n8n Lead Enrichment After Apollo

# Prospeo: finds and verifies professional emails
# Pricing: ~$39/mo for 1,000 credits, bulk endpoint available
# Best for: email discovery and verification

# n8n HTTP Request node configuration:
# Method: POST
# URL: https://api.prospeo.io/email-finder
# Headers:
#   x-api-key: YOUR_PROSPEO_KEY
# Body:
# {
#   "domain": "{{ $json.company_domain }}",
#   "first_name": "{{ $json.first_name }}",
#   "last_name": "{{ $json.last_name }}"
# }
#
# Response shape:
# {
#   "email": "john@company.com",
#   "confidence": 95,
#   "verified": true,
#   "domain": "company.com"
# }

# Prospeo strengths: high verification accuracy
# Prospeo weakness: email only, no firmographic data

# PDL: comprehensive person and company data
# Pricing: pay-per-record (~$0.03-0.10/record depending on volume)
# Best for: company data enrichment (size, industry, funding)

# n8n HTTP Request node configuration:
# Method: GET
# URL: https://api.peopledatalabs.com/v5/company/enrich
# Query params:
#   api_key: YOUR_PDL_KEY
#   website: {{ $json.company_domain }}
#
# Response includes:
# {
#   "name": "Company Inc",
#   "size": "51-200",
#   "industry": "computer software",
#   "founded": 2019,
#   "location": {"country": "us", "region": "california"},
#   "funding_total": 15000000,
#   "employee_count": 120
# }

# PDL strengths: deep firmographic data, large database
# PDL weakness: expensive at scale, data can be stale

import requests, os

def serp_enrich_company(domain: str) -> dict:
    """Enrich company data using live SERP results."""
    api_key = os.environ["SCAVIO_API_KEY"]
    headers = {"x-api-key": api_key}
    base_url = "https://api.scavio.dev/api/v1/search"

    enrichment = {"domain": domain, "web_presence": {}, "social_signals": {}}

    # Google: company info, news, reviews
    google_resp = requests.post(
        base_url,
        headers=headers,
        json={"query": f"site:{domain}", "platform": "google"},
        timeout=10,
    )
    google_data = google_resp.json()
    enrichment["web_presence"] = {
        "indexed_pages": len(google_data.get("organic_results", [])),
        "top_pages": [
            {"title": r["title"], "url": r.get("link", "")}
            for r in google_data.get("organic_results", [])[:5]
        ],
        "has_knowledge_panel": bool(google_data.get("knowledge_panel")),
    }

    # Reddit: what people say about them
    reddit_resp = requests.post(
        base_url,
        headers=headers,
        json={"query": domain.replace(".com", ""), "platform": "reddit"},
        timeout=10,
    )
    reddit_data = reddit_resp.json()
    enrichment["social_signals"] = {
        "reddit_mentions": len(reddit_data.get("organic_results", [])),
        "recent_discussions": [
            {"title": r["title"], "snippet": r.get("snippet", "")[:100]}
            for r in reddit_data.get("organic_results", [])[:3]
        ],
    }

    return enrichment

# 2 credits per company = $0.01
# Different data than Apollo: live web presence instead of static firmographics
# Best for: understanding current market presence, not historical data

// n8n Function node: normalize enrichment from any provider
// Place after the enrichment HTTP Request node

const raw = $input.first().json;
const provider = $input.first().json._provider || "unknown";

const normalized = {
  domain: "",
  company_name: "",
  emails: [],
  company_size: "",
  industry: "",
  funding: null,
  web_presence: null,
  social_signals: null,
  enriched_at: new Date().toISOString(),
  provider: provider,
};

if (provider === "prospeo") {
  normalized.domain = raw.domain || "";
  normalized.emails = raw.email ? [{ email: raw.email, confidence: raw.confidence }] : [];
} else if (provider === "pdl") {
  normalized.domain = raw.website || "";
  normalized.company_name = raw.name || "";
  normalized.company_size = raw.size || "";
  normalized.industry = raw.industry || "";
  normalized.funding = raw.funding_total || null;
} else if (provider === "scavio") {
  normalized.domain = raw.domain || "";
  normalized.web_presence = raw.web_presence || null;
  normalized.social_signals = raw.social_signals || null;
}

return [{ json: normalized }];

# Workflow structure:
#
# [Trigger: new lead in CRM / webhook / spreadsheet]
#     |
#     v
# [Split: run enrichment in parallel]
#     |--- [Prospeo: email finding]
#     |--- [Scavio: web presence + Reddit mentions]
#     |--- [PDL: firmographic data (if budget allows)]
#     |
#     v
# [Merge: combine all enrichment results]
#     |
#     v
# [Normalizer Function: consistent output shape]
#     |
#     v
# [Write to CRM / Airtable / Google Sheets]
#
# Cost per lead:
#   Prospeo: ~$0.04
#   Scavio: $0.01 (2 credits)
#   PDL: ~$0.05
#   Total: ~$0.10/lead with all three
#   Without PDL: ~$0.05/lead