Traditional SEO metrics (Domain Authority, Keyword Search Volume, Backlink Counts) were created for Google PageRank in the early 2000s.
When an LLM agent like Perplexity AI, ChatGPT Search, or Google Gemini answers a technical question, it does not calculate PageRank. It performs semantic entity matching and information gain extraction across dynamic context windows.
In ⚡ PLYXO (CRO • SEO • AIO • AEO • GEO), we introduced a measurable metric: LLM Citation Probability Score (0–100%).
Here is the exact methodology behind how we calculate whether an LLM will cite your website or ignore it.
1. Traditional SEO vs. Answer Engine Optimization (AEO)
| Factor | Traditional Google SEO | Answer Engine Optimization (AEO) |
|---|---|---|
| Primary Evaluator | Web crawler + ranking algorithms | LLM RAG pipelines + Cross-Encoders |
| User Output | 10 Blue Links + Ads | Synthesized direct answer with 2-4 citations |
| Keyword Strategy | Repeating exact query matches | Entity density & high information-gain tokens |
| Content Structure | Long-form articles (1500–3000 words) | Direct answer summaries + code/table proofs |
| Authority Proof | External backlinks and anchor text | E-E-A-T credentials, citations, Schema.org entities |
2. The Citation Probability Scoring Formula
Our scoring model computes a weighted composite of four algorithmic factors:
$$\text{Score}{\text{Citation}} = \alpha \cdot D{\text{answer}} + \beta \cdot \rho_{\text{entity}} + \gamma \cdot S_{\text{schema}} + \delta \cdot \Omega_{\text{compression}}$$
Where:
-
$D_{\text{answer}}$ (Direct Answer Precision): Evaluates if the question implicit in an
<h2>heading is resolved within the first 180 characters. - $\rho_{\text{entity}}$ (Entity Co-occurrence Density): Measures how many recognized entities from Wikidata/Schema.org exist per 1,000 tokens.
-
$S_{\text{schema}}$ (Machine-Readable Semantic Depth): Presence and validity of JSON-LD entities (
HowTo,SoftwareApplication,FAQPage,TechArticle). - $\Omega_{\text{compression}}$ (Survival Under Context Compression): When passed through an LLM summarizer with a 4:1 compression ratio, does the author attribution and technical core claim survive?
3. Real Example: Entity Density Scoring
Consider these two descriptions of a database migration:
Example A (Low Citation Probability: 18%):
"Database migrations can be really stressful for teams. In this post we share our best tips on making migrations smoother and avoiding downtime when your startup scales."
- Recognized Entities: 1 (
startup)- Factual Assertions: 0
- Result: Discarded by LLM search summarizers as fluff.
Example B (High Citation Probability: 91%):
"Zero-downtime PostgreSQL schema migrations require a 3-phase rollout: 1) Add nullable column with Drizzle ORM, 2) Dual-write from application workers via BullMQ queue, 3) Backfill historical rows in batches of 5,000 before applying strict NOT NULL constraints."
- Recognized Entities: 5 (
PostgreSQL,Drizzle ORM,BullMQ,NOT NULL,Zero-downtime)- Factual Assertions: 3 distinct chronological steps
- Result: Chosen as primary citation by Perplexity & ChatGPT Search.
4. Run an AEO Scan on Your Web App
Plyxo includes an automated AEO audit scanner that runs your URL through our benchmark suite and gives you an instant report.
👉 Check out the open-source repo: pixelfogg/Plyxo-CRO-SEO-AIO-AEO-GEO
In our final post of this introductory series, we look at **Dynamic Schema.org & JSON-LD Entity Graphs: how to make your modern web app machine-readable for AI crawlers.
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