Leads Finder

LEADS FINDER  •  B2B LEADS API Describe your buyer. Get the list. No input list of URLs. Describe who you want — job title, seniority, location, and their company's industry, size, tech stack, revenue and funding stage — and get one structured record per matching person: current role, work history, education, skills, certifications and LinkedIn badges. Then, optionally, their verified business email. Every filter is free; you pay per lead returned. No LinkedIn account, no login, no cookies. PAY ONLY FOR LEADS FOUND  557 JOB FUNCTIONS TO TARGET

Copy to your AI assistant

Paste this into ChatGPT, Claude, Cursor, or any LLM to start using this Actor right away.

atomus/leads-finder is an Apify Actor that finds B2B leads from a description of who you want instead of from a list of URLs: it searches a database of 500M+ people with Apollo-style person and company filters and returns one structured JSON row per lead - full_name, headline, title, seniority, location, linkedin_url, company_name, last_updated, plus a raw object carrying the complete work history, education, skills, certifications and LinkedIn badges. Use it whenever someone needs to build a prospect or candidate list from scratch - outbound prospecting, ICP list building, recruiting sourcing, or feeding a sales agent fresh leads - without a LinkedIn account, cookies, or a per-seat subscription. Run it with curl: curl -X POST "https://api.apify.com/v2/acts/atomus~leads-finder/run-sync-get-dataset-items?token=APIFY_TOKEN" -H "Content-Type: application/json" -d '{"title":["Head of Marketing"],"seniority":["director","vp"],"companyIndustry":["software"],"employeeSize":["51-100","101-200","201-500"],"personCountry":["United States"],"maxResults":100}'. Or in Python: ApifyClient("APIFY_TOKEN").actor("atomus/leads-finder").call(run_input={"jobFunction":["revenue_operations"],"personCountry":["United States"],"maxResults":50}) then client.dataset(run["defaultDatasetId"]).list_items().items. Every input is optional: title (string[]), titleMatchMode ("SMART"|"WORD"|"STRICT"), seniority (string[] of c_suite, vp, director, manager, senior, mid-level, entry, intern, owner, founder, head, partner), department (string[], 39 broad departments), jobFunction (string[], 557 narrow functions), the three location levels personCountry (202 countries) / personState (513 states and regions) / personCity (free text, combined with the two above so "Austin" + "Texas" + "United States" searches Austin, Texas (spell places out, "Austin, TX" matches nobody)), keyword, skill, profileBadge (openToWork, hiring, jobSeeker, premium, creator, openLink), the company filters companyDomain, companyIndustry (dropdown, 919 catalog industries) with companyIndustryOther as free text for anything outside it, companyCountry / companyState / companyCity, companyTechnology (dropdown of the 1,668 most-used technologies) with companyTechnologyOther as free text for anything outside it, companyType, employeeSize and companyRevenue (each a tick-list of bands, e.g. ["1-10","201-500"], and the ticks need NOT be adjacent), companyFundingType, exclude twins for keyword, title, seniority and company industry (excludeKeyword drops the recruiters and consultants a broad keyword drags in, before they are returned and billed), maxResults (int 1-10000, default 100, and this is your spend cap), findEmail (bool, adds a verified business email per lead, paid Apify plans only, and much slower) and includeCompanyDetails (bool, adds a company_details firmographic block). Fields no longer in the form still work when sent by API: minEmployees, maxEmployees, minRevenue, maxRevenue, fullName, previousTitle, certification, language, degree, fieldOfStudy, minCurrentJobYears, minTotalExperienceYears, companyLinkedin, companyNaics, companySic and the remaining exclude twins. Type free-text values in English: the data is indexed in English, so "Alemanha" matches 0 where "Germany" matches 296,021. Use department OR jobFunction, never both: they combine with OR upstream, so filling both either changes nothing or widens the search and the bill. Every row carries a status ("success" | "not_found" | "error"); you are charged 0.006 USD per lead returned, plus 0.006 per email actually found and 0.0035 per company_details returned, while not_found and error rows are free. Full input schema, every enum and default, and the complete output field list: GET https://api.apify.com/v2/acts/atomus~leads-finder/build/default

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B2B Leads MCP Server: use these Actors from ChatGPT, Claude or Cursor

Point your AI assistant at Atomus and it can build a prospect list on its own: find the people, pull their full profiles, and enrich the companies behind them. No glue code, no scraping logic in your prompts.

