One goal, four different judges
Each assistant weighs sources differently: Perplexity leans on live citations, Gemini on Google’s index, ChatGPT and Claude on trained knowledge plus browsing. The same fix can move one assistant in weeks and another only at the next model release. Optimize the shared foundations first, then chase per-assistant gaps.
Step one: claim a specific answer
Assistants complete the sentence “for [situation], consider [brand].” If no situation maps cleanly to you, you are unrecommendable. Define the three to five moments you genuinely win, phrase them the way buyers phrase them, and align every page and profile to those moments. This is the highest-leverage hour in all of GEO.
Step two: build third-party proof
Models trust what multiple independent sources agree on. Reviews with volume and recency, category listicles, comparison articles, community mentions, press. Study which sources carry your competitors’ recommendations (Perplexity shows them openly), then earn presence in the same set. Consensus is constructed, deliberately.
Step three: publish quotable answers
For every buying question in your category, someone’s page becomes the raw material of the AI answer. Make it yours: direct language, question-shaped headings, honest comparisons, FAQ schema, no hedging. Assistants quote pages that resolve questions, and skip pages that market around them.
Step four: fix the record
Run your brand and buyer questions through all four assistants. Wrong pricing, dead products, dated positioning all trace to correctable sources. Silence traces to fillable gaps. This audit-fix-verify loop is Aethon’s core: we monitor the answers continuously, diagnose the sources behind them, and ship the work that moves them.
