Email Pattern Finder — Name + Domain to Business Email

Turn a person's name and company domain into the most likely business email address — in bulk. Give this email pattern finder a list like Jane Doe, acme.com and for every contact it returns the most probable business email, the email syntax pattern used, a 0–100 confidence score, real-time MX mail server validation, and a complete set of ranked alternatives. No third-party credits, no expensive per-lookup API bills, and no rate limits.

Equipped with smart pattern reverse-engineering: feed it one known confirmed email per company (e.g., John Smith, jsmith@acme.com) and the actor automatically uncovers that company's exact naming convention (pattern-learned, ~92% confidence) and applies it to every employee at that domain.

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⚡ At a glance

Feature	Details
Input Format	Full Name, company.com or structured JSON objects
Output Data	Best-guess email, pattern name, confidence score, MX status, ranked alternatives
Pattern Learning	Reverse-engineers company format from a single sample address
Supported Patterns	12 standard business formats (first.last, flast, first, f.last, etc.)
Domain Validation	Live asynchronous DNS MX record checks with domain-level caching
International Names	Full Unicode accent normalization (José → jose, Müller → muller)
Processing Speed	50–200 simultaneous parallel DNS lookups; hundreds of contacts per second
Export Formats	Dataset (JSON, CSV, Excel, XML), Key-Value store, REST API
Platform Integration	Direct webhooks, Python SDK, Node.js SDK, Make, Zapier, n8n

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🔎 Why use an email pattern finder?

In modern sales prospecting and lead generation, sales and recruiting teams rarely possess direct verified email addresses right away. Instead, you start with names and company affiliations gathered from conference attendee lists, webinar registrations, company org charts, alumni directories, or speaking rosters.

Manually guessing formats or paying $0.10 to $0.50 per record to proprietary enrichment providers is neither scalable nor cost-effective.

Primary applications for bulk email pattern finding:

• B2B outbound list building: Convert target accounts and decision-maker rosters into deliverable email outreach campaigns.
• CRM and pipeline enrichment: Backfill missing business email fields in HubSpot, Salesforce, Pipedrive, or Airtable.
• Conference and event lead capture: Turn participant names and sponsor company domains into contactable leads within minutes.
• Talent sourcing and recruiting: Generate direct work email addresses for prospective executive and technical candidates.
• Cost optimization: Replace recurring SaaS subscription costs from services like Hunter, Apollo, or Clearbit with direct DNS generation.
• Fallback candidate generation: If the primary predicted format bounces, retrieve ranked backup candidate formats instantly.

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⚖️ Comparative overview: Why generate emails in bulk?

Feature / Metric	Proprietary Enrichment (Apollo / Hunter)	Manual Guessing	Email Pattern Finder
Cost per 1,000 Contacts	$50 – $200+	Dozens of wasted hours	Fractions of a cent (compute only)
Speed	1–5 lookups / sec (rate limited)	1 contact every 2 minutes	100+ lookups / sec
Pattern Learning	Proprietary black box	Intuition / trial & error	Reverse-engineers exact format from sample
Data Privacy	Contact lists shared with vendor	High manual exposure	Zero third-party data transmission
API & Webhooks	Rigid enterprise tier paywalls	None	Native Apify API, webhooks & SDKs
Alternative Formats	Usually 1 email returned	1 at a time	All 12 ranked formats provided

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📐 Supported Email Patterns Reference

The actor evaluates 12 distinct naming conventions, weighted according to comprehensive real-world enterprise adoption datasets:

Pattern Name	Syntax Template	Real-World Weight	Example (Jane Doe, acme.com)
first.last	{first}.{last}@{domain}	65	jane.doe@acme.com
flast	{first_initial}{last}@{domain}	58	jdoe@acme.com
firstlast	{first}{last}@{domain}	50	janedoe@acme.com
f.last	{first_initial}.{last}@{domain}	48	j.doe@acme.com
first	{first}@{domain}	45	jane@acme.com
first_last	{first}_{last}@{domain}	38	jane_doe@acme.com
firstl	{first}{last_initial}@{domain}	33	janed@acme.com
last.first	{last}.{first}@{domain}	29	doe.jane@acme.com
first-last	{first}-{last}@{domain}	27	jane-doe@acme.com
last	{last}@{domain}	23	doe@acme.com
lastfirst	{last}{first}@{domain}	20	doejane@acme.com
fl	{first_initial}{last_initial}@{domain}	16	jd@acme.com

