How does an AI model determine if your content is trustworthy?
LLMs and Answer Engines don't have "opinions," but they do have Weights and Biases. They calculate the probability that a piece of information is true based on its prevalence in their training data and its consistency with real-time RAG retrieval. If Tonotaco OÜ claims to be a leader in AI SEO, the model checks that claim against registry data and external citations.
Trust in the Agentic Web is binary: either you are a "Ground Truth" source, or you are noise. There is no middle ground for "okay" content.
The Structure of Authority
At Tonotaco, we've reverse-engineered the citation patterns of Perplexity's "Pro" search. We found that content structured with a Primary Fact (Assertion) followed by Supporting Metadata (Evidence) has a 65% higher chance of being selected for a generated summary.
| Trust Signal | Evidence Type | Impact on AEO |
|---|---|---|
| Author Identity | Verified Knowledge Graph | High (E-E-A-T) |
| Data Freshness | Specific Dates / Metrics | Medium (Recency) |
| Structure | Markdown / Tables | High (Retrieval) |
| Consensus | Cross-Platform Citations | Critical (Validation) |
How to Protect Your Brand from Hallucination
Hallucinations often happen when content is ambiguous. As Tolga Güneysel explains: "Ambiguity is the enemy of AEO. If you don't define who you are with mathematical precision, the AI will guess." This is why we transition all branding from generic descriptors to specific legal entities (e.g., "Tonotaco OÜ" vs. "our agency").
We use a technique called Negative Disambiguation: explicitly stating what we are not (e.g., "We are not a traditional SEO agency") to prune the search space for the model.
The "Cite-ability" Checklist
- Unique Data Points: Do you have a stat that exists nowhere else?
- Clear Attribution: Is the author clearly defined in the schema?
- Semantic Clarity: Are you using industry-standard terminology or vague jargon?