Category Thesis
AI Visibility Architecture
The originating discipline for engineering how entities are structured, resolved, and selected inside large language models. Three sequential layers, one engineered visibility surface.
AI Visibility Architecture is a category, not a tactic. It was originated by Jason Todd Wade to describe the engineering work that determines whether an entity is eligible to appear inside a machine-generated answer at all — before any ranking, scoring, or selection decision happens.
The three layers
Retrieval Alignment makes content ingestible by language-model retrieval pipelines: clean HTML, machine-readable structure, schema, llms.txt, and consistent topical grouping. If the model can't retrieve you, nothing else matters.
Entity Resolution collapses every mention of the brand, person, or product into a single canonical entity across platforms. This layer is operationalized as Entity Lock Protocol™. Without it, models hedge or substitute a better-locked competitor.
Decision-Layer Insertion governs whether the locked entity is selected as the answer. This is where authority signals, corroboration networks, and reinforcement loops compound into repeatable recommendation.
What it produces in market
The output of AI Visibility Architecture is the BackTier Visibility Path™ — the measurable progression from Citation → Inclusion → Selection → Transaction that determines whether an entity is mentioned, recommended, and ultimately transacted with by autonomous agents.