We Pointed Our Own AI Visibility Scanner at Our Category: The Raw Data (2026)

Part of the AI Search Hub, AEO Hub, and the generative engine optimization guide.

Most posts about AI visibility tools are written from the outside, by someone comparing dashboards. This one is written from the inside. I build Attrifast, and one of the things it does is run an AI Visibility scan: it asks the major AI engines a set of buyer-stage questions about your category, parses each answer, and reports whether you were cited, how you compare to competitors, and which sources the engines pulled from instead of you.

So I did the obvious, slightly uncomfortable thing: I pointed it at my own domain and published the raw output — including the parts that make us look bad. If you want to understand what an AI visibility scan actually produces before you buy one, this is the unfiltered version. For the category-level buyer's guide, see AI visibility tools: what they are and how to pick one; this is the case study that shows one running end to end.

The setup: what we actually ran

The mechanics matter, because they determine what the numbers mean. Here is exactly what happened, with nothing rounded for effect.

• Target domain: attrifast.com
• Prompts: 10, auto-generated from our own top-trafficked pages (more on why that choice backfired below)
• Engines: 4 — ChatGPT (via OpenAI), Claude (Anthropic), Gemini (Google), Perplexity (Sonar), each with live web search enabled
• Answers: 40 (10 prompts × 4 engines), 0 errored
• Competitors benchmarked: otterly.ai, datafa.st, peec.ai
• Sources collected: 320 cited URLs across all answers

Every engine ran the same prompts through its own native web-search tool, so each answer reflects what that engine actually retrieves and cites today, not a cached snapshot. The scan completed cleanly — a detail I care about because an earlier run of ours failed two-thirds of its calls to a rate limit, and a scan that silently drops 60% of its answers will hand you a garbage score with total confidence. Zero errors is the precondition for trusting anything downstream.

The prompts we asked

The ten prompts our scanner generated were broad, category-level questions:

Hold that list in mind. It explains everything about the result.

The headline number: a 0/100 score, and why that is the honest answer

Across 40 answers, our domain was cited zero times. Score: 0/100.

I could have quietly swapped in prompts I knew we'd win and shown you a flattering number. I didn't, because the zero is the more useful data point. On questions as broad as "what are the top AEO tools for 2026," the four engines reach for the brands with the most third-party coverage — Semrush, established SEO suites, the names that show up on every listicle. A two-person analytics tool does not surface there yet, and no amount of on-page optimization changes that overnight.

Here is the proof it's a prompt problem and not a detection problem. Separately, I ran a single sharper, commercial-intent prompt through the same pipeline — "best revenue attribution tool for Stripe payments" — and both ChatGPT and Claude cited attrifast.com directly [1], with Claude even pulling our /for/stripe page into its answer. Same scanner, same engines, same day. The only variable that changed was the specificity of the question.

Source: Attrifast AI Visibility scan, attrifast.com, July 2026

The takeaway for anyone measuring a young brand: broad category prompts flatter incumbents and bury challengers. If your prompt set is all "best X tool" questions, your score is really measuring your competitors' backlink history. Mix in the specific, high-intent phrasings your actual buyers type — the ones where your differentiation is the answer — or you will conclude you are invisible when you are simply asking the wrong questions.

Share-of-voice: where competitors get cited and we don't

This is where a scan earns its keep. The score tells you about you; share-of-voice tells you about the race. Because our scanner stores every answer, it computes competitor citation rates from the same 40 answers, and you can add competitors after the fact without re-running anything.

Source: Attrifast AI Visibility scan, attrifast.com, July 2026

The numbers, unrounded:

Two things jump out. First, Otterly is winning the category conversation — it's cited across three of the four engines while we're cited on none. That is not an opinion or a vibe; it is a count. Second, being a real, shipping product is not enough — DataFast is a well-known analytics tool and it also scored 0% on these prompts, which tells me this specific prompt set is skewed toward AI-visibility-monitoring queries where Otterly has invested in coverage. The gap is competitive and topical, and now it's quantified. For the full method behind this, see how to analyze your competitors' AI visibility and AI share of voice in 2026.

The most useful output: the 15 sources AI reads instead of you

If share-of-voice is the diagnosis, the cited-sources list is the prescription. Across the 40 answers, the engines pulled from 320 source URLs, and our scanner ranks the external domains by how many of your prompts they showed up for. This is, functionally, the list of places you need to get mentioned.

Source: Attrifast AI Visibility scan, attrifast.com, July 2026

The top of the list:

Look at what these are: listicles, forum threads, review sites, and comparison posts — third-party pages, not the vendors' own homepages. This is the single most important thing an AI visibility scan taught me about my own business: getting cited by AI is mostly an off-site problem. The engines trust the sites that already round up "best X tools," so the path to citation runs through earning a spot on those roundups, seeding honest comparisons, and showing up in the Reddit and LinkedIn conversations where buyers actually ask. That's the co-citation game, and it's covered in depth in AI citations vs backlinks. A monitoring tool that stops at "you scored 0" is useless; one that hands you the ranked list of where to go is a work plan.

The number no scan on this list can give you: revenue

Here is the honest limit of everything above, including our own scan. A visibility score tells you whether AI cited you. It cannot tell you whether that citation paid you. Every tool in this category shares the same blind spot, and it's structural, not a feature gap.

The reason is plumbing. AI clients strip the Referer header, so when ChatGPT sends someone to your site, the visit shows up in GA4 as Direct/(none) — indistinguishable from someone typing your URL. Independent measurement and our own data put 65–82% of ChatGPT visits in that Direct bucket [1][2]. So any "AI revenue" figure derived from a GA4 integration is built on a systematic undercount, and any monitoring tool quoting one is estimating.

The only AI revenue number you can defend is one tied to a transaction: an AI-referred session traced through to a paid Stripe invoice or Shopify order. That requires a first-party attribution layer that captures the AI referral before the header is stripped and joins it to the payment webhook — which is the actual job Attrifast does, and why we treat the visibility scan as the top of the funnel, not the whole thing. A high citation share with flat revenue-per-visitor usually means you're being cited for informational queries that don't convert — a false positive a score-only tool will never catch [3]. For the full argument, see does GEO actually drive revenue and AI visibility metrics and KPIs.

What I changed after reading my own data

A scan is only worth running if it changes what you do. Here's my actual to-do list coming out of this one:

1. Fix the prompt set. Auto-generated broad prompts undersold us. I'm replacing half of them with the specific, commercial-intent questions our buyers ask — the ones where "Stripe-native revenue attribution" is the answer — and keeping a few broad ones as a category barometer.
2. Work the cited-sources list, top-down. Semrush, the AEO listicles, the Reddit and LinkedIn threads — that ranked list is a placement backlog, and it's more actionable than any generic "build backlinks" advice because the engines told me exactly which domains they already trust for my category.
3. Watch the trend, not the day. One scan is a snapshot of a probabilistic system. The number I'll act on is whether our share-of-voice moves against Otterly's over the coming weeks — which is the whole point of running it on a schedule.
4. Keep measuring revenue separately. The score is a leading indicator. The lagging indicator that actually matters — did AI-referred visitors pay — lives in the attribution layer, and that's the number I'll report to no one but myself and, eventually, a board.

If you want to run the same scan on your own category and see your share-of-voice, your competitor gap, and your own version of that 15-domain placement list, that's a few clicks inside Attrifast.