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Company AI Visibility Audit
Find out whether AI systems understand your company as a category option.
Being indexed is not the same as being understood. Stonebridge examines whether AI systems identify the organization, describe its services accurately and surface it in the category and comparison prompts prospects use.
Specialist reviewConfidentialNo obligation
Company-specificrecognition separated from recommendation
Category-testedbuyer questions sampled
Evidence-ledvisibility gap located
Complimentary assessment
Get a complimentary initial Company AI Visibility Audit.
Share your company and target queries so we can review its visibility in AI answers.
The question behind the search
Why is my company missing when people ask AI about providers in our category?
Assessment
A documented company ai 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 company ai visibility audit.
Factsverified before action
Sourcesmapped by authority and ownership
Sequencecorrections ordered to reduce conflict
Company visibility layers
Separate recognition, category fit and recommendation.
A company can be named correctly and still be absent from the answers its buyers use.
Company AI Visibility AuditReview framework
Recognition
Does the system identify the intended company?
Description
Does it explain the services and audience accurately?
Category fit
Does the company appear in relevant category questions?
Recommendation
Is it included when the user asks for suitable providers?
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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The company is placed in the wrong category or geography
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Competitors appear in comparisons while the company remains absent
Compare like-for-like questions and record the reasons and citations actually supplied in each answer.
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
Brand and entity recognition
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Service and category classification
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Geographic and market association
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Comparison and use-case prompts
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Official content and structured data
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Independent citations and category sources
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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Company visibility benchmark
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Category and service accuracy map
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Prompt-intent findings
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Citation and source gaps
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Owned-content priorities
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Competitive monitoring framework
Process
Four stages. One accountable diagnostic path.
Each stage has a defined question, evidence threshold and next decision.
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Frame
Translate the company’s real services into prospect question groups.
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Test
Record recognition, descriptions and mentions across systems.
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Explain
Connect answer gaps to public evidence and entity signals.
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Prioritize
Build a practical correction and authority roadmap.
Scope assurance
A clear scope before work begins.
A guarantee applies only when it is expressly stated in the written service agreement. If that guaranteed deliverable is not delivered within the agreed contractual timeline, the client is entitled to a full refund for that specific undelivered service. Sales, rankings, editorial decisions and other third-party outcomes are not guaranteed.
- 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.
Company AI Visibility Audit fit depends on the available evidence, access, service boundary and the client’s willingness to accept an honest diagnostic result.
Strong fit
- ✓ Established companies with defined services
- ✓ Brands competing in research-heavy categories
- ✓ Teams able to verify company facts
- ✓ Organizations prepared for cross-channel authority work
Not a fit
- × A guarantee of top AI placement
- × Inventing locations, clients or capabilities
- × Mass-producing thin pages for prompts
- × Treating direct-name recognition as recommendation visibility
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View case study →How do I know whether AI recognizes my company?
The audit tests direct identity, service, category, comparison and recommendation prompts. Recognition means more than returning the website for an exact brand query.
Why are competitors mentioned instead?
They may have clearer category language, stronger independent evidence, more accessible sources or better entity consistency. The audit measures the specific gap rather than guessing.
Is this the same as SEO?
SEO contributes discoverability, but company AI visibility also involves entity understanding, citations, descriptions and performance across natural-language prompt categories.
Will adding schema solve the problem?
Structured data can clarify owned facts, but it is only one input. It does not replace public evidence or guarantee inclusion in AI answers.
Can you guarantee my company will be recommended?
No. The service improves the evidence and information environment; independent systems decide what they generate.
How is this different from the founder audit?
This page focuses on the organization’s services, category and recommendation context. Founder visibility focuses on the person’s identity and expertise.