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Research summary
The Index at a glance
Each Protocol v1.0 study is a controlled observational snapshot: one exact sector prompt is tested consecutively across five named AI platforms, the visible responses are preserved as evidence, and the combined result is assessed against 14 fixed metrics.
In simple terms
The Index records what five AI systems returned for the same question on a stated date, then measures that evidence consistently. It does not claim that the same answer will appear forever.
Research objective and scope
The parent page is the permanent explainer for the Index. It sets out the research purpose, protocol, metric definitions, evidence rules and limitations; individual child pages document sector-specific tests.
The NeuralAdX UK Business AI Visibility Index is an ongoing observational research programme that records how named UK-sector businesses, organisations and publications are recommended, positioned and cited in live generative AI answers across five major AI platforms.
Each published sector page is a dated retrieval snapshot. It records what the tested systems returned during that specific session. It is not presented as a permanent market ranking, a guarantee of future AI visibility or a claim to know the platforms’ proprietary ranking logic.
In simple terms
We ask the same sector-specific commercial question across five major AI platforms, preserve what they return, and measure the visible results using the same 14 rules every time.
Why the Index exists
AI answer engines can recommend brands, cite websites and present competitive choices directly inside generated responses. The Index exists to make those outputs observable, comparable and evidence-led rather than relying on unsupported visibility claims.
A business can be mentioned without its own website being cited. A company can perform strongly on one AI platform and weakly on another. A citation may be visible while the underlying destination URL remains hidden. Those are materially different outcomes, so the Index measures them separately.
By applying one published framework across different sectors, each child study becomes a comparable evidence record rather than an isolated claim.
Why five platforms?
One engine does not represent the whole AI answer environment. Testing Google AI Mode, ChatGPT, Microsoft Copilot, Claude and Google Gemini allows platform coverage and cross-platform consensus to be measured directly.
How every Index study works
Protocol v1.0 follows the same seven-stage path from sector selection to a published evidence record.
01
Define the UK sector
Choose a clearly bounded business, publication or organisational category and define which returned entities qualify for inclusion in that sector study.
02
Set the exact prompt
Use one sector-specific buyer-style prompt and preserve its exact wording.
03
Test five AI platforms
Submit the same prompt consecutively across the five fixed platforms.
04
Capture primary evidence
Preserve one screenshot per platform plus the live screen-recorded testing session.
05
Build the evidence ledger
Record eligible brands, recommendation positions, mentions, visible citations, identifiable pages and visible supporting domains.
06
Calculate 14 metrics
Apply the locked definitions and denominator rules published on this parent page.
07
Publish the study record
Present the results, evidence, test date, methodology and limitations on a child page.
Scope rule
Unless a child study explicitly states otherwise, Protocol v1.0 records one dated retrieval run per platform for that study. Future retesting may be added, but no fixed retest schedule is promised.
The five AI platforms
Every Protocol v1.0 study uses the same five named retrieval environments. The Index records the visible answers returned in the tested interfaces; it does not claim access to hidden model internals.
01
Google AI Mode
Google’s AI search experience used as one retrieval surface in the Index.
02
ChatGPT
OpenAI’s assistant response environment used as one retrieval surface in the Index.
03
Microsoft Copilot
Microsoft’s assistant/search response environment used as one retrieval surface in the Index.
04
Claude
Anthropic’s assistant response environment used as one retrieval surface in the Index.
05
Google Gemini
Google’s Gemini assistant environment used as one retrieval surface in the Index.
The 14 locked AI visibility metrics
The 14 definitions are the measurement contract for Protocol v1.0. They are grouped below to make the framework easier to understand, but the metric names, formulas and denominator rules remain fixed.
Visibility & competitive presence
How often a brand appears, where it appears and how strongly it competes across the five platforms.
01
Overall AI Visibility Rank
The final competitive order across the combined five-platform test. Brands are ranked first by Cross-Platform Consensus, then Average Brand Position, then Domain Citations, then Brand Mentions. If all four measures are identical, the brands remain tied.
02
Brand Mentions
The total number of eligible times the brand is named in the five generated answer bodies. Citation chips, source cards, reference lists and interface labels are not counted as brand mentions.
