This module is narrated by an AI voice model trained on Dr Ravichandran's own recordings — a real, working example of the AI-native approach this course teaches, not a shortcut around it.
What this module covers
Explain why AI-driven search surfaces some businesses over others — as recommendability, not keyword-matching — and build a query-match style audit rather than relying on generic SEO habits.
Lessons in this module
- 5a — Why AI search isn't Google searchClient-rendered sites and the JavaScript-crawling gap that makes some businesses invisible to AI tools despite ranking fine on Google.
- 5b — Recommendability, not keyword-matchingA five-category framework — credentials/trust, problem-specificity, named-approach clarity, low-friction entry signals, and client-felt-experience language. The counterintuitive finding: generic phrasing that's SEO-safe actively hurts AI recommendability.
- 5c — Building your own audit: the query-match methodGenerating the specific natural-language questions a real customer would ask, then scoring whether a page would surface for those exact questions.
Deliverable
Try this: Pick 5-10 natural-language questions a real customer in your category would ask an AI assistant, then audit your own content against them using the five-category framework.