Bulk LinkedIn & Email Enrichment — CSV, 50,000 Rows

$3.20 per 1,000 resolved rows, $8 per 1,000 with emails and phones that work. Built for the 10,000 to 50,000 row files too big for a lookup form, with crash-safe resume: a delivered row is never billed twice.

Upload a CSV (or link one) and every row resolves by the strongest key it carries:

1. email address
2. profile URL
3. social handle (GitHub, X/Twitter, Facebook)
4. full name, plus a company domain column when you have one

Bare names resolve only when exactly one profile carries the name. Namesakes come back as a free ambiguous row with the holder count, never a guess.

Built for big files

• Rows stream into your dataset as they resolve.
• Long runs survive platform server migrations: the Actor checkpoints and resumes instead of starting over.
• Ten consecutive service errors stop the run early instead of grinding through an outage.
• A person your file lists twice is looked up once; the repeat is a free row naming the paid one.

Input

{
  "csv": "email\nsatya.nadella@microsoft.com",
  "contacts": false,
  "mustHave": []
}

csv: upload a file, paste rows or link a CSV. Recognized columns: email, profile_url, github, twitter, facebook, full_name (or first_name + last_name), domain.

Emails and phones: contacts

Turn on contacts to get the recorded email addresses and phone numbers with each profile. The best address and a phone are lifted into their own columns: email, emailType (work / personal), employerMatch, workEmails, personalEmails, emailCount, phone, phones.

You pay the contacts price, $8 per 1,000, only for a person with a contact that reaches them today:

• a personal mailbox (gmail, outlook, ISP mail);
• an address on the domain of their current employer;
• a phone the service labels as direct (not a switchboard).

Everyone else is delivered at the $3.20 profile price, old work addresses included. The _tier column says which price each row was charged at. Phone labels arrive with the next data rebuild; until then numbers ship unlabelled and never earn the contacts price by themselves.

How often that is, measured on 30 September 2026 over 200 employed professionals per country: the contacts price applied to 53% of people in the US, 42% in India, 40% in France, 33% in the UK and 32% in Germany. Everyone else shipped at the lower price.

Only people who have what you need: mustHave

List what a person must have for you to pay: email, personalEmail, workEmail, currentWorkEmail, phone, github, twitter, facebook. A person who is found but lacks one comes back as a free missing_required row that says what was missing (and names nobody). Social links are checked on every run; the email and phone requirements need contacts on, and a run that asks for them without it is refused before anything is charged.

Output

One row per input row, tagged with its record number (the header is record 1). A real resolved row:

{
  "_status": "found",
  "_input": { "row": 2, "email": "satya.nadella@microsoft.com" },
  "_freshness": "fresh_90d",
  "profileUrl": "https://www.linkedin.com/in/satyanadella",
  "location": "Redmond, Washington, United States",
  "countryCode": "us",
  "companyName": "Microsoft",
  "companySlug": "microsoft",
  "_view": "lite-v4",
  "slug": "satyanadella",
  "fullName": "Satya Nadella",
  "headline": "Chairman and CEO at Microsoft",
  "jobTitle": "Chairman and CEO",
  "industry": "Software Development",
  "connectionsCount": 500,
  "seniority": { "totalExperienceYears": 12, "currentTenureYears": 12, "averageTenureYears": 7 },
  "experience": {
    "work": [
      { "title": "Chairman and CEO", "company": "Microsoft", "startDate": "2014-02-01", "endDate": null },
      { "title": "Member Board Of Trustees", "company": "University of Chicago", "startDate": "2018-01-01", "endDate": null }
    ]
  },
  "education": [
    { "school": "University of Wisconsin-Milwaukee", "degreeName": "Master’s Degree", "fieldOfStudy": "Computer Science" }
  ],
  "contactInformation": {
    "socialLinks": { "profileUrl": "https://www.linkedin.com/in/satyanadella", "twitterUrl": null, "githubUrl": null }
  }
}

(trimmed for display: the row also carries every past position with its description, about, skills, languages, certifications, patents, publications and articles whenever the profile has them)

With contacts on, the same kind of row gains the contact columns (values below are made up):

{
  "_status": "found",
  "_tier": "contacts",
  "fullName": "Jane Doe",
  "companyName": "Acme",
  "profileUrl": "https://www.linkedin.com/in/jane-doe-example",
  "email": "jane.doe@acme.example",
  "emailType": "work",
  "employerMatch": true,
  "workEmails": ["jane.doe@acme.example", "jdoe@oldjob.example"],
  "personalEmails": ["jane.doe@example.com"],
  "emailCount": 3,
  "phone": "+15550100123",
  "phones": [{ "number": "+15550100123", "sharedBy": 1, "companyLine": false }]
}

A row looked up by email never gets that same address sold back to it. Every non-match is a free row with a reason (ambiguous, not_found, invalid, profile_removed), so the output has the same rows as the input.

The Output tab has three views: Overview, Career & education and Contacts & signals.

Pricing

Event	Price	When
Row resolved	$0.0032	resolved to a profile, full career row
Row resolved (with contacts)	$0.008	contacts on and a contact that reaches the person today
Unresolvable / malformed / namesake / missing_required	$0	always free, with a reason
A person your file lists twice	$0	answered as a free row naming the paid one

A charge fires only after the row is in your dataset.

Use Bulk LinkedIn & Email Enrichment with AI agents and MCP

This Actor works as a tool for AI agents. Add it to Claude, ChatGPT, Cursor or any other MCP client through the Apify MCP server:

https://mcp.apify.com?tools=b2bsearch/bulk-people-enrichment

• Fast enough for a tool call. A small request finishes in seconds, so the agent gets its answer inside one call.
• The agent pays only for results. Prices are per result (see the pricing section above) and every miss is a free row. Cap what one call may spend with maxTotalChargeUsd.
• Rows explain themselves. Each row has a _status; a row that is not a result says why in _error, so the agent can decide what to do next without guessing.
• Compact rows for a context window. "compact": true returns a short row — about 2 KB for a full profile instead of 10+ KB: identity, current role, the 5 latest positions, education, top skills and any contacts. Same price; leave it off to get the complete record.

Input an agent can send as is:

{
  "compact": true
}

The same Actor is available as a tool in LangChain, CrewAI and the OpenAI Agents SDK, and as a step in n8n, Make and Zapier through the Apify integrations.

Which actor in this family?

One database, ten doors. Misses are free on every one of them.

Actor	Input → output
Profile Lookup	profile URL → full career profile
Reverse Email Lookup	email → person, profile URL and employer
Name to Profile	name + company domain → profile
Social Handle Lookup	GitHub, X/Twitter or Facebook handle → profile
Bulk People Enrichment (this one)	CSV of emails, URLs, handles or names → profiles
LinkedIn Email Finder	profile URL → email addresses on record
Work Email Finder	name + company domain → work email candidates
Company Employees	company domain → current staff
People Database Search	filters → people
Company Database Search	filters → companies

Disclaimer

This Actor is an independent product. It is not affiliated with, endorsed by or sponsored by LinkedIn, GitHub, X/Twitter or Facebook. It does not access, crawl or scrape any of them at run time: answers come from our own database of publicly available professional data, and the network names only describe the kind of data it covers. To have a person's data removed, open an issue on this Actor with only the profile link — nothing else is needed, and it is removed from every listing.