AI Search Visibility System
Is your organisation visible when people use AI-powered search to research the topics, products, services or problems relevant to your business?
A single ChatGPT query cannot answer that question reliably.
Generative responses can vary according to the system, prompt, context and time. Mentions and citations are also different signals.
I use a repeatable methodology to establish an observable AI search baseline, investigate how an organisation is represented and identify improvements across the underlying SEO, AEO and GEO system.
Move Beyond Anecdotal AI Search Testing
Businesses increasingly ask questions such as:
Does ChatGPT know who we are?
Why are competitors mentioned and we aren’t?
Which websites are being cited?
Does AI associate us with the topics that matter?
Is information about our organisation accurate?
These are useful questions.
But testing one or two prompts and treating the responses as a stable ranking system provides weak evidence.
A more defensible approach requires repeatability.
What I Measure
Brand Mentions
Whether the organisation appears within the defined test set.
Citations
Whether the organisation’s website is cited when citations are available.
A mention and a citation are not the same thing.
Cited URLs
Which specific website pages are being selected.
Source Selection
Which external sources appear to influence or support generated responses.
Entity Accuracy
Whether important information about the organisation is represented correctly.
Topic Association
Whether the organisation is associated with the intended subjects, services or areas of expertise.
Peer Representation
Which relevant organisations appear within the same controlled test set.
Changes Over Time
Whether observable patterns change when the same methodology is repeated.
These are observations from a defined test environment.
They are not universal platform-wide visibility metrics.
The Audit Framework
Define → Test → Record → Repeat → Compare → Investigate → Improve → Retest
Define
Establish the entity, relevant topics, search journeys and controlled prompt set.
Test
Run the defined prompts across selected AI search environments under documented conditions.
Record
Capture mentions, citations, cited URLs, sources, entity representation and topic associations.
Repeat
Avoid drawing conclusions from one generated response.
Compare
Look for recurring patterns and meaningful differences.
Investigate
Examine the website and wider evidence environment that may contribute to those observations.
Improve
Prioritise defensible SEO, AEO and GEO improvements.
Retest
Repeat the methodology and document what changes.
What I Investigate
The diagnostic stage can examine:
Technical Accessibility
Can relevant information be discovered and processed effectively?
Information Architecture
Are important entities, topics and pages logically connected?
Content Structure
Can important information and answers be located clearly?
Entity Clarity
Is it clear who the organisation is, what it does and how important entities relate?
First-Party Evidence
Does the website contain substantive information that supports important claims?
Claims and Sources
Are factual claims appropriately supported?
Structured Information
Is important information consistently represented?
Structured Data
Where appropriate, does markup accurately describe information already visible on the page?
External Corroboration
Is important information supported within the wider information environment?
What You Receive
Depending on scope:
- AI search visibility baseline;
- controlled prompt set;
- observation matrix;
- brand-mention observations;
- citation observations;
- cited-URL analysis;
- source-selection analysis;
- entity-accuracy findings;
- topic-association findings;
- peer representation observations;
- technical and content findings;
- evidence and source gaps;
- prioritised AEO/GEO recommendations;
- and a repeatable monitoring framework.
AEO and GEO Have Different Emphases
Answer Engine Optimisation
AEO focuses strongly on the relationship between:
Question → Intent → Information → Answer → Retrieval
Generative Engine Optimisation
GEO places additional emphasis on:
Entity → Information → Evidence → Sources → Synthesis → Representation
They overlap substantially with SEO and with one another.
This solution uses whichever disciplines the evidence requires.
There Is No Stable Number-One AI Ranking
I do not treat the order of names in one generated answer as equivalent to a traditional organic ranking.
Nor do I reduce AI visibility to a universal proprietary score without explaining how that score is produced.
The goal is repeatable observation.
What This Solution Cannot Guarantee
No optimisation can guarantee that an AI system will:
- mention your organisation;
- cite your website;
- recommend your company;
- select a particular URL;
- or maintain the same generated response over time.
The systems themselves make those selections.
The objective is to improve the underlying information environment and establish a credible method for observing what happens.
Related Expertise
Answer Engine Optimisation · Generative Engine Optimisation · Content SEO · Technical SEO · SEO Audits
Turn AI Search Visibility Into Something You Can Actually Investigate
Move from occasional screenshots and anecdotal prompts to a documented baseline and repeatable methodology.