The fundamentals - crawlability, question coverage, quotable structure - are shared. But the engines differ in how they retrieve, how they cite, and how much search demand surrounds them. Here is the per-engine map, with our investment read on each.
In short
Each AI engine retrieves and cites differently: ChatGPT blends training knowledge with live search, AI Overviews rides Google's index, Perplexity is retrieval-first and citation-dense, Claude and Copilot matter for specific audiences. Optimize the shared fundamentals once - then tune per engine where the differences are real.
Separate assistant surface; not the same pipeline as AI Overviews
Low but real demand; frequently conflated with Overviews
GEO-only: same fundamentals, measured on its own
What to do with the differences
Not five strategies - one strategy, five tuning passes. The shared work (crawler access, question coverage, citable format, corroboration) moves every engine at once. The per-engine passes are narrower: AI Overviews rewards classic ranking strength, Perplexity rewards freshness and precise sourcing, ChatGPT rewards being the consensus answer across corroborating sources. The audit measures you on each engine separately, because your gaps will not be uniform.
Measured, not guessed
Engine-by-engine mention tracking is exactly what our own monitoring account logs daily on our own topics. The recommendations on these pages are downstream of that data - and the same instrument runs on client categories during every audit.
Common questions
Which engine should we optimize for first?
Where your buyers ask - usually ChatGPT and AI Overviews, with Perplexity as the low-competition third. The audit's per-engine baseline replaces guessing with your own numbers.
Do the engines share sources?
Substantially - retrieval pipelines lean on overlapping web indexes, which is why the shared fundamentals move everything at once. The citation behaviors differ more than the source pools.
How different are the optimization tactics really?
Perhaps eighty percent shared, twenty percent engine-specific - and the twenty percent shifts quarterly. That ratio is why we sell fundamentals plus measurement, not per-engine magic.
What about new engines?
The fundamentals transfer. A site that is crawlable, question-covering, and quotable is pre-positioned for whichever engine ships next - that is the strategy's real durability. Gemini is the current example: same fundamentals, measured as its own surface rather than assumed from an AI Overviews score.
See your baseline on every engine at once.
Per-engine mention rates on your question set - the audit's first output, and usually the first time anyone in your company has seen the number.