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Tibin Jacob.
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Answer engine optimization and GEO for B2B

Answer engine optimization (AEO) and generative engine optimization (GEO) make a B2B site easy for ChatGPT, Perplexity and Google AI Overviews to read, trust and cite. I do it with server-rendered pages, direct answers under question headings, schema, consistent entity facts and llms.txt, then measure whether AI tools actually cite you.

Why AI search matters for B2B pipeline

Buyers increasingly ask AI assistants to shortlist vendors and explain problems before they ever visit a site. If your pages can't be read or quoted, you're not in that answer. AI search is a demand source like paid or outbound, so I connect it to the same CRM and pipeline reporting as everything else I build.

How I approach AEO and GEO

  1. Make every page readable without JavaScript. Server-render or statically generate pages so crawlers and AI bots see the full content, not an empty app shell.
  2. Answer the question first. Use question-style headings with a 40–60 word direct answer underneath, then go deeper.
  3. Add structured data. Person or Organization, Article, FAQPage, HowTo, DefinedTerm and BreadcrumbList schema, matching what is visible on the page.
  4. Make the entity consistent. The same name, role and facts on the site, LinkedIn and other profiles, linked with sameAs, so AI models can connect them.
  5. Publish llms.txt and open access to AI crawlers. A plain-text map of key pages for AI tools, and a robots.txt that allows GPTBot, PerplexityBot, ClaudeBot and Google-Extended.
  6. Measure citations, not just rankings. Track target questions in AI tools each month alongside Search Console and GA4 data.

Tools I use for AI search

Google Search Console, GA4, Schema.org markup, llms.txt, and direct checks in ChatGPT, Perplexity and Google AI Overviews.

See it on this site

This site is built the way I describe: every page is statically rendered, uses question headings with direct answers, carries Person, FAQPage, HowTo and DefinedTerm schema, and publishes llms.txt. Start with the AEO definition or the GEO definition.

AEO and GEO questions

What is answer engine optimization (AEO)?

Answer engine optimization is structuring content so AI assistants and search features can quote it directly. In practice that means question-style headings, a short direct answer under each one, schema markup, and pages that render fully without JavaScript.

What is generative engine optimization (GEO)?

Generative engine optimization is making a brand easy for generative AI tools like ChatGPT, Perplexity and Google AI Overviews to find, understand and cite when they write an answer. It leans on clear entities, consistent facts across the web, specific evidence and machine-readable structure.

What is the difference between AEO and GEO?

AEO focuses on being the quoted answer for a specific question, such as a featured snippet or voice answer. GEO focuses on being included and cited when an AI model writes a longer answer. The work overlaps heavily, so I treat them as one program.

How do you measure AI search visibility?

With Search Console and GA4 for search and referral traffic, plus a recurring check of target questions in ChatGPT, Perplexity and Google AI Overviews to see whether the site is cited.