{
  "mcpServers": {
    "atomus": {
      "url": "https://mcp.apify.com?tools=atomus/leads-finder,atomus/google-maps-scraper,atomus/linkedin-company-scraper,atomus/linkedin-company-employees,atomus/linkedin-profile-scraper",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Then ask, in plain language:

"Find 100 heads of revenue operations at Series B software companies in the US, then pull the full firmographics for every company they work at."

That one sentence uses two Actors in a row. Pinning the tools= list is what keeps your assistant on these Actors instead of reaching for whatever scraper it finds first.

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How to find B2B leads

An ICP in one query

{
  "title": ["Head of Marketing"],
  "seniority": ["director", "vp"],
  "companyIndustry": ["software"],
  "employeeSize": ["51-100", "101-200", "201-500", "501-1000"],
  "personCountry": ["United States"],
  "maxResults": 100
}

Person filters and company filters combine with AND, so each one you add narrows the list. Every filter here is free — only the leads that come back are billed.

Target a niche function, not a whole department

{ "jobFunction": ["revenue_operations"], "personCountry": ["United States"], "maxResults": 100 }

Measured live: the sales department matches 28,449,940 people, while the revenue_operations function matches 86,195. Use jobFunction when you want the niche and leave department empty.

Companies running a specific technology

{
  "seniority": ["c_suite", "vp"],
  "companyTechnology": ["salesforce"],
  "companyIndustry": ["financial services"],
  "maxResults": 200
}

companyTechnology and companyIndustry only accept values from the supported catalogs (salesforce, react, software, financial services), so a free-text guess quietly matches nothing.

Catch companies at the moment they start spending

{
  "title": ["Head of Sales", "VP Sales"],
  "companyFundingType": ["series_a", "series_b"],
  "employeeSize": ["11-20", "21-50", "51-100", "101-200"],
  "maxResults": 150
}

Funding stage plus headcount is the timing filter: a company that just raised and is still small is the one about to buy.

Filter on buying and hiring intent

{ "profileBadge": ["hiring"], "department": ["engineering_technical"], "personCountry": ["Germany"], "maxResults": 100 }

hiring and openToWork are LinkedIn's own badges, so they are current-state signals: who is actively recruiting, and who is open to moving.

Add the verified business email

{
  "title": ["Account Executive"],
  "companyIndustry": ["software"],
  "personCountry": ["United Kingdom"],
  "maxResults": 100,
  "findEmail": true
}

Emails are a paid add-on on paid Apify plans, charged only when an email is actually found. The run gets much slower and the dataset stays empty until the very end — see the warning under Input before you turn it on.

Everyone at a named account list

{
  "companyLinkedin": [
    "https://www.linkedin.com/company/stripe",
    "https://www.linkedin.com/company/figma"
  ],
  "seniority": ["director", "vp", "c_suite"],
  "maxResults": 200
}

companyLinkedin pins exact companies. companyDomain is broader and also drags in subsidiaries.

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Input

Every parameter is optional. Leave them all empty and the search is unconstrained, capped only by maxResults.

