Why the engines look different but rhyme

ChatGPT, Perplexity, Google AI Overviews, Bing Copilot, Claude and Gemini all answer in natural language and cite a small set of sources. Their differences are real but narrower than they look — different crawlers, different retrieval pipelines, different source weightings, different freshness windows.

The shared foundation is unchanged across all of them: a clearly-defined brand entity, schema-rich content, prompt-fit answers in the first paragraph of every section, and a citation surface that has trained the underlying models to trust the brand.

The shared foundation (do this first)

Confirm crawler access in robots.txt for GPTBot, OAI-SearchBot (ChatGPT Search), ClaudeBot (Anthropic), Google-Extended (Gemini and AI Overviews) and PerplexityBot. Blocking any of them removes you from that engine entirely.

Ship Person and Organization JSON-LD with sameAs to LinkedIn, Wikidata, Crunchbase and category-relevant profiles. Add Article, FAQPage, HowTo and Service schema across every cornerstone page. Make every section open with a 40–60-word direct answer to the question that section solves.

Build the citation surface: Wikipedia, Wikidata, G2, Capterra, top-three podcasts in your category, Reddit AMAs in active subreddits, GitHub repos for technical brands, and editorial mentions in trusted industry press.

"You do not need seven AI strategies. You need one foundation and seven dial settings."

Per-engine tuning that matters

ChatGPT and ChatGPT Search lean on entity strength and prompt-fit content. Strong Wikidata QID, clean Person schema and definitional first-paragraph answers move the needle most.

Perplexity weights live retrieval and source quality heavily. Schema density, recency and authoritative outbound links lift citation share fastest. Perplexity converts at 4–6× a Google session in client data — worth a dedicated workstream.

Google AI Overviews privileges sources that already rank in the top 10 organic and ship FAQPage / HowTo schema. Conventional SEO underneath GEO is non-negotiable.

Bing Copilot pulls heavily from Bing Webmaster Tools coverage — submit your sitemap there, not just in GSC.

Claude and Gemini lean on training-data presence (Wikipedia, GitHub, peer-reviewed content) and entity recognition. The work overlaps almost entirely with the LLM Optimization workstream.

How AI Search Optimization is measured

Cross-engine citation share — track a fixed set of 30–60 buyer prompts monthly across ChatGPT, Perplexity, AI Overviews, Bing Copilot, Claude and Gemini. Note who is cited, what is cited, and where you are closest to winning.

Generative referrals in GA4 — chat.openai.com, perplexity.ai, gemini.google.com, copilot.microsoft.com. Segment as a separate channel and attribute to pipeline.

Branded query volume — when buyers encounter you in AI answers, they verify in Google. A rising branded curve is one of the cleanest leading indicators of AI Search momentum.

Where to invest first

Order of operations for most B2B and SaaS brands: foundation (entity + schema + crawler access) → cornerstone content rewrite for prompt-fit → citation surface investment → per-engine tuning. Skip any layer and the layers above wobble.

For the full per-engine breakdown, see How to be cited by ChatGPT, Perplexity & Google AI Overviews. For the consulting engagement, AI Search Optimization services live here.