A lookalike company finder for automated ICP discovery
Find new companies matching your best customers.
The Kavex lookalike company finder takes the customers you already love and finds more like them. Paste the LinkedIn URLs of five to twenty of your best accounts and it derives the pattern that connects them, searches LinkedIn for matches, and returns the closest companies, each scored for fit. It turns a short seed list into a ranked prospect list without manual research. You pay per result returned.
A RUN OF 1,000 RESULTS ON THIS SOURCE, AT 5 PER RESULT
Five columns on every row.
These come with the run and cost nothing extra. Add-ons widen the same sheet, and you pay only for the ones you switched on.
| Company | Industry | Size | Location | Fit score |
|---|---|---|---|---|
| Helmplan | SaaS | 11-50 | Utrecht, NL | 0.94 |
| Cadenta | Software | 51-200 | Ghent, BE | 0.91 |
| Forsby Labs | SaaS | 11-50 | Malmö, SE | 0.88 |
| Tindle & Co | Information Technology | 51-200 | Manchester, UK | 0.82 |
Say what you want. Read the price first.
Everything else is a ceiling or a filter. The estimate moves with the numbers you type, so the run never surprises you at the end.
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.
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.
Charged per delivered row. If the source holds fewer than you asked for, the rest of the credits go back to your balance the second the run ends.
One thousand results, itemised.
One line, because this source has nothing bundled on top. You pay the rate times the rows the run actually delivered.
| Line | Rate | Units | Credits |
|---|---|---|---|
| Look-alike | 5 / result | 1,000 results | 5,000 |
| 1,000 results | 5 / result | 5,000 credits | $5.00 |
$0.005 PER FINISHED RESULT · ONLY DELIVERED ROWS ARE BILLED
What people point it at.
Four ways this source is used today. Every one of them is the same run, the same export and the same credit pool.
- 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.
The rest of the linkedin stack.
Same credit pool, same export. Chain the output of one run into the next without paying for the list twice.
Asked before the first run.
Short answers, no accordion to click through. If something is still missing, the help pages go deeper.
How many seed companies should I provide?
Between five and twenty of your best customers works well. Enough examples give the tool a clear pattern to learn from, while keeping the seed set focused on accounts that genuinely represent your ICP.
What does the fit score mean?
The fit score rates how closely a candidate company resembles your seed set across industry, size, location and other attributes. A higher score means a stronger match, so the list is ready to work from the top down.
How do I connect LinkedIn?
Add a LinkedIn session cookie once in Settings, stored encrypted at rest. The lookalike company finder then runs from your seed list with no further setup.
How do results export?
Results download as a CSV with one candidate company per row, its firmographics and a fit score column, so you can work the strongest matches first.