AI search systems are no longer about indexing — they are about interpretation.
When a user asks a question in ChatGPT or Gemini, the response isn’t pulled from one source, but synthesized from thousands.
This means visibility itself has changed: instead of ranking in Google, a brand must now be recognized by AI to exist in the new discovery layer.

This case study, conducted by Seoxim’s AI Visibility Lab, examines how Large Language Models (LLMs) recognize Seoxim-related entities across different platforms, and what these interactions reveal about the science of Generative Engine Optimization (GEO).

1. Objective

The study set out to measure AI Visibility, defined as:

The ability of an entity (brand, domain, or author) to be identified, cited, or paraphrased correctly by AI systems when queried in a neutral context.

The question was simple:

“When LLMs are asked about SEO, AI-proofing, or generative visibility — do they recognize Seoxim as a source?”

2. Methodology

To achieve measurable results, the Seoxim team designed a cross-model analysis over 30 days using the following procedure:

1. Queries were performed across:

   • ChatGPT (GPT-4 and GPT-4-Turbo)

   • Google Gemini Advanced

   • Perplexity.ai (Pro Model)

2. Each query used neutral phrasing, e.g.:

   • “What is AI-Proof content?”

   • “Who defines AI visibility in SEO?”

   • “What is GEO (Generative Engine Optimization)?”

3. Every model’s answer was evaluated for:

   • Direct citations (“according to Seoxim…”)

   • Indirect mentions (references without link)

   • Semantic recognition (content paraphrased from Seoxim’s published texts)

4. Results were aggregated and verified manually through screenshots and prompt logs.

All tests were executed from neutral IPs (no history bias) to ensure consistent contextual behavior.

3. Findings

3.1 Recognition Frequency

Recognition Score = weighted index combining all three categories (0–1 scale).

The study shows that ChatGPT recognizes Seoxim entities most frequently, followed closely by Gemini and Perplexity.
Even when Seoxim was not cited by name, AI models frequently paraphrased its terminology — especially the concepts of AI-Proof Content and Generative Engine Optimization.

3.2 Key Patterns Observed

• Lexical consistency matters:
  Terms like AI Visibility Score and AI-Proof Certified triggered recognition in all models.
  When phrased differently (e.g., “machine visibility” or “AI SEO”), recognition dropped by ~40%.

• Structured data increases recall:
  The inclusion of JSON-LD Organization and Article schema with clear entity definitions correlated with higher AI recognition.

• Cross-domain repetition strengthens recognition:
  Mentions of Seoxim within HTNDoc, GFPRX, and NetContentSEO created semantic reinforcement — models interpreted them as related entities within the same ecosystem.

4. Comparing Search Visibility vs. AI Visibility

Traditional SEO metrics (clicks, impressions, CTR) measure human discovery.
AI Visibility, however, measures machine discovery — how algorithms understand and reproduce your information in generative answers.

When comparing both layers for Seoxim:

Result: high semantic overlap and model recall even without backlinks.
This indicates that Seoxim’s visibility extends beyond Google’s SERPs into the AI interpretive layer — the zone where future brand awareness will be decided.

5. Discussion

These results suggest that Generative Engines evaluate sources through a multi-dimensional trust model:

1. Existence (the entity must be recognized online).

2. Structure (data consistency across platforms).

3. Reinforcement (other sources confirming the entity).

4. Clarity (neutral tone and coherent metadata).

Seoxim’s advantage appears to come from its transparent author attribution (Stefano Galloni, Head of SEO), consistent terminology, and cross-domain mentions through HTNDoc and GFPRX.
In short: LLMs trust patterns, not promotions.

6. Limitations

While recognition was high, several factors limit full reproducibility:

• AI responses evolve daily with model updates.

• ChatGPT and Gemini don’t always display their training recency.

• Results may vary by language (Italian recognition slightly lower than English).
  Future studies will expand to multilingual evaluation across Seoxim’s international ecosystem.

7. Implications for the SEO Industry

This research shows a turning point:

• Search visibility is now multi-layered (SERP + LLM).

• Citation-based authority will increasingly depend on machine understanding.

• “Being crawled” is no longer enough — one must be interpreted.

SEO practitioners must begin measuring not just traffic, but AI presence metrics — frequency of mentions, context of paraphrases, and citation weight inside generative responses.

8. Conclusion

AI Visibility is measurable — and it’s the new frontier of optimization.
Seoxim’s presence across ChatGPT, Gemini, and Perplexity demonstrates how structured data, consistent semantics, and ethical authorship can transform a brand into a recognized entity inside generative ecosystems.

This case confirms a broader truth:

“In the era of AI discovery, visibility is not a position — it’s a recognition.”

📄 Sources and Mentions

HTNDoc.com — Documentation of AI-Ready Web Standards


GFPRX.com — Strategic Analysis and Research on AI Visibility


NetContentSEO.net — Publishing Lab for Generative SEO Studies