03
Share of Voice
The brand’s percentage share of all eligible Brand Mentions recorded in the complete five-platform study. It is calculated by dividing the brand’s mentions by the total eligible mentions across all included brands and expressing the result as a percentage.
04
AI Platform Coverage
How many of the five AI platforms show the brand as a substantive part of the generated answer at least once, rather than only inside a citation chip, source card or interface label. The result is shown as both a number out of five and a percentage. The platform total is always five.
05
Average Brand Position
The brand’s average recommendation position across platforms that actually present it as an answer or recommendation. An explicit numbered rank is used where available; if the answer is unnumbered, visible top-to-bottom recommendation order is used. Incidental mentions are not given a ranking position. Lower is better.
06
Position Consistency
Measures how much the brand’s recommendation position changes across the tested AI platforms. Very High means little or no movement, High means small variation, Moderate means noticeable variation, and Low means substantial variation. Lower variation means greater consistency. For reproducibility, the label is calculated using population standard deviation: Very High covers 0.00 to 0.49, High 0.50 to 0.99, Moderate 1.00 to 1.99, and Low 2.00 or higher.
Citation & source ownership
Whether the brand’s own website is visibly used as a source, how often, and how broadly across platforms.
07
Domain Citations
How many clearly visible citations or source links point to the brand’s own domain across the five captured responses. Hidden, collapsed or unidentified sources are not guessed.
08
Citation Share
The brand’s percentage share of all visible eligible own-domain citations recorded in the complete study. It is calculated by dividing the brand’s Domain Citations by the total visible eligible own-domain citations across all included brands and expressing the result as a percentage.
09
Citation Coverage
How many of the five AI platforms visibly cite the brand’s own domain at least once. The result is shown as both a number out of five and a percentage.
10
Average Citation Position
The average visible position of the brand’s own-domain citation where a citation order can actually be identified. Unordered, collapsed or hidden source sets are not given artificial positions. Lower is better.
11
Cited Pages
How many distinct pages on the brand’s own website can be confirmed from the evidence. If at least one cited page is confirmed but the visible URL detail cannot show whether additional citations lead to different pages, the result is reported as “At least 1 page” rather than giving an unsupported exact number.
12
Citation-Backed Presence
The percentage of the five AI platforms that both surface the brand and visibly cite its own domain. The denominator is always all five platforms: one platform equals 20%, two equal 40%, three equal 60%, four equal 80%, and all five equal 100%.
Evidence breadth & agreement
How diverse the visible third-party support is and how strongly the five AI engines agree on the recommendation.
13
Source Diversity
How many different visible third-party domains support or contribute evidence around the brand’s placement. The brand’s own domain and unidentified hidden sources are excluded.
14
Cross-Platform Consensus
How many of the five AI platforms actually present the brand as an answer or recommendation to the tested prompt. Five out of five is Very High, four is High, three is Moderate, two is Low, one is Very Low, and zero is No Consensus.
Measurement contract
A child study should not change a metric’s meaning, denominator or scoring rule. Child pages may present results in clearer plain-English wording — for example, “At least 1 page” instead of a mathematical symbol — provided the underlying calculation and meaning remain unchanged. Any future methodological change should be published as a new protocol version.
How the Overall AI Visibility Rank is calculated
The Index deliberately avoids an undisclosed composite score. The ordering rule is public so readers can see why one brand is placed above another.
1
Cross-Platform Consensus
Brands recommended by more of the five engines rank ahead.
2
Average Brand Position
If consensus is tied, the lower average recommendation position ranks ahead.
3
Domain Citations
If still tied, the brand with more visibly attributable own-domain citations ranks ahead.
4
Brand Mentions
If still tied, total eligible Brand Mentions is the final tie-breaker.
No hidden weighting
The next criterion is used only when the preceding criterion does not separate the brands. If brands remain exactly tied after all four rules, the tie should be disclosed rather than broken by an unpublished score.
Evidence and transparency standards
The Index quantifies only what the captured material can support. This conservative rule is central to the methodology.
Exact prompt preserved
Every child study displays the exact prompt used in the test.
Position order rule
Use the explicit rank where an AI answer numbers its recommendations. If the recommendations are unnumbered, use their visible top-to-bottom order. Incidental mentions that are not presented as recommendations do not receive a position.
