> ## 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.
# Long Document Analysis → Persona Source
> Use Claude's 1M token context window to process entire annual reports, 10-K filings, or whitepapers in a single call — extract audience segments and decision-maker profiles, then create Mavera Custom Personas grounded in real market intelligence
## Scenario
Claude's 1M token context window can process entire annual reports, 10-K filings, or industry whitepapers in a single call — no chunking, no summarization chains, no lost context. This job feeds a full document to Claude Opus 4.6 for deep analysis, extracts audience segments and decision-maker profiles, then creates Mavera Custom Personas grounded in real market intelligence.
**Flow:** Read document → Anthropic `POST /v1/messages` (Claude Opus 4.6, full document) → Extract segments → Mavera `POST /personas` → Data-driven personas
## Code
```python Python theme={"dark"}
import os, time, json, anthropic, requests
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
client = anthropic.Anthropic()
with open("acme_annual_report_2025.txt") as f:
document_text = f.read()
print(f"Document loaded: {len(document_text):,} chars (~{len(document_text)//4:,} tokens)")
# 1. Claude Opus analyzes the full document
analysis = client.messages.create(
model="claude-opus-4-6-20250725", max_tokens=4096,
input=[{"role": "user",
"content": "Market intelligence analyst. Analyze this document and extract:\n\n"
"1. **Audience Segments** 2. **Pain Points** 3. **Decision Criteria** "
"4. **Communication Preferences** 5. **Budget Indicators**\n\n"
"Return JSON: [{name, role, industry, pain_points, decision_criteria, "
"communication_style, budget_range, key_quotes}]\n\n"
f"DOCUMENT:\n{document_text}"}],
)
raw_output = analysis.content[0].text
print(f"Claude complete — {analysis.usage.input_tokens:,} input, {analysis.usage.output_tokens:,} output tokens")
# 2. Parse extracted personas
try:
start = raw_output.index("[")
end = raw_output.rindex("]") + 1
personas_data = json.loads(raw_output[start:end])
except (ValueError, json.JSONDecodeError):
personas_data = [{"name": "Extracted Persona", "description": raw_output[:500]}]
print(f"Extracted {len(personas_data)} persona segments")
# 3. Create Mavera personas
created = []
for p in personas_data[:6]:
desc = (f"Role: {p.get('role', 'N/A')}. Industry: {p.get('industry', 'N/A')}. "
f"Pain points: {', '.join(p.get('pain_points', [])[:3])}. "
f"Style: {p.get('communication_style', 'professional')}. Budget: {p.get('budget_range', 'N/A')}.")
mv = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
"name": p.get("name", "Document Persona"), "description": desc,
}).json()
created.append({"id": mv["id"], "name": p.get("name")})
print(f" Created: {p.get('name')} → {mv['id']}")
time.sleep(0.3)
print(f"\nPersonas created: {len(created)}")
for c in created:
print(f" • {c['name']} ({c['id']})")
```
```javascript JavaScript theme={"dark"}
import Anthropic from "@anthropic-ai/sdk";
import fs from "fs";
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const client = new Anthropic();
const documentText = fs.readFileSync("acme_annual_report_2025.txt", "utf-8");
const analysis = await client.messages.create({
model: "claude-opus-4-6-20250725", max_tokens: 4096,
input: [{ role: "user",
content: `Market intelligence analyst. Extract: 1. Audience Segments 2. Pain Points `
+ `3. Decision Criteria 4. Comms Preferences 5. Budget Indicators\n`
+ `Return JSON: [{name, role, industry, pain_points, decision_criteria, communication_style, budget_range}]\n\n`
+ `DOCUMENT:\n${documentText}` }],
});
const rawOutput = analysis.content[0].text;
console.log(`Complete — ${analysis.usage.input_tokens.toLocaleString()} input tokens`);
let personasData;
try {
personasData = JSON.parse(rawOutput.slice(rawOutput.indexOf("["), rawOutput.lastIndexOf("]") + 1));
} catch { personasData = [{ name: "Extracted Persona", description: rawOutput.slice(0, 500) }]; }
const created = [];
for (const p of personasData.slice(0, 6)) {
const desc = `Role: ${p.role || "N/A"}. Pain points: ${(p.pain_points || []).slice(0, 3).join(", ")}. `
+ `Style: ${p.communication_style || "professional"}. Budget: ${p.budget_range || "N/A"}.`;
const mv = await fetch(`${MV_BASE}/personas`, { method: "POST", headers: MV_H,
body: JSON.stringify({ name: p.name || "Document Persona", description: desc }),
}).then(r => r.json());
created.push({ id: mv.id, name: p.name });
console.log(` Created: ${p.name} → ${mv.id}`);
await new Promise(r => setTimeout(r, 300));
}
console.log(`Personas created: ${created.length}`);
```
## Example Output
```text theme={"dark"}
Document loaded: 847,293 chars (~211,823 tokens)
Claude complete — 211,823 input, 2,847 output tokens
Extracted 4 persona segments
Created: VP of Operations → per_8a3f2c
Created: Chief Financial Officer → per_1b7d4e
Created: IT Director (Mid-Market) → per_9c2a1f
Created: Procurement Manager → per_4d8e3b
```
## Error Handling
Claude Opus 4.6 supports 1M input tokens. A 500-page annual report is \~200K tokens — well within limits. For documents exceeding 1M tokens, split at natural boundaries (chapters, sections) and run multiple calls.
Tier 1 allows 50 req/min. This job uses 1 Claude call + up to 6 Mavera calls. The `anthropic` SDK retries 429 errors automatically with exponential backoff. For raw HTTP, add retry logic with 30-60s waits.
Claude may wrap JSON in markdown code fences. The substring extraction handles this. If parsing still fails, retry with a system prompt that says "return raw JSON, no markdown."