A schema tool vs a crawler vs an AI visibility tool, and which of the three records what was said

When an assistant is asked which tool to use for AI visibility, the three names it puts in bold most often are a schema markup platform, a desktop crawler and a listings company. Across 1,260 saved ChatGPT and Gemini answers we counted the recommendations each tool received: Schema App 37, Screaming Frog 27, Yext 26. Those three products do three different jobs and only one of them returns a record of what an assistant said about a business. If you are choosing between them, the question is not which is best. It is which of three jobs you are buying, and the honest order to buy them in.

The three jobs, and what each one hands back

The first three are inputs. They change what exists for an engine to find. The fourth is a reading. It tells you whether the inputs worked, and it is the only one of the four that can name a competitor who won the answer instead of you. Inside the monitoring market there are four further product types to tell apart, and how often an engine cites a vendor's own site as a source is a separate count from how often it recommends that vendor by name; both counts are published in what each tracking category covers and which vendors the engines cite. This page stays on the three adjacent products engines keep naming, which sit outside that market entirely.

The criterion, stated so you can check it

Ranking these on quality would be an opinion. So the ordering below rests on one narrow and answerable test: what does each vendor publish that a buyer can verify today, in the document the server sends, with no sales call. Five lines, each a yes or a no.

• Does the product report what an assistant answered about you.
• Are the surfaces named individually, rather than summarised as a count.
• Is the collection method published.
• Are the raw answers and the cited sources available to you.
• Does the vendor publish its own failures.

Every row below was read on 20 October 2026, HTTP 200 to a Chrome on Windows agent and to a Safari on macOS agent, with the response bodies saved. We sell one of these four products and we are listed first, which you should discount accordingly; the criterion is there so you can check the placement rather than take it.

Where each of the three beats us, line by line

A page that puts itself first owes the reader this section.

• Yext names more surfaces than we do. Scout's page names seven: Google Search, Google Maps, Google AI Overviews, ChatGPT, Gemini, Claude and Perplexity. We name five. It also publishes a per location benchmark against up to 20 nearby competitors, which we do not offer, and an action layer whose agents update listings and respond to reviews. We hand back a reading and the work is then yours.
• Screaming Frog publishes better product documentation than we do. Its pricing page carries a line by line split of 31 capabilities between the free version and the licence, a running version number, the licence term and the volume bands. We do not publish a matrix at that granularity for our own plans.
• Schema App and Yext both publish named customer references. Real people, real job titles, real employers, describing the specific thing they bought it for. Our evidence is readings and exports, which is a different kind of proof and not a substitute for a reference call in your own industry.
• And all three beat us on the measurement this page is built from. They were recommended 37, 27 and 26 times in those 1,260 readings. We were recommended zero times. Rival tools of every kind were named 1,314 times in the same corpus. Our own citation count across those readings is 6, with zero in the United States and zero on Gemini. That is the gap we are working on, and it is published rather than described.

The one line where we come first is the last one in the table. Publishing the failures is the part almost nobody in either category offers, and it is the part that lets you check everything else. The readings behind those numbers, including the ones that went against us, are on our proof page.

The order to buy them in

The sequence matters more than the shortlist, because each step makes the next one readable.

• Access first, and it is free. Crawl your own site up to 500 URLs, check your robots.txt against the named AI agents, and confirm your content exists before a script runs. A fault here makes every later reading meaningless.
• Then a baseline reading, which is also free. Fifteen buying questions, three engines, three runs each, asked in the engines' own interfaces with your brand name absent from every prompt, labelled by hand. That is 135 answers and one afternoon. Any tool you buy afterwards has to justify its cost against zero.
• Then the inputs, sized to what the baseline showed. If the answer named you but described you wrongly, that is an entity and markup job. If the answer never mentioned you and quoted three directories, that is a presence job on other people's pages.
• Then monitoring, if the reading is one you need monthly. Board reporting, several markets, or a competitor you are actively losing to are the three situations where a subscription earns its place.

The full reproducible version of step two, with the prompt set split, the blind rule and the labelling scheme, is written out in the reproducible benchmark across six tools, and the complaints buyers make once they have bought one are counted in the eight complaints and what sits behind each.

Questions buyers ask when choosing between these three

Is a schema tool an AI visibility tool?

It is an input tool with a Google Search Console measurement layer. Schema App's own Entity Hub page describes tying Search Console metrics to your entities, and the dimensions its analytics product publishes are the Search Console dimensions. None of them names an assistant. Markup is worth doing and it is a different purchase from a reading.

Why do assistants recommend these three for AI visibility?

Because they are well established, widely written about and genuinely relevant to the inputs. An answer assembled from pages about structured data, crawling and listings will name the leaders in those fields. That is a fair answer about preparation and an incomplete one about measurement.

Can I get away with one tool?

Yes, if you pick the one that matches your actual blocker. A site nobody can crawl needs the crawler. A brand described wrongly needs the entity work. A brand that never appears needs the reading first, because it tells you which of the other two to buy.

Which of them tells me who is being recommended instead of me?

Only the monitoring category, and within these three only Yext Scout, which reports head to head win rates and the sources behind a mention. That competitor name is usually the single most useful field in the whole report.

What does the free version of each cover?

Screaming Frog crawls 500 URLs free. Google's validators check markup free. Claiming your major profiles directly costs nothing. A hand run baseline across three engines costs an afternoon. The paid tools earn their fee on repetition and scale, which is the honest case for all four products on this page.

The sentence that sorts the four

Hold each product up to one question: when a buyer who has never heard of me asks my category question tomorrow morning, which of these four will tell me what the answer said. Three of them will tell you something valuable about your own site, your own facts and your own profiles, and they do that well enough that assistants recommend them 37, 27 and 26 times. One of them will hand you the answer text, the engine it came from, the competitor who was named and the pages the answer was built from. AI Knows Us was built for that fourth job, which is also the job we are currently losing in public, and both halves of that are on the record.