From keywords to entities
Goodbye keyword density. Google and generative engines don't just look for strings of text: they map entities and relationships. Akendor helps you build content that covers a topic's entire graph.
AI's mental model
While old tools try to rank you for "running shoes", the semantic approach starts from the entity [Running] and its connections: [Biomechanics], [Performance], [Injury prevention].
Logical shift
SEO 1.0 (Legacy)
- Obsessive keyword repetition
- Flat texts lacking context
- Backlinks based on anchor text alone
Akendor (Semantic)
- Full coverage of the topic graph
- Related entities and smart LSI
- Authority based on depth of knowledge
Knowledge Graph extraction
// Processing user query: "best supplements for anxiety"
1. Entity identification: [Supplements], [Anxiety]
2. Context search: [Mental health], [YMYL compliance]
3. Relationship mapping: [Anxiety] -> [Cortisol] -> [Magnesium]
4. Reliability assessment: Checking E-E-A-T signals...
// Result: Authoritative answer generated from the Entity Graph.
The Akendor strategy
1. Identification
Start from the key concepts of your topic, not from individual words: they are what shapes the cluster structure.
2. Relationships
Akendor builds the network of connections (cluster): each subtopic strengthens the pillar through internal links.
3. Relevance
Each article puts the main topic at the center, so engines understand exactly what the content is about.
Authority metrics
Coverage
Ratio
Friction
Salience