Platform · Measure
Methodology
Measurement you'd bet budget on has to survive its own methodology page. This is exactly how every number on a SolvedAgain report is produced.
What we measure
Whether AI assistants mention and recommend your brand when your buyers ask them real purchase questions — and, when they don't, which competitor wins and why. The standard audit queries ChatGPT and Claude; Full Coverage and every subscription plan add Gemini, Perplexity and Grok. Question sets scale by plan — 20 on the one-time audit and Track, 50 on Grow, 100 on Command. Each report lists exactly which engines it queried.
Grounding — and why it matters
Every answer is generated with the engine's live web-search tool enabled. This is deliberate: an ungrounded answer only reflects what the model memorized during training, which measures training-data presence, not what a real user sees today. A groundedanswer reflects the live answer engine. Each engine's grounding status is labeled on the report, so you always know which one a number came from.
How the audit runs
We generate buyer-intent questions across four types (best-of, comparison, problem-led, category-explainer), which you review and edit before anything runs. Each approved question is asked to each engine three times (AI answers vary between runs). We store every raw answer. A separate extraction pass reads each answer and records which brands were mentioned, in what order, with what sentiment, and which domains were cited.
The metrics
- Mention rate — share of samples that mention your brand.
- Share of AI Voice — your mentions ÷ all tracked-brand mentions.
- Average rank — where your brand appears among named brands, when mentioned.
- Recommendation rate — share of your mentions that are positive endorsements.
- Citation share — share of cited sources that are your own domains.
- Volatility — how much the three samples disagreed, shown honestly.
- Unprompted visibility — mention rate and Share of AI Voice computed only over questions whose text names no tracked brand. A comparison question that names you (or a competitor) leads the engine toward the tracked set, so branded questions are scored separately and can never inflate the unprompted numbers.
- Source Map— every domain the engines cited while answering, ranked by how many answers cited it, labeled yours / competitor's / third-party. This is the list of sources that actually decide your category's answers — and the ranked outreach list for getting into them.
- Recognition ladder— mention rate grouped by question specificity, from questions that name a brand down to fully generic category questions. The deepest rung where you're still named is your recognition boundary; monthly re-runs show it moving.
- New-brand readiness — when unprompted visibility is 0%, the report adds a prerequisites checklist run against your own domain: site reachability, AI-crawler access in robots.txt, schema.org structured data, llms.txt, and whether any answer cited your domain at all.
Monthly re-runs
Subscription audits re-run automatically each month with the same approved questions, so month-over-month movement compares like with like. The report's “Since last audit” section shows Share of AI Voice movement, per-engine mention-rate movement, and every question whose outcome changed. An engine added since the previous run is labeled “new” rather than counted as movement.
Honesty
We invent no statistics. Every number in a report is computed from stored AI answers you (or we, as admins) can inspect. AI answers are probabilistic and change between runs — treat every figure as a dated snapshot, not a guarantee.
Start with the free scorecard.
Run it on your own brand — the methodology is the product.
$0 to start · no card required · every number traces to a stored answer