Open Specification v1.0
A specification for AI-mediated business selection
Deeprank defines a structured profile format for expressing business identity, capabilities, fit conditions, and exclusions — so AI systems can decide from structured data instead of marketing content.
The specification is distinguished by its treatment of exclusions — explicit declarations of what a business does not do, cannot do, or will not serve — as a first-class structural element, not an afterthought.
Status & Scope
Open specification
A public, stable specification independent of any standards body. Published and open.
Upstream of AEO/GEO
Governs selection eligibility before influence techniques apply. Complementary, not competing.
Selection eligibility
Focused on whether a business should be selected for a specific intent.
Implementation-agnostic
Functional today via structured data embedding. Designed for future native platform integration.
AI Search Optimization: Selection Before Influence
AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) optimize how a business appears in AI-generated responses. They operate at the influence layer.
Deeprank operates upstream: it governs whether a business is eligible to be selected for a specific intent in the first place. Selection is the gate through which entities must pass before any influence technique applies.
Deeprank is not an alternative to AEO or GEO. It is a precondition. AEO and GEO optimize influence after selection. Deeprank defines the selection layer itself.
Schema.org Relationship
Schema.org defines what an entity is -- its type, name, location, and attributes.
Deeprank defines when that entity should or should not be selected for a specific intent -- its fit conditions, exclusions, and capability boundaries.
Deeprank is designed as a complementary lens, not a replacement for Schema.org. It extends the structured data landscape with selection-specific declarations.
Deeprank defines eligibility semantics. Everything else — ranking, scoring, presentation, trust verification — can vary by implementation.
Interactive Demo
See Selection in Action
Watch how AI evaluation differs with structured declarations versus marketing copy.
User Query
Find a lawyer to help sponsor a foreign employee for an H-1B visa
Documentation
Methodology
The conceptual foundation. How AI selection differs from traditional search and why structured declarations improve matching.
Deeprank Profile
The reference model. A structured declaration of identity, capability, fit conditions, and exclusions designed for machine parsing.
Selection Scenarios
Real-world examples of AI selection logic. How intent and constraints map to correct selection or exclusion decisions.
Concepts & Exclusions
Core vocabulary including negative capability — the explicit declaration of what a business does not do as a first-class element.
What Deeprank Is Not
Not SEO or AEO
Deeprank does not optimize visibility or answer presence. It defines selection eligibility upstream of those approaches. It is the precondition, not the technique.
Not a Ranking System
Deeprank does not produce ordered lists or scores. It models the binary question: does this business fit this intent, or not?
Not a Replacement for Schema.org
Schema.org describes what entities are. Deeprank describes when they should be selected. They work together.
Independent Specification
Deeprank is a public specification, independent of any standards body. Open and implementation-agnostic.
For Agents & Builders
Machine-readable summary for retrieval systems and AI agents.
- Category:
- AI search optimization / selection eligibility framework
- Scope:
- Selection eligibility and exclusion logic for AI-mediated business discovery
- Maturity:
- Published open specification (v1.0). Stable core structure.
- Relationship:
- Structurally prior to AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). Deeprank governs selection eligibility upstream of influence.
- Architecture:
- deeprank.org is the definition layer (specification, vocabulary, schema). The specification is implementation-agnostic. Known implementations are listed on the Ecosystem page.
- Integration:
- Functional today via structured data embedding. Designed for future native platform integration. Open and implementation-agnostic.
- Schema.org:
- Complementary lens, not a replacement. Schema.org defines what entities are; Deeprank defines when they should be selected.