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AI Recommendation Visibility Audit
Measure whether AI systems recommend you, not just recognize your name.
Brand recognition answers “who are you?” Recommendation visibility asks whether the brand appears when a prospect describes a need. Stonebridge benchmarks those two states separately and explains the evidence gap.
Specialist reviewConfidentialNo obligation
Buyer-question ledrelevant prompts tested
Pattern-basedone answer is not enough
Measuredinclusion and citations recorded
Complimentary assessment
Get a complimentary initial AI Recommendation Visibility Audit.
Share your brand and target queries so we can review whether AI tools recommend it.
The question behind the search
Does my business appear when prospects ask AI for providers, experts or solutions?
Assessment
A documented ai recommendation visibility audit explanation of what is wrong, what remains uncertain and what should happen next.
This engagement does not treat a platform-controlled outcome as a Stonebridge deliverable. It establishes the readiness, correction options and implementation support relevant to ai recommendation visibility audit.
Factsverified before action
Sourcesmapped by authority and ownership
Sequencecorrections ordered to reduce conflict
Recommendation sampling plan
Test the questions a buyer would actually ask.
Recommendations are reviewed across a defined prompt set, with the platform and test conditions recorded.
AI Recommendation Visibility AuditReview framework
Define
Specify the category, buyer needs and eligible comparison set.
Sample
Run relevant recommendation questions under recorded conditions.
Record
Capture inclusion, wording, citations and alternatives.
Repeat
Check patterns over time before drawing a conclusion.
Illustrative review framework. Findings are established from the project’s evidence.
Common warning signs
The symptom tells us where to begin, not what to assume.
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Recommendations change sharply with wording, geography or buyer need
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Competitors appear repeatedly without a clear explanation
Inspect the stated reasons and citations; do not claim access to hidden recommendation weights.
What Stonebridge examines
A focused audit of the systems that shape the result.
The review covers these areas, with findings and recommended next steps documented for your project.
Request the assessment →01
Category, need and comparison prompts
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Geography and audience modifiers
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Recommendation frequency and position
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Descriptive language and qualification
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Visible citations and source patterns
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Brand recognition versus recommendation gap
What the engagement can include
Decision-ready findings, not a generic checklist.
Your project scope confirms which deliverables are needed and who is responsible for the next step.
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Prompt taxonomy
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Recommendation benchmark
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Recognition-versus-recommendation analysis
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Citation and authority findings
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Priority buyer-question plan
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Repeatable measurement protocol
Process
Four stages. One accountable diagnostic path.
Each stage has a defined question, evidence threshold and next decision.
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Model intent
Build prompts from real buyer needs and decision stages.
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Benchmark
Test selected systems with controlled variables.
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Diagnose
Explain visibility using accessible evidence and sources.
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Improve
Prioritize category clarity and authority development.
Scope assurance
A clear scope before work begins.
An accepted engagement may include a contract-backed service assurance for specific Stonebridge deliverables defined in the written agreement. If a covered deliverable is not provided within the agreed contractual terms, the remedy stated in that agreement applies. Sales, rankings, editorial decisions and other third-party outcomes remain outside Stonebridge’s control.
- Findings tied to observable evidence
- Current requirements checked where platform rules matter
- Owned corrections separated from third-party requests
- No fabricated authority, acceptance or placement claims
Project fit
A strong engagement starts with the right conditions.
AI Recommendation Visibility Audit fit depends on the available evidence, access, service boundary and the client’s willingness to accept an honest diagnostic result.
Strong fit
- ✓ Companies with defined audiences and categories
- ✓ Experts seeking relevant, not generic, visibility
- ✓ Teams prepared to measure over time
- ✓ Brands with verifiable capabilities and locations
Not a fit
- × A guaranteed recommendation
- × Prompt spam or manipulation
- × Testing only the company’s exact name
- × Unsupported “best” or market-leader claims
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View case study →What is AI recommendation visibility?
It is whether a person or company appears when a user asks an AI system for options that match a category, problem, audience or location.
How is it different from brand accuracy?
Accuracy tests facts returned about a known brand. Recommendation visibility tests whether the brand enters the answer before the user names it.
Can you calculate share of AI visibility?
Where a defined prompt set and comparison group support it, Stonebridge can calculate a directional share within that test. It is not a universal market-share statistic.
Why do results change between tests?
Outputs can vary by model, version, location, browsing mode, prompt wording and time. The methodology records those conditions and avoids treating one answer as definitive.
Can Stonebridge guarantee improvement?
No. We can improve the quality and clarity of public evidence, then measure outputs. The platforms control generation and recommendation.
What kinds of prompts are included?
The set can include best-fit provider questions, use cases, comparisons, location-qualified needs and evaluation questions relevant to the actual buyer.