Test date preserved
Every study records the date of the live retrieval session.
Five original screenshots
The visible result from each of the five AI platforms is retained as primary evidence.
Live video evidence
The testing session is screen-recorded so the retrieval sequence can be reviewed alongside the screenshots.
No hidden-source guessing
Collapsed +1/+2 controls, unreadable URLs and unidentified citation targets are not attributed to a brand.
No false URL precision
If an own-domain citation is visible but the destination page cannot be distinguished, Cited Pages is reported conservatively.
All eligible brands counted
Share of Voice and Citation Share denominators include all eligible brands in the captured test, not only the eventual top five.
Sector eligibility rule
An entity is eligible when the captured AI answer presents it as belonging to the defined sector or category being tested. Clearly out-of-category entities, institutional or government resources where the category calls for commercial businesses or publications, individuals shown only as commentators, and incidental references are excluded. Material exclusions should be disclosed on the child study page.
Limitations published
Each child study states that the result is a dated retrieval snapshot that may vary in future runs.
Evidence rule
If the screenshot does not expose enough detail to identify a citation, URL or source confidently, NeuralAdX Ltd does not guess it.
How to interpret an Index study
A live AI retrieval study observes a dynamic system. The aim is to make that observation transparent without pretending it is permanent.
What a study can show
Which eligible brands were surfaced in that session, how they were positioned, how often they were mentioned, which own domains were visibly cited, and how strongly the five tested engines agreed.
What a study cannot prove
It cannot guarantee the same answer indefinitely, reveal proprietary model ranking logic, establish causal reasons for a platform’s selection, or convert an unidentified hidden source into a known citation.
Interpretation boundary
What this Index does not claim
Not permanent: a dated study does not prove that future AI answers will reproduce the same ordering.
Not predictive: the Index does not forecast future visibility, traffic, revenue or commercial performance.
No hidden inference: unseen citations, collapsed sources and internal model reasoning are not treated as observed evidence.
Not official statistics: this is a proprietary NeuralAdX Ltd observational research index, not a government statistical publication or peer-reviewed academic paper.
Retesting
Individual sectors may be tested again in future to examine change over time. Protocol v1.0 does not promise a monthly, quarterly or annual retest schedule; unless a child page says otherwise, its figures refer only to the dated session shown there.
Methodology governance and research principles
The methodology is version-controlled. Plain-English clarifications may be made within the same protocol version where they do not change a metric’s meaning, calculation, denominator, eligibility outcome or ranking logic. Any substantive change to those rules should be published as a new protocol version rather than silently rewriting earlier studies.
Current protocol
Version 1.0
Effective from 19 August 2026. Five AI platforms, 14 locked metrics, evidence-led counting rules and transparent rank ordering.
Status
Proprietary observational index
Produced by NeuralAdX Ltd. It is not official UK statistics, not a peer-reviewed academic publication, and is not endorsed by the tested AI platforms or the external methodological bodies referenced below.
Methodological context
The proprietary NeuralAdX Ltd protocol is informed by general principles of explicit measurement, evidence traceability, transparent statistical communication and reproducible research artefacts. The references below provide methodological context only; they do not validate, certify, audit or endorse the Index.
Research archive
Published Index studies
Each child page is a separate study record beneath this methodology page. New records can be added to the archive in batches; the core protocol does not need to be rewritten when another sector is published.
| Study ID | Sector | Prompt type | Test date | Evidence | Record |
|---|---|---|---|---|---|
| NADX-UKBAIVI-001 | UK Cyber Security Trade Publications | Best / Market Leadership | 19 August 2026 | 5 screenshots + live video | Study 001 |
Mobile users: scroll horizontally. The exact tested prompt and complete results are preserved on the individual study record.
Archive rule
The study identifier is permanent. If a sector is tested again in future, the new retrieval session should receive a new study record rather than overwriting the original dated evidence.
Citation guidance
How to cite the Index
A consistent citation makes it easier for readers, businesses and third parties to identify the methodology version being referenced. Individual child studies should additionally cite their permanent study ID and test date.
Suggested parent-page citation
NeuralAdX Ltd. NeuralAdX UK Business AI Visibility Index. Research Protocol v1.0. 2026.
