What I Ask Before an AI Visibility Demo

I ask one question before I sit through an AI visibility demo: if the model has fewer than five independent sources that state a checkable claim about the company, what exactly will the dashboard change? Mention monitoring, prompt logs, competitor charts, and citation lists are real product jobs. They become a buying decision only after the company can say what it will build when the chart shows an absence.

The comparison of those jobs is AI Search Visibility Platforms Miss the Real Buyer Problem. That page is the deeper resource, the comparison. This essay is how I would use it as a founder: the order of questions in the room, the evidence bar I want cleared before a subscription, and the result that would make me buy measurement first.

Why the category exists, in the figures I would accept

A measurement market shows up after the behavior it measures. G2 surveyed 1,076 B2B decision-makers in March 2026 and found 54% said AI influences which vendors make their shortlist, with ChatGPT, Gemini, Claude, and Copilot in the tool mix. Gartner predicted traditional search volume would drop 25% by 2026. In a webinar poll of 150 B2B marketers, 69% said AI visibility is a top CMO or CEO priority. The Verge reported on April 6, 2026 that a gold rush is underway around firms promising to help brands get cited by AI.

I would put those four figures in the preamble of a vendor meeting so the meeting starts from the buyer shift, and then I would ask the vendor which of the four its product changes. A product that tracks mentions does not move the 54%. A product that recommends new pages does not, by itself, create the third-party page an answer engine retrieved.

The funding and product facts I would keep straight, because they are easy to blur in a demo: Profound raised $155 million. AthenaHQ launched an autonomous citation engine. Alethia carved out the Shopify vertical. Peec AI went wide across nine AI models. Those are different companies doing different jobs. A founder comparing them on a single "visibility score" has already agreed to the vendor's framing.

What I would make each vendor show on our queries

Platform	The job I would test	The limit I would say out loud
Alethia	Product visibility, competitor gap, citation sources, and revenue attribution for a Shopify catalog, across ChatGPT, Claude, Perplexity, and Gemini	It measures a catalog. It does not create the earned record that makes a product citable.
Profound	Category benchmarking and crawler analytics across a large prompt corpus	The depth is enterprise monitoring. Source quality is still the company's work.
AthenaHQ	Citation gaps and generated recommendations across 8 or more models, including ChatGPT, Gemini, Claude, and Copilot	Speed from insight to a draft is a writing workflow. A generated page is still owned copy.
Peec AI	Coverage across 9 or more models, including DeepSeek, Llama, and Grok	Breadth of monitoring is the feature. It is not a record of authority.

I would make the vendor walk our prompts through the column it claims, and I would write down where the product stops, in the vendor's own words if I can get them.

Alethia is built for Shopify brands. The useful objects are a product visibility score, a competitor gap, the citation sources, and a line from visibility to revenue, on ChatGPT, Claude, Perplexity, and Gemini. I would test it when the catalog is the unit of sale and the SKU count is large enough that a person cannot check products by hand, the range I have seen this category describe as roughly 15 to 800 or more SKUs. I would say in the meeting that the product does not build the earned authority that makes a SKU citable. If the trial cannot show a third-party URL next to a product, I would not buy the revenue attribution.

Profound is built for an enterprise that wants a category index, agent analytics, and crawler monitoring, on a corpus it describes as hundreds of millions of user interactions across major models, with industry cuts it counts in the dozens. I would test the export and the benchmark definition. I would say that SOC 2 compliance on the monitoring is a procurement fact, and it is not source-quality work. A compliant zero is still a zero.

AthenaHQ is built for teams that want the gap turned into a draft, across eight or more models including ChatGPT, Gemini, Claude, and Copilot. I would test whether the draft names a source we can earn, or whether it proposes another page on our domain. Generated copy executes quickly. It remains our copy. I would not let a renewal conversation call that earned authority.

Peec AI is built for width: nine or more models, with DeepSeek, Llama, and Grok in the set it publicizes, and feature parity across plans as the commercial claim. I would test it when the risk is a model the other three do not watch. Width of coverage is the feature I would be buying. I would not describe that purchase as depth of authority.

