A new metric for a new kind of visibility
Generative Engine Optimization (GEO) has emerged as a distinct discipline from traditional SEO, and its core metrics reflect that difference: rather than organic traffic, keyword rankings, and click-through rate, GEO tracks Share of Model, brand mention share, domain influence score, and sentiment distribution across AI systems — measures suited to an ecosystem where the "result" is a generated answer, not a ranked list of links.
What Share of Model actually measures
Share of Model quantifies how often a brand gets mentioned by AI systems relative to named competitors, across a defined, repeatable set of prompts representative of how real users actually query AI systems about a given category or use case. It's the AI-search analog to organic search share of voice, adapted for a fundamentally different kind of result.
Why a fixed prompt set matters
Testing with random, ad hoc prompts each time makes it impossible to compare results meaningfully across checks — a fixed, representative prompt set (a general category question, a direct brand comparison, a recommendation request, a brand-specific query) tested identically each time is what makes the resulting percentage genuinely comparable over successive checks, rather than an apples-to-oranges snapshot.
Why testing across multiple AI engines matters
ChatGPT, Claude, Gemini, Perplexity, and Google AI Overview each draw on different underlying training data, retrieval mechanisms, and citation logic — a brand's Share of Model can differ meaningfully across these engines, and testing only one gives an incomplete picture of overall AI visibility across the ecosystem users are actually distributed across.
Why fresh, incognito sessions matter for accurate testing
Testing within an account with extensive chat history can produce results influenced by that history rather than reflecting what a new user would actually see — running the testing protocol in a fresh or incognito session each time produces results more representative of a genuinely new user's experience querying that AI system.
Why order of mention matters, not just presence
Being mentioned third or fourth in a list of options carries meaningfully different visibility value than being named first or most prominently — tracking not just whether a brand is mentioned, but where in the response it appears relative to competitors, captures a dimension of Share of Model that a simple mentioned/not-mentioned binary would miss.
Why this needs to be repeated monthly, not run once
AI model outputs shift as underlying models are updated and retrained — a Share of Model calculated once provides a snapshot, but running the identical protocol monthly reveals whether a brand's actual AI visibility is improving, declining, or holding steady over time, turning a single data point into an actual trend worth acting on.
Frequently Asked Questions
Share of Model measures how often a brand is mentioned by AI systems relative to named competitors across a defined set of prompts — a GEO (generative engine optimization) metric distinct from traditional SEO measures like organic traffic and keyword rank, suited to an ecosystem where the result is a generated answer rather than a ranked list of links.
A fixed, representative prompt set tested identically each time is what makes the resulting Share of Model percentage genuinely comparable across successive checks. Random, ad hoc prompts each time would make it impossible to tell whether a change in results reflects an actual trend or just different prompt wording.
ChatGPT, Claude, Gemini, Perplexity, and Google AI Overview each draw on different underlying training data, retrieval mechanisms, and citation logic. A brand's Share of Model can differ meaningfully across these engines, so testing only one gives an incomplete picture of overall AI visibility.
Testing within an account with extensive chat history can produce results influenced by that history rather than reflecting what a genuinely new user would see. A fresh or incognito session each time produces results more representative of an actual new user's experience querying the AI system.
Yes — the Share of Model Brand Mention Checker generates a structured protocol with specific prompts to test across five major AI engines, a scoring worksheet, and a Share of Model percentage formula, designed to be run monthly to reveal genuine trends.