How AI search
picks an inspector.

AI search is the umbrella term for the new search experiences powered by large language models. The major surfaces:

• ChatGPT — when users ask "who's a good home inspector in [city]"
• Perplexity — search-focused AI assistant; cites sources directly
• Claude — Anthropic's assistant, increasingly used for local recommendations
• Google AI Overviews — the AI-generated summary at the top of Google search results
• Bing Chat / Copilot — Microsoft's answer to ChatGPT

All of these tools answer questions by pulling content from across the open web, summarizing, and (often) citing sources. Your goal is to be one of the sources they pull from when someone asks about inspection services in your market.

The signals are similar to traditional search but weighted differently. Roughly in priority order:

• Structured data — schema markup that explicitly tells the AI what your business is, where it operates, and what it offers.
• Topical depth — does your site cover home inspection topics in real depth, or just have a contact page and three blog posts?
• Citation pattern — are other sources (news articles, local resources, industry publications) referencing your business by name?
• Review signals — high review counts and positive sentiment carry into AI recommendations the same way they carry into local search.
• Geographic relevance — service area schema matters more in AI than in traditional Google because the AI is matching the question's location to your stated service area.
• Recency — AI assistants prefer recently updated content. Pages last touched in 2019 don't cite as well.

Schema.org markup is JSON-LD code in your website's HTML that tells AI assistants what your business is in a structured way they can't misinterpret. The minimum schema set for an inspector:

• LocalBusiness with subtype HomeAndConstructionBusiness — your name, address, phone, hours, service area
• Service — for each inspection service you offer (general inspection, radon, sewer scope, etc.)
• FAQPage — on any page with Q&A content; AI assistants pull directly from this schema
• Person — for the inspector(s), with credentials (InterNACHI, ASHI, license numbers)
• Review / AggregateRating — average rating and review count, pulled from your real review platforms

This is technical work but it's a one-time setup followed by light maintenance. Once it's in place, every AI assistant has a clean structured read of your business.

The patterns AI assistants prefer to pull from:

• Long-form, comprehensive coverage — 2,000+ word pieces that cover a topic exhaustively, not 500-word filler.
• Clear question-answer structure — explicit questions as H2/H3 headings followed by direct answers.
• Concrete numbers and specifics — "a sewer scope inspection in Seattle typically costs $250-$400" gets cited; "sewer scopes vary in price" doesn't.
• Named entities — explicit references to specific tools, brands, locations, people. AI builds knowledge graphs from these.
• First-person expertise — "In our experience, the most common defects in 1970s-built homes are..." reads as authoritative; generic third-person doesn't.
• Citations to authoritative sources — linking out to InterNACHI, NACHI, NAHB, government sources actually helps you, not hurts you.

Tracking AI search performance is the part of AEO that's still evolving. The current state of the art:

• Manual prompt testing — once a month, ask the major AI assistants the queries your buyers ask ("recommend a home inspector in [city]") and document who's cited.
• Referral analytics — Google Analytics 4 catches some AI referrals but most are hidden behind "direct" or unattributed traffic.
• Brand search lift — when AI starts mentioning you, you'll see branded search volume increase. That's the indirect signal.
• Specialized tools — Profound, Otterly, and AthenaHQ are emerging tools specifically for tracking AI citations. Worth it for businesses spending real budget on AEO.

None of this is as clean as Google's rank tracking. It's the price of being on a new surface — better tools come every quarter, but you have to do some manual work in the meantime.

If you're starting AEO from scratch, the 30-day checklist:

1. Audit your current schema with Google's Rich Results Test. Most inspector sites have minimal or no schema.
2. Add LocalBusiness, Service, and FAQPage schema everywhere applicable.
3. Identify the 5 most-asked questions your buyers ask in the sales process. Write a 300-500 word answer to each. Publish them as a FAQ on your site with proper schema.
4. Test the major AI assistants this week with a query like "recommend a home inspector in [your city]." Document the answer.
5. Re-test in 60 days after the schema and content work is live.