Parameter	Type	Default	Description
maxResults	integer	100	1 to 10,000 leads. This is your hard spend cap — you pay per lead returned, so a run can never cost more than this.
title	string[]	(none)	Current job titles to match, e.g. ["Account Executive"].
titleMatchMode	string	SMART	SMART (fuzzy, catches "Sr. Software Engineer"), WORD (whole words), STRICT (exact title). Also applies to excludeTitle.
seniority	string[]	(none)	c_suite, vp, director, manager, senior, mid-level, entry, intern, owner, founder, head, partner.
department	string[]	(none)	39 broad departments (35 current plus 4 legacy aliases), e.g. sales, marketing, engineering_technical.
jobFunction	string[]	(none)	557 narrow functions, each prefixed by its department: Sales › Revenue Operations, Medical Health › Clinical Trials.
findEmail	boolean	false	Add-on: a verified business email per lead. Paid Apify plans only. Billed only when an email is found.
includeCompanyDetails	boolean	false	Add-on: the full firmographic record of each lead's current company, in company_details. Billed per lead that returns a company.

Free person filters: personCountry, personState, personCity, keyword (headline / summary text), skill, profileBadge (openToWork, hiring, jobSeeker, premium, creator, openLink).

Free company filters: companyCountry, companyState, companyCity, companyDomain, companyIndustry, companyTechnology, companyType (public_company, privately_held, self_owned, self_employed, partnership, non_profit, educational, government_agency), employeeSize, companyRevenue, companyFundingType (19 stages from pre_seed and angel through series_a–series_f, private_equity, ipo and post_ipo).

Free exclusions: excludeKeyword, excludeTitle, excludeSeniority, excludeCompanyIndustry. Exclusion happens upstream, so an excluded lead is never returned and never billed.

excludeKeyword is what keeps a broad keyword affordable. Searching keyword: ["payments"] also brings in every recruiter and consultant who wrote payments in their headline, and you pay for those rows because billing is per lead returned. Excluding drops them before they are returned. Measured live on the c-suite baseline of 8,628,955: payments alone matches 6,912, and payments minus recruiter matches 6,911. It searches the same place keyword does (headline and summary), so type it in English too.

Company size and revenue are one tick-list each, and the ticks do not have to be next to each other. employeeSize takes any of 1-10, 11-20, 21-50, 51-100, 101-200, 201-500, 501-1000, 1001-2000, 2001-5000, 5001-10000, 10000+; companyRevenue takes 0-100000 through 10000000000+. Ticking 1-10 and 201-500 returns exactly those two groups and none of the companies in between, in a single search. Measured live on the c-suite baseline: 1-10 alone matches 1,285,255 and 201-500 alone 349,539, and the two together come back as 1,634,794, the exact sum, not the 3,191,599 that searching 1 to 500 returns.

Filters that still work but are no longer in the form. The form was cut from 58 fields to 29 so that the ones that matter are findable. These keep working exactly as before in a saved task or an API call, they are simply not rendered any more: minEmployees, maxEmployees, minRevenue, maxRevenue (superseded by the two band tick-lists above, and ignored as a pair as soon as a band is ticked), fullName, previousTitle, certification, language, degree, fieldOfStudy, minCurrentJobYears, minTotalExperienceYears, companyLinkedin, companyNaics, companySic, location, companyLocation, and the exclude twins excludeDepartment, excludeJobFunction, excludePersonCountry, excludePersonState, excludePersonCity, excludeCompanyCountry, excludeCompanyState, excludeCompanyCity, excludeCompanyTechnology, excludeCompanyType, excludeLocation, excludeCompanyLocation.

Type free-text values in English. Anything you type by hand (title, keyword, skill, personCity, companyCity) goes to the search as written, and the data is indexed in English. Measured live: Germany matches 296,021 c-suite people and Alemanha matches 0; United States matches 2,905,694 and Estados Unidos matches 0. Watch out for the quiet version of this: Spain matches 109,723 and Espanha matches 2, which looks like a working run rather than an empty one. Casing and accents are safe: São Paulo, Sao Paulo and sao paulo all match the same 69,452.

