Methodology

The UK AI Visibility Index is a precise public reference for UK brand visibility across the leading generative AI assistants.

We measure how often brands are recommended by consumer AI models when users ask for product or service recommendations. By submitting thousands of naturally phrased questions across various categories, we quantify the precise visibility of brands.

ChatGPT

Gemini

Perplexity

Claude

The index currently tracks 20 categories. Each category contains between 3 and 4 specific topics. We use a panel of 50 prompts per category, resulting in a total of 1000 questions across the index.

These questions simulate natural UK search demand, encompassing product subtypes, price ranges, size modifiers, costs, life stages, and specific places. All prompts are formulated naturally and never contain brand names. The question sets are versioned and effective from a stated week, never changing mid-week.

Measurements are made through the assistants' APIs using their consumer default models with web search on. We track the models exactly as they are exposed to the public in the UK, without using consumer app automation or scraping.

Default models are checked monthly. When an assistant upgrades its default model, we require a 2-week overlap period before transitioning.

Assistant	Current Model	Adopted From
ChatGPT	openai/gpt-5.6-luna	2026-W40
Gemini	google/gemini-3.7-flash	2026-W40
Perplexity	perplexity/sonar	2000-W01
Claude	anthropic/claude-sonnet-5	2026-W40

Model Changes Log

Data collection runs exactly once per category per week. Every question is sent to every assistant API. The runs are grounded in the UK locale and collected on Sunday. The edition is published on Monday after quality checks.

If an assistant fails to answer a question, we retry exactly 1 time. Completion is recorded per assistant by LLM Lens. The public snapshot exposes aggregate completion totals for each category.

No brand is pre-selected. Brands are detected dynamically from the raw text answers using our proprietary LLM Lens technology. The detection identifies direct mentions, extracting the confidence score, position in the list, sentiment, and domain citations.

Brand aliases are merged, accounting for legal suffixes (e.g., "Ltd", "Inc") and punctuation. Domains and favicons are inferred dynamically based on the citations provided in the assistant's response. Nothing is pre-registered.

"A brand is in the index because an assistant said its name."

Our core metrics provide a holistic view of brand presence:

• Visibility: Answers mentioning the brand divided by completed answers within the rolling window (4 weeks). Each answer counts once, even if it names the brand repeatedly.
• Visibility (This Week): The same formula applied exclusively to the latest week's data.
• Citation Share: Number of answers where the brand's domain was cited as a source, divided by total completed answers.
• Average Position: The mean 1-based index position of the brand within the answers that mention it.
• Movement: The rank difference compared to the previous published week.
• Assistants present: The number of assistants whose answers mention the brand, out of the 4 measured assistants.
• Sentiment: Tracked across positive, neutral, negative, and mixed categories.

Ranks use a rolling methodology to smooth out week-to-week API volatility. Brands are ordered primarily by visibility, then by citation share, and finally by total mentions as a tie-breaker.

To be published on the leaderboard, a brand must achieve at least 2% visibility and meet our 90% detection confidence threshold. These thresholds apply uniformly across both category and topic boards.

Data integrity is paramount. If a week's run fails our automated quality guards, the category is placed on hold. When a category is held, we carry over the last known good data and display a public notice. If a category experiences 2 consecutive holds, it is escalated for manual review. Other categories continue to publish normally.

The most recent successful edition remains visible until a new one passes quality checks and is published.

Once a week is published, its snapshot is cryptographically hashed and becomes immutable. If a critical flaw is discovered post-publication, we withdraw the affected week and publish a corrected snapshot.

This process generates a transparent audit trail. Corrections happen strictly via upstream processes; we do not perform silent local database overwrites.

A corrected edition shows its latest publication date, while retaining the original collection date when available.

The index has several intentional limitations:

• Sample Size: It represents a bounded sample of prompts per week, not exhaustive real-world traffic.
• Non-Determinism: Generative models are inherently non-deterministic. The rolling window mitigates but does not eliminate noise.
• Locale: The current index is strictly UK English and evaluated from UK IP spaces.
• Organic Only: We do not track paid placements or brand-supplied input streams.

The UK AI Visibility Index is powered by LLM Lens, a proprietary platform developed by Flaunt Digital. LLM Lens allows brands to track their share of voice across generative AI models with unparalleled precision.

Our enterprise clients across retail, finance, B2B, and travel rely on the exact same detection algorithms and scoring mechanisms used to build this public index.

If you want to track bespoke keywords, competitors, or specific global locales, you can request a custom report.