AI SEO Audit: Compute Your
Visibility in ChatGPT & Gemini
Stop guessing. We engineered SEO Intel to algorithmically score your website's **Generative Engine Optimization (GEO)**. Our ingestion engines map your domain's Vector Embeddings and RAG Architecture compatibility against exactly how Large Language Models parse data. We don't sell 'magic', we sell mathematical Information Gain that drives Revenue, Traffic Growth, and Brand Safety in the Answer Engine era.
Request Your Free LLM Structural Output Scan
We will email you a JSON-LD validation report and your preliminary LLM Visibility Score within 24 hours.
"Why Am I Not On Google Anymore?"
Understanding the shift from Search to Synthesis
The rules of visibility have fundamentally changed. Your customers are no longer typing keywords into a search bar; they are asking complex questions to AI assistants like ChatGPT, Perplexity, and Google AI Overviews.
Traditional SEO optimized your site for web crawlers looking for repeated keywords (TF-IDF). But an LLM isn't a crawler—it's a neural network. It doesn't read your website; it synthesizes your data to formulate a direct answer. If your website's code isn't structured to mathematically prove its authority to that AI, you simply will not exist in the answer.
Generative Engine Optimization (GEO) is the engineering discipline of structuring your proprietary brand data so that an AI machine confidently retrieves, cites, and recommends you over your competitors. We bridge the gap between your marketing message and the AI's mathematical reality.
The Mathematical Framework
How the LLM Visibility Score is Computed
We are radically transparent about our methodology. The SEO Intel crawler doesn't look at keyword density; it evaluates your codebase as a neural network would. We calculate your final visibility score by synthesizing three distinct technical pipelines:
let semantics = evaluateInformationGain(domainData.NLP_Entities);
// 2. Schema Integrity & Structured Data Validations (Weight: 30%)
let schema = validateJSON_LD(domainData.KnowledgeGraph_Nodes);
// 3. Extracted Entity Linkages (Weight: 30%)
let connections = mapSameAs_Authorities(domainData.Entity_Proximity);
return (semantics * 0.40) + (schema * 0.30) + (connections * 0.30);
Semantic Architecture for AI
Structural formatting dictates machine trust.
The "10 Blue Links" Era
Traditional search engines rely on 'Keyword Density' and brute-force backlinking. In the Neural Information Retrieval era, this unstructured framework provides zero semantic context to an LLM, resulting in brand obscurity or hallucination.
| Processing Metric | Legacy SEO |
|---|---|
| Objective Function | SERP Index Ranking |
| Data Architecture | Unstructured HTML Blobs |
| Crawling Reliance | Keyword Repetition (TF-IDF) |
| Business Outcome | Traffic Attrition / Hallucination |
Generative Engine Optimization
GEO structures your digital assets into discrete Knowledge Graphs. By maximizing **Semantic Information Gain**, we provide the exact mathematical proofs LLMs require to confidently cite and recommend your proprietary brand data.
| Processing Metric | GEO Implementation |
|---|---|
| Objective Function | Direct LLM Zero-Click Citation |
| Data Architecture | Machine-Readable JSON-LD |
| Crawling Reliance | Semantic Triples (Entity Relations) |
| Business Outcome | Dominate AI Overviews / Conversion Growth |
Technical Ingestion Pipeline
How our system scrapes and analyzes your domain
1. Vector Search Emulation
We deploy headless scraping instances to bypass standard robots.txt caching. The system executes an abstract syntax tree (AST) parse of your raw HTML, mapping your explicit H-tag hierarchies and isolating existing JSON-LD objects. It does not look at visuals; it looks solely at data relationships.
2. NLP Entity Extraction
Your parsed text corpus is ingested via Gemini 1.5 Pro to simulate how Answer Engines summarize context. We run a comparison against Wikipedia and established ontological knowledge bases to determine your Entity Proximity. Are you the source of truth, or just noise?
3. RAG Alignment Roadmap
Upon calculating your LLM Visibility Score, the system compiles a JSON/PDF diagnostic
isolating the precise schemas (e.g., Missing DefinedTerm, fragmented FAQ
arrays) and contextual gaps you must resolve to achieve priority extraction via
Retrieval-Augmented Generation workflows.
Compute Your Visibility
Secure your diagnostic data package
The DIY Audit Report
Acquire the raw diagnostic scrape data. Uncover the structural blind spots immediately.
- Semantic Architecture Analysis
- JSON-LD / Knowledge Graph Audit
- Computed LLM Visibility Score
- PDF/JSON Delivery (15 mins)
Strategy Blueprint
The complete diagnostic payload, plus a 1-on-1 architect session to interpret the variables.
- Includes Complete Audit Data
- 45-Min GEO Architecture Consult
- Gemini vs ChatGPT Comparison Parsing
- Prioritized 30-Day Resolution Syntax
- Entity Gap Analysis (Recapture AI Traffic)
Enterprise Deployment
SEO Intel does *not* offer automated code fixes. For "Done For You" codebase refactoring and continuous RAG integration, you must consult directly with our engineering team.
Documentation & Variables
Clarifying Neural Information Retrieval Parameters
How does Neural Information Retrieval differ from traditional SEO?
Traditional SEO relies on keyword density and pagerank algorithms for index placement. Neural Information Retrieval relies on statistical probability, Vector Embeddings, and entity proximity. SEO Intel maps your site's data structure specifically against these LLM citation requirements.
How is the LLM Brand Visibility Score calculated?
The proprietary score is synthesized by evaluating three core metric sets: Semantic Density & Keyword Proximity (40%), JSON-LD Schema Integrity & Validations (30%), and Extracted Entity Linkages to known Knowledge Graphs (30%).
Do you provide API access for continuous Vector Scraping?
Continuous RAG pipeline integration and custom JSON-LD ontology generation are available exclusively for enterprise clients within our Custom 'System Execution' diagnostic tier. The standard tiers execute a one-time point-in-time snapshot.
System Definition: SEO Intel is a custom GEO assessment layer built by Empire AI Studio that algorithmically parses a website's semantic architecture against LLM Vector Search parameters to compute mathematical indexability ratios.
[ Download Sample json_ld_audit_report.pdf ]