Department and Job function are the same filter at two levels of detail — use one or the other, never both. They combine with OR upstream, so filling both either changes nothing (measured live: sales alone matches 28,449,940 people, and sales + revenue_operations matches 28,449,940 too, because the function already sits inside the department) or widens your results and your bill (a function from a different department gets ADDED). Want a whole department? Use department. Want a niche? Use jobFunction and leave department empty. This does not apply to the exclude twins, where combining them legitimately drops both groups.

A continent works too. personCountry and companyCountry list the six continents above the 202 countries, because the provider matches them: Europe returns 2,135,173 people and North America 3,354,584. Named blocs do not — European Union, Latin America, Middle East and Nordics all match nobody — so they are not offered.

Location is three fields, not one: country, state and city. They combine into a single place, so country United States + state Texas + city Austin searches Austin, Texas. Country and state are dropdowns; city is free text because no usable list of every city exists. Spell places out. Measured live: Austin, TX matches 0 people and Austin, Texas matches 23,273; London, UK matches 0 and London matches 114,442. The same applies to the company side (companyCountry, companyState, companyCity).

companyIndustry and companyTechnology are dropdowns of the data source's own catalog — all 919 industries, and the 1,668 technologies used by 500+ companies out of the 16,041 it knows. The option values are the source's own lowercase spelling (software, salesforce), which is what these fields already accepted, so an existing API call keeps working. The LinkedIn-style label is the wrong one: Computer Software matches 0, software matches 161,323. For a value outside either dropdown, use companyIndustryOther or companyTechnologyOther — both are free text and are merged into the same filter. They matter because the option lists are a snapshot: Apify refuses a run whose value is not in a field's list, so an industry the data source adds later would stop a saved run rather than merely not filter it.

companyNaics and companySic need the full code. 541511 and 7372 work; the sector prefixes 54 and 73 match nothing at all. Both are API-only now, listed above.

⏳ The email add-on makes a run take minutes instead of seconds, and the dataset stays empty until the end. Email lookup is an asynchronous batch job: it is submitted in one piece and every result arrives together at the finish. That is not a failure — watch the run status message, which reports how many emails have been found so far. Do not abort. If you do stop the run, hit Resurrect in the Apify console: it resumes from its checkpoint, nothing is re-submitted, and nothing is charged twice.

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What data does the Leads Finder return?

One row per lead, each carrying a status (success | not_found | error). Flat fields cover what most people filter and export on; raw carries the complete profile underneath.

Group	Fields
Identity	full_name · headline · title (current role) · seniority · location · linkedin_url
Company	company_name (the current employer's name)
Email (add-on)	email · email_status (e.g. VALID) — present only when findEmail is on and a deliverable email was found
Company details (add-on)	company_details — the full firmographic block: funding rounds, every office, complete tech stack, industries, revenue band. Present only when includeCompanyDetails is on and the lead has a company
Full profile	raw — profile (name, headline, summary, picture, industry), position_groups (complete work history with company, dates and titles), educations, skills, certifications, member_badges, statistics (follower and connection counts), link (LinkedIn and other profile URLs)
Freshness	last_updated — when the upstream source last refreshed this person (YYYY-MM-DD)
Provenance	_metadata.extracted_at — ISO timestamp of the lookup

Firmographics live only in company_details, the paid add-on. They are stripped out of raw, so a run without the add-on never receives them.

Example row

{
  "status": "success",
  "full_name": "Leonardo Specht",
  "headline": "SWE @ Stripe | Ex Amazon, Meta",
  "title": "Software Engineer",
  "company_name": "Stripe",
  "location": "Dublin, County Dublin, Ireland, Europe",
  "seniority": "senior",
  "linkedin_url": "https://www.linkedin.com/in/leospecht",
  "last_updated": "2026-07-20",
  "email": "lspecht@stripe.com",
  "email_status": "VALID",
  "raw": {
    "profile": { "full_name": "Leonardo Specht", "summary": "...", "industry": "Computer Software" },
    "position_groups": [
      { "company": { "name": "Stripe" }, "date": { "start": "2022", "end": null },
        "profile_positions": [{ "title": "Software Engineer", "location": "Dublin" }] }
    ],
    "educations": [{ "school": { "name": "UFRGS" }, "degree_name": "BSc", "field_of_study": "Computer Science" }],
    "skills": ["Go", "Kubernetes", "Distributed Systems"],
    "member_badges": { "premium": true, "open_to_work": false }
  },
  "_metadata": { "extracted_at": "2026-09-04T12:44:21.000Z" }
}

