Does AuraCite guarantee rankings or recommendations in AI engines?

No. AuraCite measures configured evidence and helps teams improve source quality, answer clarity, and verification loops. It does not guarantee rankings or recommendations in third-party AI systems.

What evidence labels does AuraCite use?

AuraCite labels evidence as measured, derived, stored, sample, or unknown, depending on whether it comes from a configured run, first-party source data, stored rollups, public samples, or insufficient evidence.

AI visibility methodology

How AuraCite separates evidence from AI visibility guesswork.

AuraCite measures AI visibility by labeling where each signal comes from: Direct/provider-backed measurement, GSC-derived signals, Stored rescore, Zero-cost website evidence, and LLM-judge or proxy evidence. The goal is a decision trail, not an opaque score.

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Evidence labels

Every signal needs a source label.

Label	Meaning	How AuraCite uses it
Measured	Observed through a configured run, API response, Search Console record, or production smoke.	Strongest basis for claims and customer-facing proof.
Derived	Calculated from measured signals, such as GSC query clusters or source influence summaries.	Useful for prioritization when the underlying source is clear.
Stored	Recomputed from existing stored rollups without refreshing external source data.	Useful for safe rescoring, replay checks, and trend interpretation.
Sample	Public sample or demo evidence with a clear date, method, and boundary.	Useful for explaining the method without exposing customer data.
Unknown	Insufficient evidence, missing source context, or unsupported claim.	Do not treat as a ranking fact, citation fact, or recommendation fact.

Direct/provider-backed measurement

Configured AI or search provider runs can observe mentions, citations, answer context, competitors, and source URLs for a defined prompt basket and market.

GSC-derived signals

Google Search Console data helps map real query demand into AI visibility prompt baskets, source opportunities, and content gaps.

Stored rescore

Stored rescore recomputes opportunity logic from existing rollups. It does not refresh external source data by itself.

Zero-cost website evidence

The Free Brand Check evaluates public website readiness signals such as schema, sitemap, robots, llms.txt, titles, and key pages without paid provider calls.

LLM-judge or proxy evidence

LLM-judge and proxy evidence can help explain a hypothesis, but AuraCite labels it separately from direct measurement.

Claim boundary

AuraCite does not guarantee rankings or recommendations in ChatGPT, Google AI Mode, Perplexity, Gemini, Claude, or any third-party AI system. The platform measures configured evidence, explains source gaps, and helps teams run governed improvement loops.