Optimizing per engine
Why this lesson
Section titled “Why this lesson”Most people treat “AI search” as a single target and optimize for it one way. That’s the mistake this lesson kills. Each engine has its own index, its own biases, and its own idea of what a good source looks like — so a page that gets cited constantly in Perplexity can be invisible in ChatGPT. You’ve already done the universal work (citability in 3.2, crawler access in 3.3, entity + sameAs in 3.4). Now you learn where each platform actually pulls from, so you can prioritize the two or three moves that matter for the engines your audience uses instead of spreading effort thin across all of them.
The explainer
Section titled “The explainer”The video’s headline is the whole strategy: AI search is not one thing. Only ~14% of the top-cited domains overlap across the three big engines. So instead of one plan, you carry five short playbooks — and you score a page separately for each. That per-engine fit is what we call platform readiness.
Google AI Overviews
Section titled “Google AI Overviews”AI Overviews reward structure and existing authority. The lever is content shape: a question as an H2/H3, immediately followed by a 40–60 word “answer target” paragraph, plus comparison tables and clean definition patterns — the citability work from 3.2, aimed at Google. AI Overviews optimization also leans on pages already ranking in the top 10 (though that link is weakening — from ~76% of citations down to ~38%), and on Google-owned surfaces: YouTube video and Reddit threads now feed a large share of citations. If you rank and you’re structured, you’re most of the way there.
ChatGPT Search
Section titled “ChatGPT Search”ChatGPT runs on the Bing index and crawls with OAI-SearchBot, so first confirm both: your site is in Bing, and OAI-SearchBot isn’t blocked in robots.txt. Then it’s an entity and publisher game. ChatGPT’s most-cited domains skew to high-authority publishers (median domain rating around 90, partly from OpenAI’s licensing deals) and to strong entities — which is exactly why your Wikipedia/Wikidata presence and Organization sameAs from 3.4 matter here. Factual, quotable, dated content with real author bylines is what it pulls.
Perplexity
Section titled “Perplexity”Perplexity is the closest thing to traditional Google — roughly 28.6% of its citations rank in Google’s top 10 — so if you already rank, this is your fastest AI win. Two extra tilts: community validation (Perplexity indexes Reddit, forums and reviews heavily — third-party corroboration of your brand counts) and freshness (recent dates win). One technical catch: PerplexityBot has limited JavaScript handling, so your content must be server-rendered to be seen (the subject of 3.6).
Gemini
Section titled “Gemini”Gemini lives inside the Google ecosystem. It draws on YouTube, Google Business Profile, Scholar and News, and it trusts the Knowledge Graph — your entity, sameAs, and consistent NAP consistency (Name, Address, Phone identical everywhere). For a local business this overlaps almost perfectly with the local SEO you did in Level 2. Long-form, clustered, topically-deep content is the content signal.
Bing Copilot
Section titled “Bing Copilot”Copilot is Bing plus the Microsoft ecosystem. The technical accelerator is IndexNow — a protocol (via Bing Webmaster Tools) that pings Bing the moment you publish or update a page, so it’s indexed in minutes instead of days. Add the msvalidate.01 verification, keep a strong LinkedIn and GitHub presence, and you’ve covered its main preferences.
Cross-platform synergy
Section titled “Cross-platform synergy”Here’s the payoff. Some moves aren’t platform-specific — they lift several engines at once. The biggest is cross-platform synergy: a Wikipedia presence strengthens ChatGPT, Perplexity and Gemini simultaneously, because all three lean on the knowledge graph you just joined. Getting mentioned on Reddit helps both Perplexity and AI Overviews. So when you prioritize, do the synergy plays first — one Wikipedia article or one well-placed Reddit thread beats five single-engine tweaks. Then spend the rest of your effort on the one or two engines your audience actually uses.
- Take the page you’ve been improving through Level 3. Give it a platform readiness score from 0–100 for each of the five engines, using the levers above as your checklist.
- AI Overviews: does it have a question-H2 + 40–60 word answer target? Is it ranking top-10? Are there supporting YouTube/Reddit signals?
- ChatGPT: is the site in Bing? Is OAI-SearchBot allowed? Is there entity/
sameAs+ author bylines? - Perplexity: does it rank in Google? Is content server-rendered and fresh? Any Reddit/review corroboration?
- Gemini & Copilot: is NAP consistent across GBP and citations? Is IndexNow set up in Bing Webmaster Tools?
- Find your lowest two scores and your highest-leverage fix. If Wikipedia is missing, that single cross-platform synergy move usually tops the list. Write the prioritized action plan in the workbook.
Terms introduced
Section titled “Terms introduced”Check yourself
Across the top 50 most-cited domains on Google AI Overviews, ChatGPT and Perplexity, roughly how much overlap is there?
Which platform is most aligned with traditional Google rankings, making it the fastest AI win if you already rank in Google’s top 10?
What is the highest-leverage cross-platform GEO action — one move that lifts several engines at once?
You can move on when you can… score a page’s platform readiness for all five AI engines, name what each one pulls from, and pick the highest-leverage fix — including the cross-platform synergy moves that lift several engines at once.
Go deeper
Section titled “Go deeper”- Google — “Optimizing for AI features in Google Search”: the primary, first-party guidance for AI Overviews and AI Mode.
- Bing Webmaster Tools — IndexNow documentation: set up instant indexing for Copilot in a few minutes.
- Next: 3.6 · SSR & technical GEO — the render fix several of these playbooks depend on.