LLM analytics is the practice of measuring and analyzing how large language models like ChatGPT, Claude, and Gemini represent a brand: whether they mention it, how they rank it, what they say about it, and which sources they cite. It applies an analytics discipline to AI answers the way web analytics applies one to website traffic.
As AI assistants become a primary way buyers discover and shortlist products, the answers those models give are a measurable surface that directly influences revenue. Without LLM analytics, that surface is a blind spot: you cannot tell whether AI is recommending you or your competitors. Treating AI answers as analyzable data turns a vague worry into a managed channel.
LLM analytics works by sending a fixed set of prompts through each model on a schedule and structuring the responses into data: presence, position, sentiment, competitors, and citations. Repeating the same prompts over time produces trends, and comparing models reveals where coverage diverges. The exact response text is kept as evidence behind every number.
Rankry is a purpose-built LLM analytics platform that structures AI answers into trends and benchmarks.