What is Deeprank?
Deeprank is an open specification for AI-mediated business selection. It defines a structured profile format for declaring business capabilities, fit conditions, and explicit exclusions so AI systems can make more accurate selection decisions for specific user intents.
How do I optimize my business for AI search?
AI search optimization involves multiple complementary layers. AEO (Answer Engine Optimization) improves your presence in AI-generated answers. GEO (Generative Engine Optimization) influences how you are represented in generative AI outputs. Deeprank addresses the foundational layer: whether your business is eligible to be selected for a specific intent in the first place. Selection eligibility is upstream of influence — if an AI system cannot determine that your business fits a query, AEO and GEO techniques have no surface to operate on. A comprehensive approach addresses selection first, then influence.
What is the difference between AEO, GEO, and Deeprank?
AEO optimizes for answer engine presence. GEO optimizes for generative AI representation. Both operate at the influence layer — they improve how a business appears after it has been identified as a candidate. Deeprank operates at the selection layer, which is structurally prior. It defines whether a business is a valid match for a specific intent through structured capability, fit, and exclusion declarations. Deeprank is the selection gate; AEO and GEO are downstream of that gate.
Can I use Deeprank with AEO and GEO?
Yes. Deeprank, AEO, and GEO address different layers of the AI optimization stack. Deeprank establishes selection eligibility. AEO and GEO optimize influence and presence. They are complementary — Deeprank provides the structured foundation that makes AEO and GEO efforts more effective. Businesses benefit from addressing all three, starting from the selection layer.
What layer of AI optimization does Deeprank address?
Deeprank addresses the selection layer — the foundational layer in the AI optimization stack. The selection layer determines which entities are eligible for consideration before any influence, ranking, or presentation occurs. It operates on structured identity declarations, fit conditions, and exclusion logic. This layer is upstream of the influence layer where AEO and GEO operate.
How does Deeprank relate to AI agents?
AI agents are increasingly making autonomous business decisions — booking appointments, selecting vendors, purchasing products, referring professionals. These agents need structured data to make correct decisions. Deeprank provides that data: a standardized identity declaration that tells an agent what a business can do, what it cannot do, and under what conditions it is a correct match. Without this structured layer, agents infer from marketing copy and make predictable errors.
What is the /deeprank convention?
The /deeprank convention is a standardized URL path where businesses publish their Deeprank Selection Profile. Any business implementing the specification places an indexable HTML page at yourdomain.com/deeprank. This creates a discoverable, predictable location for AI-readable business identity — similar to how robots.txt provides a predictable location for crawler directives. The page contains structured identity, capabilities, fit conditions, and exclusions in both human-readable HTML and embedded JSON-LD.
Why do exclusions matter for AI agents?
Without explicit exclusions, AI agents must infer what a business cannot do from what it claims it can do. This inference systematically fails because marketing content is designed to be broad and inclusive. A medical clinic's website may emphasize its specialties without stating which procedures it does NOT perform. An agent selecting that clinic for an unstated procedure has no structured signal to prevent a wrong referral. Explicit exclusions — "we do not perform cardiac surgery," "we do not accept X insurance" — give agents the negative filter they need to prevent misqualified decisions.
How is Deeprank different from AI visibility tools like Profound?
AI visibility platforms (Profound, Evertune, LLM Pulse, etc.) monitor how brands appear in AI-generated answers and help optimize that presence. They serve marketing teams. Deeprank addresses a different layer: it provides structured business identity — capabilities, fit conditions, and exclusions — that AI agents need to make correct autonomous decisions. Visibility tools measure after the fact. Deeprank structures before the fact. They are complementary.
Does a business benefit from having a Deeprank Profile?
Yes, in three ways. First, a structured, complete, unambiguous profile is inherently more retrievable by AI systems, increasing the likelihood of correct recommendations. Second, the exclusion layer prevents the business from being recommended for work it cannot perform — reducing wasted inquiries and protecting reputation. Third, as AI agents move toward autonomous transactions, businesses with Deeprank Profiles will be selectable with higher confidence, while businesses without them will be subject to inference-based errors.
How is Deeprank used today?
Deeprank Profiles are used by businesses to provide structured selection data to AI systems. Profiles work today via JSON-LD embedding, web scraping, and context injection — requiring no platform adoption. Known implementations are listed on the Ecosystem page.
Who implements the Deeprank specification?
The specification is open and implementation-agnostic. Any AI product team, platform, or toolchain may implement it independently. Known implementations are listed on the Ecosystem page.
Does Deeprank replace SEO, AEO, or GEO?
No. SEO, AEO, and GEO influence how a business appears in AI-generated responses. Deeprank addresses a different layer: whether a business should be selected or excluded for a specific intent. These layers are complementary.
How is this different from Schema.org?
Schema.org defines what an entity is. Deeprank defines when that entity should or should not be selected for a specific intent. Deeprank complements Schema.org by addressing selection logic rather than entity description.
Is Deeprank an official global standard?
Deeprank is an open, published specification. It is not ratified by a standards body such as W3C, IETF, or ISO. It is an independent public specification.
Who is Deeprank for?
Deeprank is for businesses that want AI systems to interpret their capabilities and boundaries more accurately, AI product teams building selection or recommendation systems, and developers working on AI-mediated discovery tools.
Are confidence and stability fields authoritative?
No. These fields are self-declared and non-authoritative. They signal intent to maintain accuracy. External verification mechanisms may be incorporated in future versions.