Methodology
How AIglebot structures prompt tracking, reads AI engine answers, and turns these observations into actionable indicators.
This page explains the main principles used to track a brand’s visibility, reputation and recommendation in AI engines.
The goal is not to claim a perfect or universal measurement, but to offer a clear, reproducible framework that is useful for reading gaps between engines, prompts and competitors.
Principles of the method
Targeted prompts
We start from queries that carry real business value: discovery, comparison, shortlist, recommendation and reassurance questions.
Multi-engine reading
Answers are compared engine by engine, since ChatGPT, Gemini, Perplexity and Claude do not produce the same wording.
Comparative view
The analysis is not conducted in absolute terms. It takes into account competitor presence and the cases where they are recommended in your place.
Tracking over time
A single snapshot is not enough. Tracking must be able to show changes, drifts and improvements after action.
Prompt selection
- Category prompts: how engines answer on your market or your type of offering.
- Comparison prompts: best tools, alternatives, comparisons, shortlists and top lists.
- Recommendation prompts: which player to choose, which solution to recommend, which brand to cite.
- Reassurance prompts: reliability, reviews, pricing, limitations, customer stories, credibility.
How answers are read
Presence and absence
We look at whether the brand appears or not on a query deemed strategic.
Citations and sources
We observe whether the brand is merely mentioned, genuinely cited, or backed by visible third-party sources.
Recommendation
We identify whether the brand is explicitly recommended, placed in a shortlist, or on the contrary replaced by competitors.
Sentiment and framing
We distinguish the tone of the answer, the level of confidence and any caveats rather than reducing this to a simple positive or negative score.
Update cadence
An initial audit is used to define the priority prompts, engines and competitors.
Regular monitoring then makes it possible to track significant variations and framing changes.
The cadence depends on the market, seasonality, the volume of changes and the sensitivity of the topic for the brand.
Limitations to keep in mind
AI answers are not perfectly stable. The same query can vary depending on context or timing.
A single answer does not reflect an entire engine on its own. Several prompts and their evolution must be observed.
Sentiment and framing signals require a qualitative reading. They cannot be reduced to a single universal score.
Read next
GEO audit
See how this methodology translates into an initial diagnosis.
Multi-engine monitoring
See how the method applies in continuous answer tracking.
GEO guide
Go back to the general GEO framework before entering the measurement method.
How to measure GEO performance
See how the method translates into concrete KPIs and readable management.
Brand visibility in AI
Connect the method to the central question of brand presence.
AI competitor tracking
See how the same method applies to comparison against designated competitors.
AIglebotBetaAIglebot helps companies track their visibility, reputation and recommendation in ChatGPT, Gemini, Perplexity and Claude.