You can rank on Google and still be invisible in ChatGPT. That’s the gap. This guide closes it with a builder-first playbook you can ship in 90 days.
Why now
AI answer engines—ChatGPT, Gemini, Perplexity, Copilot are where users get answers first. Links are optional; citations are decisive. If models don’t cite you, your brand vanishes in the moment that matters.
LLM SEO: making your pages easy for large language models to find, understand, and cite.
GEO (Generative Engine Optimization): optimizing for answer engines, not just web SERPs.
Citable Content: blocks a model can lift with confidence and attribution (claims, sources, steps, tables).
AEO: older idea of “answer engine optimization”; overlaps with GEO but less model-aware.
The problem we solve
Traditional SEO tools optimize for blue links. AI engines optimize for evidence. Your job shifts from ranking to being the source a model trusts enough to reference—consistently, across prompts and platforms.
What’s inside
Part 1 (foundations): the shift to AI answers, how models discover/understand content, citable patterns.
Part 2 (frameworks): entity-first IA, technical access (robots/llms.txt), surface-specific play, measurement (Findability Score™).
Part 3 (applications): 30/60/90 plan, seeding & distribution, guardrails, examples, and next actions.
The Shift: From SERPs to AI Answers
Users ask an LLM and move on. That reduces your margin for error to one answer. We need a new goal: becoming the cited source across engines.
Pros
- •Faster discovery: AI answers surface your brand early.
- •Higher trust: citations in answers act like instant endorsements.
- •Cross-platform: one citable asset can influence multiple engines.
Cons
- •Fewer clicks: zero-click answers compress traffic.
- •Opaque systems: model behavior changes faster than search algorithms.
- •Measurement gap: classic SEO KPIs miss AI visibility.