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ProofHow It WorksGEO OverviewResearchAboutTest Your Domainhello@answershare.aiYour turnTest Your Domain
Run a free audit on your domain to see where you stand and how we can improve it.
Before you run itHow the AnswerShare™ audit works.
- What it measuresYour overall friendliness to AI — whether engines can retrieve, parse, and cite your content. A live 5-model panel, median with outlier-drop.
- How long it takesThis usually takes about 3 minutes. We query five AI engines live, so it is not instant — the wait is the measurement.
Scored the way AI systems actually read it
Enter your apex domain and the audit runs the same methodology behind Top10Lists.us against your site: a live multi-model panel asks five major AI engines to evaluate your domain — both from their blind prior and after fetching your live content.
The result is a per-engine matrix plus an overall row. The overall row carries the headline median (with outlier-drop) for GEO, SEO, ASQ™, and ΔNPS™; each engine row shows that engine's own GEO and λNPS™-derived ΔNPS. It runs in about a minute and returns raw receipts you can verify from outside the system.
The 5-model panelFive engines, one median — so no model swings the verdict
Every headline figure is the median across the five models with outlier-drop: a model is dropped from the median when its spread is extreme (max − min greater than roughly ten points), so a single divergent engine never determines the headline number.
PerplexitysonarRetrieval-grounded answer engine — live web grounding gives a current-day read.
OpenAIgpt-4oFrontier general-purpose model with a broad training corpus.
Google Geminigemini-2.5-flashSearch-grounded model weighted toward Google index signals.
Anthropic Claudeclaude-sonnet-4-5-20250929Strict-rubric grader — deductions are common and intentional.
xAI Grokgrok-4.3Fifth voice; completes coverage of the top conversational engines.
The 13-signal frameworkEight structural checks, five quantitative metrics
The audit evaluates 13 signals — 8 binary structural readiness checks plus 5 quantitative metrics — and reports the median across the five model families with outlier-drop. The structural checks ask whether the bot-facing surface is present and parseable; the quantitative metrics measure how much of it AI retrieval can actually use.
ASQ & the ΔNPS familyOne 0–100 composite, and the gap AI optimization moves
ASQ — the AnswerShare QuotientASQ is a 0–100 composite KPI for AI answer-share potential, blending GEO readiness and SEO foundation. Schematic formula: ASQ = 100 × (0.438 × GEO + 0.562 × SEO), with weights derived from the Cyrus Shepard / Zyppy AI Citation Ranking Factors synthesis (2026-05-07). It computes for every audited property.
ΔNPS — the verdict-lift signalΔNPS = λNPS − μNPS per engine. μNPS™ is the engine-independent reading of what people say online; λNPS is a given engine's favorable/unfavorable prior. A positive ΔNPS means AI engines surface favorable material at or above what people say online; a gap is coverage the translation layer can close.
λNPS is what an engine says from memory with no site context; the receipt-first verdict is what it says after reading your fetched content. When the evidence lifts the verdict above the blind prior, the gap is closed by retrievable, citable material — not by reputation.
Read the full NPS-family methodology→It's your turnPrefer to talk first?Email us at hello@answershare.ai, or schedule a call — technical, business, or both.
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