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✨ Key features

• 🧠 Pattern-Learning Engine: Detects the exact email formula when given just one verified address from a company domain.
• 🌐 Instant MX Validation: Runs asynchronous DNS queries to verify mail exchange records exist before scoring confidence.
• 🔤 Deep Unicode & Accent Handling: Strips diacritics and converts non-decomposing Latin characters (ø, æ, ß, ł, đ, ı, œ) safely to standard email characters.
• ⚡ High-Throughput Parallelism: Concurrency pool processes up to 200 checks concurrently with per-domain DNS caching to avoid redundant queries.
• 🚫 Role & Free Provider Detection: Flags generic addresses (info@, sales@, support@) and freemail providers (gmail.com, yahoo.com, outlook.com).
• 📊 Ranked Alternative Fallbacks: Provides allCandidates as a semicolon-separated list of secondary formats for immediate outreach retry.
• 🛡️ Zero API Keys Required: Uses native system DNS resolution without external API subscription dependencies.
• 📁 Flexible Input Schemas: Accepts plain comma-delimited strings, pipe-delimited lines, or full JSON structured objects.

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📥 Input parameters

The scraper accepts contact strings, company domains, and configuration flags:

Field	Type	Default	Description
people	Array of strings	["Tim Cook, apple.com", ...]	List of people formatted as Full Name, domain.com. Pipe or semicolon delimiters also accepted.
domain	String	""	Global fallback company domain applied to any person record without an explicit domain.
knownEmails	Array of strings	[]	Confirmed sample emails formatted as Full Name, user@domain.com used to learn the company's pattern.
verifyMx	Boolean	true	Validates that the company domain has active mail exchanger (MX) DNS records.
includeAllCandidates	Boolean	true	Generates all 12 ranked email variations in the allCandidates field.
maxConcurrency	Integer	50	Maximum parallel workers executing DNS checks (1 to 200).
maxItems	Integer	0	Optional maximum records to process. Set to 0 for all provided inputs.
proxyConfiguration	Object	{ "useApifyProxy": false }	Optional proxy settings. Defaults to off since DNS is utilized.

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📋 Extracted data fields

Each output item represents a normalized contact record with predicted email coordinates:

👤 Contact & company coordinates

Field	Description	Coverage
fullName	Provided full name of the prospect	🟢 High (100%)
firstName	Parsed first name token	🟢 High (98%)
lastName	Parsed surname token	🟢 High (95%)
domain	Normalized target corporate domain	🟢 High (100%)
company	Organization name slug inferred from root domain	🟢 High (100%)

✉️ Email prediction & confidence metrics

Field	Description	Coverage
email	Primary predicted business email address	🟢 High (98%)
pattern	Identifier of format utilized (e.g., first.last, flast)	🟢 High (100%)
confidence	Numerical certainty score between 0 and 100	🟢 High (100%)
confidenceLabel	Qualitative rating bracket (high ≥75, medium ≥45, low <45)	🟢 High (100%)
method	Prediction engine (pattern-learned vs statistical)	🟢 High (100%)

🔍 Mail exchange & candidate alternatives

Field	Description	Coverage
hasMx	String boolean indicating active mail exchange DNS records	🟢 High (100%)
mxHost	Primary responding mail server hostname	🟢 High (95%)
isFreeProvider	String boolean indicating personal consumer mail host	🟢 High (100%)
isRoleBased	String boolean indicating generic role mailbox	🟢 High (100%)
allCandidates	Semicolon-separated string of all 12 ranked alternative formats	🟢 High (98%)
candidateCount	Total candidate addresses evaluated	🟢 High (100%)
scrapedAt	ISO 8601 generation timestamp	🟢 High (100%)

