Compare how AI models describe your brand.

What we analyse

We fetch the page as a crawler would, without executing your client-side application, then isolate the text an engine can actually quote: headings, paragraphs, lists, tables and structured data. Anything that only appears after JavaScript hydration is invisible in this view, exactly as it is to most retrieval pipelines.

From that text we assess answer density — how quickly a reader reaches a direct statement — and identify passages that are self-contained enough to be lifted into an answer without surrounding context.

• Server-rendered text volume and structure
• Presence of direct, quotable answer passages
• Heading clarity and question coverage
• Structured data available for grounding
• Content that depends on JavaScript to appear

Why answers get attributed elsewhere

When a page buries its answer under marketing copy, models frequently cite a competitor, a forum thread or a review site that stated the same fact plainly in the first sentence. Attribution follows clarity, not authority alone.

Ambiguous pronouns and unnamed subjects are the other common cause. A sentence that says 'our platform supports this' is not attributable; one that says 'Reflyma tracks brand mentions across ChatGPT, Gemini, Claude and Perplexity' is.

How to improve a weak preview

Lead each section with the answer and follow with the reasoning. Name your product and the entity explicitly in the sentence that carries the claim. Keep definitional statements short enough to survive being quoted in isolation.

Then make sure that text is in the HTML the server sends. Rendering the same copy through a client-side framework alone is the single most common reason a well-written page performs badly here.

• Answer first, elaborate second
• Name the entity in every key claim
• Use question-shaped headings
• Server-render the text, do not hydrate it in
• Add dates and figures a model can attribute

Preview versus live measurement

This tool inspects one page and shows what is extractable. It does not query live models or predict whether an engine will cite you — that depends on prompt wording, competitor coverage and provider-specific retrieval.

For measured results, Reflyma runs real prompts against real providers on a schedule and records mentions, citations and positions with the raw response stored as evidence.