Structured Data Implementation
JSON-LD schema markup implementation for AI visibility.
Guide content
Structured data foundations
Structured data explicitly defines entities, properties and relationships for machine consumption. It is the technical layer that enables AI systems to parse your information programmatically.
Step 1: Schema audit
Review existing structured data implementation. Identify missing schemas, inconsistent data and opportunities for expansion.
Step 2: Organization schema
Implement comprehensive Organization schema defining your brand name, description, URL, logo, founding date, address and key attributes.
Step 3: Product and Service schemas
Define your products and services with structured data. Include name, description, features, pricing and relationship to your organization.
Step 4: FAQ and HowTo schemas
Implement FAQPage schema for Q&A content. Use HowTo schema for instructional content. These formats are directly usable by AI systems.
Step 5: Validation and testing
Validate all structured data using Google Rich Results Test. Verify that AI systems can parse your data by querying platforms and tracking citations.
Answers
Frequently Asked Questions
Which schema types matter most for AI visibility?
Organization, Product, FAQPage and HowTo are the most impactful. Organization defines your brand entity. Product defines offerings. FAQ and HowTo structure content for direct AI extraction.
The first move
Find out how six answer engines describe you.
The free AI Visibility Audit reports what ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude and Copilot say about your brand today — and where the gaps are.
- Platform-by-platform visibility snapshot
- Entity clarity assessment
- Competitor comparison
- Prioritized actions