The AI Brand Visibility Index: How We Score What AI Thinks of Your Brand

Bottom line: The AI Brand Visibility Index is AskLLM's 0–100 score for how often and how strongly ChatGPT and Gemini name and endorse your brand on realistic buyer queries — weighted by mention frequency, position, sentiment, and cross-model consistency. The score only matters if it tracks real buyer behavior — we designed it so a brand at 78 reflects stronger recommendation signal than a brand at 45, not vanity mentions.

Here's how we built it, and what the score is (and isn't) telling you.

What We're Actually Measuring

Bottom line: We score from query responses, not crawls — we generate realistic buyer questions, run them across two models with repetition and phrasing variation, then aggregate whether you appeared, where, and how you were described.

The starting point is a query, not a crawl.

When you submit your brand to AskLLM, we generate 6–8 buyer queries based on your category, use case, buyer persona, and competitive context. These are the kinds of questions a real potential customer would ask an AI model — not queries designed to fish for your brand name, but genuine problem-oriented questions that your category addresses.

We then run those queries across ChatGPT 5 and Gemini. Not once — multiple times, with variation in phrasing, to get a stable signal rather than a single snapshot.

The raw data is: for each query, across each model, did your brand appear? If it appeared, where? How was it described? With what level of confidence?

The AI Brand Visibility Index takes that raw data and turns it into a score.

The Four Components

Bottom line: The score combines mention frequency (weighted by position), sentiment of how you were described, and cross-model consistency — fragmented presence in one model is not the same as durable visibility across both.

Mention frequency is the most direct signal. Across all tested queries and all models, what percentage of responses included your brand? A brand mentioned in 70% of relevant responses is more visible than one mentioned in 20%.

But frequency alone is misleading. A brand mentioned once at the end of a long list ("and you might also consider X") is not equally visible to a brand mentioned first with a specific recommendation rationale. So frequency gets weighted by position in response — first mention, unprompted lead recommendation, and mention with specific endorsement language all score higher than buried appearances.

Sentiment captures not just whether you appeared but how you were characterized. "X is a popular option for teams that need Y" is a neutral mention. "X tends to be the go-to for teams that need Y because of Z" is a positive recommendation with a reason. "Some teams use X, though it has limitations with Y" is qualified. These distinctions matter for whether a mention translates into buyer consideration, so they're captured in the score.

Cross-model consistency is the component that surprises people most. A brand that's recommended strongly by ChatGPT 5 but not at all by Gemini has a fragmented presence — it exists strongly in one model's training data but not the other. That's meaningfully different from a brand that appears across both models with consistent description.

High consistency across models is a leading indicator of durable AI visibility — it means your brand has been absorbed into a wide range of training sources, not just the sources that weighted towards one model's corpus.

What the Score Ranges Actually Mean

Bottom line: Below 30 = weak or fragmented recall; 30–60 = in the consideration set; >60 = default recommendation territory; 100 = category-defining saturation — use these ranges as directional bands, not precision instruments.

A score below 30 typically means one of two things: either your brand is genuinely new and hasn't accumulated enough third-party coverage to register, or your brand has coverage but it's fragmented and inconsistent enough that AI models aren't confident recommending you.

A score in the 30–60 range is where most established brands land. You exist in AI model recall, you appear in some queries, but you're not yet a default recommendation. You're in the consideration set, not the lead recommendation.

A score above 60 means you're consistently appearing across multiple models and query types, usually with specific endorsement language. Brands in this range tend to show up as the first or second recommendation more often than not.

A score of 100 is reserved for category-defining brands — the ones where AI models volunteer the recommendation unprompted, describe them with specific feature-level detail, and maintain consistent characterization across both models. These are brands that have genuinely saturated their category's training representation.

What the Score Doesn't Tell You

Bottom line: The index is a snapshot, not a trend line — and it does not diagnose why you have a given score without reading the report's recommendations.

The AI Brand Visibility Index is a snapshot, not a trend. A score of 65 today tells you where you stand against your queries today. It doesn't tell you whether you're improving or declining — for that, you need to run audits over time and track the delta.

It also doesn't tell you why you have the score you have. A score of 40 could mean you need more third-party coverage, or it could mean your current coverage describes you inconsistently, or it could mean your product category is one where AI models are genuinely uncertain (a new category without established vocabulary is harder to score well in, even with good coverage).

The score is designed to be a starting point for diagnosis, not a final verdict. The recommendations section of your report is where the diagnostic work happens.

The Honest Limitation

Bottom line: Outputs are stochastic — expect small run-to-run variance; relative ordering (70 vs 35) is reliable even when absolute points drift.

AI model behavior is not fully deterministic, and training data isn't fully transparent. We're measuring outputs — what models say when asked — not the underlying weights that produce those outputs. This means there's noise in the signal. Two audits run a week apart might produce slightly different scores for the same brand.

We handle this with query repetition and variation — running each query multiple times with slight phrasing differences — which smooths out most of the stochastic noise. But we'd be misleading you if we said the score is as stable as, say, a PageRank or a G2 star rating.

What it is, reliably, is a directional signal. A brand with a 70 has meaningfully stronger AI visibility than a brand with a 35, even if the exact numbers shift slightly between measurements. The relative ordering is stable even when the absolute values have noise.

Why We Built This

Bottom line: We built the index because almost nobody runs manual two-model queries consistently — a single number forces action where qualitative "maybe" answers do not.

The reason we built the AI Brand Visibility Index wasn't to create a metric for its own sake. It was because the alternative — running manual queries across ChatGPT and Gemini and trying to synthesize the results qualitatively — is something almost nobody actually does.

Most brands have no idea how AI models describe them. They assume they're visible because they're on Google. They assume they're described accurately because they have a good website. Neither assumption is reliable.

A number gives you something to act on. A 42 tells you there's work to do in a way that "ChatGPT mentioned us but Gemini didn't really say much" doesn't. And a 42 that becomes a 61 three months later tells you that work paid off — in a way that matters for actual buyer behavior.

That's what we were trying to build. A signal that's honest about its limitations, but genuinely useful for the brands trying to understand and improve how AI sees them.

Startups & lean teams: Put the index in context for early-stage positioning with our AI brand visibility audit for startups.

Run your free AI brand visibility audit at askllm.io. See your score across ChatGPT 5 and Gemini — and what's driving it.