Actionable guides on LLMO, GEO, AEO, and AI search optimization. Written by Astral.
21 articlesShoppers now ask AI for the best product to buy. Here is how to get your products, categories, and store cited across ChatGPT, Perplexity, and Google AI search.
02AI engines cite sources they trust. Here is how to build the Experience, Expertise, Authoritativeness, and Trust signals that get you into AI answers.
03Google AI Overviews now sit above the classic results. Here is how AI Overviews pick their sources, the ranking factors that matter, and how to structure pages to get cited.
04A step-by-step GEO audit checklist: baseline your AI visibility, then check crawlability, content extractability, schema, authority, and measurement, and turn gaps into a roadmap.
05B2B buyers now ask AI engines which tools to pick. Here is how to win the queries that drive SaaS pipeline, from comparison pages to docs, reviews, and SoftwareApplication schema.
06Crypto users ask AI engines whether projects are legit, which L2 is cheapest, and where it is safe to stake. This is the trust-first GEO playbook for getting your Web3 project cited.
07A category-by-category breakdown of the best GEO tools in 2026, from AI visibility tracking to schema and analytics, with honest cost tiers and a starter versus pro stack.
08GEO is harder to measure than SEO because there is no universal rank tracker. Here are the metrics that matter, how to build a citation-tracking system, and how to prove ROI.
09A technical, copy-pasteable guide to schema markup for GEO: which JSON-LD types actually help AI citations, how structured data feeds retrieval, and the mistakes that get you ignored.
10The editorial playbook for content LLMs actually quote. How retrieval works, what makes a passage citable, and a before/after rewrite that turns weak prose into AI-citable content.
11Complete breakdown of GEO pricing in 2026. Agency retainers, one-time projects, in-house costs, DIY budgets, and the tier that fits your stage. With ROI benchmarks and a budget framework.
12The umbrella discipline covering GEO, LLMO, and AEO. How AI search works, the six core pillars, step-by-step implementation process, and the metrics that matter.
13Step-by-step guide: schema markup, llms.txt, content structure, AI crawler access, and entity authority building.
14GEO optimizes for AI citations. SEO optimizes for Google rankings. Full comparison of how they differ, where they overlap, and why you need both.
15Complete guide to GEO: what it is, how AI engines decide what to cite, implementation steps, and the key ranking factors that drive AI citations.
16Step-by-step technical guide to creating and deploying llms.txt and llms-full.txt. Includes templates, real examples, and deployment instructions.
17Complete comparison of LLMO and SEO. How they differ, how they complement each other, and the recommended combined strategy for maximum visibility.
18Compared: Astral, Victoria Olsina, High Vibe PR, ColdChain, ICODA, Coinbound. Specialization, pricing, and which one fits your project.
19The 6-step process to get your project cited in AI search results. From audit to schema, llms.txt, content, authority, and monitoring.
20Everything about Large Language Model Optimization: definitions, LLMO vs SEO, how each AI model works, and implementation steps for projects.
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