Patnick
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🔮 Pillar · AI Visibility

Keywords don't rank.
Entities do.

LLMs answer queries by retrieving entities — not by matching keywords. Patnick measures how often ChatGPT, Claude, and Gemini surface your brand entity across semantic queries, then quantifies the signals you need to become unavoidable in AI answers.

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What is it?

Keywords don't rank. Entities do., defined.

Keywords don't rank. is an entity-oriented measurement discipline that quantifies a brand's presence across large language model answers — scoring coverage, historical engagement, and cross-model consensus as three independent signals of AI search authority, each grounded in published research and Google patents on entity resolution and knowledge graph integration.

Patnick's core service audits every site across 8 patent-backed dimensions: Business Identity, Technical Integrity, Structured Data, Semantic Coverage, Trust & Proof, Conversion Readiness, Geo Coverage, and Content Architecture. AI Visibility is the additional measurement layer I added to that analysis in 2026 — because ChatGPT, Claude, and Gemini don't rank documents the way Google does. They retrieve entities from knowledge graphs, reason over them, and surface the ones with the clearest, most consistent signals. So for every client I now probe all three major LLMs in parallel, classify their brand as an entity (not a keyword string), and surface three additional transparent dimensions — Demand, Clarity, Saturation — on top of the 8 core dimensions. Forward-looking query simulation — our exclusive capability — lets you lock in entity associations for 2026-2027 queries before their search volume arrives. If you're on the $499 Implementation plan, the AI visibility layer comes included. $799 Full Management covers it end-to-end.

3

LLMs probed in parallel

ChatGPT · Claude · Gemini

425%

Documented growth

Entity-oriented multilingual case study

0

Black-box scores

Every dimension is a published formula

How it works

4-step pipeline.

1

Entity resolution

01

2

Query network expansion

02

3

Parallel multi-LLM probing

03

4

Three-dimension scoring

04

  1. 01

    Entity resolution

    Patnick maps your brand to its EAV (entity-attribute-value) profile: name, type, attributes, canonical relationships. Every downstream signal is measured against this resolved entity, not a keyword string.

  2. 02

    Query network expansion

    We generate a semantic query network: GSC-observed queries, entity-expanded variations, competitor gap queries, and forward-looking 2026-2027 queries. Network coverage drives the Coverage score.

  3. 03

    Parallel multi-LLM probing

    Every query runs through ChatGPT, Claude, and Gemini simultaneously. Deterministic parsers extract entity mentions, positions, citations, sentiment, and competing entities from each response.

  4. 04

    Three-dimension scoring

    Scores are computed across three independent dimensions: topical coverage, historical engagement, and cross-LLM consensus. The output is three transparent numbers — Demand, Clarity, Saturation — each mapped to an actionable fix path and grounded in Google patent research.

Inside Patnick

Your 3-score dashboard.

A preview of how this capability surfaces in the real dashboard. Enter the your audit to click through every block.

patnick.com/dashboard

Demand

84

Clarity

67

Saturation

42

What you get

Three things change.

Measure entity presence, not keyword ranking

Traditional SEO tools count blue-link positions. Patnick counts how often LLMs recognize your brand as an authoritative entity — the signal that actually drives AI answer inclusion. Built on entity-oriented search research and the knowledge graph integration patents Google has filed since 2012.

Beat competitors to future query networks

Predictive ranking research shows LLM answers calcify months before search volume arrives. Patnick's forward-looking queries lock in your entity association for emerging topics — the only way to secure first-mover advantage in AI search.

Three transparent dimensions, not one opaque score

Demand, Clarity, Saturation — each dimension is independent, each has visible components, each maps to a specific fix path. When a score drops, you know exactly which lever to pull. No black-box composite that hides where the problem lives.

Who it's for

Built for these teams.

SEO Strategists

Move beyond keyword rank tracking. Measure entity authority and topical coverage as the real drivers of 2026+ visibility.

Brand Managers

See which competing entities LLMs surface instead of yours — and use the diff as a topical gap roadmap.

International Teams

Entity semantics persist across languages. Patnick probes each market separately and maps cross-lingual topical authority.