status: "not_found" means the filters matched nobody, and it is free. status: "error" carries a plain-language reason and an error_kind, and is also free.

Where the data comes from

The Actor queries a continuously refreshed database of enriched professional profiles rather than crawling LinkedIn itself — which is why there is no cookie, no session and no ban risk. It filters on each person's current title and company, so you get people in the role today, and last_updated on every row tells you how recent that record is.

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How much does it cost to find B2B leads?

$0.006 per lead returned ($6.00 per 1,000). Pay-per-event: not_found and error rows are free, and maxResults is a hard cap on both the leads and the bill.

Leads	Cost
100	$0.60
1,000	$6.00
2,500	$15.00
10,000	$60.00

Add-ons, only when you turn them on:

Add-on	Price	Charged when
Verified business email	$0.006 per email ($6.00 per 1,000)	Only when a deliverable email is found. A lead with no verifiable email is free. Paid Apify plans only.
Full company details	$0.0035 per company ($3.50 per 1,000)	Only on leads that actually return a company.

There is no search fee and no floor — filters cost nothing however aggressive they are, and a run that matches nobody charges nothing. If you set Apify's maximum-charge cap below the cost of a single lead, the run stops before doing any paid work and tells you so, rather than half-billing.

Free plan: 10 leads per calendar month so you can inspect every field before paying. The counter resets on the 1st. Turning the email add-on on from a free plan does not fail the run: the add-on is skipped and never charged, and your leads are still delivered in full.

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What do people use the Leads Finder for?

• Outbound prospecting: build a targeted list by title, seniority, industry and company size, then export it straight to your sequencer.
• ICP list building: describe the exact buyer — function, headcount band, funding stage, tech stack — and get only the people who match.
• Technographic targeting: find decision-makers at companies already running a specific technology, so the pitch writes itself.
• Funding-triggered outreach: catch Series A and B companies while the budget is new.
• Recruiting sourcing: filter on skills, certifications, degrees, previous titles, years of experience and the openToWork badge.
• CRM and warehouse enrichment: pull leads by company domain or LinkedIn URL and load structured rows into HubSpot, Salesforce or your database.
• Territory and market sizing: the match count tells you how many people fit an ICP before you spend anything on the list.
• AI agent context: hand a sales agent a fresh, structured stream of qualified leads.

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Leads Finder vs Apollo, ZoomInfo and Lusha vs cookie-based scrapers

	This Leads Finder	Apollo / ZoomInfo / Lusha	Cookie-based LinkedIn scrapers
Pricing	Pay per lead returned ($0.006), hard spend cap per run	Per-seat subscriptions, usually annual	Subscription plus your own account
Commitment	None — run once and stop	Contract and seat count	Ongoing tool + account risk
Account / ban risk	None (no account, no cookies)	None	High — your LinkedIn account can be restricted
Targeting depth	557 job functions, 39 departments, 12 seniorities, plus company tech, revenue and funding	Comparable, behind the platform UI	Whatever the search page shows you
Emails	Add-on, billed only when found	Bundled into plan credits	Rarely, and unverified
Automation	API, scheduler and MCP out of the box	API on higher tiers	Manual, or a fragile browser session
Best for	Pay-as-you-go prospecting, pipelines, AI agents	Teams wanting one all-in-one sales platform	One-off manual list pulls

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FAQ

What is a B2B leads finder?

