Measure AI visibility
Measure AI visibility with a defined set of buyer questions, repeatable test conditions, and separate counts for mentions, citations, and recommendations. These guides help you inspect answers, compare competitors, and describe what a sample can establish before you make a marketing decision.
Measure
Measure your brand’s visibility in AI search
Measure AI mentions, recommendations and accuracy with a reproducible baseline, clear event definitions and evidence from the actual answers.
Read the guide →Inspect cited sources in AI answers
Build a claim-by-claim citation ledger, verify supporting passages and distinguish disclosed AI sources from a complete reading history.
Read the guide →Test whether AI answers stay consistent
Repeat a buyer question with recorded settings, inspect material exceptions and decide what the answer variation supports in your test.
Read the guide →Compare how AI models describe your product
Compare product descriptions across available AI models using matched questions, verified facts and clearly recorded test conditions.
Read the guide →Compare your share of AI recommendations
Build a fair AI recommendation benchmark with fixed questions, explicit counting rules, competitor checks and separate model results.
Read the guide →Investigate why a competitor gets recommended
Investigate competitor recommendations through buyer fit, stated reasons and source checks, then choose a defensible content or product action.
Read the guide →How to build evidence for category recommendations
Turn missing category recommendations into a focused content brief, using buyer criteria, cited evidence and a repeatable question panel.
Read the guide →Check executive information in AI answers
Review public professional facts about an executive, distinguish namesakes and prepare a sourced correction log without unsupported monitoring claims.
Read the guide →How to check employer information in AI answers
Audit candidate questions against current public employment policies, separate factual accuracy from opinions and protect employee privacy.
Read the guide →Distinguish mentions, citations and recommendations
Learn to count brand mentions, source citations and recommendations separately, check evidence and avoid misleading interpretation.
Read the guide →Evaluate an AI visibility platform before committing
Evaluate an AI visibility platform through real team tasks, evidence quality and current account terms, with a practical acceptance scorecard.
Read the guide →Assess paywalls and crawler access
Review paywalls, search and training controls, then evaluate access and visibility evidence without assuming a citation gap proves a loss.
Read the guide →How to build publisher differentiation
Define a publisher offer around audience knowledge, original evidence, editorial work and transparent campaign reporting when rivals use similar tools.
Read the guide →Compare cited sources over time
Measure retained, new and missing cited domains across equivalent answer samples, with clear denominators and limited interpretation.
Read the guide →Estimate AI research use in your market
Combine a scoped buyer survey with analytics and sales evidence to estimate how your audience uses AI, Google, social and referrals.
Read the guide →Review sources cited beside negative AI claims
Check negative AI claims against cited passages and current facts, then choose proportionate corrections while preserving legitimate criticism.
Read the guide →Connect Rankfor data to an internal AI assistant
Connect an approved assistant to Rankfor MCP, read authorized existing records and validate the result while keeping credentials protected.
Read the guide →Compare channel budgets with evidence
Assess influencer, publisher and AI visibility work using distinct outcomes, complete costs and a bounded test before changing budgets.
Read the guide →Measure AI answers for ambiguous category terms
Map ambiguous category meanings, test clearly labeled buyer contexts and preserve a fixed question set for useful AI visibility comparisons.
Read the guide →Control persona context in AI answer tests
Test how explicit buyer context changes AI answers while controlling the question, engine and settings, and keeping simulation limits clear.
Read the guide →How to assign AI visibility ownership
Assign one accountable owner for AI visibility work, with clear evidence, editorial, technical and commercial responsibilities across the buyer journey.
Read the guide →Code win/loss patterns in AI answers
Build a consistent answer-coding process for recommendations, ties and abstentions, then verify stated reasons before choosing corrective work.
Read the guide →Compare feature claims in AI answers
Check feature-level AI comparisons against current public product evidence, with matched plans, explicit unknowns and separate preference scoring.
Read the guide →Fact-check binary product claims in AI answers
Fact-check AI product claims against versioned ground truth, score useful answer labels and prioritize material errors in a review matrix.
Read the guide →Set project inputs and analysis scope
Define project identity, scan scope, custom questions and content settings in a versioned register that keeps AI comparisons interpretable.
Read the guide →