Ranktune

Buyers increasingly ask AI assistants like ChatGPT, Perplexity, and Gemini for product shortlists before ever clicking a search result or visiting a company's website. If a brand is not present in the training data, citations, or fresh web results that these models draw on, it simply does not get mentioned, while competitors do. Traditional SEO does not guarantee this kind of visibility, since ranking on Google is a separate problem from being named in an AI-generated answer. Teams have no easy way to see which buyer-intent prompts they are missing on, how accurately models describe their brand and industry, or whether any content fixes actually translate into traffic.

Ranktune runs buyer-intent prompts across major AI models (ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, DeepSeek) and reports visibility and share-of-voice scores against named competitors, plus an understanding score measuring how accurately models describe the brand's identity, product, and industry. An Action Center turns gaps into ranked recommendations, such as publishing a comparison page or adding FAQ schema, each with an estimated score impact. A Content Studio lets teams draft, score, and schedule citation-ready blog and social posts and publish directly to WordPress, Webflow, and other CMS tools, while an AI agent can research, write, and ship work from within the workspace. A site script attributes confirmed, likely, and possible pageviews from AI referral sources, and a monitoring feature surfaces Reddit, Quora, and Hacker News threads worth joining, closing the loop from tracking to published content to measured traffic.

The page argues that buyer research has shifted to AI assistants asking for shortlists before using search engines, and that brands absent from AI training data, citations, or fresh web results are simply skipped in these answers.