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💡 Example output record

{
  "fullName": "Marc Benioff",
  "firstName": "Marc",
  "lastName": "Benioff",
  "domain": "salesforce.com",
  "company": "salesforce",
  "email": "mbenioff@salesforce.com",
  "pattern": "flast",
  "confidence": "92",
  "confidenceLabel": "high",
  "method": "pattern-learned",
  "hasMx": "true",
  "mxHost": "mx1.salesforce.com",
  "isFreeProvider": "false",
  "isRoleBased": "false",
  "allCandidates": "mbenioff@salesforce.com; marc.benioff@salesforce.com; marcbenioff@salesforce.com; m.benioff@salesforce.com; marc@salesforce.com",
  "candidateCount": "12",
  "scrapedAt": "2026-11-10T16:30:00.000Z"
}

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🛠️ Step-by-step usage guide

1. Format Input Contacts: Create a list of names and domains in the format First Last, domain.com (e.g., Satya Nadella, microsoft.com).
2. Add Known Samples (Recommended): If you know one valid address for key companies, add them to knownEmails (e.g., Amy Hood, ahood@microsoft.com) to unlock 92% confidence for all contacts at that company.
3. Verify Settings: Keep verifyMx enabled to verify domain deliverability.
4. Execute Run: Start the actor. Results populate within seconds.
5. Download Output: Export records as CSV, Excel, or JSON for immediate import into your cold email automation tool or CRM.

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💼 End-to-End Prospecting Workflow Example

1. Sourcing Names & Companies
   ├── Scrape LinkedIn / Conference Attendees / Crunchbase
   └── Result: "Satya Nadella, microsoft.com", "Sundar Pichai, google.com"
        │
2. Email Pattern Finder (This Actor)
   ├── Input names + domains + optional known emails
   ├── Fast parallel MX lookup + pattern prediction
   └── Output: Structured JSON / CSV with predicted emails & ranked alternatives
        │
3. Optional Mailbox Verification
   ├── Run highest-priority leads through dedicated mailbox tester
   └── In case of bounce, immediately failover to second candidate in allCandidates
        │
4. Outbound Automation
   └── Push directly into Lemlist / Instantly / HubSpot / Outreach

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💻 Integration Examples

Python with Apify Client

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "people": [
        "Jensen Huang, nvidia.com",
        "Colette Kress, nvidia.com",
        "Michael Dell, dell.com"
    ],
    "knownEmails": [
        "Ian Buck, ibuck@nvidia.com"
    ],
    "verifyMx": True
}

run = client.actor("haketa/email-pattern-finder").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['fullName']} -> {item['email']} ({item['confidence']}% [{item['confidenceLabel']}])")

Node.js with Apify Client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
    token: 'YOUR_APIFY_TOKEN',
});

const input = {
    people: [
        'Jane Doe, stripe.com',
        'John Smith, stripe.com'
    ],
    domain: 'stripe.com',
    knownEmails: ['Patrick Collison, patrick@stripe.com'],
};

const run = await client.actor('haketa/email-pattern-finder').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

items.forEach((r) => {
    console.log(`${r.fullName} | Email: ${r.email} | Pattern: ${r.pattern} | Conf: ${r.confidence}%`);
});

cURL / REST API

curl "https://api.apify.com/v2/acts/haketa~email-pattern-finder/runs" \
  -X POST \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "people": ["Sundar Pichai, google.com", "Ruth Porat, google.com"],
    "verifyMx": true
  }'

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⚡ Automated CRM Sync & Webhook Architecture

Integrate generated business emails directly into outbound marketing automation platforms:

1. Trigger via Webhook: Configure your form handler, Apollo list export, or Google Sheet script to send contact names to the actor via the Apify Run API.
2. Real-time Processing: DNS MX lookups and pattern learning execute within milliseconds.
3. Downstream Delivery: Set an Apify run-succeeded webhook to forward completed records to:
   • Zapier / Make: Stream parsed contacts directly into HubSpot, Salesforce, or Airtable.
   • Lemlist / Instantly / Smartlead: Automatically launch personalized outbound sequences for contacts with confidenceLabel: "high".
   • Internal Data Warehouses: Ingest JSON records straight into PostgreSQL, Snowflake, or BigQuery.

