Show IH: I built 18 industry pages for the small businesses AI search ignores — which one did I leave out?

Good flag — and you're onto something real. Voice search (Siri/Alexa) and AI search (ChatGPT/Gemini) aren't identical, but they share the thing that matters: people phrase queries as full conversational questions, not keyword fragments. That conversational angle is actually the core of what the tool tests — buyer questions instead of keywords — so we're partly there already.

Where I think your point adds something is the structural side: FAQ content written as literal Q&A, question-form headers, and speakable/FAQ schema that makes it easy for an engine to lift a clean answer. The reports already push FAQs, but the schema angle is worth building in more explicitly.

Curious what specifically you added on your site — was it the schema markup, the question-style content, or both? Would genuinely help me sharpen this.

The three-layer split is a useful way to put it — discovered → understood → recommended. Agree the first two are the easy part; the third is the whole game, and it's exactly where the 2/8 businesses are getting stuck. They're discovered and understood fine, they just don't have citable proof for the specific buying decision.

Your last point is the one I'm most interested in: tracking, across all 18, which questions surface a business vs. which swap in a competitor. That's basically the dataset I'm accumulating one report at a time, and you're right that it may end up more valuable than the pages.

One early signal that pushes against the "AI bias toward local businesses" theory: a metal-art studio — not a local-services business at all — also came back 2/8, same pattern, competitors named in its place. Small n, but it hints the gap is content depth rather than a local vs. non-local bias. Whether that holds as businesses actually add the FAQ/service content is the thing I don't know yet — which is exactly the experiment.

This is a great add, and coming from 20 years running one it carries weight. You've named the exact thing the tool is built on — owners knowing their trade cold but having no idea what buyers actually type into ChatGPT. An MSP owner can be genuinely excellent and still be invisible for "how do I pick an IT provider for a 50-person office," because nothing on the site is written in that language.

MSPs are also a near-perfect fit for the 2/8 pattern: high-trust, high-consideration purchase, tons of small owner-run firms, and site copy that (as you said) often reads like 2005 — so the model has plenty to recognize the firm by name but nothing citable for the actual buying decision.

Adding IT/MSP to the next batch. And if you still know owners in that world, I'll happily run reports on a couple and post the real results — good or bad. That'd be a better test of the hook than anything I could argue for it.

Thanks — you zeroed in on the two things I care most about. The "known by name vs. discoverable for real queries" gap is the whole reason the tool exists; a business can be fully understood by the model and still never get named when it counts.

And yeah, "it takes an hour" is deliberate — a visibility score just tells you you're losing, an hour-long fix tells you what to do about it before you close the tab. If you run one of the 18 industries (or know someone who does), happy to run a real report and post what it finds.

Boutique PR/creative agencies is a great call — and the holding-company thing is a perfect example of the 2/8 pattern: the model defaults to the big recognizable names because that's what it has citable proof for, and the actual boutique the buyer wants gets skipped. Adding it to the next batch.

Your point about context beyond citation is the sharpest thing in here, and it's honestly a layer past what the tool measures today. Getting named is necessary but not sufficient — if the answer files you under the wrong category or shows a stale detail, the citation doesn't convert. That's a real gap in what I'm auditing, and it's the kind of thing your side (actual AI-referred visit data) sees that a static test can't. Worth me thinking about how to fold that in.

And I ran yours, since you dropped the link. Amami came back at 2 of 8 — same pattern as the post's example. ChatGPT and Gemini both describe it accurately on branded searches, but on the unbranded ones ("privacy-focused GA alternative," that kind of thing) they surface Matomo, Plausible, and PostHog — 4 mentions each — with Amami absent. So it's not identity; it's the lack of citable sector proof (case studies, deployment stories, a real comparison page) for the specific migration decision. Ironic given what you do, but the quick wins the report flagged are a privacy/data-access statement, a GA-alternative comparison block, and a dedicated MCP setup page — all fast. Happy to send the full thing if useful.

