Track Your AI Search Rankings: See How LLMs and AI Search Engines Rank, Describe, and Recommend Your Brand

An AI visibility tracker like RivalSweeper runs the same brand and category prompts against every major LLM on a recurring schedule, parses each response, and records four signals per engine: share of voice (how often your brand is mentioned), position (where you appear in the recommended list), citation sources (which web pages the LLM pulled from), and sentiment (how the LLM frames your brand). RivalSweeper currently samples Google Gemini and OpenAI GPT models (OpenAI's open-weight GPT-OSS family) and shows the results per model and combined. It does not query ChatGPT, Claude, Copilot or Perplexity as products, so read the results as how large language models describe you, not as a ranking inside one assistant.

There is no native ChatGPT or Gemini analytics tool, so companies need a dedicated AI visibility platform. With RivalSweeper you point the tracker at your domain and RivalSweeper samples large language models - currently Google Gemini and OpenAI GPT models - with buying-intent questions about your category. It does not log into ChatGPT or read ChatGPT's live answers, which can also use web browsing and memory; spot-check those yourself. The platform runs those prompts on your schedule, captures each AI response verbatim, and turns the responses into measurable signals: brand mention rate, recommendation position, citation sources, sentiment, and competitor comparisons. You get a unified dashboard plus alerts when your AI visibility drops or a competitor takes over a slot you used to own.

The LLM visibility score is a single 0-100 number that summarises how visible your brand is inside large language model responses for the prompts your customers use. RivalSweeper calculates it from a weighted blend of four components: (1) mention rate - what share of LLM responses mention your brand at all, (2) position - average rank when you are mentioned, (3) sentiment - how positive or negative the surrounding description is, and (4) cross-model consistency - whether you appear consistently across the models RivalSweeper samples or only in one. The score updates every run so you can track week-over-week movement and benchmark yourself against the competitors that appear in the same answers.

Traditional SEO optimises for a search engine results page (SERP) where the user sees ten blue links and chooses one. LLM visibility monitoring optimises for a generative answer where the model writes a single recommendation paragraph and most users never click through to anything. SEO measures rankings and clicks; LLM visibility measures mention rate, share of voice in the answer, citation sources, and sentiment. The same brand can rank #3 on Google and be completely absent from an AI model's answer to the same question. As shoppers shift from typing keywords to asking AI assistants, LLM visibility becomes a parallel discovery channel that traditional SEO tools cannot see or fix.

Yes. The Competitor Comparison report runs head-to-head analysis on the exact same prompts and shows which brands the LLM recommends ahead of you, alongside you, or behind you - per sampled model and combined. You get share-of-voice deltas, sentiment deltas, and a citation-source overlap view that reveals which competitor content the LLM is drawing from. This is the report most teams use to prioritise the AEO/GEO work that actually moves your standing inside AI answers.

Analyses run on your configured schedule - daily, every three days, weekly, or monthly per prompt set. You can also trigger a fresh analysis at any time for any domain, which is useful after you publish new content, change pricing, or earn a major PR mention and want to see whether the LLMs have picked it up. Manual runs surface in the same dashboard as scheduled runs and feed the LLM visibility score history.

It tracks Gemini, and it is not limited to Gemini. RivalSweeper currently samples Google Gemini and OpenAI GPT models from one dashboard. It does not query ChatGPT, Claude, Copilot or Perplexity as products, so read the results as how large language models describe you, not as a ranking inside one assistant. For Gemini specifically it records how often Gemini mentions your brand, where you appear in its recommendation list, the sentiment of its description, and which competitors it names alongside you - so you can benchmark your Gemini visibility against the other sampled models and against rivals in the same answers.

AI search rankings, yes: RivalSweeper tracks the position your brand holds inside generative answers from the models it samples, for the buying-intent questions your customers ask, with rank, mention rate and sentiment over time and who is named ahead of you. Google AI Overviews, not directly: RivalSweeper samples Google Gemini, the model family behind AI Overviews, but it does not read the AI Overviews shown on Google Search, which also depend on live search results. Treat the Gemini read as a related signal and check AI Overviews for your key queries in Google itself.