Noma Blog
Practical guides on Answer Engine Optimization, AI visibility, and building a brand that AI engines actually recommend.
AI assistants cite a small, repeatable set of sources for any given question. Here is what determines whether a page makes that set, and what to change if yours doesn't.
AI share of voice measures how much of the conversation your brand holds inside AI answers, relative to competitors. Here is the formula, what it misses, and how it differs from visibility.
Ask five AI engines the same question about your category and you get five different answers. Here is why the disagreement happens, and why averaging it away is a mistake.
AI engines state wrong pricing, features and facts about brands with complete confidence. There is no support line to call. Here is the sequence that actually corrects the record.
Getting mentioned in an AI answer and getting cited as its source are different achievements. Here is the work that moves each, in the order that pays.
A tracked prompt set is the measurement instrument for AI visibility. Choose it badly and every number after it is misleading. Here is how to build one that holds up.
AI visibility metrics do not map cleanly onto revenue, and pretending otherwise is how budgets get cut. Here is how to report it honestly and still make the case.
Your brand can rank on page one of Google and still be invisible in AI-generated answers. Here's why that happens, what AI engines actually measure, and what to do about it this quarter.
llms.txt is a plain-text file that gives AI language models explicit context about your brand. Here's what it is, what to put in it, and how to implement it in Next.js in under five minutes.
AEO is the practice of making your brand visible in AI-generated answers — not just search rankings. Learn how LLMs decide who to cite, and three practical first steps to get started.