Track your brand's presence in Google AI Overview citations by running daily search queries for your target keywords and parsing the ai_overview field for brand mentions. Google's AI Overview now appears on 30-40% of informational queries, and citation placement drives significant traffic. Brands that monitor their AI Overview presence can detect drops early, identify new citation opportunities, and measure the impact of AEO optimization. This tutorial builds a daily tracker that stores citation history and alerts on changes.
Prerequisites
- Python 3.8+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A list of target keywords where you expect AI Overview citations
Walkthrough
Step 1: Set up monitoring queries
Define the brand name, domain, and target keywords to check for AI Overview citations.
Python
import os, requests, json, datetime
API_KEY = os.environ['SCAVIO_API_KEY']
BRAND = 'YourBrand'
DOMAIN = 'yourbrand.com'
TARGET_KEYWORDS = [
'best project management tool 2026',
'project management software comparison',
'how to manage remote teams',
'team collaboration tools',
'agile project tracking',
]
HISTORY_FILE = 'ai_overview_mentions.json'Step 2: Search with AI Overview parsing
Query each keyword through Scavio and check whether the AI Overview mentions your brand or links to your domain.
Python
def check_ai_overview(query: str) -> dict:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY},
json={'platform': 'google', 'query': query}, timeout=15)
data = resp.json()
ai_overview = data.get('ai_overview', {})
overview_text = ai_overview.get('text', '') if isinstance(ai_overview, dict) else str(ai_overview)
citations = ai_overview.get('citations', []) if isinstance(ai_overview, dict) else []
brand_in_text = BRAND.lower() in overview_text.lower()
domain_in_citations = any(DOMAIN in str(c) for c in citations)
return {
'query': query,
'has_ai_overview': bool(overview_text),
'brand_mentioned': brand_in_text,
'domain_cited': domain_in_citations,
'citation_count': len(citations),
}
result = check_ai_overview(TARGET_KEYWORDS[0])
print(json.dumps(result, indent=2))Step 3: Parse citations for detail
Extract citation URLs and titles from the AI Overview to understand which competitors also appear.
Python
def parse_citations(query: str) -> list:
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY},
json={'platform': 'google', 'query': query}, timeout=15)
data = resp.json()
ai_overview = data.get('ai_overview', {})
citations = ai_overview.get('citations', []) if isinstance(ai_overview, dict) else []
parsed = []
for c in citations:
if isinstance(c, dict):
parsed.append({'title': c.get('title', ''), 'url': c.get('url', c.get('link', ''))})
elif isinstance(c, str):
parsed.append({'url': c})
return parsed
citations = parse_citations('best project management tool 2026')
for c in citations:
is_ours = DOMAIN in c.get('url', '')
print(f"{'[OURS]' if is_ours else ' '} {c.get('url', 'N/A')}")Step 4: Track daily and store history
Run all queries, record results, and compare against previous days to detect changes.
Python
def daily_scan(keywords: list) -> dict:
today = datetime.date.today().isoformat()
scan = {'date': today, 'results': []}
for kw in keywords:
result = check_ai_overview(kw)
result['citations'] = parse_citations(kw)
scan['results'].append(result)
mentioned = sum(1 for r in scan['results'] if r['brand_mentioned'] or r.get('domain_cited'))
scan['summary'] = {'total': len(keywords), 'mentioned': mentioned}
history = []
try:
with open(HISTORY_FILE) as f:
history = json.load(f)
except FileNotFoundError:
pass
history.append(scan)
with open(HISTORY_FILE, 'w') as f:
json.dump(history, f, indent=2)
print(f'{today}: Brand mentioned in {mentioned}/{len(keywords)} AI Overviews')
return scan
daily_scan(TARGET_KEYWORDS)Step 5: Alert on changes
Compare today's scan with the previous day and alert when citations are gained or lost.
Python
def detect_changes() -> list:
try:
with open(HISTORY_FILE) as f:
history = json.load(f)
except FileNotFoundError:
return []
if len(history) < 2:
return []
prev = {r['query']: r for r in history[-2]['results']}
curr = {r['query']: r for r in history[-1]['results']}
alerts = []
for query in curr:
was_cited = prev.get(query, {}).get('brand_mentioned', False) or prev.get(query, {}).get('domain_cited', False)
is_cited = curr[query].get('brand_mentioned', False) or curr[query].get('domain_cited', False)
if was_cited and not is_cited:
alerts.append({'query': query, 'change': 'LOST', 'action': 'investigate'})
elif not was_cited and is_cited:
alerts.append({'query': query, 'change': 'GAINED', 'action': 'celebrate'})
for a in alerts:
print(f"[{a['change']}] {a['query']}")
return alerts
detect_changes()Python Example
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def check_brand_in_ai_overview(query, brand):
data = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': query}).json()
ai = data.get('ai_overview', {})
text = ai.get('text', '') if isinstance(ai, dict) else ''
return {'query': query, 'mentioned': brand.lower() in text.lower()}
print(check_brand_in_ai_overview('best crm 2026', 'HubSpot'))JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function checkBrand(query, brand) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H, body: JSON.stringify({platform: 'google', query})
});
const ai = (await r.json()).ai_overview || {};
const text = ai.text || '';
return {query, mentioned: text.toLowerCase().includes(brand.toLowerCase())};
}
checkBrand('best crm 2026', 'HubSpot').then(console.log);Expected Output
JSON
A daily monitoring system that tracks brand presence in Google AI Overview citations, stores history, and alerts when citations are gained or lost.