InsightsAI visibility
AI search answers a question in running text, with sources attached, instead of only a list of links.
The system retrieves passages, weighs them, and writes an answer. You win if your passage survives that weighing.
The question is parsed: intent, entities, what a usable answer looks like. Then the model searches indexed pages, sometimes plus live retrieval.
It does not paste random sentences. It looks for blocks that already almost answer the question. If you only hit the question in paragraph four, you drop out before the answer is written.
Clarity, overlap with the question, and whether the passage is still true on its own. Brand fame helps; hollow authority claims do not.
In Palan scans the pattern is often the same: two brands with similar services, one written to be cited and the other in slogans. Only the first comes back in answers.
AI search is search where a model answers the question in text, with citations from web sources, instead of only showing ten links. Google AI Overviews, ChatGPT with search and Perplexity work this way, each with a different mix of index and live retrieval. Visibility then means being cited, not only ranked.
Google is still a search engine, now with an answer layer on top. ChatGPT and Perplexity start from the answer. For content that barely matters: all three pull passages that already answer the question. Write for that, not for an abstract algorithm. Palan optimises those passages, not rankings as a product.
No. Indexing, a clean site and one real question per page still matter. Palan is not an SEO agency; we make the content those systems can cite. Technical SEO without citable copy does not solve AI search. First the question, then the build, then the tech if needed.
Not automatically, and not at once. Some systems crawl or search live; others lean on older training. Publish as if someone will open the page tomorrow. That is the same discipline as writing for people. Waiting until the model 'catches up' means publishing too late.
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