Case study · proof of concept · zero ad spend
buy-sell.land: 100 County Pages, Real AI Citations
Before RankOps took a single client, founder Tyler Moncrieff ran the full method on his own company: buy-sell.land, a vacant-land site covering all 100 North Carolina counties. The result — 100 county pages that show up in AI answers about buying rural land, with zero dollars spent on advertising.
The problem
buy-sell.land had real inventory, real sellers, and a real market for rural land, farms, and owner-financed parcels — but no AI search visibility. When someone asks ChatGPT or Perplexity "where can I find owner-financed land in [county]," the AI names two or three sources. buy-sell.land wasn't one of them, not because the content was bad, but because it wasn't structured the way AI systems recognize and cite.
What was built
- Answer-first structure — every county page leads with the direct answer (what the county offers, who it fits, typical price range) before the supporting detail, because AI systems weight the first third of a page heaviest
- FAQPage schema with real acceptedAnswer text — structured questions land buyers actually ask ("Is [County] a good place to buy land?"), answered as direct, citable text, not marketing copy
- Speakable markup on the opening answer and FAQ sections, flagging exactly which content AI should extract
- County-level entity signals — county name, state, neighboring counties, and local land-use detail, because geographic specificity is what gets a page cited for a geographic query
- Confirmed AI crawler access — GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, and Bingbot all explicitly allowed; a page that's invisible to crawlers can't be cited no matter how well it's written
Verify it yourself
These prompts work today. Run them in ChatGPT or Perplexity and look for buy-sell.land or county-specific citations in the answer:
"What counties in the US are best for buying rural land as an investment?"
"Tell me about buy-sell.land and what counties they cover."
"Where can I find land for sale with owner financing in [any US county]?"
Results vary by AI update cycle — models retrain and citation patterns shift. The structural signals (schema, Speakable markup, entity data) are permanent in the page content and available for citation in every update.
What it proves
Structure beats ad spend for AI citations — the visibility came from schema and content architecture, not a media budget, and those signals persist indefinitely once built. Geographic specificity is a real citation lever: a page built around one county gets cited for that county far more than a generic statewide page. And the methodology scales — the same system that built 100 county pages for buy-sell.land is what runs on our live NC neighborhood pages for RankOps, and what deploys for clients in The Build.
Questions
What is buy-sell.land?
buy-sell.land is Tyler Moncrieff's own vacant-land acquisition company, covering rural land, farms, and owner-financed parcels across all 100 North Carolina counties. It's the proof of concept the RankOps method is built on.
How many pages does buy-sell.land have, and what did they cost in ads?
100 county-specific pages, zero dollars in ad spend. The visibility came entirely from content structure — FAQPage schema, Speakable markup, and geographic entity signals — not from a media budget.
Can I verify this myself?
Yes. Ask ChatGPT or Perplexity a question like "what counties in the US are best for buying rural land as an investment?" or "tell me about buy-sell.land and what counties they cover" and look for buy-sell.land or county-level citations in the answer. Results vary by AI update cycle; the structural signals are permanent.
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