Move from monitoring to execution.

Adopting GEO should not mean rebuilding your site or running a second publishing pipeline alongside the one your team already uses. Recommendations arrive as page-level plans, each tied to a cluster of real prompts, and each one names the page to change and the point in it where the change belongs.

A plan is specific enough to act on without further analysis. It carries a draft content block, the questions to answer as a FAQ, suggested meta title and description, internal links worth adding and the schema markup to publish, together with a short task list your editor can work through and tick off.

Your editor stays in control. Meikai drafts and prioritises, your team reviews, edits and publishes in the content management system you already run. That division is deliberate: we do not push changes into your CMS, and if fully automated publishing is your main requirement, weigh that against workflow-first tools before choosing us.

On-site work only reaches part of the problem. When a model answers a question about your category, it draws on third-party sources as well as your own pages, so communications and content need to be planned against the same prompt set, not in separate quarterly cycles.

Off-site recommendations name individual publishers instead of describing a media category. Each one comes with an editorial brief: the questions the article should answer, a suggested angle and format, the elements worth making quotable, a pitch direction, and examples of pieces on that publisher that AI systems have already cited.

That gives a public relations team a target list ordered by the prompts each placement would affect, and it gives content and PR a shared basis for deciding which of the two is the faster route to a given answer. Our note on measuring which pages shape AI answers sets out the method behind it.