Answer Engine Optimization (AEO) es una línea de servicios en crecimiento para las agencias de SEO, pero la mayoría de las herramientas de seguimiento de clasificación no rastrean bien las citas de AI Overview ni cobran precios superiores por ello. La creación de un panel de control AEO personalizado con una API de búsqueda brinda a las agencias control total sobre los datos, las vistas por cliente y mantiene los costos predecibles en $0,005 por verificación de palabra clave. Este tutorial crea un panel de control AEO multicliente que rastrea la presencia de citas, el porcentaje de voz y los datos de tendencias, y luego genera JSON listo para cualquier marco de interfaz.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Listas de palabras clave del cliente para monitorear
Guia paso a paso
Paso 1: Definir la estructura de datos multicliente
Establezca una configuración que asigne a cada cliente su dominio y palabras clave de destino. Esta es la entrada al tablero.
Python
clients = {
'client_a': {
'domain': 'clienta.com',
'name': 'Client A - SaaS CRM',
'keywords': [
'best crm for small business',
'crm software comparison 2026',
'what is a crm system',
'crm vs spreadsheet'
]
},
'client_b': {
'domain': 'clientb.io',
'name': 'Client B - Project Management',
'keywords': [
'best project management tools 2026',
'how to manage remote teams',
'project management software comparison',
'agile vs waterfall 2026'
]
}
}
total_keywords = sum(len(c['keywords']) for c in clients.values())
print(f'{len(clients)} clients, {total_keywords} total keywords')
print(f'Monthly cost (daily checks): ${total_keywords * 30 * 0.005:.2f}')Paso 2: Construir la función de recopilación de datos OEA
Para cada palabra clave, verifique la presencia de SERP para la descripción general de AI y extraiga datos de citas. Registre si se cita el dominio del cliente.
Python
import requests, os, time
API_KEY = os.environ['SCAVIO_API_KEY']
def collect_aeo_data(keyword: str, client_domain: str) -> dict:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'query': keyword, 'country_code': 'us'})
data = resp.json()
aio = data.get('ai_overview', {})
has_overview = bool(aio and aio.get('text'))
citations = aio.get('citations', []) if has_overview else []
cited_domains = [c.get('domain', '') for c in citations]
# Also check organic position
organic_position = None
for r in data.get('organic_results', []):
if client_domain in r.get('link', ''):
organic_position = r['position']
break
return {
'keyword': keyword,
'has_ai_overview': has_overview,
'total_citations': len(citations),
'client_cited': any(client_domain in d for d in cited_domains),
'citation_position': next(
(i + 1 for i, d in enumerate(cited_domains) if client_domain in d), None),
'organic_position': organic_position,
'competitor_domains': [d for d in cited_domains if client_domain not in d]
}Paso 3: Generar datos de panel por cliente
Agregue datos a nivel de palabras clave en métricas a nivel de cliente: tasa de citas, posición promedio de citas, superposición orgánica y dominios de los principales competidores.
Python
from collections import Counter
def generate_client_dashboard(client_id: str, config: dict) -> dict:
domain = config['domain']
results = []
competitor_counter = Counter()
for kw in config['keywords']:
data = collect_aeo_data(kw, domain)
results.append(data)
for d in data['competitor_domains']:
competitor_counter[d] += 1
time.sleep(0.3)
total = len(results)
with_aio = sum(1 for r in results if r['has_ai_overview'])
cited = sum(1 for r in results if r['client_cited'])
avg_cite_pos = 0
cite_positions = [r['citation_position'] for r in results if r['citation_position']]
if cite_positions:
avg_cite_pos = sum(cite_positions) / len(cite_positions)
return {
'client_id': client_id,
'client_name': config['name'],
'domain': domain,
'total_keywords': total,
'keywords_with_aio': with_aio,
'aio_rate': round(with_aio / max(total, 1) * 100, 1),
'citations': cited,
'citation_rate': round(cited / max(with_aio, 1) * 100, 1),
'avg_citation_position': round(avg_cite_pos, 1),
'top_competitors': competitor_counter.most_common(5),
'keyword_details': results
}Paso 4: Cree el resultado completo del panel de la agencia
Ejecute el panel para todos los clientes y genere una estructura JSON lista para una interfaz. Incluir seguimiento de costos por cliente.
Python
import json
from datetime import date
def build_agency_dashboard(clients: dict) -> dict:
dashboard = {
'generated': date.today().isoformat(),
'clients': [],
'summary': {}
}
total_credits = 0
for client_id, config in clients.items():
print(f'Processing {config["name"]}...')
client_data = generate_client_dashboard(client_id, config)
dashboard['clients'].append(client_data)
total_credits += len(config['keywords'])
# Agency summary
all_clients = dashboard['clients']
dashboard['summary'] = {
'total_clients': len(all_clients),
'total_keywords': sum(c['total_keywords'] for c in all_clients),
'avg_citation_rate': round(
sum(c['citation_rate'] for c in all_clients) / max(len(all_clients), 1), 1),
'total_credits': total_credits,
'cost': f'${total_credits * 0.005:.2f}'
}
with open(f'aeo_dashboard_{date.today()}.json', 'w') as f:
json.dump(dashboard, f, indent=2)
return dashboard
dashboard = build_agency_dashboard(clients)
for c in dashboard['clients']:
print(f'{c["client_name"]}: {c["citation_rate"]}% citation rate, {c["citations"]}/{c["keywords_with_aio"]} cited')Ejemplo en Python
Python
import os, requests, time, json
from datetime import date
from collections import Counter
API_KEY = os.environ['SCAVIO_API_KEY']
def check_aeo(keyword, domain):
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'query': keyword, 'country_code': 'us'})
aio = resp.json().get('ai_overview', {})
citations = aio.get('citations', []) if aio.get('text') else []
domains = [c.get('domain', '') for c in citations]
return {'keyword': keyword, 'has_aio': bool(aio.get('text')),
'cited': any(domain in d for d in domains), 'domains': domains}
def client_report(name, domain, keywords):
results = [check_aeo(kw, domain) for kw in keywords]
cited = sum(1 for r in results if r['cited'])
with_aio = sum(1 for r in results if r['has_aio'])
print(f'{name}: {cited}/{with_aio} cited ({len(keywords)} keywords, ${len(keywords)*0.005:.3f})')
client_report('Demo Client', 'example.com', ['best crm 2026', 'crm comparison'])Ejemplo en JavaScript
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
async function checkAeo(keyword, domain) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: keyword, country_code: 'us' })
});
const data = await resp.json();
const aio = data.ai_overview || {};
const domains = (aio.citations || []).map(c => c.domain || '');
return { keyword, hasAio: Boolean(aio.text), cited: domains.some(d => d.includes(domain)) };
}
async function main() {
const keywords = ['best crm 2026', 'crm comparison'];
let cited = 0;
for (const kw of keywords) {
const r = await checkAeo(kw, 'example.com');
if (r.cited) cited++;
console.log(`${r.hasAio ? '+' : '-'}AIO ${r.cited ? '+CITED' : ''} ${kw}`);
}
console.log(`Citation rate: ${(cited/keywords.length*100).toFixed(0)}%`);
}
main().catch(console.error);Salida esperada
JSON
2 clients, 8 total keywords
Monthly cost (daily checks): $1.20
Processing Client A - SaaS CRM...
Processing Client B - Project Management...
Client A - SaaS CRM: 50.0% citation rate, 1/2 cited
Client B - Project Management: 33.3% citation rate, 1/3 cited
Agency summary:
Total clients: 2
Total keywords: 8
Avg citation rate: 41.7%
Cost: $0.04