A lookalike company finder for automated ICP discovery

The lookalike company finder starts from example, not from filters. You give it the LinkedIn pages of your strongest customers, and it reads their firmographics — industry, size, location and more — to build an ICP profile from what they actually have in common.

It then searches LinkedIn for companies that fit that profile and scores each candidate on how closely it matches your seed set. The output is a ranked list, so the companies most like your best customers sit at the top, ready to work first.

This replaces hours of manual prospecting. Instead of guessing filter values and hoping, you let your existing wins define the target and get back a fresh list that resembles them — useful both for finding new accounts and for sanity-checking who your ICP really is.

Working from examples beats working from filters because your best customers know things about your ICP that you have not written down. They may share a growth stage, a tech choice or a market position that no simple size-and-industry filter would ever capture, and the lookalike company finder picks that up from the seed set rather than from a guess. The scored output also doubles as a check on your own assumptions: if the top matches surprise you, your real ICP may differ from the one in your pitch deck. Either way you end with a ranked list grounded in companies that already bought — a far stronger starting point than a blank advanced search.

• Sales teams turning their ten best customers into a list of fifty similar companies to pursue.
• Founders discovering an ICP from real wins instead of guessing it on a whiteboard.
• RevOps expanding a small, proven seed list into a scored pipeline of fresh accounts.
• Agencies building a lookalike prospect list for a client from that client’s own best logos.

Each candidate company returns with a fit score and why it matched:

The lookalike company finder works through your own LinkedIn session — you connect a session cookie once in Settings, where it is encrypted at rest. It reads the firmographics of your seed companies and derives the shared ICP pattern.

It then searches LinkedIn for candidates and scores each one with Google Gemini, which weighs how well a company matches the seed set across multiple attributes rather than a single filter. The result is a ranked list where a higher score means a closer resemblance to the customers you started with.