MyAibo turns your existing content into machine-readable knowledge — FAQ schema, HowTo markup, and full knowledge graph optimization — so Google's AI Overviews, Perplexity, and any LLM can select, extract, and cite your answers precisely.
MyAibo covers FAQPage and HowTo JSON-LD, Speakable schema, Q&A architecture, and knowledge graph entity alignment (Organization, Product, Service, Person with sameAs chains) — fixing content that answers buyer questions but lacks the schema layer machines need to find it.
Schema is the confidence signal AI Overviews use to recognize content type. Without it, even the best-written page reads as ambient prose to machine extractors.
We map every buyer question across your funnel and implement FAQPage, HowTo, and Q&APage schema at the template level, validated against Google's Rich Results Test.
Eligible pages gain FAQ Rich Results and higher AI Overview selection odds at scale, without per-page manual work.
We build Organization, Person, Product, Service, and Event schema with verified sameAs chains to Wikidata, Crunchbase, LinkedIn, and GitHub, cross-validated against Google's Knowledge Panel.
Strong entity graphs correlate with lower hallucination rates, higher AI Overview selection, and stronger buyer trust.
We engineer 40–60 word answer blocks, <dfn> definitions, labeled tables, and Speakable / Article schema with author and citation properties.
Re-architected pages see measurable gains in Featured Snippets, AI Overview inclusion, and Perplexity citations within 60–90 days.
Audit existing schema and map buyer questions across your page taxonomy.
Design JSON-LD templates and entity architecture; document content block guidelines.
Deploy schema and restructure priority pages; validate with Rich Results Test.
Monthly validation sweeps and citation-rate tracking.