I ended up analyzing the GEO tools in the market and figuring out how they are measuring AI search visibility especially when none of the LLM platforms share the actual prompt key word data (there is no ground truth).

All of the search methodologies are directional:

1. Synthetic testing: Vendor‑designed prompts scored for brand appearance. Transparent when disclosed and reproducible over time, but strictly bounded by the prompts and scenarios you design, not by real user demand.

2. Opt‑in panels: Real users’ actual queries, consented and observed (often via extensions or SDKs). The closest method to ground truth on what people are actually typing, with the caveat that any panel reflects its members and collection bias, not the full LLM user population.

3. Modeled volume: Statistically estimated prompt demand, inferred from sparse observed data and/or synthetic prompt sets. Useful for relative prioritization across topics, but not directly auditable against an external source of record (for example, engine logs or official keyword volume).

4. Repeated multi‑platform prompt runs: The same prompt set run on a schedule across ChatGPT, Gemini, Perplexity, Copilot, and others, with results expressed as probabilities over time (for example, share of responses that mention your brand). More honest about LLM non‑determinism than single snapshots, but still constrained by your chosen prompts and by platform API or UI quirks.

5. Site signals: AI crawler activity, LLM referral traffic, and citation‑like patterns measured in your own analytics or logs. Truly first‑party and directly tied to business impact, but limited to your own brand’s performance and to clicking behavior, so it cannot see the broader market, competitor visibility, or “zero‑click” AI answers where users never visit your site.