See Exactly How LLMs and AI Search Engines Rank, Describe, and Recommend Your Brand

An AI visibility tracker like Trendos 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). Trendos does this across ChatGPT (OpenAI), Claude (Anthropic), Google Gemini, Microsoft Copilot, and Perplexity from a single dashboard, then aggregates the results so you can see total cross-engine visibility, per-engine breakdowns, and week-over-week movement. New AI engines are added automatically as they launch - no configuration needed on your end.

There is no native ChatGPT or Gemini analytics tool, so companies need a dedicated AI visibility platform. With Trendos you point the tracker at your domain, select the AI engines you want to monitor (ChatGPT, Claude, Gemini, Copilot, Perplexity), and add the buying-intent prompts your customers actually ask. 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. Trendos 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-engine coverage - whether you appear consistently across ChatGPT, Claude, Gemini, Copilot, and Perplexity 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 ChatGPT'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 engine and aggregated. 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 and AI engine combination, 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.