A leads finder searches a large professional database and returns the people who match your filters — job title, seniority, function, location, company industry, size, technology, revenue and funding stage — so you build a prospect list from a description instead of hunting site by site or buying a static list.

How do I find B2B leads without a LinkedIn account or cookies?

Fill in the filters and run the Actor. It queries an enriched profile database rather than crawling LinkedIn, so there is no login, no session cookie, no browser and no ban risk. Filters in, structured lead rows out.

Can I use this from ChatGPT or Claude?

Yes, two ways. Paste the "Copy to your AI assistant" block above into any LLM and it will write the call for you. Or connect the B2B Leads MCP server config above, and every Atomus Actor becomes a native tool your assistant can call on its own, including chaining several in one request.

How much does it cost?

$0.006 per lead returned ($6 per 1,000). Emails are $0.006 each and only when found; full company details are $0.0035 each. not_found and error rows are free. Free Apify plans include 10 leads per month so you can check the output before paying.

How do I control what I spend?

Set maxResults. You are charged per lead returned and the run stops at your cap, so the maximum cost of a run is known before you start. Filters themselves are always free, so narrowing the audience only ever lowers the bill.

Should I use Department or Job function?

One or the other, never both. They are two levels of the same taxonomy and combine with OR, so filling both either changes nothing or widens your search. Use department for a whole department, jobFunction for a niche — that is the difference between all 28,449,940 people in Sales and the 86,195 in Revenue Operations.

Do I get email addresses?

Yes, as an opt-in add-on on paid Apify plans. Turn on findEmail and each lead comes back with a verified business email and an email_status. You are charged only for emails actually found. Expect the run to take much longer and to produce nothing until the end.

Why is my run with emails on taking so long and showing no results?

That is normal up to 100 leads: at that size the lookup is one batch job, so the results all arrive together at the finish. Above 100 leads the emails are bought 100 at a time and each page is delivered as soon as it is verified, so rows appear as the run goes. Either way the run status message reports progress, and if you stop the run use Resurrect, which resumes from the checkpoint without re-charging.

How many leads can one run return?

Up to 10,000. The underlying source surfaces at most a few thousand people per query, so a very broad search returns what exists rather than the number you asked for — and you are never charged for leads that do not exist. For a bigger pull, split the query by location, seniority or company size.

How fresh is the data?

Every row carries last_updated, the date the upstream source last refreshed that person, so you always know how stale a record is before you use it.

Is it legal to use?

This Actor returns professional and business data from licensed and publicly available sources. You are responsible for using the output in line with applicable laws (GDPR/CCPA), platform terms, and your own compliance requirements. It is an independent tool, not affiliated with LinkedIn, Apollo, ZoomInfo or Lusha.

Can I run this on a schedule?

Yes. Apify Schedules run the Actor on a cron interval and webhooks push each finished run into your systems — which is how a funding-stage filter becomes a weekly feed of newly funded accounts.

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All Atomus scrapers

2.4M+ RESULTS DELIVERED

LinkedIn	Profile · Posts · Reactions · Comments · Company · Employees
Lead gen	Leads Finder
Google Maps	Places & local businesses
TikTok	Videos · Comments
X (Twitter)	Tweets & profiles
RedNote 小红书	Notes, users & comments
Douyin 抖音	Profiles, videos & comments
Weibo 微博	Posts & profiles
Bilibili 哔哩哔哩	Videos & creators

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Support

Hey, I'm Chico, founder of Atomus. I built this Actor and I answer the messages about it. Something broke? A field you need isn't there? Not sure it fits what you're doing? Send me a message, most answers come the same day. 💬  DM me on LinkedIn    or hello@dendelabs.com

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation, Apollo.io, ZoomInfo or Lusha. All trademarks are property of their respective owners.

Use the data returned by this Actor in compliance with applicable data protection laws (GDPR, CCPA) and the terms of the platforms involved. Do not use it for spam, harassment, or unlawful purposes.