LinkedIn Lookalike Companies — Similar Companies Finder

Give it LinkedIn companies — linkedin.com/company/… URLs or just the handle — and get back the lookalike companies LinkedIn lists for them: name, industry, location, URL, and a closeness score (timesListed: how many of the pages it read list that company). Add depth: 2 to read the pages of the lookalikes too, and the companies that many neighbours point at rise to the top. $1 per 1,000 lookalike companies, $2 from November 13, 2026 ($4 per 1,000 with company details switched on), no login, no cookies.

Try it now — the input is already filled in. Press Start: the 10 companies LinkedIn lists as similar to Stripe, in about 5 seconds, for about $0.02.

rank	name	industry	location
1	Atlassian	Software Development	Sydney, NSW
3	Revolut	Financial Services	London, England
10	Ramp	Financial Services	New York, New York

Give it several companies (or depth: 2) and timesListed becomes a closeness score: how many of the pages read list that company. Scheduled, each row says whether LinkedIn newly listed it (isNewSinceLastRun) and how its rank moved (rankChange).

Next step: paste the url column into LinkedIn Company Scraper — Employee Count & Headcount Growth for each lookalike's real headcount, and from your second run, how many people it added.

Run it again — or put it on a schedule — and every row also tells you what changed since the last run: isNewSinceLastRun (LinkedIn did not list this company last time), previousRank and rankChange (positive = moved up). On the first run these are null.

Call it from a script or a schedule

$curl -X POST "https://api.apify.com/v2/acts/northbell~linkedin-lookalike-companies/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" -H "Content-Type: application/json" -d '{"companies":["stripe","notion"]}'

The response is the rows themselves (a JSON array). To keep a lead list fresh, save the input as a task in Apify Console and add a weekly Schedule, and send each run to Google Sheets or your CRM with an integration on the task. n8n, Make and Zapier run it through their Apify app with the same input, and an MCP client can add https://mcp.apify.com?tools=northbell/linkedin-lookalike-companies.

What you get

One row per lookalike company (type: "lookalike"), closest first.

Field	What it is
name, slug, url, kind	the company; kind is company, or showcase for a product or brand page
industry, location	as shown on the similar-page card (location is missing on some cards)
timesListed, listedBy	closeness: how many of the pages read list this company, and the handles of those pages
rank	position in the list (1 = closest), by timesListed, then depth, then the order LinkedIn shows them (null on the companies you gave)
seed, depth	the first company you gave that led to it, and how many steps away it is (1 = on a page you gave, 2 = on a lookalike's page, 0 = a company you gave)
isNewSinceLastRun, previousRank, rankChange	the change since your previous run (null on the first run)
enriched	true when the company's own page was read for details (only possible with Add company details on), otherwise false
employees, followers, website, sizeBand, founded, headquarters	only with Add company details on: read from the company's own page. employees is the headcount LinkedIn counts; sizeBand is what the company states about itself
enrichError	only when details were asked for and that company's page could not be read
observedAt	when the row was made

Companies that could not be read come back as type: "error" rows (seed, errorKind, message) and are free.

Example, measured on 7 Oct 2026: given Stripe and Adyen, Revolut and Wise are on both pages (timesListed: 2) and come first; the other 15 are on one page each.

Use it for

• Account lists — start from your best customers, get the companies that look like them, and feed the handles into your CRM or enrichment
• Competitor and market maps — see who LinkedIn puts next to a company, and who sits next to everyone (depth 2)
• Watching a space — schedule it and see which companies newly appear in a competitor's neighbourhood
• A US account list by size instead of by likeness — every US employer in a state and industry with 200 to 1,000 employees, from their Department of Labor filings, comes from the US search of Official Company Headcount (Texas manufacturers: 470 in 6 seconds); paste their names here to widen the list

Input

{ "companies": ["stripe", "https://www.linkedin.com/company/adyen"], "depth": 1, "maxCompanies": 100, "enrich": false, "includeSeeds": false }

