An AI visibility checker tests whether AI agents name your brand when someone asks for a recommendation in your category. It runs buyer-intent prompts across the engines your market uses, records whether you were mentioned, recommended, cited or absent, and shows which sources shaped the answer. Most teams stop after the first two. The sources are where the cause sits.
What does an AI visibility checker actually measure?
Conventional SEO tools tell you where a page ranks for a keyword. That is useful, and it says nothing about what an engine tells a buyer who asks for a shortlist. A first-place organic result can be left out of the answer, and a competitor sitting four positions below you can be named in every one, because its content is clearer, its product facts are more consistent, or other people describe it in terms the model can reuse.
A checker measures the answer instead. For each prompt it records four states: you were recommended, you were mentioned in passing, you were absent, or there was no visible source to judge. Each state needs different work, which is why collapsing them into one score loses the useful part.
If an engine says a rival is better for enterprise teams and lists you as an alternative, you have visibility with weak recommendation status. If it names you for a use case you do not serve, you have a positioning problem rather than a visibility one. If it cites a third-party review carrying outdated pricing, you have a source-control problem, and publishing more of your own content will not fix it.
Why does ranking first not guarantee a mention?
Because the engine is assembling an answer, not ordering a list of pages. It needs claims it can state confidently and sources it can defend citing. Pages that make a direct claim, name specific products and admit limitations are easier to quote than pages that explain why a category matters for seven hundred words first.
Buyer asks: “What is the best AI visibility platform for a small B2B SaaS team that needs prompt tracking and competitor benchmarking?”
For a small B2B SaaS team, three options are worth shortlisting. Vendor A is the most established and suits teams that already run structured SEO reporting. Vendor B is cheaper and covers fewer engines. Vendor C is the usual choice when competitor benchmarking is the main requirement.
The brand ranked third organically for its category term. It is absent from the answer, and the two cited sources are both third-party roundups rather than vendor sites.
The brand there is findable in search and invisible in the answer, and nothing in its analytics records the loss. See which pages AI engines cite about your brand for how to trace the sources behind an answer like that one.
Where does your visibility actually get decided?
On other people's websites, for the most part. When we looked at the citations behind real answers, the large majority pointed somewhere other than the brand being discussed: roundups, review directories, community threads, video transcripts and comparison posts written by other people.
Read your checker output through that number. If your score is low and your own pages are already clear and well structured, the missing work is distribution: you need to appear in the pages the engine is already reaching for. We covered the mechanics in how to get cited by AI through content you don't own.
It also explains a pattern that confuses a lot of teams. A competitor gets recommended while its homepage is never cited, because the engine learned to trust the product from third-party evidence and never needed the vendor's own marketing.
How do you check your brand's AI visibility?
- 1
Write prompts a buyer would actually type
Not keywords. Full questions, in the wording someone uses immediately before a trial, demo or purchase. Pull them from sales objections, support tickets, lost-deal notes and comparison questions. Ten to twenty five prompts close to a buying decision beat a spreadsheet of two thousand vague terms.
- 2
Freeze the wording, then run them across every engine
Fix the prompt text, the engine and the location before you look at any answer, and do not reword it afterwards. ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot, Google AI Overviews and Google AI Mode can return materially different recommendations from identical input.
- 3
Record recommendation and citation separately
For each answer, note the products named, the order they appear in, and every source URL. Keep them in different columns. A vendor recommended without its own domain being cited is telling you something specific about where its authority comes from.
- 4
Classify the reason you lost, not just the fact
Missing page for the decision, inconsistent product facts across the web, competitor claims with third-party support you lack, or a good page nobody influential has seen. Each of those points at different work.
- 5
Re-run the same set after the work ships
Movement at prompt level is the signal. An overall score can rise while your highest-value comparison prompts get worse, so check the prompts that touch revenue individually.
You can do all of that by hand. It is slow rather than difficult, and the discipline that usually breaks first is prompt freezing: it is tempting to reword a prompt until the answer flatters you, at which point you are comparing moving targets rather than tracking a benchmark.
