Lead Enricher Pro

All-in-one B2B lead enrichment. Give it a company website, get back complete contact intel + tech stack + lead scoring.

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What you get (per company)

Category	Fields
Identity	domain, company_name, description
Emails	emails, personal_emails, generic_emails, scored_emails (with confidence 0–100)
Phones	phones (strict extraction — no garbage)
Social	linkedin, twitter, facebook, instagram
Tech Stack	40+ technologies (WordPress, React, Shopify, Stripe, HubSpot, AWS, …)
Domain	whois.domain_age_days, whois.registrar, whois.expiry_date
Security	ssl.ssl_valid, ssl.ssl_issuer, ssl.ssl_days_left
Deliverability	has_mx_records (real DNS lookup via dnspython)
Patterns	executive_emails — role-based guesses (only when MX exists, clearly marked unverified)
Scoring	lead_score (0–100), lead_grade (A Hot / B Warm / C Cool / D Cold)

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How to use

1. Simple URLs

Just paste a list of websites:

{
  "leads": [
    { "url": "https://stripe.com" },
    { "url": "https://vercel.com" },
    { "url": "https://freshworks.com" }
  ]
}

2. With personal names (for email pattern prediction)

If you know a contact at the company, include their name — the actor will generate personalised email guesses like sridhar.vembu@zoho.com:

{
  "leads": [
    { "url": "https://zoho.com", "firstName": "Sridhar", "lastName": "Vembu" }
  ]
}

3. Bulk (recommended)

Paste 50–500 URLs at once. Each takes ~5–10 seconds. Results stream into the Dataset tab as they complete.

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Reading the output

Check these fields first:

1. lead_grade — quick quality indicator (A/B/C/D)
2. has_mx_records — ⚠️ if false, the domain cannot receive email. Skip it.
3. scored_emails — sorted by confidence. confidence ≥ 70 is usually safe.
4. tech_stack — tells you how modern/technical the company is.

After the run, check the key-value store for RUN_SUMMARY — it has totals, grade distribution, and timing for the whole batch.

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Example output (abbreviated)

{
  "domain": "stripe.com",
  "company_name": "Stripe | Financial Infrastructure...",
  "lead_grade": "A - Hot Lead",
  "lead_score": 85,
  "email_count": 3,
  "scored_emails": [
    { "email": "press@stripe.com", "confidence": 90 },
    { "email": "sales@stripe.com", "confidence": 85 }
  ],
  "phones": ["+1 888-926-2289"],
  "linkedin": ["https://www.linkedin.com/company/stripe"],
  "tech_stack": ["React", "Next.js", "Stripe", "AWS", "Segment"],
  "has_mx_records": true,
  "ssl": { "ssl_valid": true, "ssl_issuer": "DigiCert Inc", "ssl_days_left": 46 },
  "whois": { "domain_age_days": 4521, "registrar": "MarkMonitor Inc." },
  "executive_emails": {
    "note": "These are role-based patterns — not individually verified.",
    "verified": false,
    "patterns": { "CEO": "ceo@stripe.com", "CTO": "cto@stripe.com" }
  }
}

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Tips & limitations

• ✅ Real MX check — if a domain has no MX records, we don't fabricate executive emails.
• ✅ Strict phone extraction — only tel: links, international format, or US (XXX) XXX-XXXX. No version numbers or random digits.
• ⚠️ Executive emails are predictions, not verified hits. Always validate before sending cold outreach.
• ⚠️ JavaScript-rendered sites may have less data (actor uses HTTP, not a headless browser).
• 💡 Best results on company websites with public contact/about pages.

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Version history

• v1.1 — Fixed phone regex (no more garbage), real dnspython MX checks, MX-gated executive emails with clear unverified labels, python-whois fallback, run summary in KV store, improved dataset views.
• v1.0 — Initial release.