AI Visibility · Research & advisory
Search visibility got you found.
AI visibility gets you recommended.
When a buyer asks ChatGPT, Claude, Gemini or Perplexity for the best tool in your category, the assistant names a handful of brands and moves on. There is no page two. RecommendByAI shows you whether you are in that answer, who is there instead, and what it would take to change it.
Answer first
What changed between search rankings and AI recommendations?
Search told buyers where to look. AI assistants tell them what to choose — and only a handful of brands get named.
A ranked list distributed attention across ten results and let the reader judge. An assistant reads the same web, forms a view of your category, and returns two to five options with reasons attached. There is no page two to climb from and no impression to optimise. You are either inside the answer or absent from the decision entirely.
The problem
Your buyers are getting a shortlist you never see
Discovery moved from a list of links you could rank on to a single synthesised answer you either appear in or you don't. Most companies have no idea which version of them the models are describing.
You are not mentioned
The assistant confidently lists five vendors in your category and none of them is you.
You are described wrongly
You are named, but positioned in the wrong category, with outdated pricing, features or use cases.
A competitor owns the intent
One rival is returned for the exact phrase you built your product around, in every assistant, every time.
Why now
This is the earliest moment to act, and the cheapest
Assistants are becoming the first stop in software research. A buyer describes their problem in plain language and receives an opinionated answer with a small number of named options. That answer is assembled from sources the model trusts — comparison content, documentation, community discussion, reviews, structured facts about your company.
Those sources are being written and re-crawled right now, and they compound. Companies that make their positioning legible to models early become the default answer in their category. Everyone else spends the next few years trying to dislodge them, exactly as happened with search rankings a decade ago.
What we do
Three pillars: audit, monitoring, optimization
We start with the audit because you cannot fix what you have not measured. Monitoring and optimization follow only if the findings justify them.
Audit
A manual review of how the major assistants answer the buying questions in your category, which brands they name, and what evidence they lean on.
Monitoring
Repeat the same prompt set on a schedule so you can see whether your presence in AI answers is improving, drifting, or being overtaken.
Optimization
Work on the sources models actually read — comparisons, documentation, third-party coverage, structured facts — so your brand is easier to recommend correctly.
Dogfooding principle
We run our own methods on ourselves first
RecommendByAI is a new company in a new category. Rather than claim authority we do not have yet, this site is the first thing we audit, monitor and optimise — in public, with the results reported as they are.
Product rule
If recommendbyai.com isn't recommended by AI, we haven't finished building the product.
This site is the test subject
Every page here is written to be readable, quotable and attributable by an assistant — the same standard we apply to client pages.
One page per real question
We publish answer-first pages for the questions buyers actually ask, because that is the shape a model can lift and cite.
No proof we haven't earned
No customer logos, no testimonials, no invented case studies. Our own visibility results are the proof we are building.
Same prompt sets, same tracking, same optimization work we would run for a client, applied here first. If the method cannot make us recommendable, we have no business selling it.
How the audit works
Four steps, run by a human analyst
No scraper, no dashboard, no synthetic score. A person reads the answers and tells you what they mean.
- 01
You tell us the intent
Your website, your main competitors, and the one thing you want AI assistants to recommend you for.
- 02
We build a prompt set
We write the realistic natural-language questions a buyer in your category would ask an assistant, from broad discovery to direct comparisons.
- 03
We run them manually
Across ChatGPT, Claude, Gemini, Perplexity and Google's AI answers, reading how each one frames the category and who it names.
- 04
You get a written audit
Findings, competitor share of voice, the reasoning behind the gaps, and a prioritised list of what to change first.
Who this is for
Companies whose buyers research with AI first
B2B SaaS companies
Your category is crowded and buyers now ask an assistant to narrow it down before they book a single demo. Being left off that shortlist is a silent pipeline loss.
AI tools
Ironically, AI products are often poorly described by AI. If assistants confuse what you do or compare you to the wrong category, you are being mis-recommended.
Agencies
Clients are already asking what AI Visibility means for them. An audit gives you a concrete, defensible answer instead of speculation.
FAQ
Common questions
What is AI Visibility?
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AI Visibility is how often, how accurately, and how favourably a brand is surfaced when people ask AI assistants for recommendations. Instead of ranking on a page of blue links, you are either named inside the answer or you are not mentioned at all.
Why does ChatGPT recommend competitors instead of us?
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Usually because the sources an assistant trusts describe your category without describing you: comparison articles, community threads, documentation, reviews, and directories. If those sources do not clearly connect your brand to a specific use case, the model has nothing to recommend you for.
How do I get recommended by AI?
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Start by measuring what assistants say today, then make your positioning legible to a model: one clear use case per page, consistent facts across every source, and presence in the comparison articles, review sites, directories and documentation that answers are built from. Recommendation follows evidence, not persuasion.
How do I measure AI Visibility?
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Fix a prompt set for your category, run it across the major assistants, and track four numbers plus one qualitative read: mention rate, share of voice against named competitors, accuracy of the description, intent coverage, and the role you are given in the answer. Re-run the same prompts on a schedule so the numbers are comparable.
How is AI Visibility different from SEO?
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SEO optimises for ranked lists of documents. AI Visibility optimises for synthesised answers. An assistant reads across many sources, forms an internal view of a category, and then names a handful of options. You can rank on page one of Google and still be absent from that shortlist.
Answers index
Keep reading
Each answer is a page. Each page ends in the same place: a free, manually run audit of your own category.
What is AI Visibility?
How often, how accurately and how favourably AI assistants name your brand when asked for a recommendation.
AI Visibility vs SEO — what's the difference?
SEO wins a position in a list of links. AI Visibility wins a mention inside an answer that already made the choice.
How do I get recommended by AI?
Find out what the assistants currently say, why competitors are named instead, then fix the sources those answers are built from.
How do I measure AI Visibility?
Mention rate, share of voice, accuracy and intent coverage across a fixed prompt set, re-run over time.
Why does ChatGPT recommend competitors instead of us?
Because the sources a model trusts describe your category without clearly connecting your brand to a specific job.
Find out what AI assistants say about you
We run a manual AI Visibility Audit across the assistants your buyers actually use, then show you exactly where you appear, where competitors win, and what to change first.