The short answer: start with five — ChatGPT (largest consumer usage), Google Gemini (Google's model family), Google Search (what Google's own AI answers say, grounded in live search results), 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 five before adding anything niche.
Why these five
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 is Google's assistant, answering largely from what the model has learned. It tells you what Google's model family believes about your brand before any search happens — the memory half of the Google picture.
Google Search is the other half, and the one most of your customers meet first: the AI answers inside the world's dominant search engine, AI Overviews and AI Mode. brandflare measures it as Gemini with live Google Search grounding, so every answer is built from current search results and shows its sources. The same model can say different things with and without the search results in front of it, which is why the two are tracked as separate engines: a brand Gemini knows well can still be missing from the pages Google retrieves, and the reverse.
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, like Google Search's, 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 five 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 and Google Search, 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 five
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 five are instrumented and stable, not instead. Most teams find the five-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 five engines don't compound into a trend. That's the job of scheduled sweeps: brandflare runs your panel across all five engines every week and scores every answer with the same LLM judge, and every brand's profile in the index shows that engine-by-engine picture.
Once the panel is running, the next question is what the numbers should look like — see what counts as a good AI visibility score.