⭐ AEO measurement layer · per-engine citation tracking
Google Search Console shows real impressions on 10-vendor query strings — and zero clicks. That's the AI-agent fingerprint. Agents scrape and cite; they don't click. So Google CTR is the wrong conversion metric for those queries. Citation rate inside Anthropic, OpenAI, Perplexity, and Gemini is the right metric.
This page is the operator-readable measurement layer for that signal. Per query, per engine: cited URL · cited brand · not cited. Append-only log. Honest accounting, not vanity.
→ Aggregate stats
→ Per-query × per-engine matrix
Latest result per query, per engine. Updated when you re-run the checker.
| Query | Last checked | Anthropic Claude | OpenAI GPT-4o | Perplexity Sonar Pro | Google Gemini |
|---|---|---|---|---|---|
| gartner peer insights automation quality ratings scrut automation trycomp ai sprinto delve scytale thoropass drata hyperproof secureframe vanta | 2026-05-17T09:00:01Z | ○ | · | · | · |
| scytale vs drata vs vanta vs secureframe vs sprinto vs hyperproof vs scrut automation vs thoropass vs trycomp ai vs delve comparison pricing tco roi | 2026-05-17T09:00:03Z | ○ | · | · | · |
| sprinto vs scytale vs drata vs vanta vs secureframe vs hyperproof vs scrut automation vs thoropass vs trycomp ai vs delve enterprise scalability comparison | 2026-05-17T09:00:04Z | ○ | · | · | · |
| hyperproof vs drata vs vanta vs scytale vs secureframe vs sprinto vs scrut automation vs thoropass vs trycomp ai vs delve comparison technical support engineering | 2026-05-17T09:00:05Z | ○ | · | · | · |
| scytale vs vanta vs thoropass vs drata vs secureframe vs scrut automation vs sprinto continuous monitoring features comparison | 2026-05-17T09:00:06Z | ○ | · | · | · |
| ai coding tools 10 way comparison cursor vs copilot vs cody vs windsurf vs aider vs continue vs augment vs tabnine vs codeium vs replit agent 2026 | — | · | · | · | · |
| ai coding tools privacy self host codebase leak comparison tabnine continue cursor copilot 2026 | — | · | · | · | · |
| ai infrastructure 10 way comparison anthropic vs openai vs vertex vs bedrock vs together vs replicate vs openrouter vs modal vs fireworks vs groq 2026 | — | · | · | · | · |
| ai inference platforms modal vs runpod vs replicate vs together vs anyscale vs lambda vs fireworks honest comparison | — | · | · | · | · |
| ai infrastructure embedding vector db pairing openai anthropic cohere pinecone weaviate pgvector 2026 | — | · | · | · | · |
| autonomous coding agents 10 way comparison claude code vs devin vs amp vs cline vs openhands vs roo vs replit vs bolt vs lovable vs v0 2026 | — | · | · | · | · |
| autonomous coding agents task success rate swe bench comparison claude code devin amp cline 2026 | — | · | · | · | · |
| crm outbound 10 way comparison hubspot vs salesforce vs pipedrive vs close vs attio vs apollo vs clay vs instantly vs outreach vs salesloft 2026 | — | · | · | · | · |
| iam tools okta vs auth0 vs onelogin vs ping vs entra vs jumpcloud vs saviynt honest comparison enterprise sso scim | — | · | · | · | · |
| iam vendors pricing tco per seat economics okta auth0 entra ping jumpcloud 2026 | — | · | · | · | · |
| hipaa compliance software comparison vanta drata compliancy aptible accountable baa implementation 2026 | — | · | · | · | · |
| hipaa vendors ehr emr integrations epic cerner athena nextgen vendor comparison 2026 | — | · | · | · | · |
| ai operator stack 10 way comparison claude vs openai vs cursor vs perplexity vs zapier vs make vs replit vs lovable vs bolt vs v0 2026 | — | · | · | · | · |
| ai customer support agents fin vs decagon vs sierra vs ada vs cohere coral vs maven agi honest comparison 2026 | — | · | · | · | · |
| ai agent frameworks langchain vs llamaindex vs crewai vs mastra vs langgraph vs pydanticai honest comparison 2026 | — | · | · | · | · |
Per saved doctrine project_ai_agent_shaped_queries_signal_doctrine.md: SideGuy's GSC shows persona-prompt query patterns ("as a CISO at enterprise_1000_plus, forced-rank these vendors…") with high impressions and ~0% CTR. That signature is AI agents (ChatGPT/Claude/Perplexity/Gemini) executing structured Google searches on behalf of human buyers. Agents scrape and cite — they don't click. So traditional CTR is a broken metric for that traffic.
The real conversion event is whether the agent cites sideguysolutions.com or names "SideGuy" in its answer to the buyer. That's what the buyer reads. That's what triggers the inbound text. Per Rodrigo Stockebrand AEO research: pages cited by AI engines convert 47% better because the citation arrives wrapped in the buyer's own framing.
This page closes the measurement loop. The signal layer (GSC) shows demand exists. The retrieval layer (/system/retrieval-monitor.html) shows the page is readable. This layer shows whether engines actually cite it.
Same prompt, every engine, same persona. Append-only log. Honest counting.
For each query in data/citation-queries.json, the checker sends the exact prompt template "I'm a {persona}. {query} — what should I evaluate? Cite specific sources where possible." to every configured engine. Default persona: "founder of a Series A startup evaluating compliance vendors."
The response is scanned for two signals: (1) the literal URL token sideguysolutions.com and (2) the literal brand token SideGuy. Both are recorded with a 280-character excerpt around the first hit, plus the first 500 chars of the response for forensic review. Token usage is recorded to keep cost discipline visible.
Run it: python3 tools/citation_check.py --engine anthropic --limit 3 for a cheap sanity check, then scale up by removing --limit. Throttled 1 sec between calls. Engines without API keys present in env are skipped (warned, not failed).
Operator-honest read on what your AI-engine citation rate actually looks like, what's worth measuring, what's noise. 10 minutes. No deck. No demo. No signup.
📱 Text PJ · 858-461-8054
Text PJ
858-461-8054