> ## Documentation Index
> Fetch the complete documentation index at: https://docs.videntic.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Visibility Score

> How often your brand is mentioned across the LLM providers you track.

**AI Visibility Score** is the headline metric on your dashboard. It answers the core GEO question in one number: *when customers ask an LLM about your category, how often does your brand come up?*

## Overview

For every analysis run, Videntic simulates customer queries against the major LLMs and checks each response for a text mention of your brand or domain. It calculates a mention rate for each provider, then averages providers equally.

<Info>
  Higher is better. A score of **35%** means one in three AI answers about your topics mentions you — a meaningful position in most categories.
</Info>

## How AI Visibility is calculated

```
Provider visibility = responses mentioning your brand / responses from that provider × 100
AI Visibility Score = equal-weight mean of provider visibility values
```

A response counts when the answer text contains your brand name, domain, or a known alias recognized by Videntic's mention detection. URL citations are measured separately and do not add weight to this score.

The score is computed per run and per model, then aggregated:

* **Overall score** — averaged across all models in your plan, weighted evenly.
* **Per-model score** — the same metric broken out by ChatGPT, Perplexity, Google AI Overviews, and Gemini (where included in your plan).

Providers that produced no responses are excluded from the overall mean rather than counted as 0.

Per-model scores often tell you more than the average. It's common to be strong on one model and weak on another, because each one sources information differently.

## How to read the number

Scores fall into four human-readable tiers:

| Tier                    | Range  | What it means                                                                                             |
| ----------------------- | ------ | --------------------------------------------------------------------------------------------------------- |
| **Building visibility** | 0–5%   | You're invisible in most AI answers. Focus on content gaps and baseline GEO‑16 fixes.                     |
| **Gaining traction**    | 5–15%  | You show up in a meaningful minority of answers. Work on quality (Citation Position) and coverage.        |
| **Well positioned**     | 15–35% | You're a recurring answer in your category. Consolidate wins and defend against competitors.              |
| **Category leader**     | 35%+   | LLMs consistently mention you. At this point, the lever is depth — richer content, more specific queries. |

Benchmarks vary by category. In high-competition consumer categories, 35% is excellent. In niche B2B domains, 60%+ is achievable.

## Why AI Visibility matters

### Business lens: demand capture

A rising share of research and purchase decisions is now happening inside AI assistants. Every prompt where you're *not* cited is a prospect who saw a competitor's answer instead.

### Marketing lens: channel measurement

AI visibility is to GEO what keyword rankings were to SEO. If you can't measure it, you can't report on it, budget for it, or defend its value internally.

### Product lens: positioning

Which queries you win tells you how the market *frames* your product. If you win "best CRM for startups" but lose "CRM with advanced workflows", the LLMs — trained on the open web — are telling you something real about your brand's current associations.

## How to improve it

In rough order of leverage:

1. **Fix GEO‑16 pillars** flagged red on your audit, especially Structured Data, Evidence & Citations, and Metadata & Freshness. These are the baseline requirements for being quoted.
2. **Publish content targeting your top content gaps**. Videntic ranks gaps by impact; start from the top.
3. **Work on Citation Position**. Being cited 5th is worth less than being cited 1st. See [Citation Position](/metrics/citation-position) for why.
4. **Expand to under-served LLMs**. Your per-model breakdown will show you which model you're weakest on; often one content fix lifts all four.

## Related metrics

<CardGroup cols={2}>
  <Card title="Share of Voice" icon="chart-pie" href="/metrics/share-of-voice">
    Same data, but normalized against your tracked competitors.
  </Card>

  <Card title="Citation Position" icon="list-ol" href="/metrics/citation-position">
    How prominent your mention is when you are cited.
  </Card>

  <Card title="GEO audit" icon="list-check" href="/features/geo-audit">
    The on-site signals that drive citation likelihood.
  </Card>

  <Card title="Content gaps" icon="lightbulb" href="/features/content-gaps">
    Prompts where you're missing. The biggest lever for AI Visibility.
  </Card>
</CardGroup>
