Structured Data & JSON-LD
Schema Architecture
Make structured facts agree with the content customers can read. We audit conflicting markup, connect the relevant entities, implement template changes, and validate the result. Recommendation outcomes are measured separately.
How We Implement Schema Architecture
- 1Audit
We audit your current schema and identify gaps, errors, and conflicts.
- 2Design
We design a schema architecture (types, hierarchy, scope) for your site.
- 3Implement
We implement and validate JSON-LD across key pages.
- 4Maintain
We monitor and update schema as your site and content evolve.
Learn the implementation patterns and limits in our schema guide for AI search. Google’s AI search documentation explains why special AI markup is not required.
Ad-Hoc Schema vs. Schema Architecture
Schema architecture means a planned, consistent approach, not one-off or conflicting markup.
| Aspect | Ad-Hoc Schema | Schema Architecture |
|---|---|---|
| Focus | No formal schema plan | Planned schema architecture across pages |
| Structure | Ad-hoc or duplicate markup | Consistent, validated JSON-LD |
| Validation | Conflicting or untested facts | Facts checked against visible content and supported feature requirements |
| Maintenance | Reactive fixes | Ongoing architecture and validation |
Frequently Asked Questions
- It is the design and maintenance of structured facts across a site. Organization, Article, Product, and other types are selected to match the actual page content and supported platform uses.
- Valid schema does not guarantee more citations or recommendations. Google says no special schema is required for AI Overviews or AI Mode. We correct factual and technical issues, then measure outcomes separately.
- An inventory of existing markup, a map of entities and identifiers, proposed template changes, validation results, and a maintenance plan. The written scope specifies page types and implementation responsibilities.
- We compare markup with visible facts, check syntax and relationships, and use platform-specific validators for supported features. A passing validator does not establish search eligibility or AI inclusion by itself.
Ready to Fix Your Schema?
Get a schema architecture audit and a clear plan for structured data.