Rankry is an AI visibility analytics platform for tracking how a brand is recommended by ChatGPT, Claude, Gemini, Perplexity, and Grok. It focuses on visibility, ranking position, sentiment, and competitive share of voice across 23 proprietary metrics, with the stated aim of showing where a brand appears in AI-generated answers and what may be blocking that appearance.
Its dashboard includes coverage across five AI models, buyer-intent prompt tracking, share-of-voice reporting, sentiment and claims analysis, and an action plan. The prompt lab supports up to 100 buyer prompts per report, and the interface surfaces metrics such as visibility, position, sentiment, and diversity. The page also shows competitor-oriented views, source and citation counts, and recommended actions such as adding answer paragraphs, publishing a competitor comparison page, refreshing lastmod dates, and adding SoftwareApplication schema to product pages. Rankry also mentions an AI Website Readiness score and an AI Readiness score, and it says the product can connect AI tools and pull visibility data because it now speaks MCP.
The product is described for brands that want to understand how they are represented across leading LLMs and to review prompts, competitors, sources, and citations in one place. The page includes example brand and category views, such as developer APIs and omnichannel customer engagement queries, to illustrate how the analytics are used.
Rankry is delivered as a web-based service. The site offers a 7-day trial with no credit card required and says setup takes under two minutes.
Rankry sits in PulseGate's Analytics (BI, web, product) category. It focuses on understanding and improving how brands are recommended and perceived by leading AI models and LLMs. Rankry is a B2B product aimed at brand managers and digital marketers. There is a free tier, and paid plans start at $49. Rankry is available on the web.
Rankry first shipped in 2024. Among its 5 catalogued features are AI visibility metrics, sentiment analysis, and competitor tracking. It exposes integrations via an MCP server.
Summary written by a language model from the project’s public pages.
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