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Schema That Actually Matters for Local AEO

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Schema That Actually Matters for Local AEO

AEO Snippet (for AI & Answer Engines): Local AEO schema is the structured data that allows AI systems like Google AI Overviews, ChatGPT, and Perplexity to identify, trust, and recommend a local service business.

The schema that matters most for Local AEO is LocalBusiness, supported by Service, Review / AggregateRating, and areaServed markup.

Together, these act as a machine-readable nutrition label that tells AI:

  • who the business is
  • what services it provides
  • where it operates
  • and whether it is trusted

If this information is missing, incomplete, or inaccessible to crawlers, the business is unlikely to be included in AI-generated local recommendations.

Most schema advice is junk.

Not wrong, just useless.

It’s either:

  • A copy-paste of Schema.org definitions with no context
  • Or a giant JSON-LD blob that technically validates but does absolutely nothing for visibility

Local AEO doesn’t care if your markup is correct. It cares if your markup is useful.

Think of schema as a nutrition label for AI systems.

Not marketing copy. Not rankings hacks. Just clean, machine-readable facts that answer:

  • Who are you?
  • What do you do?
  • Where do you do it?
  • Why should I trust you?

If an AI can’t answer those questions confidently, you don’t get selected.

This guide covers the only schema that consistently matters for Local Answer Engine Optimization, how to implement it without overengineering, and how to make sure AI systems can actually read it.

The Mental Model: Schema as a Nutrition Label

AI systems don’t rank pages. They synthesize answers.

When someone asks:

“Who’s the best estate planning attorney in Austin?”

The AI is not crawling your blog post like Google did in 2018.

It’s assembling a shortlist from:

  • Known business entities
  • Verified attributes
  • Reputation signals
  • Geographic relevance

Schema is the structured input layer for that decision.

Done right, it gives the AI a clean label:

  • Business type
  • Services
  • Coverage area
  • Reviews
  • Contact details

Done wrong, it’s ignored.

Below are the four schema types that actually move the needle for service-based SMBs.

1. LocalBusiness Schema (The Foundation)

If you only implement one schema type, this is it.

LocalBusiness schema defines your core entity. Everything else attaches to it.

For AEO, this is what tells AI systems:

  • You are a real-world business
  • You operate in a specific location
  • You belong to a known service category

What actually matters

At minimum:

  • @type (use the most specific subtype you qualify for)
  • name
  • address (fully structured)
  • telephone
  • url
  • openingHours

Example for a family law firm:

{
"@context": "https://schema.org",
"@type": "LegalService",
"name": "Smith Family Law",
"url": "https://smithfamilylaw.com",
"telephone": "+1-512-555-0123",
"address": {
"@type": "PostalAddress",
"streetAddress": "123 Main St Suite 400",
"addressLocality": "Austin",
"addressRegion": "TX",
"postalCode": "73301",
"addressCountry": "US"
},
"openingHours": "Mo-Fr 09:00-17:00"
}

No fluff. No marketing language. Just facts.

Common mistakes

  • Using Organization instead of a LocalBusiness subtype
  • Leaving address fields unstructured
  • Stuffing keywords into name

AI systems treat this as an identity record. Keep it boring.

2. Service Schema (What You Actually Do)

Most local sites talk endlessly about themselves and barely define their services.

AI systems care about the opposite.

They want a clean list of what you offer.

The simplest and most reliable pattern is:

  • makesOffer → Offer → Service

Example:

"makesOffer": [
{
"@type": "Offer",
"itemOffered": {
"@type": "Service",
"name": "Divorce Consultation",
"serviceType": "Family Law"
}
},
{
"@type": "Offer",
"itemOffered": {
"@type": "Service",
"name": "Child Custody Representation",
"serviceType": "Family Law"
}
}
]

This does two important things for AEO:

  1. It creates discrete service entities
  2. It lets AI match conversational queries to exact offerings

If someone asks:

“Who handles child custody cases near me?”

This is the data layer that gets you included.

What not to do

  • Don’t list every keyword variation
  • Don’t duplicate service pages
  • Don’t embed pricing logic unless it’s stable

Clarity beats completeness.

3. Review & AggregateRating Schema (Trust Signals)

AI systems are extremely conservative with recommendations.

They prefer:

  • Businesses with verified reputation
  • Consistent sentiment
  • Real volume

Schema helps make that explicit.

Minimum viable review markup

"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.8",
"ratingCount": "45"
}

Optionally add individual reviews:

"review": {
"@type": "Review",
"author": "Jane Doe",
"reviewBody": "Clear advice and very responsive.",
"reviewRating": {
"@type": "Rating",
"ratingValue": "5",
"bestRating": "5"
}
}

Important caveats

  • Reviews must exist on the page or a linked source
  • Don’t fabricate or scrape
  • Keep counts accurate

AI models cross-check. Sloppy review schema is worse than none.

4. AreaServed Schema (Local Relevance)

This is critical for service-area businesses.

Without areaServed, AI often assumes:

  • You serve everywhere
  • Or nowhere useful

Example:

"areaServed": [
{ "@type": "City", "name": "Austin" },
{ "@type": "City", "name": "Round Rock" }
]

This directly influences inclusion for queries like:

“Best HVAC company in Round Rock”

Pro tip

Match this to:

  • Your GBP service areas
  • Your core location pages

Consistency matters more than coverage.

Validation: Making Sure AI Can See It

Validation isn’t about green checkmarks. It’s about accessibility.

Use these tools

  1. Google Rich Results Test
  2. Schema.org Validator
  3. Google Search Console → Enhancements

Fix errors. Ignore warnings unless they block parsing.

Critical AEO detail

Most AI crawlers do not execute JavaScript.

If your schema is:

  • Injected via GTM
  • Rendered client-side

There’s a good chance ChatGPT, Claude, and Perplexity never see it.

Your JSON-LD must be present in the raw HTML response.

Server-rendered or static only.

Monitoring: Schema Is Not “Set and Forget”

Schema breaks silently.

Common causes:

  • Theme updates
  • Plugin changes
  • CMS migrations
  • Content edits

Build a simple routine:

  • Quarterly schema audit
  • Revalidate after major site changes
  • Spot-check AI visibility for brand queries

If AI answers change, check schema first.

Final Takeaway

It’s about being understandable.

Local AEO isn’t about gaming algorithms.

Schema is how you explain your business to machines without distortion.

If your schema clearly states:

  • Who you are
  • What you do
  • Where you do it
  • Why you’re trusted

AI systems can safely select you.

Everything else is noise.

Treat schema like infrastructure, not decoration.