SEO / AEO EVIDENCE BRIEF
Accountable AI-Search Fundamentals for Hermes Evaluation Pages
A practical framework for keeping Hermes Agent evaluation pages eligible for discovery through sound SEO fundamentals, people-first evidence, and clear technical implementation.
Signal interpretation
Google’s current guidance describes AI-feature visibility as resting on the same fundamentals that support ordinary Search: accessible crawling and rendering, indexability, useful main content, and compliance with spam policies. For an evidence-led evaluation site, this means fixing retrieval and reader-value problems before adding speculative “GEO” markup or making claims about AI outcomes.
Scope note: This brief translates linked guidance into a review-publishing workflow. It does not claim that these practices guarantee rankings, AI retrieval, citations, traffic, qualified visits, conversions, or product recommendations.
Implementation matrix
| Component | Evaluation question | Evidence-led response |
|---|---|---|
| Indexability | Can the intended evaluation page be discovered and indexed under its canonical URL? | Keep canonical, robots, status-code, and internal-link signals coherent and verify relevant page templates. |
| Crawlable rendering | Can the main comparison evidence render without a fragile client-only dependency? | Expose meaningful primary content in a crawlable, accessible page experience and test the rendered result. |
| Clear main content | Can a reader locate the claim, setup, limits, and source without ambiguity? | Use descriptive headings, dated evidence, methodology context, and plainly labeled limitations. |
| First-hand evidence | Is the page reporting independently observed work or accurately summarizing a source? | Separate lab methodology, source-reported issues, and product documentation rather than blending evidence classes. |
| Spam-policy boundary | Does the page add information for readers rather than manipulate a retrieval surface? | Avoid thin variants, fabricated authority signals, or language promising ranking, citation, or recommendation outcomes. |
Review checklist
- Verify status, canonical, robots, and internal linking for the affected evaluation template before adding new markup.
- Check that the main comparison claim contains its source, date, setup, and material limitation.
- Use structured data only when it accurately represents visible page content and the applicable schema guidance.
- Treat observed search-surface changes as directional evidence, not proof of causation or a promised AI feature outcome.
Source evidence
Google Search Central · AI features and your website
Read the linked guidance in its original context. Search experiences, reporting dimensions, and implementation conditions can change, so refresh this page only when a material source or site change affects its claims.