> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Entity-Based Competitive Intelligence
### Scenario
Perigon tags articles with structured entities — companies, people, topics, and locations. This job filters articles by competitor company entities, enriches them with related person entities (executive mentions), and feeds the structured data to Mave for deep competitive analysis that goes beyond keyword matching.
**Flow:** Perigon `GET /all?companyName={competitor}` → Extract entities → Mavera `POST /mave/chat` → Entity-level intelligence report
### Code
```python Python theme={"dark"}
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])
```
```javascript JavaScript theme={"dark"}
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));
```
### Example Output
```text theme={"dark"}
Salesforce: 20 articles, 14 person mentions
HubSpot: 18 articles, 9 person mentions
Adobe: 15 articles, 11 person mentions
ENTITY INTELLIGENCE REPORT
============================================================
## Salesforce
**Executive Moves:** Marc Benioff keynote at AI conference — positioning as
"AI-first CRM." New VP of AI hired from Google DeepMind.
**Product Signals:** Einstein Copilot expansion. Acquiring data pipeline startup.
**Partnership Map:** AWS (deepening), Snowflake (new integration), Anthropic.
**Sentiment:** Positive (65%) — Wall Street bullish on AI pivot.
**Implication:** Their AI narrative is credible. We need differentiated AI story.
THREAT/OPPORTUNITY MATRIX:
| Competitor | Threat | Opportunity |
|------------|--------|-------------|
| Salesforce | 8/10 — AI credibility | Mid-market gap as they go enterprise |
| HubSpot | 5/10 — SMB loyalty | They're slow on AI features |
| Adobe | 6/10 — Creative suite | Marketing ops underserved |
```
### Error Handling
Perigon uses NER — `companyName=Apple` may include Apple Records. Add `topic` or `category` filters to narrow results.
Not all articles have entity tags. Filter with `len(companies) > 0` to ensure structured data is available.
Each competitor = 1 API call. 3 competitors = 3 calls. Safe on Starter plans for daily runs.