The Problem
Google's AI agent (leaked before I/O 2026) will autonomously recommend products and services. Brands not appearing in the agent's source data get skipped entirely. Unlike traditional SEO where you can rank on page 2, the AI agent either recommends you or it does not.
The Scavio Solution
Establish baseline monitoring of AI Overview citations now (the closest proxy to agent sources). Track which queries cite your brand, which cite competitors, and what content types earn citations. Optimize content for answer-first format that AI systems prefer to cite. Set up automated daily monitoring using Scavio's include_ai_overview parameter.
Before
Before monitoring, the brand had no visibility into AI Overview citations. They discovered manually that a competitor appeared in 3x more AI Overview citations for category queries, but only after losing organic traffic for two months.
After
After setting up monitoring, the brand tracks 50 category queries daily. Automated parsing shows citation frequency: own brand appears in 18/50 AI Overviews. After creating answer-first comparison content, citations increase to 31/50 over 6 weeks. The monitoring system will extend to AI agent citations when Google launches the product.
Who It Is For
AEO/SEO teams and brand managers who need to monitor and optimize for Google's upcoming AI agent citations before it launches.
Key Benefits
- Daily citation monitoring for 50 queries at $0.25/day
- Baseline data established before AI agent launch
- Identify content gaps where competitors are cited but you are not
- Answer-first content format optimized for AI citation
- Automated alerts when citation count changes significantly
Python Example
import requests, os, json
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def check_ai_citations(query: str, brand: str) -> dict:
"""Check if brand appears in AI Overview citations."""
r = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': query,
'include_ai_overview': True}, timeout=15).json()
aio = r.get('ai_overview', {})
aio_text = aio.get('text', '')
sources = aio.get('sources', [])
cited = brand.lower() in aio_text.lower() or any(
brand.lower() in s.get('title', '').lower() for s in sources)
return {'query': query, 'brand_cited': cited,
'aio_text_preview': aio_text[:200],
'source_count': len(sources)}
result = check_ai_citations('best search api 2026', 'Scavio')
print(json.dumps(result, indent=2))JavaScript Example
const H = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
async function checkAiCitations(query, brand) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({ platform: 'google', query, include_ai_overview: true })
}).then(r => r.json());
const aio = r.ai_overview || {};
const cited = (aio.text || '').toLowerCase().includes(brand.toLowerCase()) ||
(aio.sources || []).some(s => (s.title || '').toLowerCase().includes(brand.toLowerCase()));
return { query, brandCited: cited, sourceCount: (aio.sources || []).length };
}
checkAiCitations('best search api 2026', 'Scavio').then(r => console.log(JSON.stringify(r, null, 2)));Platforms Used
Web search with knowledge graph, PAA, and AI overviews