Contact → Email

Three workflows depending on what you already have. Always validate at the end.

Which workflow to use?

Workflow A: Name + Company

Tries Apollo → Crustdata → PDL in order, stops at first match:

deepline enrich --input leads.csv --in-place --rows 0:1 \
  --with-waterfall "email" \
  --type email \
  --result-getters '["data.email","email","data.0.email"]' \
  --with 'apollo=apollo_people_match:{"first_name":"{{First Name}}","last_name":"{{Last Name}}","organization_name":"{{Company}}"}' \
  --with 'crustdata=crustdata_person_enrichment:{"linkedinProfileUrl":"{{LinkedIn}}","fields":["email","current_employers"],"enrichRealtime":true}' \
  --with 'pdl=peopledatalabs_enrich_contact:{"first_name":"{{First Name}}","last_name":"{{Last Name}}","domain":"{{Company Domain}}"}' \
  --end-waterfall \
  --with 'validation=leadmagic_email_validation:{"email":"{{email}}"}'

Required columns: First Name, Last Name, Company

Optional columns: LinkedIn, Company Domain (improves match rate)

Workflow B: LinkedIn URL

When you have LinkedIn profile URLs but no email:

deepline enrich --input leads.csv --in-place --rows 0:1 \
  --with-waterfall "email" \
  --type email \
  --result-getters '["data.0.email","data.email","emails.0.address"]' \
  --with 'apollo=apollo_people_match:{"first_name":"{{First Name}}","last_name":"{{Last Name}}","organization_name":"{{Company}}","domain":"{{Company Domain}}"}' \
  --with 'crustdata=crustdata_person_enrichment:{"linkedinProfileUrl":"{{LinkedIn URL}}","fields":["email","current_employers"],"enrichRealtime":true}' \
  --with 'pdl=peopledatalabs_enrich_contact:{"linkedin_url":"{{LinkedIn URL}}","first_name":"{{First Name}}","last_name":"{{Last Name}}","domain":"{{Company Domain}}"}' \
  --end-waterfall \
  --with 'validation=leadmagic_email_validation:{"email":"{{email}}"}'

Required columns: LinkedIn URL

Optional columns: First Name, Last Name, Company, Company Domain

Workflow C: Pattern matching

When you have name + domain but no LinkedIn. Generates common email patterns (first.last, flast, etc.) and validates each. Most cost-effective for bulk lists:

deepline enrich --input leads.csv --in-place --rows 0:1 \
  --with 'patterns=run_javascript:{"code":"const f=(row[\"First Name\"]||\"\").trim().toLowerCase(); const l=(row[\"Last Name\"]||\"\").trim().toLowerCase(); const d=(row[\"Domain\"]||\"\").trim().toLowerCase(); if(!f||!l||!d) return {}; return {p1:`${f}.${l}@${d}`,p2:`${f[0]}${l}@${d}`,p3:`${f}${l[0]}@${d}`,p4:`${f}@${d}`};"}' \
  --with-waterfall "email" \
  --type email \
  --result-getters '["data.email","email","data.0.email"]' \
  --with 'v1=leadmagic_email_validation:{"email":"{{patterns.p1}}"}' \
  --with 'v2=leadmagic_email_validation:{"email":"{{patterns.p2}}"}' \
  --with 'v3=leadmagic_email_validation:{"email":"{{patterns.p3}}"}' \
  --with 'v4=leadmagic_email_validation:{"email":"{{patterns.p4}}"}' \
  --with 'apollo=apollo_people_match:{"first_name":"{{First Name}}","last_name":"{{Last Name}}","organization_name":"{{Company}}","domain":"{{Domain}}"}' \
  --end-waterfall \
  --with 'final_validation=leadmagic_email_validation:{"email":"{{email}}"}'

Required columns: First Name, Last Name, Domain

Understanding validation results

After validation, check validation.data.email_status:

Note: catch_all is not a failure — many business domains use catch-all. Continue with outreach, just expect slightly higher bounce rates.

Scale after pilot

# After reviewing --rows 0:1 output, run remaining rows:
deepline enrich --input leads.csv --in-place --rows 1: \
  # ... same flags as pilot

Get started

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npm install -g @deepline/cli
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