Agencies

Whitelabel entity-oriented audits for clients. Deliver formula-based authority reports instead of vague 'do more content' advice.

Deep dive

Explore each capability.

Multi-LLM Probing

Test your entity across ChatGPT, Claude, and Gemini in parallel — language-agnostic semantic signals reveal true topical authority.

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Forward-Looking Queries

Predictive ranking in action: probe 2026-2027 query networks before search volume arrives, lock in early entity associations.

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Three-Score Model

Demand · Clarity · Saturation. Three independent, research-backed dimensions replace the black-box composite score.

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Cross-LLM Consensus

When entity identity is consistent, every LLM agrees. Consensus is the strongest signal that your brand is genuinely resolved.

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Share of Voice

Entity share across generated answers. Measures your topical dominance vs. competing entities in the same semantic network.

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Citation Tracking

LLM citations are historical data in the AI era. Every link compounds your author rank and entity authority.

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People also ask

Frequently asked.

What is AI search visibility?
AI search visibility measures how often a brand entity — not a keyword string — is surfaced in answers generated by large language models. Unlike traditional SEO that tracks blue-link rank, AI visibility measures whether LLMs treat your brand as an authoritative resolved entity in the relevant semantic network. This is grounded in entity-oriented search theory, which holds that search engines interpret queries as entity-attribute-value triples rather than keyword matches.
How is entity-oriented search different from keyword SEO?
Keyword SEO optimizes for exact-match string retrieval: you pick a keyword, you write a page about it, you monitor position. Entity-oriented search is fundamentally different — engines (and LLMs) interpret the query as an entity with attributes and expected values, then retrieve pages whose entity profile best matches the query's resolved entity. In practice this means keywords rank because they serve entities, not the other way around. LLMs take this further: they bypass document retrieval entirely and return the entity itself. Patnick's 3-score model is designed around this paradigm, not the legacy keyword model.
Why three independent scores instead of a composite?
A composite score hides its weights. When a composite drops 10 points, you don't know whether it's coverage, engagement, entity consistency, or competitive pressure that moved. The three independent dimensions — Demand (topical breadth + real search demand), Clarity (entity consistency + cross-LLM consensus), Saturation (competitive crowding) — are each transparent, each grounded in published Google patents and peer-reviewed research, and each mapped to a distinct fix path. One score tells you there's a problem; three scores tell you which problem.
What are forward-looking queries and why probe them?
Forward-looking queries are hypothetical 2026-2027 searches that don't yet have significant volume but will — emerging technology categories, product launches, new behavior patterns. Patnick generates them from trend signals and probes LLMs with them. Why? Because predictive ranking research shows LLM answers for emerging categories calcify months before search traffic arrives. When 'best AI agents for Shopify in 2027' becomes a popular query, the top 5 LLM answers will already be locked in. Forward-looking probing is the only way to secure your entity association before the window closes — and Patnick is the only platform that does it.
Why probe three LLMs instead of just ChatGPT?
Different LLMs have different training corpora, retrieval strategies, and alignment fine-tuning. A brand that's firmly embedded in Claude's training data can be invisible in Gemini's, and vice versa. Measuring only one LLM gives you a 33% view of real AI visibility. More importantly, consensus across models is a stronger signal than any single probe: when three independently-trained LLMs agree on mentioning your brand, it means your entity identity is robustly encoded across the training data landscape — not a fluke of one model's corpus. Cross-LLM consensus directly feeds the Clarity dimension in the 3-score model.
Does Patnick measure share of voice or backlinks?
Share of voice, yes — measured at the entity level across LLM answers (not just SERP positions). Backlinks, no — deliberately. Semantic SEO research consistently shows that historical engagement data (clicks, dwell time, satisfaction signals) predicts ranking stability far better than link graphs in the post-2022 algorithm landscape. Patnick's framework treats backlinks as a secondary signal at best. The primary authority driver is entity consistency + historical data accumulation — exactly the signals LLMs use when deciding which entities to surface in generated answers.

Ready to start?

Log into the demo dashboard. Click any block to learn exactly what it does and why it matters.

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