The ordering is basically a speed-to-proof ratio — what's fastest to ship that gives the model something concrete to cite.

Quick wins go first: things like a services block, an FAQ, or a comparison section, because they're a few hours of writing, not a rebuild, and they directly answer the exact questions the business dropped out on. If a buyer question surfaced a specific gap (say, no pricing or process info anywhere), the fix that closes that exact gap jumps the queue over anything more general.

The bigger moves — case studies, dedicated comparison pages, in-depth guides — come after, not because they matter less, but because they take real time and are more valuable once the cheap fixes are live and the business has proof to draw on. There's also a "what's actually true today" filter: I won't recommend a case-study page before the business can say who a real client was and what the outcome looked like. The fix has to be something they can honestly write, not just a page shape that looks good to a model.

Fair worry, since "18 industry pages" could easily become one template with find-and-replace on the industry name.

The main defense is that the report itself is never templated — it's generated fresh per business from the actual test results: which questions it dropped out of, which real competitors got named instead, what specific gap showed up (pricing, process, sector proof, whatever). That's pulled from live data every time, so even two businesses in the same industry get different reports because their actual gaps are different — see the ZEAM (art studio) and Amami (analytics) reports above, both 2/8 but nothing alike in the specifics.

Where the landing pages themselves could drift is in the industry framing around the report — the intro copy, the example gap described, the language used for "what buyers ask." I keep those industry-specific by actually building each industry's buyer-question set from real scraped language for that trade, not reusing a generic set with nouns swapped. If two industries start pulling from suspiciously similar question sets, that's my signal something's gone lazy and needs a rebuild, not a copy-paste.

The pages are meant to be thin on purpose — the report is where the real specificity has to live, and that's the piece I protect from templating.

Good question, and honestly yes — that's turned out to be a bigger effect than I expected going in.

Most owners start from "am I ranking," which is a Google-era mental model that doesn't map onto how AI search actually works. The reports seem to shift that framing just by showing the mechanic directly: you're recognized by name, you're just not being recommended for the question a buyer actually asks, and here are the specific competitors sitting in your spot. That "known vs. recommended" split does more to change how someone thinks about the problem than any fix on the list — a few people in this thread have said basically that, that the gap itself was the surprising part, not the to-do list.

The competitor-names line does something similar — seeing "Wappenschmied" or "Matomo" named in your place makes it concrete and personal in a way a visibility score never does. It stops being an abstract AI-trend problem and becomes "that specific business is answering a question I should be answering."

I don't have hard data yet on whether that reframing changes behavior (do they actually go make the fixes, or just feel unsettled and move on) — that's the thing I'd want to track next, maybe by following up with people who got a report and seeing what they actually shipped.

Good distinction, and it comes down to volume and intent overlap rather than any single question being "special."

A question becomes its own page when it's asked often enough across an industry's buyer language, and answering it well requires enough depth (process, pricing logic, comparisons, examples) that cramming it into an FAQ entry would bury the useful part. If a question keeps showing up in slightly different phrasings across many businesses in the same trade, that's a signal there's real search volume behind it and it can carry a dedicated page.

Everything else — the narrower questions, the ones specific to one business's niche, the ones that are more clarifying than decision-driving — stays as supporting content like FAQ entries. Those still matter for the AI test (they're often exactly what trips a business up in the 8 buyer questions), they just don't have the volume or depth to earn a standalone page.

Rough rule: if multiple businesses in an industry would all want the same page, it's a page. If the answer is specific to one business's situation, it's a fix I recommend in their report instead.

Both — the scraped data gets it onto the shortlist, but the AI responses decide what actually makes the final 8.

The flow: I pull candidate questions from real buyer language (PAA boxes, Reddit/forum threads, review text), which gives me 20-30 per industry. Then I run them against ChatGPT and Gemini to see how they behave. The ones that make the cut are where the model would have to know the specific business to answer well — not too generic (everybody gets named) and not too niche (nobody does). A question only earns its spot if the AI response shows there's a real gap to surface.

