The process where an AI model runs a live web search, reads the results, and weaves some of those pages into its answer.
Web search grounding is the process where an AI model answers a fact-seeking question not from memory but by running its own web search, reading what comes back, and weaving some of those pages into its reply. It's the umbrella term for how Google, OpenAI and Anthropic pull live sources into generated answers.
Under the hood every platform runs the same funnel: a search returns pages received, a subset have readable content, and a smaller subset get cited in the answer. The gap between received and cited is where each platform's personality shows. In our head-to-head test on a single query, Google received 7 pages and cited all 7, OpenAI received 39 and cited just 2, and Anthropic received 14 and cited 9.
For AI visibility this is the whole game: your page has to survive the funnel — get retrieved, be readable, and earn the citation. It works through grounding chunks and the grounding snippet, and it is distinct from document grounding, where you hand the model a specific URL.
