AI Search Optimization refers to the practice of structuring content so AI systems can accurately retrieve, interpret, and cite it in generated answers. It encompasses Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO), focusing on how models evaluate clarity, authority, and relevance rather than traditional rankings.
AI search is reshaping how news is discovered, summarized, and attributed. This guide outlines a practical AEO framework for media publishers to maintain narrative control, increase citation visibility, and turn AI answers into a new source of subscriber growth.
llms.txt is a markdown file that gives AI systems a structured, token-efficient map of a website’s most important content. This guide explains how it works, how AI engines use it, and where it fits in a GEO strategy.
AI product discovery is shaping which brands get seen in generative answers. Learn 5 tactics top e-commerce brands use to get cited in ChatGPT, Google AI Overviews, Gemini and more.
AI search engines increasingly analyze public social media content to detect real-world usage patterns, sentiment trends, and expert signals. This article explains how platforms like YouTube, Reddit, TikTok, Instagram, and LinkedIn influence AI-generated answers, and why crawlability determines which platforms matter most.
AI search engines do not rely on static training data to decide what to cite. They retrieve, evaluate, and rank sources in real time using Retrieval-Augmented Generation (RAG). This article explains how AI engines fetch candidate documents, assess extractability and authority, and synthesize final answers, and why structure determines whether your brand is cited.
GEO requires a new measurement framework. This guide breaks down the three layers of GEO measurement that define AI search performance: Infrastructure, Visibility, and Business Impact.
In e-commerce GEO, the Product Detail Page (PDP) is the primary unit of optimization because AI systems extract, compare, and evaluate products at the SKU level. This article explains what makes a PDP citation-ready and how to structure product data for AI visibility.
A practical GEO framework for evaluating whether your website is structured to be crawled, interpreted, trusted, and cited by AI engines.
Most websites fail in AI search not because they lack content, but because their content isn’t extractable. This post breaks down the 10 most common GEO mistakes preventing AI citation. And the specific fixes that unlock visibility in ChatGPT, Google AI Overviews, Gemini, and other answer engines.
SEO, AEO, and GEO describe different generations of search optimization. This article explains how they relate, where they overlap, and why GEO is the clearest framework for AI-driven search today.
AI engines treat commerce sites differently than informational sites. This article explains how AI systems understand, compare, and recommend e-commerce products, and what brands must expose at the product level to be eligible for citation.
AI systems assess five distinct dimensions that determine whether content is extractable, trustworthy, and worthy of citation. This article elaborates on Stellar’s GEO Framework and explains how AI engines actually evaluate websites along Five Pillars.
AI search engines don’t cite sources based on writing quality or rankings. They follow a structured citation pipeline that filters content based on retrievability, extractability, and trust. This article explains how that process works, and why most content never gets cited.
GEO focuses on making content extractable and citable by AI systems, not just rankable in search engines. This article explains what GEO is, why it matters, and how AI decides which sources to include in its answers.
To get started with optimizing your website for AI search visibility, submit your information here. Stellar will perform a mini-assessment to give you a AEO-readiness score along the different pillars of our framework. Your free report will be emailed to you within 2–3 business days.
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