So the scraped data tells me what people ask, but the AI validation tells me which of those questions actually separate a recommended business from an invisible one.

Real language first, then filtered by hand — not just what I'd ask myself, though that's a sanity check at the end.

The raw material comes from actual buyer phrasing: People Also Ask boxes, Reddit/forum threads where people ask "how do I find a good X," and review text where customers explain who they picked and why. That gives me 20-30 candidates per industry. I don't have direct access to private AI chat logs, so search-side query data plus that review language is my closest proxy for how people actually talk when they're deciding.

Then I narrow to 8 by running them against ChatGPT and Gemini and keeping the ones where the model has to actually know the business to answer well. The "what would I ask as the customer" gut-check is really just the last filter to catch anything that reads like marketer language instead of buyer language.

That gap is the whole point, and it's why I think most visibility scores miss it — being known isn't the same as being recommended.

Accountants is a great pick, especially given how trust-driven that decision is. Adding it to the next batch.

Here's a reply draft — answers the question and gives them the real report result like you promised in the post:

Thanks! On the questions: it's mostly real query language — People Also Ask boxes, Reddit/forum threads, review text where people explain who they picked and why — filtered down by hand to 8 that are common but specific enough that the AI has to actually know the business to answer well.

And I ran yours. ZEAM came back at 2 of 8 — same as the example in the post, funnily enough.

The pattern: ChatGPT and Gemini both know you when someone asks by name — they'll describe your metal illustration, album art, and logo work fine. But ask the questions a real buyer asks ("artist for dark metal album artwork," "death metal logo design," "reliable album-art commissions") and you drop out, with names like Wappenschmied, Mario Nevado Art, and Loner Illustration showing up instead.

So it's not an identity problem — it's a "no citable proof for the specific buying decision" problem. The quickest fixes the report flagged:

A short services block on the homepage naming exactly what you do (custom metal album art, dark illustrations, death-metal logo design, merch artwork) — ~45 min, gives AI something direct to quote.
A concise FAQ covering process, deliverables, revisions, timelines, and usage rights — ~1 hour, these were the exact things the album-art reliability question dug into.
Project credits on portfolio pieces linking each artwork to the band and release — verified context is what recommendation answers lean on.

Happy to share the full report if you want it — the bigger moves (a dedicated dark-metal commission page, case studies) are where the real lift is, but those three are the fast wins.

Mostly real query language, with some hand-curation on top. For each industry I'm scraping/reading actual buyer questions from places like PAA snippets, Reddit threads, and niche forums, plus review text where people explain their decision criteria ("looked for someone who specializes in X" type language). That gives me the raw material.

From there I narrow it down to 8 by hand — picking ones that are common enough to matter but specific enough that an AI model would have to actually know the business (not just recognize the industry) to answer well. I'm not fully happy with the rigor of that narrowing step yet, so if you've got ideas for validating question sets at scale, I'm all ears.

It's a blend, but weighted toward real signal rather than my own guesses. The starting point is scraped buyer language — People Also Ask questions, forum/Reddit threads, review text — for each industry. That's the raw pool. I then hand-pick the 8 that show up most consistently and are specific enough that getting recommended actually requires the AI to know something about the individual business, not just the category.

Where it's still me making judgment calls is the final filtering — deciding which 8 are the most representative without it turning into 40 near-duplicate questions. That's the part I'd like to make more systematic, maybe by validating against actual model query logs instead of just search-engine proxies.

Good question, and honestly the honest answer is "both, in stages." I start by pulling real language — Google's "People Also Ask" boxes, Reddit/forum threads where people are actually asking "how do I find a good X," review sites where customers explain why they picked someone, and for industries I know well, actual sales call notes or intake questions. That gives me a rough set of 20-30 candidate questions per industry.