The decision
Should you choose ShareOfAsk?
Brands and agencies inspecting answer-level AI visibility evidence
Best for
Brands and agencies inspecting answer-level AI visibility evidence.
Choose something else if
Buyers requiring independently measured ranking or revenue gains
Evidence
Verified research · rating withheld
Pricing checked
See current vendor pricing · Nov 4, 2026
No verified permanent free plan is recorded.
Confirm billing, usage limits, seats, and renewal terms with the vendor.
In this guide
Short answer
ShareOfAsk is worth shortlisting when you want to inspect the answers behind an AI visibility score. Choose it for a defined buyer-question panel, rather than as a substitute for website conversions or a forecast of all potential customers. Its one-time scan can help establish whether the evidence is useful before committing to monitoring.
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Evidence and review scope
This review follows submission #628. We checked the official product, pricing, methodology and privacy policy on November 4, 2026. We did not create an account, run a scan, connect MCP or independently verify extraction accuracy. Ratings are withheld. No affiliate destination is configured.
What the tracker documents
The product records brand mentions, recommendation order and sources in answers, then suggests actions. MCP access is documented on every plan; ShareOfAsk itself does not edit your website. A connected assistant and other tools create a separate permission boundary. Check that boundary before giving an assistant a CMS connection.
Free scan versus continuing monitoring
Free Scan is a one-time check of ten questions across three models, with one action analysis and seven-day result access. Starter is $49 monthly or $490 yearly: one workspace, up to 25 active questions, three models and daily checks. Growth is $199 monthly or $1,990 yearly, with three workspaces and up to 100 questions shared across the organization. Prices exclude taxes. Annual billing does not enlarge monthly allowances.
The budget question is whether the question limit covers your real decisions. An agency should map brands and clients to workspaces before comparing sticker prices. Do not buy a larger panel merely to make a small percentage movement look precise.
Four metrics with different meanings
The methodology distinguishes answer-level presence, competitive share of voice, question coverage and first position. Failed requests are excluded, and some overview tiles use different periods. Provider integrations may differ from consumer app sessions. A source appearing beside an answer does not prove it caused a recommendation.
Before a report leaves your team, inspect its denominator, date range and original answers. A higher share can result from a changed competitor pool. It can also coexist with fewer completed observations. Report missing data alongside the percentage.
Proposed acceptance test
Write ten neutral questions from real buying decisions. Include one ambiguous brand name and a case where a competitor is named in a negative context. Read each returned answer before accepting a detected mention as a recommendation. Save enough evidence to reproduce the interpretation.
Repeat the same setup before making a content change. If an action proposes a comparison page, inspect the current page inventory first. A useful test is whether an editor can trace a recommendation to evidence and identify a concrete reader problem. This is a proposed pilot, not a test we ran.
Data handling and verdict
The privacy policy documents processing of questions, answers and workspace information through service providers. Use public business facts for the first panel and review sharing settings before distributing a report.
Shortlist ShareOfAsk for evidence-led brand monitoring. Compare the broader SEO workspace in MrKeyword’s review and the tool profile. Use the buyer matrix to score the evidence you actually inspect.
Transparency
How this guide was checked
Editorial accountability
Who checked this guide
- Evaluation type
- Research-based verification
- Last materially checked
- Evidence
- 4 listed sources
Hands-on testing is identified explicitly. Research-based coverage uses cited product documentation and other named sources; it does not imply every paid plan was used. Read the full methodology.
Editorial freshness
Pricing and material product claims were checked November 4, 2026.
Editorial basis
What this guidance is based on
- Editorial basis
- Source-led analysis
- Primary references
- 4
- Products covered
- 1
- Last checked
- 2026-11-04
Important limits
- • Features, availability, and pricing can change after publication; confirm consequential details with the provider.
Standardized benchmark coverage
How this review maps to the research benchmark
Citation accuracy
Not applicableThe reviewed workflow does not produce source-grounded research answers, so a citation score would be misleading.
Read protocol v2026.10-v1 →Thematic analysis
Not applicableThe reviewed workflow is not qualitative evidence analysis, so a thematic-analysis score would be misleading.
Read protocol v2026.10-v1 →Eligibility is not a product score. DiscoverAI publishes results only after output collection, blinded adjudication, reproducibility checks, and severe-error review.
Open the optional evaluation worksheet
Reusable trial worksheet
Test ShareOfAsk before you commit
Turn this review’s buyer test into evidence. Your entries autosave only in this browser and are never added to shared shortlist links.
Confirm the tool meets every must-have workflow and stakeholder requirement.
Review starting point: Brands and agencies inspecting answer-level AI visibility evidence
Run the same representative work you would use in production; do not score a polished demo.
Review starting point: Run a bounded set of representative tasks with known acceptable outcomes, then compare the result with your current workflow.
Calculate the effective cost per accepted result, including usage, review, corrections, and required add-ons.
Review starting point: One-time Free Scan; Starter $49/month or $490/year; Growth $199/month or $1,990/year, excluding taxes. Limits differ by workspaces, questions and models.
Define an acceptance threshold, test known answers and edge cases, and record every correction.
Review starting point: Editorial quality signals: features 0.0/5. Validate these signals in your own work.
Verify what data enters the product, who can access it, how long it is retained, and whether it trains models.
Review starting point: Use approved low-risk data first. Check roles, consent, deletion, subprocessors, model-training settings, and the contract—not only the marketing page.
Test the real handoffs, permissions, failure states, and export path your team depends on.
Review starting point: ShareOfAsk integration requirements are not fully structured in this review; list every required system and test each connection.
Record training, governance, reliability, accessibility, ownership, and change-management risks before rollout.
Review starting point: Free scan is one-time; Question limits require planning; No independent extraction or outcome test
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Community evidence
How verified users put ShareOfAsk to work
Structured, editor-moderated experience—not star ratings. This complements our independent review and never changes its score.
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Community choice
Recommend ShareOfAsk
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Sources and references
References cited by this article. Check the original source for scope, dates and current terms.
Frequently asked questions
Is the free scan recurring?
No. It is a one-time check with seven-day access to results.
Does ShareOfAsk edit my site?
The product says it does not. Your separately connected tools may act with their own permissions.
Does a brand mention prove a sale or recommendation?
No. Inspect the context and measure visits and commercial outcomes separately.
Tools mentioned in this article
ShareOfAsk
Evaluate ShareOfAsk’s one-time free scan, daily brand monitoring, answer-level evidence and paid allowances before choosing an AI search visibility tracker.
Evaluate ShareOfAsk’s one-time free scan, daily brand monitoring, answer-level evidence and paid allowances before choosing an AI search visibility tracker.
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