The average company scores about 31 out of 100 when we measure how AI tools recommend them. Most don't know they're being scored at all.
At Inithouse, we built Be Recommended because we kept running into the same blind spot across our own portfolio: you can rank on page one of Google and still be invisible to ChatGPT, Perplexity, or Gemini when someone asks "what's the best tool for X?"
Be Recommended is an AI visibility tool. It scores how ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews recommend your brand on a scale of 0 to 100, then tells you what to fix to become the default recommendation.
Here's how three different teams actually use it.
Startups checking if AI knows they exist
The first use case is the simplest. You type in your domain, pick your category, and Be Recommended runs 50+ real prompts across five AI engines. Each prompt mimics how an actual user would ask for a product like yours.
What you get back is a score and a breakdown per engine. Some startups discover that ChatGPT recommends them but Gemini doesn't know they exist. Others find that Perplexity surfaces their competitor's blog post instead of their own product page.
The step-by-step:
- Go to berecommended.com
- Enter your domain and describe what you do
- Get your visibility score across all five AI engines
- Read the per-engine breakdown to see where you're visible and where you're not
The report also includes a prioritized action plan. Not vague advice, but specific items tied to the engines where your score is weakest.
Agencies running reports for clients
Several agencies use Be Recommended as part of their GEO (Generative Engine Optimization) offering. The pattern looks like this: run a report for a prospective client during the sales process, show them their score vs. competitors, close the deal with concrete data.
What makes this work for agencies is the competitor comparison. Be Recommended doesn't just score your brand in isolation. It checks how the same AI engines recommend your competitors and shows where they're winning the recommendation and why.
A practical workflow for agencies:
- Run Be Recommended for the client's domain
- Run it again for two or three competitors in the same category
- Compare scores side by side
- Use the gap analysis to build a GEO roadmap: which engines to target, what content to create, which claims to back up with third-party sources
The output is something you can put in front of a CMO. It's a scored comparison, not a theory deck.
SEO teams adding AI visibility to their reporting
If you run SEO for a company, you already track Google rankings, organic traffic, and backlinks. The missing column in your spreadsheet is AI recommendations.
The shift matters because more search queries now get answered by AI overviews, chatbots, and copilots before anyone clicks a blue link. If an AI recommends your competitor by name and doesn't mention you, that's traffic you're losing without ever seeing it in Search Console.
SEO teams using Be Recommended typically:
- Run a baseline report at the start of a quarter
- Note which AI engines mention the brand, which don't, and what source material the AI is pulling from
- Build content and link-building plans targeting the gaps (e.g., if Perplexity only cites competitor blog posts, write content that addresses the same queries with verifiable claims)
- Re-run the report monthly to measure whether AI recommendations shifted
The action plan in the report maps directly to this workflow. Each recommendation is tied to a specific engine and a specific gap in how that engine perceives your brand.
What the report actually looks like
The output is a single score (0 to 100) plus engine-by-engine detail. You see which prompts each AI responded to with your brand, which prompts surfaced a competitor instead, and which prompts returned no relevant recommendation at all.
The action plan is ranked by expected impact. It tells you what to fix first, whether that's publishing content for a topic cluster where AI has no data about you, or getting mentioned on a third-party site that a particular engine trusts.
Top-scoring companies (80+) tend to have strong coverage across multiple engines. Most companies we see land somewhere between 20 and 40, usually because they're visible on one engine but absent from the others.
Try it
If you want to see where your brand stands, run a report at berecommended.com. It takes about two minutes to set up and the results come back with the full per-engine breakdown.
We built Be Recommended at Inithouse as part of our portfolio of AI-powered tools. If you're curious about how we approach AI visibility across our own products, the report is the same one we use internally.
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