• companies — LinkedIn company URLs or handles, one per line. A website (stripe.com) or a company name (Scale AI) also works: the Actor tries a few likely company pages and takes one only when its website (for a website) or its name (for a name) matches; the ones it cannot find come back as error rows (not charged)
• depth — 1 (default) reads only your companies' pages; 2 also reads the pages of the lookalikes, roughly 10 times more pages
• maxCompanies — the most lookalikes to return (default 100, up to 1,000); with depth 2 the Actor stops reading more pages once it has found that many
• enrich — read each lookalike's own page for employees, followers, website, size band, founding year and headquarters
• includeSeeds — also return a row for each company you gave (depth 0)

The change since the last run is remembered per set of companies and depth in a named key-value store in your Apify account (linkedin-lookalike-history), so the second run of the same input already shows it. Schedule it (Apify → Schedules) to keep watching.

Pricing

$1 per 1,000 lookalike companies, plus $0.01 per run; from November 13, 2026 the plain row is $2 per 1,000 (company details and the run fee stay as they are). With Add company details on, each row whose company page was read costs $4 per 1,000 instead — one row is charged one price, never both. Error rows are free. A row whose own page could not be read comes back without the details and is charged the plain-row price. The $0.01 run-start fee is charged together with the first result; a run that returns nothing costs nothing.

100 lookalikes cost $0.11; 100 with company details cost $0.41.

Notes

• Public data only: what anyone sees on a company page without signing in. Companies, not people — no names of employees, no emails, no phone numbers.
• The similar companies are LinkedIn's own list (usually 10 per page). The Actor adds the counting across pages, the ranking and the change tracking. Some company pages show no list; those come back as a free error row.
• timesListed counts only the pages that were read. With depth 2, a small maxCompanies stops the reading early, so the counts are then based on fewer pages.
• Pages are read at about 20 a minute (a polite, shared budget). Depth 1 with 100 companies given is 100 pages, about 5 minutes; depth 2 for one company and 100 lookalikes is roughly 10 to 20 pages; company details add one page per row. With company details on, rows are written closest first while the pages are read, so a stopped run still has the best ones.
• If LinkedIn answers "too many requests", the Actor waits 5 seconds and then 10 seconds and tries again. If four pages in a row still fail, it stops reading more pages: companies you gave that were not read come back as free error rows, and lookalikes whose details were not read come back without the details (charged the plain-row price) — run it again a little later.
• location is empty on some cards, founded on companies that do not state it, employees on pages without a count (for example some showcase pages); headquarters is usually there. Empty fields are null, never guessed.
• The change since the last run compares with the last earlier run that read the same pages: the same companies, the same depth, and at depth 2 the same maxCompanies (it decides how many pages are read). It counts what LinkedIn listed, not only the top maxCompanies. If a page that was read last time could not be read this time (LinkedIn refused it, or the company is gone), nothing fair can be compared and that run shows null; the next normal run compares with the last normal one, so one bad day does not turn the whole list "new". A company that is simply not on LinkedIn does not switch the comparison off.
• School pages (linkedin.com/school/…) are accepted as input, but LinkedIn may refuse to show them without a sign-in (HTTP 999); such a school comes back as a free error row.
• Websites and company names are matched with a few tries each (one LinkedIn request per try). When none fits, Wikidata is asked which LinkedIn page belongs to that website or company name, and a page is used only when its own website agrees with Wikidata's (on 29 Oct 2026 "Snowflake Inc." found snowflake-computing and "Zoom Video Communications" found zoom this way). For a long list of websites, Domain to LinkedIn Company URL finds more of them and gives each match with its confidence; feed its handles in here.

For headcount and how fast a company is growing, see LinkedIn Company Scraper — Headcount, Employee Growth & Details — its rows already carry the 10 similar companies for each company you give; this Actor is for ranking and widening them across many companies.

For AI agents

{"companies": ["stripe"], "maxCompanies": 20} returns up to 20 lookalike companies as rows with name, slug, industry, location and timesListed; add "enrich": true for employees and website, "depth": 2 for a wider net. Rows have type: lookalike or error. Feed slug values back into companies to walk the graph one step further.

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