Entity-Based Competitive Intelligence

import os, requests, time

PG_KEY = os.environ["PERIGON_API_KEY"]
PG_BASE = "https://api.goperigon.com/v1"
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

COMPETITORS = ["Salesforce", "HubSpot", "Adobe"]

# 1. Fetch articles per competitor entity
all_intel = {}
for comp in COMPETITORS:
    r = requests.get(f"{PG_BASE}/all", params={
        "apiKey": PG_KEY, "companyName": comp, "sortBy": "date",
        "size": 20, "sourceGroup": "top100",
        "from": (requests.utils.default_headers(), None)[1],
    })
    if not r.ok:
        print(f"{comp}: API error {r.status_code}")
        continue
    articles = r.json().get("articles", [])

    entity_data = []
    for a in articles:
        companies = [c.get("name","") for c in a.get("companies", [])]
        people = [p.get("name","") for p in a.get("people", [])]
        topics = [t.get("name","") for t in a.get("topics", [])]
        entity_data.append({
            "title": a.get("title",""),
            "source": a.get("source",{}).get("name",""),
            "date": a.get("pubDate","")[:10],
            "summary": a.get("summary", a.get("description",""))[:300],
            "companies": companies,
            "people": people,
            "topics": topics,
            "sentiment": a.get("sentiment",""),
        })
    all_intel[comp] = entity_data
    print(f"{comp}: {len(entity_data)} articles, {sum(len(e['people']) for e in entity_data)} person mentions")
    time.sleep(1)

# 2. Build structured corpus
corpus_parts = []
for comp, articles in all_intel.items():
    corpus_parts.append(f"\n## {comp} ({len(articles)} articles)")
    for a in articles:
        corpus_parts.append(
            f"- [{a['source']}] {a['title']} ({a['date']})\n"
            f"  Summary: {a['summary'][:200]}\n"
            f"  Entities: Companies={a['companies'][:5]}, People={a['people'][:3]}, Topics={a['topics'][:3]}\n"
            f"  Sentiment: {a['sentiment']}"
        )

# 3. Mave analysis
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Competitive intelligence analyst. Analyze entity-tagged news for these competitors.\n\n"
        + "\n".join(corpus_parts[:80])
        + "\n\nFor EACH competitor:\n"
        "1. **Executive Moves** — Who's being mentioned and why (hiring, departures, keynotes)\n"
        "2. **Product Signals** — What they're building or acquiring\n"
        "3. **Partnership Map** — Which companies appear alongside them\n"
        "4. **Topic Clusters** — What themes dominate their coverage\n"
        "5. **Sentiment Trajectory** — Getting better or worse coverage?\n"
        "6. **Strategic Implication** — What this means for our positioning\n\n"
        "End with a THREAT/OPPORTUNITY matrix."
}).json()

for comp in COMPETITORS:
    cnt = len(all_intel.get(comp, []))
    print(f"{comp}: {cnt} articles analyzed")
print(f"\n{'='*60}\nENTITY INTELLIGENCE REPORT\n{'='*60}")
print(analysis.get("content", "")[:3000])

const PG_KEY = process.env.PERIGON_API_KEY;
const PG_BASE = "https://api.goperigon.com/v1";
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const COMPETITORS = ["Salesforce", "HubSpot", "Adobe"];

// 1. Fetch per competitor entity
const allIntel = {};
for (const comp of COMPETITORS) {
  const r = await fetch(
    `${PG_BASE}/all?apiKey=${PG_KEY}&companyName=${encodeURIComponent(comp)}&sortBy=date&size=20&sourceGroup=top100`);
  if (!r.ok) { console.log(`${comp}: error ${r.status}`); continue; }
  const articles = (await r.json()).articles || [];

  allIntel[comp] = articles.map(a => ({
    title: a.title || "", source: a.source?.name || "", date: (a.pubDate||"").slice(0,10),
    summary: (a.summary || a.description || "").slice(0, 300),
    companies: (a.companies||[]).map(c => c.name||""),
    people: (a.people||[]).map(p => p.name||""),
    topics: (a.topics||[]).map(t => t.name||""),
    sentiment: a.sentiment || "",
  }));
  console.log(`${comp}: ${allIntel[comp].length} articles, ${allIntel[comp].reduce((s,e)=>s+e.people.length,0)} person mentions`);
  await new Promise(r => setTimeout(r, 1000));
}

// 2. Corpus
let corpus = "";
for (const [comp, articles] of Object.entries(allIntel)) {
  corpus += `\n## ${comp} (${articles.length} articles)\n`;
  for (const a of articles)
    corpus += `- [${a.source}] ${a.title} (${a.date})\n  Summary: ${a.summary.slice(0,200)}\n  Entities: Companies=${a.companies.slice(0,5)}, People=${a.people.slice(0,3)}\n  Sentiment: ${a.sentiment}\n`;
}

// 3. Analysis
const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Entity-tagged competitive intelligence.\n\n${corpus.slice(0,5000)}\n\nPer competitor: Executive Moves, Product Signals, Partnership Map, Topic Clusters, Sentiment Trajectory, Strategic Implication.\n\nEnd with THREAT/OPPORTUNITY matrix.` }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}\nENTITY INTELLIGENCE REPORT\n${"=".repeat(60)}`);
console.log((analysis.content || "").slice(0, 3000));