AEO Strategy: How Agencies Track LLM Citations
Agencies building AEO practices need to track Google AI Overview citations, measure citation share, and monitor competitors.
Agencies building AEO (Answer Engine Optimization) strategies need to track where LLMs cite their clients in AI-generated answers. This means monitoring Google AI Overviews, checking which domains appear in citation lists, and measuring citation share over time. The agencies that track LLM citations in 2026 win the contracts that traditional SEO agencies miss entirely.
What LLM citation tracking means
When a user asks Google a question and gets an AI Overview, that overview cites specific websites. When someone asks ChatGPT or Perplexity, those tools cite sources too. AEO strategy is about getting your client cited in these AI-generated answers -- and tracking whether that is happening.
Tracking Google AI Overview citations
import os, requests, json
from datetime import datetime
from collections import defaultdict
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
HEADERS = {"x-api-key": SCAVIO_KEY}
def track_citations(domain: str, keywords: list) -> dict:
"""Track how often a domain appears in AI Overview citations."""
stats = {
"domain": domain,
"date": datetime.utcnow().strftime("%Y-%m-%d"),
"keywords_checked": len(keywords),
"aio_present": 0,
"domain_cited": 0,
"competitor_citations": defaultdict(int),
"details": [],
}
for kw in keywords:
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=HEADERS,
json={"query": kw, "num_results": 10,
"include_ai_overview": True},
)
data = resp.json()
ai_overview = data.get("ai_overview", {})
if not ai_overview:
stats["details"].append({
"keyword": kw, "aio": False, "cited": False
})
continue
stats["aio_present"] += 1
citations = ai_overview.get("citations", [])
cited = any(domain in str(c) for c in citations)
if cited:
stats["domain_cited"] += 1
# Track competitor citations
for c in citations:
c_str = str(c)
if domain not in c_str:
# Extract domain from citation URL
for part in c_str.split("/"):
if "." in part and " " not in part:
stats["competitor_citations"][part] += 1
break
stats["details"].append({
"keyword": kw,
"aio": True,
"cited": cited,
"total_citations": len(citations),
})
stats["citation_rate"] = (
stats["domain_cited"] / max(stats["aio_present"], 1) * 100
)
stats["competitor_citations"] = dict(stats["competitor_citations"])
return statsBuilding the AEO dashboard data
def aeo_dashboard(clients: list) -> list:
"""Generate AEO metrics for all agency clients."""
dashboard = []
for client in clients:
stats = track_citations(client["domain"], client["keywords"])
dashboard.append({
"client": client["name"],
"citation_rate": f"{stats['citation_rate']:.1f}%",
"aio_coverage": f"{stats['aio_present']}/{stats['keywords_checked']}",
"top_competitor": max(
stats["competitor_citations"].items(),
key=lambda x: x[1], default=("none", 0)
)[0],
})
return dashboard
clients = [
{"name": "Client A", "domain": "clienta.com",
"keywords": ["best crm software", "crm for small business",
"crm vs spreadsheet"]},
{"name": "Client B", "domain": "clientb.com",
"keywords": ["project management tool", "agile project management",
"remote team collaboration"]},
]
report = aeo_dashboard(clients)
for r in report:
print(f"{r['client']}: {r['citation_rate']} citation rate, "
f"AIO coverage {r['aio_coverage']}")What agencies charge for AEO services
- AEO audit (one-time): $500-2,000
- Monthly AEO monitoring + recommendations: $300-1,000/month
- Full AEO strategy + content optimization: $2,000-5,000/month
- Data cost: $5-50/month per client depending on keyword count
Content optimization for citations
Getting cited in AI Overviews is not about schema markup or technical SEO tricks. It is about content structure: answer the question directly in the first paragraph, use clear headers, provide specific data points, and cite your own sources. AI models prefer content that is structured as direct answers rather than content that buries the answer after an introduction.
Tracking over time
import csv
from pathlib import Path
def append_to_history(stats: dict, csv_path: str = "aeo_history.csv"):
"""Append daily stats to a CSV for trend tracking."""
file_exists = Path(csv_path).exists()
with open(csv_path, "a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=[
"date", "domain", "keywords_checked",
"aio_present", "domain_cited", "citation_rate"
])
if not file_exists:
writer.writeheader()
writer.writerow({
"date": stats["date"],
"domain": stats["domain"],
"keywords_checked": stats["keywords_checked"],
"aio_present": stats["aio_present"],
"domain_cited": stats["domain_cited"],
"citation_rate": f"{stats['citation_rate']:.1f}",
})
# Run daily via cron, build trend data over weeks/months
stats = track_citations("clienta.com", ["best crm", "crm software"])
append_to_history(stats)Key takeaway
AEO is the next service line for digital agencies. The data infrastructure costs under $50/month per client. The service commands $300-5,000/month. Agencies that build this capability now own the market before enterprise tools add it as a checkbox feature.