📧 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.

How much Email data can you extract in one run?

This Actor returns one row per item you supply, so the volume is yours to set: give it ten rows and you get ten rows, give it a thousand and you get a thousand. The run measured below handled 3 in 136 seconds. That is roughly 1 rows a minute.

There is no hard ceiling built into this Actor. What bounds a run is the result limit you set, the breadth of the input you give it, and how much Email itself publishes. Because billing is per result, a wider run costs proportionally more and a narrow one costs almost nothing — so it is worth starting small and scaling once the output looks right.

Email scraping — frequently asked questions

Do I need a Email API key, account or login?

No. This Actor reads publicly available pages and feeds, so there is nothing to register for and no credentials to manage.

How do I export Email data to CSV, Excel, JSON or Google Sheets?

Open the Storage tab after a run and pick the format you want — CSV, Excel, JSON, XML or an HTML table. For Google Sheets, Airtable or a database, use the dataset URL or the Apify API so the sheet refreshes itself on every run.

Can I schedule this Email scraper to run daily or hourly?

Yes. Use the Schedules tab and pick a cadence. Daily is enough for most tracking work; pick hourly only if the rows you care about really do change that fast.

How many rows can I get, and what does it cost?

See the volume section above for a measured figure. Billing is per result, so you pay for the rows you actually receive and a run that finds nothing costs nothing.

Is scraping Email legal?

This Actor collects only information that Email publishes publicly. It does not log in, does not defeat access controls and does not touch private data. You remain responsible for how you use the output, including under GDPR and similar rules where personal data is involved.

What happens if Email blocks the run or changes its layout?

A refused exit IP is swapped for a fresh one within a quarter of a second and retried, and rate limiting is waited out rather than fought. If nothing gets through, the run fails with the reason instead of reporting success with zero rows — so an empty result always means "no matches", never "the source would not answer".

Can I use this with an AI agent, LangChain or an MCP client?

Yes. The Actor is callable over the Apify API and through Apify's MCP server, so an agent can run it and read the dataset back as a tool call.

⚖️ Disclaimer

This Actor is an independent tool. It is not affiliated with, endorsed by, or sponsored by Email; Email and all other trademarks mentioned here are the property of their respective owners.

📝 Changelog

2026-11-01

• Monthly health check (2026-11-01): a live run on the current build completed successfully and returned data. Input options, output fields and pricing are unchanged.
• README refreshed: independence disclaimer added.

2026-10-15

• Every request now has a hard time limit, so a proxy connection that stalls can no longer hold up the run or push it past its timeout.
• A run that reaches its maximum cost per run now stops cleanly with its results saved and a status message, instead of ending as aborted.
• A proxy group your account cannot use no longer stops the run at start-up; it falls back to the default Apify Proxy.
• Memory is managed within the run's own limit, which removes a rare out-of-memory crash on large pages.

2026-09-23

• Fleet-wide quality audit. Verified end to end against live data and re-checked the input schema, the output columns and the run configuration.
• Run reliability reviewed: 92.3% of public runs succeeded in the last 30 days.
• Input schema, output schema and pricing configuration reviewed.

2026-09-01

• Fleet-wide health check. Verified against this Actor's real run history: 30-day success rate, output row counts, per-field fill rates, and peak memory against the configured memory limit.
• Reviewed for the failure patterns that have cost this fleet runs — unguarded proxy setup, retry loops that can outlast the run's own time budget, and full-page HTML parsing that can exhaust a small container.
• No change to input, output fields or scraping logic.

2026-08-11

• Maintenance release: refreshed the build and dependencies.
• Re-verified live execution, non-empty structured output and dataset field/type integrity.
• Reviewed reliability (retries, pagination) and output quality as part of a full-fleet QA pass.

2026-08-01

• Completed the August 2026 full health check: verified empty/programmatic default, Console UI default, and two source-informed alternative inputs on Apify.
• Confirmed successful live execution, non-empty structured output, dataset-field/type integrity, and logical sample quality within the 5-minute quality window.
• Declared 15 dataset fields from typed live cloud samples so the output contract is no longer an empty placeholder.
• Declared 15 nullable dataset fields from typed live cloud samples so the output contract is no longer an empty placeholder or brittle to sparse modes.

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*Maintained: last verified 2026-11-01 — checked end-to-end against live run history (reliability, output fields and types, and time/memory budgets).

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• TikTok Email & Profile Scraper (Bulk)
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