See where your brand shows up in AI search
Enter your website and get your AI Visibility Score across 7 engines. First score in under 5 minutes, no setup.
Koalr runs this as a scan: 25 real buyer prompts against seven AI agents, scored 0 to 100, with the prompts, competitors and cited sources behind the result. The score is the benchmark leadership asks for. The prompt-level evidence underneath it is the part your team can act on. How the AI Visibility Score works covers the calculation and why tools disagree with each other.
What should you do with a low score?
Start where buying intent and competitor advantage overlap. Being absent from a broad educational prompt can wait. Being absent from "best alternatives to [competitor] for enterprise security teams" while two direct rivals are recommended is a demand problem this quarter.
Before you publish anything in response to a gap
- Does the page answer one specific buying decision rather than a category in general?
- Is the direct answer near the top, above the context?
- Are competing options named and compared on the same criteria?
- Is there at least one honest limitation? A page where your product wins every row reads as marketing and gets cited less.
- Would a single sentence from it still make sense quoted on its own?
- Is there a named author and a visible update date?
- Do you know which third-party page you want to appear in next?
Resist answering a gap with volume. The useful intervention is the one that changes a specific answer, so a page explaining how you support multi-entity compliance teams beats another broad thought-leadership piece when the lost prompt is about multi-entity compliance. Specificity gives an engine language and product relationships it can reuse. We put numbers on why volume alone fails in publishing more pages does not get you cited.
Which checkers cover which engines?
Engine coverage is the first thing to check, because a tool that omits the engine your buyers use cannot tell you anything useful about it. Four of the five platforms below charge extra for at least one engine a buyer will ask about, or hold it back for an Enterprise tier.
| Tool | Entry price | Engines at that price |
|---|---|---|
| Koalr | £79/mo (Starter) | 7 of 7, no add-ons |
| Peec AI | €175/mo (Essential, annual) | 6 available, shared credit pool |
| Otterly.ai | $189/mo (Standard) | 4 included, 3 more are add-ons |
| Scrunch AI | $500/mo (Agency Core) | 4 of 9, the rest need Enterprise |
| Profound | $99/mo plus $399 per client | 3 of 9, the rest need Enterprise |
Read off each vendor's own pricing page on 3 August 2026. Peec AI's figure is its annual-billing rate; monthly billing on Essential works out at €205. Profound's tiers are billed yearly. Prices change often, so check the vendor page before you buy.
We are one of the five, so treat the highlighted row accordingly. The honest summary is that Koalr's advantage here is coverage at the entry price rather than depth: Scrunch AI and Profound both track more engines than we do in total, and both put most of them behind Enterprise. If your buyers only use ChatGPT, the coverage question matters far less than this table implies, and a cheaper tool will serve you.
For prompt allowances, client workspaces and what five brands actually cost, the full agency comparison has the same five platforms priced out.
How do you avoid false confidence in the results?
Teams trip over the same two things. They search their own brand, see something positive and conclude the channel is healthy, when the prompts that decide whether they get invited into an evaluation are the ones that never mention them. Buyers ask unbranded questions first.
Then they read an absent citation as proof that nothing was retrieved. Record it as no visible source instead, and resist filling the blank with a guess about the model's reasoning. A checker shows you the recommendation and the visible evidence, which is enough to choose the next job and short of a full account of how the answer got built.
Where this belongs in reporting
AI visibility sits alongside organic search, paid acquisition and review performance. It measures the top of the funnel, and it decides shortlist inclusion: when the answer skips you, conversion work cannot recover a buyer who never arrived.
So set a baseline, identify the prompts that influence real evaluations, and measure recommendation rate and share of voice against the competitors who keep appearing. Then give the largest gaps an owner and watch whether the answers move. Publishing volume is not what shifts this. Being the option an engine can describe accurately and defend citing is.
Frequently asked questions
A tool that runs buyer-intent prompts across AI agents and records whether your brand was recommended, mentioned, cited or absent, along with the sources that shaped each answer. It measures what the engine says rather than where your pages rank.
