Prospect Domain Enriquecimiento for Cold Email Pipeline

import requests, os, json, csv
from io import StringIO

H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}

# Sample prospects (in production, load from CSV)
PROSPECTS = [
    {"domain": "example-saas.com", "name": "Example SaaS"},
    {"domain": "acme-ai.io", "name": "Acme AI"},
    {"domain": "dataflow-labs.com", "name": "Dataflow Labs"},
]

def enrich_domain(prospect):
    """Search for a prospect domain and extract enrichment signals."""
    r = requests.post("https://api.scavio.dev/api/v1/search", headers=H,
        json={"platform": "google",
              "query": f"{prospect['name']} {prospect['domain']}",
              "ai_overview": True}, timeout=10).json()
    snippets = [o.get("snippet", "") for o in r.get("organic", [])[:5]]
    titles = [o.get("title", "") for o in r.get("organic", [])[:5]]
    aio = r.get("ai_overview", {})
    all_text = " ".join(snippets + titles).lower()
    # Simple signal extraction
    signals = {
        "has_funding_mention": any(w in all_text for w in ["raised", "funding", "series", "seed"]),
        "has_hiring_signal": any(w in all_text for w in ["hiring", "careers", "open roles", "job"]),
        "has_api_mention": "api" in all_text,
        "result_count": len(r.get("organic", [])),
    }
    score = sum([
        signals["has_funding_mention"] * 30,
        signals["has_hiring_signal"] * 20,
        signals["has_api_mention"] * 25,
        min(signals["result_count"], 10) * 2.5,
    ])
    return {
        **prospect,
        "description": snippets[0][:200] if snippets else "",
        "ai_summary": aio.get("text", "")[:200] if aio else "",
        "signals": signals,
        "score": round(score)
    }

enriched = []
for p in PROSPECTS:
    result = enrich_domain(p)
    enriched.append(result)
    print(f"[{result['score']}] {result['name']} | {result['description'][:80]}")

enriched.sort(key=lambda x: x["score"], reverse=True)
print(f"\nEnriched {len(enriched)} prospects. Top: {enriched[0]['name']} (score: {enriched[0]['score']})")

const H = {"x-api-key": process.env.SCAVIO_API_KEY, "Content-Type": "application/json"};

const PROSPECTS = [
  {domain: "example-saas.com", name: "Example SaaS"},
  {domain: "acme-ai.io", name: "Acme AI"},
  {domain: "dataflow-labs.com", name: "Dataflow Labs"},
];

async function enrichDomain(prospect) {
  const r = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST", headers: H,
    body: JSON.stringify({
      platform: "google",
      query: `${prospect.name} ${prospect.domain}`,
      ai_overview: true
    })
  }).then(r => r.json());
  const snippets = (r.organic || []).slice(0, 5).map(o => o.snippet || "");
  const titles = (r.organic || []).slice(0, 5).map(o => o.title || "");
  const allText = [...snippets, ...titles].join(" ").toLowerCase();
  const aio = r.ai_overview || {};
  const signals = {
    hasFunding: ["raised", "funding", "series", "seed"].some(w => allText.includes(w)),
    hasHiring: ["hiring", "careers", "open roles", "job"].some(w => allText.includes(w)),
    hasApi: allText.includes("api"),
    resultCount: (r.organic || []).length,
  };
  const score = Math.round(
    (signals.hasFunding ? 30 : 0) + (signals.hasHiring ? 20 : 0) +
    (signals.hasApi ? 25 : 0) + Math.min(signals.resultCount, 10) * 2.5
  );
  return {
    ...prospect, description: (snippets[0] || "").slice(0, 200),
    aiSummary: (aio.text || "").slice(0, 200), signals, score
  };
}

(async () => {
  const enriched = [];
  for (const p of PROSPECTS) {
    const result = await enrichDomain(p);
    enriched.push(result);
    console.log(`[${result.score}] ${result.name} | ${result.description.slice(0, 80)}`);
  }
  enriched.sort((a, b) => b.score - a.score);
  console.log(`\nEnriched ${enriched.length} prospects. Top: ${enriched[0].name} (score: ${enriched[0].score})`);
})();