The Problem
Google published its official Generative Engine Optimization guide in 2026, but most content teams have no way to audit their existing pages against it at scale. Manually checking whether each page includes structured data, entity markup, concise answer blocks, and citation-worthy formatting takes hours per page. With hundreds or thousands of pages, manual audits are impossible. Teams end up guessing which pages need GEO optimization rather than measuring.
The Scavio Solution
Build an automated audit pipeline that searches Google for each of your target keywords, checks whether your content appears in AI Overviews, and flags pages missing GEO-critical elements. Scavio's Google endpoint returns AI Overview content, Knowledge Graph panels, and People Also Ask data as structured JSON, letting you programmatically compare your pages against what Google actually surfaces.
Before
Before: A content team manually spot-checked 20 pages per week against Google's GEO guide. Each check took 15 minutes of browser work. Coverage was 4% of their 500-page library. They had no data on which pages appeared in AI Overviews.
After
After: An automated pipeline audits all 500 pages daily in under 30 minutes. Each keyword check costs $0.005. Monthly cost: $75 for 500 keywords checked daily. The team sees a dashboard showing AI Overview citation rates, missing structured data, and GEO compliance scores.
Who It Is For
Content teams and SEO managers who need to audit their content library against Google's 2026 GEO guide at scale. Anyone tracking whether their pages appear in AI Overviews.
Key Benefits
- Audit 500+ pages daily against Google's GEO guide for $75/mo
- Detect AI Overview citations automatically with structured JSON parsing
- Flag pages missing GEO-critical elements (entity markup, answer blocks)
- Track GEO compliance trends over time with daily snapshots
- Replace 120+ hours/mo of manual checking with a 30-minute cron job
Python Example
import requests
import json
API_KEY = "your_scavio_api_key"
def audit_keyword(keyword: str, target_domain: str) -> dict:
r = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": keyword, "ai_overview": True},
timeout=15,
)
data = r.json()
ai_overview = data.get("ai_overview", {})
cited = target_domain in json.dumps(ai_overview)
organic_pos = None
for item in data.get("organic", []):
if target_domain in item.get("link", ""):
organic_pos = item["position"]
break
return {
"keyword": keyword,
"ai_overview_present": bool(ai_overview),
"cited_in_ai_overview": cited,
"organic_position": organic_pos,
"paa_count": len(data.get("people_also_ask", [])),
}
keywords = ["best search api for agents", "llm grounding api"]
for kw in keywords:
result = audit_keyword(kw, "scavio.dev")
print(f"{result["keyword"]}: AI Overview={result["ai_overview_present"]}, Cited={result["cited_in_ai_overview"]}, Pos={result["organic_position"]}")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function auditKeyword(keyword, targetDomain) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "google", query: keyword, ai_overview: true }),
});
const data = await res.json();
const aiOverview = data.ai_overview || {};
const cited = JSON.stringify(aiOverview).includes(targetDomain);
const organic = (data.organic || []).find(r => r.link.includes(targetDomain));
return {
keyword,
aiOverviewPresent: !!data.ai_overview,
citedInAiOverview: cited,
organicPosition: organic?.position ?? null,
paaCount: (data.people_also_ask || []).length,
};
}
const keywords = ["best search api for agents", "llm grounding api"];
for (const kw of keywords) {
const r = await auditKeyword(kw, "scavio.dev");
console.log(`${r.keyword}: AI Overview=${r.aiOverviewPresent}, Cited=${r.citedInAiOverview}, Pos=${r.organicPosition}`);
}Platforms Used
Web search with knowledge graph, PAA, and AI overviews