Lookalike prospecting
from your best customers

A tool that finds companies similar to a reference set instead of a filter you define. You supply customer domains or account names, the tool reads the shared pattern across them and returns net-new companies that match it. The difference between tools is what they match on, how many seeds they need, and whether the result comes with verified contacts. For how the lookalike tools inside Claude and ChatGPT compare, see best MCP for lookalike prospecting.

A filter returns every company that meets criteria you chose. A lookalike finder returns companies that resemble the ones you closed, including attributes you didn’t think to filter on. Lusha’s model matches on the underlying profile and ignores brand or name similarity.

Finding new accounts by matching the pattern across companies you’ve already won, industry, size, technologies, growth, who buys, rather than by choosing filters. It surfaces the ICP you actually close, and it works from five or more examples.

At least five. Below five the matches degrade, because the model is reading a pattern across the set, not copying one company. Up to 100 seeds sharpens the pattern.

The underlying profile of the seed set: industry, headcount, technologies in use, growth pattern, and the titles that bought. It does not match on brand or name similarity.

Yes. Pass your customer list, open opportunities, or owned accounts as an exclusion list and they never appear in results.

Ask for the next page. Lusha keeps a session for the same seed set so the next results are ones you haven’t seen.

You get the companies with the decision makers found and a flag showing whether each has a verified email or direct dial on file, drawn from 515M+ contacts. Revealing a contact costs credits, the preview does not.

Yes. With the Lusha connector, paste five or more customer domains and ask for lookalikes. The Lookalike Skill handles the exclusion list, the filtering, and the next page for you.