No system prompts — buyer-style questions only.
Unmodified responses — what the model returned.
Neutral questions — category-level, not leading.
We query models with buyer-style questions only. We don't inject instructions that bias answers toward your brand.
What you see in ShareOfAsk is what the model returned — not a cleaned-up or re-ranked version.
Questions are category-level and buyer-realistic — not "why is [Brand X] great."
Mentions, recommendations, position, competitor context, sentiment framing, and cross-model consistency — with raw snippets to verify.
Non-determinism, sampling, model drift, and extraction error are real. We'd rather state the bounds than sell fake certainty.
Neutral (good): "What CRM platforms should a 50-person B2B SaaS company evaluate?" — category-level; mirrors real buyer research.
Biased (avoid): "Why is Acme CRM the best choice for growing teams?" — leading; measures prompt optimization, not market presence. Snapshots use neutral question sets per project — not cherry-picked prompts.
Whether your brand appears
When models rank or list options
Who appears when you don't
How the model describes you
Agreement across providers
NLP edge cases exist — we surface raw text
Non-determinism. Models change outputs run-to-run. Trends matter more than single snapshots.
Sampling. We measure configured question sets — not every possible buyer question in your category.
Model drift & extraction. Providers update models without notice; brand detection can miss nicknames. We'd rather tell you the bounds upfront.
ShareOfAsk turns real AI model outputs into visibility intelligence — with neutral questions, unmodified responses, and transparent limits.
Get started to explore the demo project, or see Entry and Pro pricing — demo first, subscribe when ready.
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