When buyers ask ChatGPT, Perplexity or Google’s AI Overview for a recommendation, you want your brand named. Here’s the practical playbook — the technical foundations and content moves that make AI engines read, trust and recommend you.
Two common, fixable problems make sites invisible to AI: blocking AI crawlers in robots.txt, and shipping client-rendered pages that non-JS crawlers fetch as nearly-empty HTML. Check both first — a free Beacon scan reads your robots.txt and measures your render gap in seconds.
Language models reward content they can parse and quote.
AI engines disproportionately cite high-authority, well-structured sources. A notable Wikipedia presence, inclusion in category roundups and listicles, and active community mentions all increase the odds the model recommends you.
Define the real prompts your buyers ask, then track mentions, position and Share of Voice over time. Honest measurement matters: a single AI call is stochastic, and no tool can show a real “ChatGPT position” for an engine it never queried. Beacon labels what’s measured vs modeled so you can trust the numbers.
You don’t “rank” — you get recommended. Make sure ChatGPT’s crawler (GPTBot/OAI-SearchBot) can read your site, structure your content so it’s extractable, earn citations on trusted sources, and track the prompts where you want to appear.
Yes. If GPTBot or OAI-SearchBot is disallowed in robots.txt, ChatGPT can’t read your content and is far less likely to recommend you. Beacon checks this for free.
You can track AI-answer visibility for your buyer prompts. Be wary of tools claiming live per-engine readings they don’t actually take — Beacon clearly marks which engine is queried live vs modeled.
It shares the technical foundations (crawlability, schema, authority) but adds tactics for being cited inside AI answers — direct-answer formats, AI-crawler access, render-gap fixes and prompt tracking.
Free scan: AI-crawler access, render gap, schema & more.