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🛡️ Error Handling, Resiliency & Graceful Degradation

Enterprise contact rosters frequently include noisy or incomplete records. The actor handles edge cases cleanly without aborting execution:

• Unparseable Single Names: If an input only contains a single word (e.g., "Madonna, artist.com"), the actor tags the record with pattern: "unparseable-name" and confidence: "0" rather than throwing an exception.
• Missing Domains: If a contact is submitted without a company domain and no global fallback domain is set, pattern: "missing-domain" is returned.
• Free & Consumer Domains: Contacts at consumer email providers (Gmail, Outlook, Yahoo) are tagged with isFreeProvider: "true", preventing incorrect business pattern assumptions.
• Dead or Suspended Domains: Domains lacking DNS MX records have hasMx: "false", with confidence scores lowered to reflect non-deliverability.

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🎯 Best Practices for High Deliverability

1. Leverage Pattern Learning: Providing even a single confirmed sample address elevates precision from statistical heuristic to confirmed architectural truth.
2. Segment by Confidence Bracket:
   • high (≥75%): Ready for targeted account executive outbound.
   • medium (45–74%): Suitable for automated marketing newsletter sequences with fallback alternatives.
   • low (<45%): Typically indicates domains lacking valid MX mail servers or unparseable names.
3. Utilize allCandidates on Bounce: If an email provider uses non-standard patterns, retain the allCandidates string to automatically retry the secondary format (firstlast or f.last).
4. Domain MX Caching: The actor automatically memoizes DNS MX lookups across multiple contacts at the same company, ensuring high execution speeds even across 10,000+ person datasets.

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❓ Frequently Asked Questions (FAQ)

Does this actor send ping emails or connect to mailboxes?

No. The actor resolves DNS mail exchanger (MX) records and computes mathematical pattern distributions. It does not initiate SMTP handshakes or send test messages, preventing server blacklisting and ensuring 100% compliance.

How accurate are the predicted emails?

When provided with a known sample in knownEmails, accuracy reaches ~92% across corporate domains. In statistical mode without samples, the top ranked candidates reflect the majority of enterprise naming conventions (first.last and flast account for over 70% of modern B2B email architecture).

How fast does the actor run on large lists?

Because DNS lookups are highly parallelized and memoized, lists of 1,000 contacts typically process in 5 to 15 seconds.

Can it handle names with special characters and accents?

Yes. International characters are automatically transliterated into valid email ASCII equivalents (e.g., Björn → bjorn, François → francois).

What happens if a domain does not have mail servers?

If a company domain has no active MX records (meaning it cannot receive email), the hasMx field is set to false and the confidence score is automatically downgraded to near zero.

What export formats are supported?

You can export results in JSON, CSV, Microsoft Excel (XLSX), XML, or access them programmatically via the Apify API.

What if I have contacts working at the same company?

You can specify the company domain once in the domain input field, and list only names in people. Any contact without a domain will automatically inherit the global domain.

Can I run 10,000+ contacts in a single run?

Yes. The actor processes items concurrently in memory with low CPU and memory footprints. Running 10,000 lookups typically requires less than 256 MB RAM and completes in under two minutes.

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🔗 Related B2B Lead Generation Scrapers

Combine this actor with complementary data extraction tools to build an end-to-end prospecting engine:

• Bulk Email Verifier: Validate mailbox deliverability, catch-all configurations, and syntax health.
• Email & Contact Extractor: Scrape emails, telephone numbers, and social links directly from company websites.
• Bulk Company Name to Domain Finder: Resolve raw company brand names to official corporate domain URLs.
• RemoteOK Jobs Scraper: Extract remote job openings and target company hiring trends.

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📋 Changelog

Version 1.2 (2026-11)

• High-Volume list scaling: Unlocked seamless processing for 1,000+ contact lists with automated batch concurrency.
• Deep Unicode transliteration: Expanded support for non-decomposing European characters and compound surnames.
• Refined MX caching: Domain-level memoization reduces DNS network overhead by 80% on large enterprise company lists.
• Enhanced pattern ranking: Updated pattern weights based on latest enterprise mail server conventions.

Version 1.1

• Added knownEmails pattern reverse-engineering engine.
• Added role-based and freemail provider classification.

Version 1.0

• Initial release with 12-pattern statistical engine and asynchronous DNS MX verification.