Build an AI Overview change alert system that establishes a baseline of AI Overview content for your target queries, runs daily searches with AI Overview parsing, diffs the current content against the baseline, and alerts when citations are added, removed, or when the summary text changes significantly. Google's AI Overview is now a primary traffic source for many queries, and citations can appear or disappear without warning. An automated monitoring system catches these changes within 24 hours instead of weeks.
Prerequisites
- Python 3.8+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A list of target queries to monitor
Walkthrough
Step 1: Baseline queries
Run initial searches to establish a baseline of AI Overview content for each target query.
Python
import os, requests, json, datetime, hashlib
API_KEY = os.environ['SCAVIO_API_KEY']
QUERIES = [
'best search api for developers',
'how to add search to ai agent',
'serp api comparison 2026',
]
BASELINE_FILE = 'ai_overview_baseline.json'
def fetch_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 = data.get('ai_overview', {})
if isinstance(ai, dict):
return {
'text': ai.get('text', ''),
'citations': ai.get('citations', []),
}
return {'text': str(ai), 'citations': []}
def create_baseline(queries: list) -> dict:
baseline = {}
for q in queries:
overview = fetch_ai_overview(q)
baseline[q] = {
'date': datetime.date.today().isoformat(),
'text_hash': hashlib.md5(overview['text'].encode()).hexdigest(),
'text': overview['text'][:500],
'citations': overview['citations'],
}
print(f'{q}: {len(overview["text"])} chars, {len(overview["citations"])} citations')
with open(BASELINE_FILE, 'w') as f:
json.dump(baseline, f, indent=2)
return baseline
baseline = create_baseline(QUERIES)Step 2: Daily search with AI Overview parsing
Run daily searches and parse the current AI Overview content for comparison.
Python
def daily_scan(queries: list) -> dict:
current = {}
for q in queries:
overview = fetch_ai_overview(q)
current[q] = {
'date': datetime.date.today().isoformat(),
'text_hash': hashlib.md5(overview['text'].encode()).hexdigest(),
'text': overview['text'][:500],
'citations': overview['citations'],
}
return current
current = daily_scan(QUERIES)
for q, data in current.items():
print(f'{q}: {len(data["citations"])} citations')Step 3: Diff citations
Compare current citations against baseline to find additions and removals.
Python
def extract_urls(citations: list) -> set:
urls = set()
for c in citations:
if isinstance(c, dict):
urls.add(c.get('url', c.get('link', '')))
elif isinstance(c, str):
urls.add(c)
return urls
def diff_citations(baseline_entry: dict, current_entry: dict) -> dict:
base_urls = extract_urls(baseline_entry.get('citations', []))
curr_urls = extract_urls(current_entry.get('citations', []))
added = curr_urls - base_urls
removed = base_urls - curr_urls
text_changed = baseline_entry.get('text_hash') != current_entry.get('text_hash')
return {
'added': list(added),
'removed': list(removed),
'text_changed': text_changed,
'has_changes': bool(added or removed or text_changed),
}
# Example:
base = {'citations': [{'url': 'https://a.com'}, {'url': 'https://b.com'}], 'text_hash': 'abc'}
curr = {'citations': [{'url': 'https://b.com'}, {'url': 'https://c.com'}], 'text_hash': 'def'}
diff = diff_citations(base, curr)
print(f'Added: {diff["added"]}, Removed: {diff["removed"]}, Text changed: {diff["text_changed"]}')Step 4: Alert on changes
Generate alerts for any detected changes and print a summary report.
Python
def generate_alerts(baseline: dict, current: dict) -> list:
alerts = []
for query in current:
if query not in baseline:
continue
diff = diff_citations(baseline[query], current[query])
if diff['has_changes']:
alert = {
'query': query,
'date': current[query]['date'],
'added_citations': diff['added'],
'removed_citations': diff['removed'],
'text_changed': diff['text_changed'],
}
alerts.append(alert)
print(f'CHANGE DETECTED: {query}')
if diff['added']:
print(f' Added: {diff["added"]}')
if diff['removed']:
print(f' Removed: {diff["removed"]}')
if diff['text_changed']:
print(f' AI Overview text modified')
if not alerts:
print('No changes detected in AI Overviews')
return alerts
alerts = generate_alerts(baseline, current)Step 5: Log change history
Maintain a running log of all detected changes for trend analysis over time.
Python
HISTORY_FILE = 'ai_overview_changes.jsonl'
def log_changes(alerts: list):
if not alerts:
return
with open(HISTORY_FILE, 'a') as f:
for alert in alerts:
f.write(json.dumps(alert) + '\n')
print(f'Logged {len(alerts)} changes to {HISTORY_FILE}')
def read_history() -> list:
entries = []
try:
with open(HISTORY_FILE) as f:
for line in f:
if line.strip():
entries.append(json.loads(line))
except FileNotFoundError:
pass
return entries
# Update baseline after processing
def update_baseline(current: dict):
with open(BASELINE_FILE, 'w') as f:
json.dump(current, f, indent=2)
print('Baseline updated')
log_changes(alerts)
history = read_history()
print(f'Total historical changes: {len(history)}')Python Example
Python
import requests, os, hashlib
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def check_ai_overview(query):
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 str(ai)
citations = ai.get('citations', []) if isinstance(ai, dict) else []
return {'hash': hashlib.md5(text.encode()).hexdigest(), 'citations': len(citations)}
print(check_ai_overview('best search api 2026'))JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function checkAiOverview(query) {
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 {length: text.length, citations: (ai.citations || []).length};
}
checkAiOverview('best search api 2026').then(console.log);Expected Output
JSON
An automated daily system that monitors Google AI Overview changes, detects citation additions/removals, tracks text modifications, and maintains a change history log.