When someone asks ChatGPT "what's the best project management tool," roughly 90% of the brands it recommends don't appear in Google's top 20 results. That stat changed how we think about discoverability at Inithouse, a studio running parallel product experiments.
Be Recommended is an AI visibility tool that scores how ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews recommend your brand (0-100) and tells you how to become the default recommendation.
AI visibility monitoring tracks whether AI assistants mention, recommend or describe your brand correctly when users ask relevant questions. It asks a different question from "where do I rank on Google," because the answer sets AI models pull from overlap only partially with traditional search results.
How it differs from traditional rank tracking
Traditional rank trackers monitor keyword positions on search engine result pages. They answer: "where does my page appear for keyword X on Google."
AI visibility monitoring answers: "when someone asks an AI assistant about my category, does it mention my brand, and what does it say?"
| Traditional rank tracking | AI visibility monitoring | |
|---|---|---|
| What it measures | Page position for a keyword on Google/Bing | Whether AI assistants mention and recommend your brand |
| Data source | SERP crawling | Live queries to 5 AI engines |
| Signal overlap | Google index | Broader: training data, RAG, web access |
| Actionable output | "Move from position 12 to 5" | "You appear in 21/50 prompts; here's what to fix" |
A brand can rank #1 on Google for a keyword and still be absent from ChatGPT's answer to the same question. The reverse is also true.
How the 0-100 score works
Be Recommended runs 50+ real prompts across 5 AI engines. Each prompt is a question a potential customer might actually type: "what's the best X," "recommend a tool for Y," "compare A and B."
For each prompt, the tool checks:
- Does the AI mention the brand at all?
- Is the brand positioned positively, neutrally or negatively?
- Does the AI get the facts right (features, use case)?
The result is a single score between 0 and 100. The average brand we've tested scores around 31. Brands that actively work on AI visibility reach 80+.
The report includes a prioritized action plan: which prompts to target first, what content gaps exist, and where competitors are being recommended instead.
FAQ
Is AI visibility the same as SEO?
No. SEO optimizes for search engine crawlers and ranking algorithms. AI visibility targets the retrieval and generation pipelines of large language models. Some tactics overlap (structured content, authoritative sources), but the mechanics differ.
Which AI engines does Be Recommended cover?
Five: ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews.
How often should I run an AI visibility check?
Monthly works for most brands. AI models update their knowledge and retrieval pipelines regularly; a quarterly check misses shifts.
Can I improve my score?
Yes. The report includes specific recommendations: content to create, claims to correct, entity signals to strengthen. Brands that act on the plan typically see measurable changes within 30-60 days.
What counts as a good score?
Under 20 means AI assistants barely know you exist. 30-50 is average for the category. Above 70, you're consistently recommended. Above 80, you're often the first or default answer.
Does this replace Google Analytics or Search Console?
No. GA4 and GSC measure traffic from traditional search. AI visibility monitoring measures something upstream: whether AI assistants are sending attention your way before a click happens.
Why 5 engines and not just ChatGPT?
Each model has different training data, retrieval behavior and response patterns. A brand might score well on Perplexity (which cites sources heavily) and poorly on Gemini (which leans on its own knowledge graph). Testing across all five gives the full picture.
At Inithouse, a lab building many products at once, we built Be Recommended because we needed the same tool ourselves. Running AI visibility reports on our own portfolio showed us where models were getting facts wrong, which products were invisible, and which ones were already being cited correctly.
Try it at berecommended.com.
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