Suggested citations are provided for identification and traceability. They do not imply academic peer review, institutional endorsement or official-statistics status.
Frequently asked questions
These answers explain how the parent Index and its sector child studies should be read.
What does the NeuralAdX UK Business AI Visibility Index measure?
It measures observable brand visibility and citation behaviour in a dated five-platform AI retrieval test. The 14 metrics cover recommendation presence, mentions, share of voice, position, citation ownership, citation coverage, source diversity and cross-platform agreement.
Is the Index claiming a permanent ranking of UK businesses?
No. Every child study is a dated retrieval snapshot. AI responses can change between runs and over time, so the result is evidence of what was returned during the recorded session, not a permanent guarantee.
Why does each study use five AI platforms?
Using five fixed platforms reduces the risk of treating one engine as representative of the whole AI answer environment and makes cross-platform coverage and consensus directly measurable.
How is the overall ranking calculated?
Protocol v1.0 orders brands first by Cross-Platform Consensus, then Average Brand Position, then Domain Citations, then Brand Mentions as the final tie-breaker. Only visibly attributable own-domain citations count as Domain Citations. The Index does not use a hidden composite score.
Why are hidden or collapsed sources not counted?
Because the Index is evidence-led. If a screenshot does not reveal the underlying domain or URL, NeuralAdX Ltd does not guess which brand received the citation.
What is the difference between AI Platform Coverage and Cross-Platform Consensus?
AI Platform Coverage records whether the brand has a substantive presence on a platform. Cross-Platform Consensus is stricter: it records whether the platform actually presents the brand as an answer or recommendation to the tested prompt. A brand can therefore have platform presence without receiving recommendation consensus.
How is sector eligibility decided?
An entity is included when the captured AI answer presents it as belonging to the defined sector or category being tested. Clearly out-of-category entities and incidental references are excluded, with material sector-specific exclusions disclosed on the relevant child study page.
Will every sector be retested on a fixed schedule?
No fixed retest schedule is promised under Protocol v1.0. NeuralAdX Ltd is initially expanding the Index across business sectors. Individual sectors may be retested later to examine changes over time.
Are the results official statistics or peer-reviewed academic research?
No. The Index is a proprietary observational research programme produced by NeuralAdX Ltd. It uses published methodology and evidence standards, but it is not official UK statistics, a university study or a peer-reviewed journal publication.
Are Google, OpenAI, Microsoft or Anthropic involved in the Index?
No endorsement or affiliation is implied. The named platforms are the retrieval environments tested by NeuralAdX Ltd; the Index methodology and published analysis are produced independently by NeuralAdX Ltd.
NeuralAdX Ltd UK Business AI Visibility Index · Research Protocol v1.0 · Effective 19 August 2026 · Dated observational retrieval studies · No affiliation with or endorsement by the tested AI platform providers is implied.
Author and GEO methodology context
Paul Rowe
Paul Rowe
Founder, Chief Generative Engine Optimisation Officer and CEO.
Paul Rowe is the Founder, Chief Generative Engine Optimisation Officer and CEO of NeuralAdX Ltd, a UK-based Generative Engine Optimisation agency focused on helping brands become visible, retrievable, cited, mentioned and trusted inside AI-generated answers.
His work focuses on AI citation visibility, answer-engine retrieval, entity clarity, structured content, source trust, prompt coverage and measurable AI answer visibility across ChatGPT, Google AI Mode, Google Gemini, Microsoft Copilot, Perplexity, Grok, Claude and other major AI search and answer platforms.
Paul’s optimisation process is built around the 11-factor GEO methodology, combining citation addition, statistics, quotations, fluency, easy-to-understand content, authority signals, schema markup, recency, author bios, source diversity and technical-term clarity.
NeuralAdX Ltd publishes proof-led GEO work through live AI retrieval testing, the Proof That Generative Engine Optimisation Works evidence hub, the AI Citation Benchmark and the AI Answer Visibility and Share of Voice Benchmark. This author bio is used to connect each article with clear expertise, transparent methodology and verifiable AI visibility evidence.
CEO
11-factor GEO
AI citation visibility
Answer-engine retrieval
Entity clarity
Evidence-led GEO
Live AI retrieval