LocalBusiness Schema: What AI Engines Actually Read

Schema markup is structured data that tells machines — search engines, AI models, knowledge graph crawlers — exactly what your business is, where it is, and what it does.

For local businesses, LocalBusiness schema is the single most impactful technical implementation for AI visibility.

Why AI models care about schema

When an AI retrieves information about a business, schema markup is unambiguous. It's structured, machine-readable, and requires no inference. A model doesn't need to guess whether "Burlington plumber" means Burlington, Ontario or Burlington, Vermont — the schema tells it.

This reduces the chance of an AI giving confident but wrong information about your business, and increases the chance it cites you with accurate details.

What to include

At minimum: business name, description (specific, including your city and service type), street address, phone number, opening hours, and areaServed. The areaServed field is frequently missing — without it, AI doesn't know you serve surrounding cities.

What most local sites get wrong

We see three common schema mistakes in our audits. First, using generic "Organization" instead of the specific type (Plumber, Dentist, LegalService, etc.). Second, missing areaServed. Third, stale hours data — businesses that changed hours and never updated their schema.

The service-specific types

LocalBusiness has subtypes that are more precise. Using "Plumber" instead of "LocalBusiness" as your @type consistently improves AI citation rates in our data. The more specific the type, the more confidently AI models will match your business to relevant queries.

Implementation

The schema block goes in the head of your homepage as a JSON-LD script tag. Most modern website platforms (WordPress, Squarespace, Webflow) have plugins or built-in fields for this. If you're on a custom site, it's about 20 lines of JSON.

Not sure where your schema stands? Run a free Signal Check at sourcepull.ca — it checks for JSON-LD blocks and reports exactly which schema types you have and which ones you're missing.