Email Finder — Name + Domain → Best-Guess Work Email

Give this actor a person's name and their company domain, and it returns the most likely work email — with a numeric confidence score, MX validation, and a ranked list of alternative candidates. No API key, no signup, no monthly seat.

What you actually get: a ranked, MX-gated best guess, not a hard-confirmed mailbox. The actor attempts live SMTP verification, but most cloud networks (including Apify's) block outbound port 25, so it degrades to MX + pattern ranking and labels the method mx_pattern. SMTP confirmation only fires where the run network allows it.

Why this one

Most email-finder actors hand back a single guessed address and, at best, a catch_all flag. That leaves you to guess whether the address is real. This actor instead:

• Builds frequency-ranked candidate patterns — first.last, flast, first, firstlast, and ~9 more — weighted by how common each scheme actually is in corporate email.
• Validates what it can — one MX lookup per domain to confirm the mail server exists, then (where the network allows) a catch-all probe and RCPT verification of candidates in priority order. A confirmed mailbox is marked verified; on port-25-blocked networks the best guess is marked unverified_guess with its MX host and confidence.
• Tells you the truth — every record carries confidence (0–1), the verificationMethod (smtp / mx_pattern / none), isCatchAll, isDisposable, isRoleAccount, and the ranked alternativeEmails so you can fall back if the top pick bounces.

If the run network blocks outbound port 25 (common on cloud), the finder degrades gracefully to MX + pattern ranking and labels the method mx_pattern — you still get a ranked, MX-gated best guess instead of a hard failure.

Input

{
  "people": [
    { "firstName": "Patrick", "lastName": "Collison", "domain": "stripe.com" },
    { "firstName": "Tobias", "lastName": "Lutke", "domain": "shopify.com" }
  ],
  "verifySmtp": true,
  "maxAlternatives": 5,
  "smtpTimeoutSecs": 8,
  "maxItems": 0
}

Field	Type	Notes
people	array	Each item: firstName (required), lastName or surname, optional fullName, and domain (required).
verifySmtp	boolean	Attempt live SMTP RCPT verification. Auto-falls back to MX + pattern if port 25 is blocked. Default true.
maxAlternatives	integer	Ranked alternatives returned per person. Default 5.
smtpTimeoutSecs	integer	Per-probe SMTP timeout. Default 8.
maxItems	integer	Total output cap, 0 = unlimited.

Output (per person)

{
  "firstName": "patrick",
  "lastName": "collison",
  "domain": "stripe.com",
  "email": "patrick.collison@stripe.com",
  "confidence": 0.45,
  "status": "unverified_guess",
  "verificationMethod": "mx_pattern",
  "isCatchAll": null,
  "isDisposable": false,
  "isRoleAccount": false,
  "mxFound": true,
  "mxHost": "aspmx.l.google.com",
  "patternUsed": "first.last",
  "alternativeEmails": [
    { "email": "pcollison@stripe.com", "pattern": "flast", "confidence": 0.319, "smtpStatus": "not_checked", "smtpCode": null }
  ],
  "parse_confidence": 0.9,
  "warnings": ["smtp_unreachable_port25"]
}

The example above is a real Apify-cloud result (port 25 blocked → mx_pattern). On a network with outbound SMTP open, status becomes verified/accept_all, verificationMethod becomes smtp, and confidence rises accordingly.

status values: verified (SMTP confirmed the mailbox) · accept_all (domain accepts all mail — can't single one out) · unverified_guess (MX present, SMTP not usable, ranked best guess) · no_valid_pattern (SMTP worked but no candidate accepted) · no_mx (domain has no mail server).

Pricing

Pay-per-result. You are charged once per person record produced — never for an invalid input. You run it on your own Apify account, so compute is billed to you at platform cost.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Epa Data Scraper
• Firmy Cz Scraper
• Gelbeseiten Directory
• Google Maps Leads
• Google Maps Scraper
• Hiring Signal Monitor

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.