Overview
Layered Architecture
AI systems that recommend, select, or refer businesses operate across multiple distinct layers. Each layer serves a different function and operates on different inputs. Conflating these layers leads to misapplied optimization efforts.
The AI optimization stack, from foundational to surface, consists of four layers: Selection, Representation, Influence, and Retrieval/Ranking. Each layer depends on the layers beneath it.
Selection Layer
Deeprank
The selection layer determines which businesses are eligible for consideration. This is the foundational layer. If a business is not selected at this layer, no amount of optimization at higher layers matters.
Selection operates on identity, capability, fit conditions, and exclusions. The logic is binary: select or exclude. There is no partial selection, no ranking, no scoring at this layer.
Function
The selection layer answers: "Is this business a valid candidate for this query?" It does not answer: "Is this the best candidate?" or "How should this business appear?"
Deeprank defines the protocol for this layer. It specifies how businesses declare their identity, capabilities, fit conditions, and exclusions in a form that AI systems can parse and evaluate.
Failure at This Layer
If a business is incorrectly excluded at the selection layer, it cannot be recovered by influence techniques. The business simply does not exist in the candidate set. This is the most consequential layer for businesses seeking AI-driven referrals.
Representation Layer
Profiles and Schema
The representation layer provides the structured data that enables selection. This includes business profiles, schema markup, and machine-readable declarations.
Representation bridges human-readable content and machine-parseable data. Without proper representation, AI systems must infer business attributes from unstructured content, leading to errors.
Relationship to Selection
The representation layer feeds the selection layer. A Deeprank Profile is a representation artifact that enables selection decisions. The quality of representation directly affects selection accuracy.
Influence Layer
AEO / GEO
The influence layer contains techniques that attempt to affect AI outputs. This includes Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and related methodologies sometimes called AI SEO.
These techniques operate on content, citations, authority signals, and other factors that may affect how AI systems present or prioritize information.
Dependency on Selection
Influence techniques operate only after selection eligibility exists. A business cannot be "optimized" into the candidate set. If the selection layer excludes a business, influence techniques have no effect.
AEO and GEO assume that selection has already occurred. They do not define or replace the selection mechanism.
Scope Limitation
Influence techniques cannot fix selection problems. If a business is excluded because its capabilities are unclear, no amount of content optimization will resolve the issue. The selection layer must be addressed directly.
Retrieval / Ranking Layer
Traditional Search Systems
The retrieval and ranking layer encompasses traditional search and ordering systems. This includes search engine results pages, directory listings, and any system that produces ordered lists based on relevance, popularity, or other scoring mechanisms.
Distinct from Selection
Ranking produces ordered lists. Selection produces binary decisions. These are fundamentally different operations. Optimizing for ranking does not optimize for selection. A business may rank highly in search results while being excluded from AI selection due to unclear fit declarations.
Canonical Relationship
Canonical Statement
AEO and GEO operate after an AI system has already decided which entities are eligible. Deeprank defines the selection layer those approaches assume but do not specify.
Layer Dependencies
The layers have strict dependencies. Retrieval/Ranking sits atop Influence, which sits atop Selection, which depends on Representation. Optimizing an upper layer without addressing lower layers produces limited results.
A business seeking AI visibility should address the stack from the bottom up: first ensure accurate representation, then verify selection eligibility, then consider influence techniques.
Non-Fit
What Deeprank Is Not
Deeprank is not an AEO technique. It does not attempt to influence AI outputs through content optimization, citation building, or authority signals.
Deeprank does not optimize ranking or placement. It does not produce ordered lists or scores. It does not compete with or replace SEO, marketing, or reputation systems.
Deeprank operates at the selection layer. It defines how businesses declare their identity so that AI systems can make accurate select/exclude decisions. Everything that happens after selection is outside the scope of this protocol.