Most teams still optimize for blue links, while their buyers already ask AI assistants for shortlists, trade‑offs, and next steps.
That gap is what Generative Engine Optimization (GEO) is about.
Instead of asking “How do we rank for this keyword in Google?”, GEO asks:
• When someone asks ChatGPT, Claude, Gemini, or Perplexity for tools like ours, are we in the answer?
• If we are, what does the model actually say about us?
• Which competitors appear more often or higher in the reasoning?
From what we see working on AI visibility tooling, GEO has three practical layers:
1) AI visibility
Are you mentioned at all for your core use cases across major models?
2) Perception and positioning
How do models describe your category, ICP, pricing, and differentiators? Do they understand your unique angle, or collapse you into “another SEO tool”?
3) Execution loop
Can you push structured fixes (better product facts, FAQs, schemas, repo metadata) and then measure ranking jumps in AI answers and traffic coming from AI‑mediated sessions?
In upcoming articles I’ll turn these into checklists you can wire into your existing stack.
Practical starting point for developers
Pick one realistic buying query for your product, then:
1) Ask that exact question in 3–4 AIs.
2) For each answer, note:
– Did we appear?
– Are the basics right (category, ICP, pricing band, geography)?
– Which competitors show up more often or higher?
That snapshot is the baseline for your first GEO sprint.
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