Schema & Structured Data
Give AI the structured signals it needs to trust and cite your brand.
Technical GEO implementation: entity disambiguation, Schema.org markup, and knowledge-graph signals that make your brand unambiguous to LLMs.
Key Benefits
Entity disambiguation
Establish your brand as a distinct, unambiguous entity in LLM knowledge graphs. This eliminates confusion with similarly named businesses.
Schema.org implementation
Full technical implementation of Organisation, Product, FAQPage, HowTo, and Review schema that structured AI extractors read first.
Knowledge-graph seeding
Submit structured brand data to Wikipedia, Wikidata, and authoritative directories that LLMs use as ground-truth knowledge sources.
Canonicalisation audit
Ensure every page that mentions your brand uses consistent naming, descriptions, and structured data so LLMs form a coherent entity model.
Why technical signals determine AI trust
LLMs don't just read prose. They extract structured data. A brand with clean Schema.org markup, consistent entity references, and knowledge-graph presence is far more likely to be recommended accurately than one with unstructured content alone. Technical GEO is the foundation everything else is built on.
The OmniGro technical audit
We crawl your entire domain, audit every page for schema coverage, check entity consistency across your web presence, and compare your structured data footprint against the top-cited brands in your category. The output is a prioritised technical fix list with clear implementation guidance.
Implementation and ongoing maintenance
Our team can implement schema changes directly or provide developer-ready code. We then monitor your technical health monthly. LLMs update their training data and new schema types emerge, so your technical GEO needs to stay current.