SEO at the meaning layer.
Semantic SEO engineers the entity, topic and ontology signals modern search systems and LLMs actually run on. It's how brands move from ranking for keywords to owning categories.
Search rewards meaning, not strings.
Modern ranking systems — Google's MUM, RankBrain, AI Overviews, and every LLM-powered answer engine — operate on entities, relationships and topics. Optimizing for keywords without optimizing the underlying semantic structure leaves the moat unbuilt.
Six capabilities, one architecture.
Entity Optimization
Disambiguate your brand, product and people entities across Wikidata, Wikipedia, and Google's Knowledge Graph so every retrieval surface knows what you are.
Topic Cluster Architecture
Pillar + cluster + hub-and-spoke structures that demonstrate end-to-end topical authority — the surface signal every modern ranking system rewards.
Knowledge Graph Strategy
Engineered schema, structured data and entity relationships that publish your facts into the graphs LLMs ingest and cite.
Ontology Engineering
Domain taxonomies and concept maps that make your corpus machine-comprehensible at the meaning layer — not the keyword layer.
Search Intent Mapping
Multi-modal intent modeling across informational, transactional, navigational and conversational queries — including AI follow-ups.
Semantic Audits
Continuous gap analysis between what your category asks and what your corpus actually answers.
From audit to category authority in five stages.
Audit
Entity, topic and ontology baseline across your corpus and the open knowledge graph.
Architect
Pillar + cluster design, schema templates, internal linking topology.
Engineer
Production-grade implementation across CMS, structured data and metadata.
Publish
Knowledge graph submissions, third-party graph alignment, entity reconciliation.
Compound
Weekly monitoring of entity strength, topical authority and downstream retrieval impact.
Where Semantic SEO earns its keep.
New category, undefined vocabulary
Define and own the canonical terms before competitors crowd in. Engineer the entity graph that becomes the category's source of truth.
Multi-product portfolios
Resolve entity confusion across product families, sub-brands and acquisitions so each surfaces correctly in retrieval.
Knowledge-dense industries
Healthcare, legal, finance: where accuracy, authority and structured facts decide which brands AI engines feel safe citing.
International expansion
Locale-aware ontologies, multilingual entity reconciliation and cross-market topic cluster transfer.
Coverage you can measure.
Topic-by-engine coverage matrix and entity graph health, refreshed continuously.
Frequently asked
Map your category's semantic surface.
In 45 minutes we'll walk you through the entity and topic gaps separating you from category authority — and what a 90-day program would address first.