📧 Email Finder — Name + Company Domain → Work Email

Find anyone's work email from just their name and company domain.
This email finder learns each company's real email format from its own website, returns published addresses when they exist, and validates every domain over DNS — so you reach real inboxes, not dead guesses.

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🔍 What is this Email Finder?

Email Finder is a bulk email lookup tool that turns a list of names and company domains into real, reachable work email addresses. Give it Anna Roth, acme.com and it returns the most likely business email for that person — with a confidence score, ranked alternatives, and proof of how it was found.

Unlike basic tools that slap first.last@ on every name, this email address finder reads each company's website, discovers the real email pattern the company uses, and applies it — the same way premium tools like Hunter and Apollo work, but with no API key, no login, no monthly subscription, and no credit caps. Pay only per result, from $3.50 per 1,000 emails.

💡 Use it for: cold email, B2B sales prospecting, lead generation, recruiting, and CRM enrichment — anywhere you need to find a work email by name and company.

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⚙️ How the Email Finder works — a 4-layer ladder

The Finder works down a ladder of decreasing certainty and tells you exactly which rung it used (the method field), so you always know how much to trust a result.

#	Method	What happens	Confidence
1️⃣	found_on_site	The person's email is published on the company site (contact / about / team). Returned verbatim — a real, verified address.	~96
2️⃣	discovered_pattern	The company publishes other real emails, so the Finder learns its true format (sees mark.davies@acme.com → learns Acme uses first.last) and applies it to your person.	75–92
3️⃣	frequency_guess	The site publishes nothing usable, so it falls back to the most common corporate pattern — and clearly labels it as a guess.	19–33
4️⃣	Domain validation	Always on. Every candidate domain is checked for MX (mail server) records over DNS, so unreachable domains are flagged before you send.	—

The result: real answers where the data exists, honest best-guesses where it doesn't, and an MX validation on every single row.

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🎯 Confidence score, explained

The confidence field (0–100) combines how the email was found with whether the domain accepts mail:

• 80+ → send with confidence
• 30–80 → verify, then send
• under 30 → treat as a lead to confirm, not a confirmed address

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📬 Email patterns it understands

For John Smith at acme.com, the Finder ranks and applies these corporate email formats:

Pattern	Example	Frequency
first.last	john.smith@acme.com	~33%
first	john@acme.com	~19%
flast	jsmith@acme.com	~14%
firstlast	johnsmith@acme.com	~9%
first_last	john_smith@acme.com	~5%
f.last	j.smith@acme.com	~5%
firstl · first.l · last.first · lastfirst · last · first-last	…	~1–4% each

When a company's real email pattern is discovered on its site, that format jumps to the top with high confidence.

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📤 What you get — output fields

Field	Description
mostLikelyEmail	The single best work email for this person
confidence	0–100 score for the top result
method	found_on_site · discovered_pattern · frequency_guess
pattern	The email format applied (e.g. first.last)
patternEvidence	The real published email(s) the pattern was learned from
candidates	Every plausible format, ranked, each with email + probability
status	found · pattern_discovered · guessed · personal_domain · invalid_domain
domainHasMx / mxHost	Whether the domain accepts mail, and its mail server
fullName · firstName · lastName · domain	Parsed input

🧪 Example — discovered pattern (high confidence)

{
  "fullName": "Anna Roth",
  "domain": "acme.com",
  "mostLikelyEmail": "anna.roth@acme.com",
  "confidence": 86,
  "method": "discovered_pattern",
  "pattern": "first.last",
  "patternEvidence": ["mark.davies@acme.com"],
  "domainHasMx": true,
  "mxHost": "aspmx.l.google.com",
  "candidates": [
    { "pattern": "first.last", "email": "anna.roth@acme.com", "probability": 86 },
    { "pattern": "first", "email": "anna@acme.com", "probability": 19 },
    { "pattern": "flast", "email": "aroth@acme.com", "probability": 14 }
  ]
}

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📥 How to use — 3 ways to feed it

1. A simple list — one person per line as Full Name, domain:

   Anna Roth, acme.com
   Marques Brownlee, mkbhd.com
   Sarah Johnson, stripe.com
   

2. Pasted text — drop a whole block into People (paste); commas, semicolons, or pipes all work.
3. Objects from another Actor — pipe records with firstName / lastName / fullName + domain straight from a LinkedIn scraper, company scraper, or Google Maps scraper.

Then click Start — every person comes back with their best email, the method used, confidence, and ranked alternatives. Export to CSV, Excel, or JSON, or pull via the API.

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💼 Email finder use cases

• Cold email & outbound sales — turn a prospect list into reachable inboxes with a confidence score on each.
• B2B lead generation — add validated work emails to any list of names and companies.
• Recruiting & talent sourcing — reach candidates and hiring managers directly.
• Agency & partnership outreach — go from a roster to real contacts.
• CRM enrichment & hygiene — re-derive and re-validate emails for stale records.
• Account-based marketing — build verified contact lists for target accounts.

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🔗 Integrations & automation

Wire the Email Finder into your stack: Apify API / SDK (Python & Node), Make, Zapier, n8n, webhooks, and scheduling. Chain it in an enrichment pipeline:

Company / LinkedIn scraper → 📧 Email Finder → ✅ Bulk Email Verifier → your sequencer

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🆚 Why this over other email finders

	This Finder	Typical guesser	Hunter / Apollo
Learns the company's real format	✅ live site	❌ always first.last	✅ database
Returns published addresses	✅	❌	✅
MX validation on every row	✅	sometimes	✅
Transparent method + evidence	✅	❌	partial
API key required	❌	❌	✅
Monthly fee / credit cap	❌	varies	✅ $49+/mo
Price	from $3.50 / 1,000	varies	subscription

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💰 Pricing

Pay per result — from $3.50 per 1,000 emails. No subscription, no API fees, no monthly credit ceiling. Compared to a $49–$99/month email-finder SaaS seat with hard caps, bulk runs here cost a fraction.

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✅ Honest note on accuracy

A live-crawl email finder is not a giant historical database. Big SaaS companies that hide every address behind JavaScript will fall back to a frequency guess (clearly labeled). Its accuracy sweet spot is exactly what cold email targets: agencies, service firms, manufacturers, consultancies, local and mid-market companies that publish real contact emails. Every result — discovered or guessed — is domain-validated and carries an honest confidence score. For a final deliverability pass, pair it with Bulk Email Verifier.

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⚖️ Compliance

This Actor reads only publicly available data and does not send email. You are responsible for lawful outreach under GDPR / ePrivacy, CAN-SPAM, CASL and similar — including lawful basis, clear identification, and a working opt-out.

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❓ FAQ

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🔧 Related Actors

• Bulk Email Verifier — validate and clean found emails (MX, disposable, role, syntax) before outreach.
• Company / LinkedIn / Google Maps scrapers — feed names + domains straight into this Finder.

Find the email → verify it → reach the right inbox — without a single API key.