Valid markup
We remove contradictions and duplicates so crawlers receive a clear semantic picture.
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Schema.org
JSON-LD
Entity SEO
Schema.org & structured data for entity SEO
Technical semantic optimization for search systems and AI assistants.
We build consistent entity graphs for services, FAQs, offers, and organization data so content stays machine-readable and unambiguous.
Published: • Updated:
Entity linking
We connect pages, services, and organization entities in one consistent graph structure.
Better discoverability
Clean JSON-LD strengthens relevance signals for rich results, AI overviews, and assistant answers.
| Package | Starting price | Typical scope | Best for |
|---|---|---|---|
| Markup Audit | from 290 € | Audit + error list + priorities | Fast quality control |
| Entity Setup | from 790 € | Service/FAQ/Article/Organization Setup | New and existing sites |
| Cluster JSON-LD | from 1,490 € | Multi-page graph, local clusters, governance | Scaling SEO setups |
Guideline values depending on page count, existing markup quality, and entity complexity.
Comparison: basic markup vs strategic entity graph
| Criteria | Basic markup | Entity-Graph |
|---|---|---|
| Machine readability | Medium | High |
| Scalability | Limited | Very good |
| AI/assistant relevance | Lower | Higher |
Local context & cluster
This service connects SEO, content clusters, and technical implementation. That is why it is linked directly with our core service pages.
Structured data as a competitive advantage in search and AI
Many websites already include JSON-LD, but semantic quality is often weak: duplicate entities, inconsistent properties, or isolated snippets without relationships. For search systems and AI assistants, this creates uncertainty. We therefore do not ship isolated schema blocks; we build consistent entity graphs across all relevant page types.
A clean graph connects organization, services, locations, authors, offers, and FAQs logically. This helps systems interpret content reliably and attach the right context. Especially for local and service-driven websites, this increases the chance of accurate inclusion in rich results, summaries, and assistant responses.
In the audit, we review technical validity and content consistency. Valid JSON alone is not enough if relationships are wrong or key entities are missing. We also analyze how markup, visible content, and internal linking work together. Only when these layers are aligned does a robust machine-readable signal emerge.
During implementation, we focus on maintainability. Structured data must evolve with the content process, otherwise it becomes outdated quickly. We define clear rules for when fields must be updated, how new pages are integrated into the graph, and which schema types are required per page type.
The goal is not more code, but better interpretability for systems that influence visibility. With a clean entity setup, clear relationships, and ongoing quality control, structured data becomes a sustainable SEO lever.
Structured data with measurable impact
For structured data, success is measurable through consistency, validity, and semantic coverage of key entities.
We measure how completely core entities are modeled: organization, service, offer, FAQ, and their @id relationships. This is the foundation of machine readability.
Validation is not a one-time step. It is repeated after content updates so markup remains in sync with visible content.
We also monitor whether new pages are integrated correctly into the existing graph without creating semantic breaks.
Implementation principles
- Schema architecture follows a consistent entity model.
- Validation and deduplication are part of the operational process.
- Markup is strictly tied to real visible content.
- New pages are integrated into existing @id relationships.
Related service clusters
View all servicesFrequently asked questions
Why is structured data important?
They help search systems and assistants interpret content correctly and map entities accurately.
Do you correct existing markup?
Yes, including deduplication, mapping corrections, and consistent entity logic.
Is this only for Google?
No, semantic markup also improves machine readability for other search and AI systems.
Do you also implement breadcrumb and organization markup?
Yes. Depending on page type, we implement breadcrumb, organization, service, and FAQ schemas consistently.
Do you review markup after content updates?
Yes. We recommend and support regular validation so structured data stays consistent after content updates.
We also establish clear validation rules for new content so markup stays consistent on future pages. This allows the entity graph to grow in a controlled way without losing semantic quality.
Next step
We review your current markup, fix errors, and build a semantic graph for scalable visibility.