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45
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Agentic Readiness Score
+25 llms.txt +15 structured API missing +20 ai-plugin
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Agentic Ready 45/100

            

llms.txt Content

# ShareOfAsk > ShareOfAsk is an AI visibility platform for brands and agencies. It asks AI assistants the questions your buyers ask, records whether your brand appears, which competitors show up, how you are described and which sources are cited, then analyzes those answers in depth and turns the findings into evidence-based recommendations and prioritized Actions. On paid plans you choose which AI models to track from the current model list (up to 3 per workspace on Starter, up to 4 on Growth), which includes ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews and Google AI Mode. ShareOfAsk measures answers to a saved set of buyer questions from the AI models you select. Each result belongs to a particular question, model and point in time, and every result links back to the recorded answer and the cited sources behind it. The measurement request does not add instructions asking a model to favor your brand. The product has five views: Visibility, Competitors, Perception, Sources and Actions. How the analysis becomes action: an action analysis uses the findings, supporting evidence and further research to produce a set of prioritized recommendations with suggested steps. Your team reviews the evidence, decides what to change and carries out the work; you can record completed or dismissed actions in the product. ShareOfAsk measures and recommends. A recommendation does not guarantee an improvement or establish what caused a change. Key metrics (formulas published on the methodology page): - AI Presence: answers mentioning your brand / measured answers in scope x 100. Failed requests are excluded; a brand counts once per answer. - Competitive Share of Voice: your brand's counted mentions / all counted brand mentions x 100. - Question Coverage: questions with a brand mention / measured questions in scope x 100. - Average First Position: average recorded position of your brand in the extracted recommendation order; lower values mean earlier placement. Ho