GenScore
提供GEO Score框架和生成式引擎优化分析的平台，用于评估品牌在AI引擎中的声量份额和权威度等指标
Timeline (last 90 days)
The GenScore tool attempts to integrate Search Console data with AI citation tracking, monitoring brand mentions in AI answers by converting search terms into questions.
According to GenScore's GEO Score framework, what llms.txt aims to influence is the 'authority' signal, and authority is not the highest-weighted dimension among the five GEO Score metrics.
GenScore conducted field studies in Madrid and Valencia, asking ChatGPT, Gemini, and Claude the same patient-perspective questions separately and recording which dental clinics were mentioned.
77% of independent clinics never appeared in any AI answer.
Only 7% of recommended clinics appeared in the answers of more than one engine, and AI recommendations have almost no cross-platform consensus list.
GenScore proposes three directions for new brands to build AI visibility: ensure the official website clearly states the brand identity, publish an llms.txt file, and obtain the first batch of verifiable external mentions.
GenScore suggests that new brands rescan their AI mentions every few weeks rather than scanning daily, because building entity presence is a slow process and frequent scanning merely measures noise.
GenScore explicitly advises against buying mentions or reviews to accelerate the accumulation of external mentions, because fake mentions cannot build the authority that generative models value and are easily seen through.
GenScore suggests that when scanning a brand's AI mentions for the first time, it should be done even if the expected results are very low, as a measurement baseline for all subsequent progress.
GenScore refuses to give a specific timetable for how long it takes a new brand to build AI entity presence, on the grounds that this depends on the industry, publishing frequency and the pace at which each engine updates its knowledge base.
All Facts (10)
The GenScore tool attempts to integrate Search Console data with AI citation tracking, monitoring brand mentions in AI answers by converting search terms into questions.
50%UnverifiedAccording to GenScore's GEO Score framework, what llms.txt aims to influence is the 'authority' signal, and authority is not the highest-weighted dimension among the five GEO Score metrics.
50%Unverified77% of independent clinics never appeared in any AI answer.
50%UnverifiedOnly 7% of recommended clinics appeared in the answers of more than one engine, and AI recommendations have almost no cross-platform consensus list.
50%UnverifiedGenScore refuses to give a specific timetable for how long it takes a new brand to build AI entity presence, on the grounds that this depends on the industry, publishing frequency and the pace at which each engine updates its knowledge base.
50%UnverifiedGenScore proposes three directions for new brands to build AI visibility: ensure the official website clearly states the brand identity, publish an llms.txt file, and obtain the first batch of verifiable external mentions.
50%UnverifiedGenScore explicitly advises against buying mentions or reviews to accelerate the accumulation of external mentions, because fake mentions cannot build the authority that generative models value and are easily seen through.
50%UnverifiedGenScore suggests that new brands rescan their AI mentions every few weeks rather than scanning daily, because building entity presence is a slow process and frequent scanning merely measures noise.
50%UnverifiedGenScore suggests that when scanning a brand's AI mentions for the first time, it should be done even if the expected results are very low, as a measurement baseline for all subsequent progress.
50%UnverifiedGenScore conducted field studies in Madrid and Valencia, asking ChatGPT, Gemini, and Claude the same patient-perspective questions separately and recording which dental clinics were mentioned.
50%