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Scenario

You’re running ABM and need personas representing the buying committee at target accounts. You pull companies from HubSpot, enrich with Mave’s research, then create role-specific personas for each account’s buying committee.

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Architecture

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Code

import os, requests, time

HS = os.environ["HUBSPOT_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

ROLES = [
    {"role": "Economic Buyer", "desc": "C-level controlling budget. Cares about ROI and strategic alignment."},
    {"role": "Technical Evaluator", "desc": "Engineering/IT lead. Evaluates integration, security, scalability."},
    {"role": "End User Champion", "desc": "Day-to-day user and internal advocate. Cares about UX and time savings."},
    {"role": "Procurement Gatekeeper", "desc": "Legal/procurement. Reviews compliance, terms, and risk."},
]

# 1. Target companies
companies = requests.post("https://api.hubapi.com/crm/v3/objects/companies/search",
    headers={"Authorization": f"Bearer {HS}"},
    json={
        "filterGroups": [{"filters": [{"propertyName": "numberofemployees", "operator": "GTE", "value": "100"}]}],
        "properties": ["name", "industry", "numberofemployees", "annualrevenue", "domain"],
        "sorts": [{"propertyName": "annualrevenue", "direction": "DESCENDING"}],
        "limit": 5,
    }).json().get("results", [])

all_personas = []
for co in companies:
    p = co.get("properties", {})
    name = p.get("name", "Unknown")

    # 2. Enrich with Mave
    enrichment = requests.post("https://app.mavera.io/api/v1/mave/chat", headers=MH,
        json={"message": f"Research {name} ({p.get('industry','N/A')}, ~{p.get('numberofemployees','?')} employees). Strategic priorities? Key challenges? Recent news? Buying committee for B2B SaaS?"}
    ).json()
    context = enrichment.get("content", "")[:300]

    # 3. Create buying committee personas
    for role in ROLES:
        persona = requests.post("https://app.mavera.io/api/v1/personas", headers=MH, json={
            "name": f"{name} — {role['role']}",
            "description": f"{role['desc']} Context: {p.get('industry','N/A')}, ~{p.get('numberofemployees','?')} employees. {context}",
            "demographic": {"industries": [p.get("industry", "Technology")]},
        }).json()
        all_personas.append({"company": name, "role": role["role"], "id": persona["id"]})
        time.sleep(0.2)
    time.sleep(0.5)

print(f"Created {len(all_personas)} ABM personas for {len(companies)} companies")
for p in all_personas:
    print(f"  {p['company']} | {p['role']} | {p['id']}")

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Example Output

{ "companies": 5, "personas": 20,
  "sample": { "company": "Acme Corp", "committee": [
    { "role": "Economic Buyer", "id": "per_acme_eb_1" },
    { "role": "Technical Evaluator", "id": "per_acme_te_2" },
    { "role": "End User Champion", "id": "per_acme_eu_3" },
    { "role": "Procurement Gatekeeper", "id": "per_acme_pg_4" }
  ], "enrichment": "Digital transformation. $50M Series C. Supply chain automation." }}

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Error Handling

Mave has separate credits/limits. Code includes 500ms between companies. For 50+, add exponential backoff.
Re-running creates duplicates. Check with GET /api/v1/personas?search={name} before creating.

HubSpot Integration

Personas API