contacts that survive your filter.
A LinkedIn URL seed works too: ctc lookalike <linkedin_url> returns similar people instead of companies, at the same 0.35 credits per row. Use ctc search filters to list every accepted filter value before composing a search.
03 — Commands and credits
What this workflow uses
Every command reports its credit cost in the output. Preview any spend with --estimate at zero cost.
| Command | Purpose | Credits |
|---|---|---|
| ctc lookalike <domain> | Similar companies from a seed | 0.35 credits per returned row |
| ctc search people --domain D --titles T | People at a named account | 0.25 credits per search |
| ctc find <name> <domain> | Verified work email | 1 credit per result found |
Full datasheet: credit costs in the docs · plan pricing on /pricing