> For the complete documentation index, see [llms.txt](https://rankavi.gitbook.io/rankavi-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://rankavi.gitbook.io/rankavi-docs/readme.md). # Rankavi Docs Rankavi is a software as a service (SaaS) platform that publishes brand mentions in articles on indexed third-party websites. Brands use Rankavi to build mentions that AI search tools such as ChatGPT, Gemini, and Perplexity can find. A network of over 20,000 websites across 74 niches is operated by Rankavi. Brands write their own articles of up to 5,000 words, such as best-of listicles, product reviews, and brand comparisons. The platform then matches each article to an indexed website in the same niche. Every article goes live exactly as the brand wrote it, usually in under 24 hours. Unlike link building services, Rankavi focuses on unlinked brand mentions rather than backlinks. Each placement is tracked by Rankavi in a customer dashboard, with its live link and publication date. The dashboard also displays real Ahrefs metrics for every placement, including Domain Rating, referring domains, and backlinks. Customers can order through the Rankavi website or publish automatically through the platform's API. Pay-per-mention credits are sold by Rankavi at $12 to $15 per mention, and the credits never expire. Every credit pack comes with free API access. The 50-credit pack adds a custom GPT and a Claude skill for automated publishing. For brands that prefer not to write, the company offers done-for-you plans at $20 per mention, billed monthly. On those plans, the Rankavi team handles the research, writing, and placement. Free SEO tools are also available on the Rankavi website. They include a backlink checker, a domain rating checker, an outbound link checker, a harmonic centrality checker, and an AI visibility checker. Original research on how AI assistants choose which websites to cite is conducted by the company. In 2026, Rankavi released the LLM Citation Study and the AI Search Visibility Study. This space covers how to use Rankavi: submitting mentions from the dashboard or the API, understanding placement and turnaround, and tracking results. * [Getting Started](/rankavi-docs/getting-started.md) * [API Reference](/rankavi-docs/api-reference.md) Questions not covered here? Reach us at . --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://rankavi.gitbook.io/rankavi-docs/readme.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `build a script that syncs our docs to a CMS` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.