AI Visibility
The AI Visibility module checks how the client's brand is represented when people ask ChatGPT, Claude, and Gemini questions related to the client's industry and topics. As more users turn to AI assistants for discovery and recommendations, appearing in AI responses is becoming a meaningful channel alongside traditional search rankings.

How it works
- You enter up to 10 topics relevant to the client's business (e.g. "car rental in Crete", "best SUV rental Greece", "cheap car hire Heraklion").
- Minorank sends structured prompts to ChatGPT, Claude, and Gemini asking about each topic.
- It analyses the responses to detect whether the client's brand is mentioned, how prominently, and in what context.
- The results are compiled into a visibility score and per-topic breakdown.
Summary metrics
| Metric | Description |
|---|---|
| Brand visibility % | Percentage of all AI responses that mentioned the brand |
| Responses analysed | Total number of AI responses evaluated across all topics and models |
| Mentions found | Total number of brand mentions across all responses |
| Per-AI scores | Separate visibility scores for Claude, ChatGPT, and Gemini |
Per-AI scores
Scores are broken down by AI model:
- Claude — visibility score and mention count from Claude responses
- ChatGPT — visibility score and mention count from ChatGPT responses
- Gemini — visibility score and mention count from Gemini responses
Different AI models draw on different training data and browsing behaviour — a brand can be highly visible in one and invisible in another. Identifying which models recognise the brand helps prioritise optimisation efforts.
Visibility by topic table
For each topic entered, the table shows:
| Column | Description |
|---|---|
| Topic | The entered topic or question |
| Claude | Whether the brand was mentioned (✓ / ✗ / partial) |
| ChatGPT | Same |
| Gemini | Same |
| Prompts analysed | Number of prompt variations run for this topic |
| Visibility score | Weighted score across all models for this topic |
Topics where the brand is not mentioned across all three models are the highest-priority gaps.
AI suggestions for improvement
Below the results table, Minorank generates AI-written recommendations for improving brand visibility in AI responses. These are tailored to the specific gaps identified in the analysis. Common recommendation categories:
- Content creation — which topics need authoritative pages or articles that AI models can reference
- Brand mentions — where to earn editorial mentions that AI crawlers are likely to index
- Structured data — schema types that help AI models understand the brand's context
- LLMs.txt — ensuring the site's
/llms.txtfile accurately represents the brand for AI crawlers - Wikipedia/knowledge graph — whether establishing a knowledge graph presence would help
Each recommendation has a Task button and an Asana button.
Limitations
AI visibility analysis is an approximation. AI models respond differently to prompt variations, update their training data on different schedules, and may not consistently reproduce the same responses. The scores reflect a snapshot in time based on the prompts run.
:::note Topic selection Choose topics that real users would actually ask an AI assistant about when looking for a service like the client's. Generic topics ("SEO services") are less useful than specific ones ("best SEO agency for e-commerce in Athens"). :::
Next steps
- AI SEO Reports — include AI visibility context in client reports
- Blog Post Planner — create content targeting AI visibility gaps
- WordPress SEO Management — ensure LLMs.txt is enabled via the plugin