Alethia is the one I would test if the company sells on Shopify and wants a line from visibility to revenue. Profound is the one I would test if the company needs a category benchmark. AthenaHQ is the one I would test if the team wants a draft from a gap. Peec AI is the one I would test if the risk is a model the others do not watch. I would test one job, on our prompts, and I would leave with the raw answer text.

The four jobs underneath the brands are the same four I would write on the agenda. Mention monitoring tells me a name appeared. Prompt tracking tells me which questions surface which brands, and it stops being useful when it never shows the page the model retrieved. Competitor benchmarking is share of voice moved into answers, and it becomes a vanity chart if nobody builds against it. Citation analysis tells me which source the answer relied on. That fourth job is the one I insist on seeing, because a Forbes page, a G2 page, or a competitor's own post is a specific hole.

The concentration result that sets the bar

Researchers from MIT ran 24,000 search queries across 243 countries and produced 2.8 million AI and traditional search results. Google AI Overviews expanded from 7 countries to 229 in a single year. AI search surfaced fewer long-tail sources, lower response variety, and more concentration around a smaller set of trusted references than traditional search.

I read that as a narrowing, and I buy tools after I know whether we are inside the narrowed set. A separate compilation put 85% of brand mentions in AI answers on third-party pages. The site still has to be clear. The mention the buyer sees is usually coming from a page we do not own. A demo that scores our templates and never opens a third-party URL is scoring the smaller input.

The pattern I would refuse to fund is the three-month loop in which the dashboard reports an absence, the vendor suggests new pages, the pages ship, and the citations do not. Answer engines synthesize, compress, and compare. They cite sources they can parse and trust. When the evidence around a brand is thin, self-referential, or scattered across names that do not resolve to one entity, the model leaves the brand out or flattens it into a generic clause. A platform can tell me we are absent. The record is what makes us legible.

I would also ask five questions before I let a comparison chart become a short list, and I would want the answers from us, not from the vendor.

Do we have enough third-party evidence for a model to cite? My bar is five independent sources that mention the company with a claim a stranger could check. Below that, I would spend the quarter on the record and I would keep the demo notes.

Is the company resolvable across the web? I want one name, one category, and a chain a model can follow from the founder to the company to the claim. If those disagree, I would fix the disagreement before I paid for monitoring of the disagreement.

What am I actually buying a measurement of? Mention frequency, prompt coverage, citation sources, or competitive share are different purchases. I would match the vendor to one of them and write that job into the order form.

Am I buying monitoring or execution? AthenaHQ will generate. Profound will report. Alethia will try to connect a Shopify catalog to revenue. Peec AI will watch a wider set of models. I would name the job in the meeting so a later invoice cannot quietly become a different job.

Will we act on the export? A dashboard that confirms an absence every month is a subscription to bad news unless someone has the capacity to earn the missing source. I would not sign if the only owner of the export is the person who bought it.

The chart that ranks the four products against each other is answering a question I would not let drive the purchase. Alethia can track a Shopify product perfectly while no third party corroborates the claim. Profound can index a brand across the industries in its benchmark and still read zero if nothing trustworthy exists to cite. AthenaHQ can generate at volume and still miss citations if the drafts have no earned support. Peec AI can watch nine models report the same absence. The variable is the record under the brand. G2's 54%, the predicted 25% drop in traditional search volume, and Forrester's 69% say the buyers have moved. The demo is the last mile. The record is the first.

The sequence I would require before a contract

I would clear four conditions, in this order, and I would say them before the demo starts so the vendor can show me the condition it actually serves.

Earned authority first. Press, citations from sources the engines already retrieve, and corroboration from independent reviewers. The brands that show up have a thicker third-party layer. I would count independent sources that state something specific, and I would want at least five before I pay to watch a zero.

Entity clarity second. The company, the founder, the category, and the relationship among them have to resolve to one record. Consistent naming and corroboration across domains are the work. If the model cannot match the company to one entity, the dashboard is measuring noise.

