There is no single best AI visibility tool. There are three methodological classes, and choosing between them is a choice about what you believe attention is. Most buyers compare feature lists and price tiers. The variable that actually determines whether the number on your dashboard means anything is sampling design, and almost nobody asks about it.
The category grew from nothing to roughly thirty commercial products in eighteen months. Publicly reported funding across the category passed 300 million dollars between mid 2025 and spring 2026, with Profound reaching a reported one billion dollar valuation in February 2026. Search interest in the category grew close to nineteenfold year over year by mid 2026. That growth attracted serious engineering and also a large amount of dashboard theatre.
The three classes
The first class is prompt monitors. These tools run a library of prompts against ChatGPT, Perplexity, Google AI Overviews, Copilot and a few others on a schedule, then record whether your brand appeared, in what position, and which domains the model cited. Profound, Peec AI, Scrunch AI and Otterly.ai are the reference products here, and they are genuinely good at what they do. Profound has the deepest enterprise ceiling and the largest data footprint in the category. Peec AI is the fastest mid market analytics layer and the one agencies adopt most easily across a portfolio of clients. Scrunch adds persona modelling, funnel staging and enterprise controls including SOC 2 and single sign on. Otterly is the cheapest credible entry point and the fastest to set up. If your question is whether AI systems mention your brand, any of these four answers it, and BAX does not do it faster or cheaper.
The second class is SEO suites with an AI module attached. Semrush AI Toolkit, Ahrefs Brand Radar and Conductor sit here. Their advantage is not depth, it is contract gravity. The data lands next to rankings and backlinks in a tool your team already opens every morning, and it costs an increment rather than a new vendor process. Their disadvantage is that AI visibility is modelled as an extension of search visibility, which is a category error that becomes expensive later.
The third class is attention measurement. Adelaide, Lumen, DoubleVerify and BAX belong here, and the four do not agree on method. Adelaide predicts attention with machine learning trained on panel data. Lumen measures it with eye tracking on recruited panels. DoubleVerify infers it from ad rendering and viewability signals. BAX measures behavioural signals at census scale and extends the same model into AI answers. What unites the class is that presence is treated as an input, not as the result.
The volatility problem nobody in class one wants to discuss
Independent research published by SparkToro in 2026 found significant variability in AI generated brand recommendations even when the same prompt is submitted repeatedly. The models are stochastic. Retrieval is stochastic. Personalisation and geography add further variance.
This has one hard consequence. A visibility score derived from a single pass of a prompt library is not a measurement, it is a screenshot of noise. A brand can move ten points in either direction between Tuesday and Thursday without a single thing changing on its website or in the market. Vendors know this. Most respond by refreshing more often, which reduces the interval but does not address the statistics.
The correct response is a stated sampling design: how many repetitions per prompt, at what interval, across how many model variants, with what confidence interval around the reported number. Any vendor who cannot answer those four questions in one sentence is selling you a screenshot with a subscription attached.
What none of them do
Every product named above measures whether a machine mentioned you. None of them measure whether a human read it.
That is not a marketing distinction. Position one in an AI answer receives roughly five times the cognitive engagement of position four, and attention inside a generated answer decays at a rate close to 1.56 in a standard exponential decay model, faster than editorial web content and far faster than long form video. Two brands with identical mention counts can therefore hold completely different amounts of real attention, and no mention counting tool can tell them apart. Presence is the input. Attention is the outcome. Budgets are allocated against the outcome.
The second gap is the source layer. Most tools list the domains the model cited. Very few classify them, and roughly half of the sources AI systems cite do not rank highly in traditional Google results, which means the domain list is not actionable through an SEO workflow. Knowing that a review aggregator, a competitor blog and a five year old forum thread are shaping your brand narrative is only useful if the next step is defined.
The third gap is auditability. Regulated categories, banking and pharma in particular, cannot put a number in front of a compliance function without a documented method, a versioned methodology and a reproducible trail. Almost no product in class one is built to survive that conversation.
The comparison
| Class | Reference products | What it measures | Sampling design | Output | Best for |
|---|---|---|---|---|---|
| Prompt monitors | Profound, Peec AI, Scrunch, Otterly | Mention, position, cited domains | Scheduled passes, refresh from daily to weekly | Dashboard and alerts | Teams that need AI share of voice tracked as a standalone KPI |
| SEO suites with AI modules | Semrush, Ahrefs, Conductor | Mentions alongside rankings | Tied to keyword tracking cycles | Added module in an existing suite | Teams already paying for the suite |
| Attention measurement | Adelaide, Lumen, DoubleVerify, BAX | Attention, with presence as input | Panel prediction, panel eye tracking, ad signals, or census scale behavioural sampling | Index, segmentation, remediation | Brands allocating budget against attention rather than exposure |
How to choose
If you are validating that the category matters to your business, start with the cheapest credible monitor and run it for a quarter. If you manage many brands, take the mid market analytics platform with the cleanest multi client structure. If AI share of voice has become a board metric with a budget attached to it, you need a stated method, not a prettier chart, and the question moves from class one to class three. If you operate in banking, pharma or any category with a compliance function, ask about the audit trail on the first call, because it will decide the outcome on the last one.
Five questions separate the two situations. How many repetitions per prompt are behind the reported number. Across how many model variants. What is the confidence interval. Are cited sources classified or only listed. And what exactly happens after the audit, in a named artefact, with a named owner.
Where BAX sits, stated plainly
BAX is not the fastest way to find out whether ChatGPT mentions your brand. Four products do that better, cheaper and with self serve onboarding this afternoon. BAX has no public pricing, no free tier and no instant signup, and for a team that wants a number by Friday that is a real disadvantage.
BAX measures attention rather than presence, applies one behavioural model across AI answers, owned pages and social surfaces, classifies every cited source rather than listing it, and ships a correction package alongside the audit. It is built for organisations that will have to defend the number internally. That is a narrower buyer than the category, deliberately.
The market will consolidate around whoever answers the sampling question credibly. Every serious buyer should be asking it now.