The short answer: start with four — ChatGPT (largest consumer usage), Google Gemini (feeds Google's AI surfaces), Perplexity (retrieval-first, citation-heavy), and Claude. Together they span the two architectures that produce AI brand recommendations, memory-heavy and retrieval-heavy, and they routinely disagree about the same brand — which is precisely the signal worth catching. Instrument these four before adding anything niche.
Why these four
ChatGPT is where the volume is: the largest consumer AI audience asking the most buying-intent questions. It blends answering from model memory with live web search, so it exercises both of your visibility channels at once.
Google Gemini matters beyond its own app because Google's model family powers AI Overviews and AI Mode inside the world's dominant search engine. Tracking Gemini gives you a leading indicator for how the Google side of AI search treats your brand.
Perplexity is the purest retrieval engine: nearly every answer is grounded in live sources with visible citations. That makes it both a distinct discovery surface and a diagnostic instrument — its citations show you exactly which pages are feeding answers in your category, which is intelligence you can act on for every other engine.
Claude rounds out the panel with a distinct model lineage and its own web search. Because its training data and retrieval behavior differ from OpenAI's and Google's, it regularly diverges on the same prompt — surfacing brands the others skip, or repeating an error the others have shed.
The real reason to track more than one
The four engines represent different mixes of the two ways models know about brands: memorized associations versus live retrieval (RAG). A brand can score well on retrieval-heavy engines and be invisible to memory-heavy ones, or the reverse — and the split is the diagnosis:
- Strong on Perplexity, weak on memory-heavy answers — your recent content is working, but your long-term footprint is thin. Invest in coverage and entity building.
- Strong from memory, weak when engines retrieve — the model knows you, but the pages engines currently cite omit you. Chase placements in those sources.
- One engine states a false claim the others don't — a stale source or model-specific hallucination worth tracing before it spreads.
Track only one engine and every one of these situations looks identical: a number, with no explanation. The methodology behind cross-engine comparison is covered in how to measure AI visibility.
When to expand beyond the core four
Additional surfaces — Microsoft Copilot, Meta AI, Grok, regional engines — earn a slot when your audience demonstrably lives there: enterprise buyers deep in Microsoft tooling, or a market where a local engine dominates. Add them after the core four are instrumented and stable, not instead. Most teams find the four-engine panel already surfaces more actionable disagreement than they can work through.
The practical constraint is consistency, not coverage. Comparable numbers require running the same prompt panel against every engine on the same schedule and scoring answers the same way — one-off manual checks across four engines don't compound into a trend. That's the job of scheduled sweeps: Brandflare runs your panel across all four engines and scores every answer with the same LLM judge, and a free audit gives you the four-engine baseline in one pass.
Once the panel is running, the next question is what the numbers should look like — see what counts as a good AI visibility score.