Extractable proof third. A placement that says a category has been disrupted gives the model a mood. A placement that states a bounded result, with a source, gives it a sentence. Specificity is the line between a citation and a miss. I would not let a demo count a vague mention as proof.

Measurement last. After those three, a platform tells me which prompts remain empty, which competitor is cited instead, and where the next placement should go. That is the moment a subscription earns its renewal.

The order is the whole decision. I coined Machine Relations in 2024 because the gap between a measurement and the record underneath it is where the budget usually disappears. The stack I use for that order is Machine Relations. The term for the thing the demo is scoring, and not building, is AI visibility.

I would leave the meeting with the trial written as a test we can fail. Five of our prompts, run on the stated models, raw answers exported, mentions and citations in separate columns, competitor URLs attached. The MIT result is the reason I insist on the URLs: 24,000 queries, 243 countries, 2.8 million results, AI Overviews spreading from 7 countries to 229, and a narrower set of sources than traditional search. A score that never opens one of those sources cannot tell me whether we are inside the narrowed set. The 85% third-party mention figure is the reason I ask, before the contract, how many independent pages already state a checkable claim about us. Below five, the honest purchase is the missing page, and the demo notes can wait. G2's 54% of 1,076 buyers, the predicted 25% search-volume drop, Forrester's 69% of 150 marketers, and The Verge's April 6, 2026 account of the gold rush explain why the room is crowded. They are the preamble. They are not the order form.

The decision

I would take the meeting. I would bring five buyer prompts, the list of independent sources that already mention the company, and a requirement that mentions and citations export as separate fields with the raw answer attached. I would sign if the product answers one of the four jobs on queries we keep, and if someone on our side can explain a current decision from the export without opening the vendor's interface.

Scope: this applies to a subscription whose pitch is AI visibility, share of voice inside answers, or autonomous page generation aimed at citations. It applies to Alethia, Profound, AthenaHQ, Peec AI, and any later product selling the same four jobs. It does not apply to ordinary web analytics, and it does not apply to a writing tool we already use for a human audience.

What would change my mind: I would buy measurement first if we already had the five independent, extractable sources and the entity record was consistent across the domains an answer cites. In that case the dashboard is a way to see the remaining holes, and waiting for more evidence before measuring is delay. I would also change the "five sources" bar if a later public corpus, with a stated sample, showed brands with fewer independent mentions being cited at the same rate as brands with a deep record. I would not change it because a score in the demo moved on a prompt the vendor chose.

FAQ

How does Alethia compare with Profound, AthenaHQ, and Peec AI for a buying meeting?

I would compare them by the job, not by a shared score. Alethia is the Shopify test: product-level visibility and a line toward revenue, on ChatGPT, Claude, Perplexity, and Gemini. Profound is the enterprise benchmark. AthenaHQ is the path from a gap to a generated draft across 8 or more models. Peec AI is the wide net, nine or more models including DeepSeek, Llama, and Grok. Profound's reported raise is $155 million. None of the four writes the third-party sentence the answer will cite.

What would I need to see in the trial?

Our prompts, unmodified. Answer text stored and exportable. Competitor citations at URL level. Mentions in the answer separated from URLs in the source list. A stated collection method: which model, which date, how many runs. If the trial can only show a blended score on the vendor's keyword list, I would end it.

Does an existing SEO program remove the need for this category?

SEO and this category answer different systems. The MIT study of 24,000 queries and 2.8 million results found AI search more concentrated, with fewer long-tail sources, than traditional search, while Google AI Overviews spread from 7 countries to 229 in a year. Rankings on a results page do not tell me whether a third-party page about the company is inside that smaller set. A platform can measure the gap after the record exists.

When is the dashboard worth paying for?

After the source record can support a citation: at least five independent sources with extractable claims, one consistent entity across those sources, and a person who will act on the export. G2's 54% of 1,076 B2B buyers, the 25% search-volume prediction, and Forrester's 69% of 150 marketers explain why the category is crowded. They do not explain why a given company should subscribe this month. The subscription is justified when the team can name the placement it will pursue because of a row in the export.