Una investigación de prospectos eficaz requiere datos nuevos: noticias de la empresa, tecnología, patrones de contratación y financiación reciente. La investigación manual por cliente potencial tarda entre 15 y 30 minutos. Este tutorial crea una canalización automatizada que utiliza la API de Scavio para buscar señales de prospectos, extraer datos clave de fragmentos de SERP y compilar un perfil de prospecto estructurado. Cada perfil cuesta alrededor de $0,015 (3 búsquedas) y tarda menos de 5 segundos en generarse.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Una lista de nombres de empresas potenciales
Guia paso a paso
Paso 1: Búsqueda de antecedentes y noticias de la empresa
Realice búsquedas específicas para obtener noticias recientes, descripciones de la empresa y señales clave sobre cada cliente potencial.
Python
import os, requests, time, re
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
URL = 'https://api.scavio.dev/api/v1/search'
def search_company(company: str, query_suffix: str, num: int = 5) -> list:
resp = requests.post(URL, headers=H,
json={'query': f'{company} {query_suffix}', 'country_code': 'us', 'num_results': num})
return resp.json().get('organic_results', [])
def get_company_news(company: str) -> list:
results = search_company(company, 'news 2026')
return [{'title': r['title'], 'snippet': r.get('snippet', ''), 'url': r['link']} for r in results]
def get_tech_stack(company: str) -> list:
results = search_company(company, 'technology stack engineering blog')
all_text = ' '.join(r.get('snippet', '') for r in results).lower()
techs = ['python', 'react', 'kubernetes', 'aws', 'gcp', 'azure', 'terraform',
'postgresql', 'mongodb', 'redis', 'docker', 'typescript', 'go', 'rust']
return [t for t in techs if t in all_text]
news = get_company_news('Stripe')
print(f'Stripe news: {len(news)} articles')
for n in news[:3]:
print(f' {n["title"][:60]}')Paso 2: Extraer señales de contratación y crecimiento
Busque noticias sobre actividad de contratación y financiación como indicadores de la etapa de crecimiento de la empresa y la disponibilidad de presupuesto.
Python
def get_hiring_signals(company: str) -> dict:
results = search_company(company, 'hiring jobs careers 2026')
all_text = ' '.join(r.get('snippet', '') for r in results).lower()
roles = re.findall(r'(engineer|developer|manager|director|vp|head of)', all_text)
return {
'active_hiring': len(results) > 0,
'job_signals': len(roles),
'role_types': list(set(roles)),
'sources': [r['link'] for r in results[:3]],
}
def get_funding_signals(company: str) -> dict:
results = search_company(company, 'funding raised valuation 2026')
all_text = ' '.join(r.get('snippet', '') for r in results)
amounts = re.findall(r'\$(\d+(?:\.\d+)?\s*(?:million|billion|M|B))', all_text)
return {
'recent_funding': len(amounts) > 0,
'amounts_mentioned': amounts[:3],
'snippets': [r.get('snippet', '')[:100] for r in results[:2]],
}
hiring = get_hiring_signals('Stripe')
print(f'Hiring: {hiring["job_signals"]} role mentions, types: {hiring["role_types"]}')Paso 3: Compilar el perfil del cliente potencial
Combine todas las señales en un perfil de cliente potencial estructurado que un representante de ventas pueda revisar en menos de 30 segundos.
Python
def research_prospect(company: str) -> dict:
news = get_company_news(company)
time.sleep(0.3)
tech = get_tech_stack(company)
time.sleep(0.3)
hiring = get_hiring_signals(company)
time.sleep(0.3)
funding = get_funding_signals(company)
profile = {
'company': company,
'top_news': [n['title'] for n in news[:3]],
'tech_stack': tech,
'hiring_active': hiring['active_hiring'],
'role_types': hiring['role_types'],
'recent_funding': funding['recent_funding'],
'funding_amounts': funding['amounts_mentioned'],
'credits_used': 4,
'cost': 0.020,
}
print(f'Prospect Profile: {company}')
print(f' News: {len(news)} articles')
for n in profile['top_news']:
print(f' - {n[:55]}')
print(f' Tech: {", ".join(tech) if tech else "Unknown"}')
print(f' Hiring: {"Active" if hiring["active_hiring"] else "No signals"} ({hiring["job_signals"]} mentions)')
print(f' Funding: {", ".join(funding["amounts_mentioned"]) if funding["amounts_mentioned"] else "None found"}')
print(f' Cost: ${profile["cost"]}')
return profile
research_prospect('Stripe')Ejemplo en Python
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def research_prospect(company):
queries = [f'{company} news 2026', f'{company} technology stack', f'{company} hiring careers']
for q in queries:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': q, 'country_code': 'us', 'num_results': 3})
results = resp.json().get('organic_results', [])
print(f' [{q.split(company)[1].strip()}]')
for r in results[:2]:
print(f' {r["title"][:55]}')
time.sleep(0.3)
print(f' Cost: $0.015 (3 searches)')
for co in ['Stripe', 'Datadog']:
print(f'\n{co}:')
research_prospect(co)Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function researchProspect(company) {
const queries = [`${company} news 2026`, `${company} technology stack`, `${company} hiring`];
for (const q of queries) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: q, country_code: 'us', num_results: 3 })
});
const results = (await resp.json()).organic_results || [];
console.log(` [${q.replace(company, '').trim()}]`);
results.slice(0, 2).forEach(r => console.log(` ${r.title.slice(0, 55)}`));
}
console.log(' Cost: $0.015');
}
researchProspect('Stripe');Salida esperada
JSON
Prospect Profile: Stripe
News: 5 articles
- Stripe Launches AI-Powered Fraud Detection in 2026
- Stripe Revenue Surpasses $20B Annual Run Rate
- Stripe Expands to 15 New Markets in Asia Pacific
Tech: python, react, aws, kubernetes, postgresql, redis
Hiring: Active (8 mentions)
Funding: $6.5 billion
Cost: $0.020