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

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.