Answer Engine Optimization (AEO) is a growing service line for SEO agencies, but most rank tracking tools do not track AI Overview citations well or charge premium prices for it. Building a custom AEO dashboard with a search API gives agencies full control over the data, per-client views, and keeps costs predictable at $0.005 per keyword check. This tutorial builds a multi-client AEO dashboard backend that tracks citation presence, share of voice, and trend data, then outputs JSON ready for any frontend framework.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- Client keyword lists to monitor
Walkthrough
Step 1: Define the multi-client data structure
Set up a configuration that maps each client to their domain and target keywords. This is the input to the dashboard.
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}')Step 2: Build the AEO data collection function
For each keyword, check the SERP for AI Overview presence and extract citation data. Record whether the client domain is cited.
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]
}Step 3: Generate per-client dashboard data
Aggregate keyword-level data into client-level metrics: citation rate, average citation position, organic overlap, and top competitor domains.
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
}Step 4: Build the full agency dashboard output
Run the dashboard for all clients and output a JSON structure ready for a frontend. Include cost tracking per client.
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')Python Example
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, ${len(keywords)*0.005:.3f})')
client_report('Demo Client', 'example.com', ['best crm 2026', 'crm comparison'])JavaScript Example
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
async function checkAeo(keyword, domain) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: keyword, country_code: 'us' })
});
const data = await resp.json();
const aio = data.ai_overview || {};
const domains = (aio.citations || []).map(c => c.domain || '');
return { keyword, hasAio: Boolean(aio.text), cited: domains.some(d => d.includes(domain)) };
}
async function main() {
const keywords = ['best crm 2026', 'crm comparison'];
let cited = 0;
for (const kw of keywords) {
const r = await checkAeo(kw, 'example.com');
if (r.cited) cited++;
console.log(`${r.hasAio ? '+' : '-'}AIO ${r.cited ? '+CITED' : ''} ${kw}`);
}
console.log(`Citation rate: ${(cited/keywords.length*100).toFixed(0)}%`);
}
main().catch(console.error);Expected Output
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