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Unlike search rankings, an AI model's answer isn't indexed, logged, or exposed through any dashboard a business can check on its own — the only way to know whether ChatGPT, Claude, Gemini, or Grok mentions a business favorably is to ask the models directly, repeatedly, with the real questions its buyers would use. AI share of voice is the resulting metric: across a defined set of buyer-intent prompts, how often the business is named at all, how accurately, and how it's framed relative to named competitors. This requires systematic, repeatable probing across multiple models rather than a single spot-check, since each model draws on different training data and live retrieval sources and can disagree sharply about the same business. AI HALO's free audit establishes this baseline across six major assistants, and the thirty-day re-scan quantifies the actual lift after the structured-data and citation work is complete — turning an otherwise invisible metric into a concrete, comparable number.
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Each model trains on different data snapshots and uses different live-retrieval methods, so one may rely on outdated directory data while another pulls fresher structured signals — producing inconsistent or even contradictory descriptions of the same business.
Not in the way traditional rank trackers work, since AI answers aren't publicly indexed; accurate measurement requires directly querying each model with defined prompts on a recurring basis and comparing the responses.
There's no universal benchmark — the meaningful comparison is a business against its own named competitors across the same set of buyer questions, and against its own baseline over time as visibility work takes effect.
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