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AI Reputation & Hallucination Audit
Find out what AI systems get wrong about you and why.
Wrong founders, obsolete addresses and invented company histories usually cannot be fixed with one prompt. Stonebridge records the answers, traces conflicts across public sources and builds an evidence-led correction plan.
Specialist reviewConfidentialNo obligation
Claim-by-claimincorrect facts isolated
Source-tracedpublic evidence compared
Correctableupstream actions identified
Complimentary assessment
Get a complimentary initial AI Reputation & Hallucination Audit.
Share the AI responses that concern you so we can review factual errors about your brand.
The question behind the search
Why does ChatGPT or another AI system give incorrect information about my company?
Assessment
A documented ai reputation & hallucination 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 reputation & hallucination audit.
Factsverified before action
Sourcesmapped by authority and ownership
Sequencecorrections ordered to reduce conflict
AI answer source trace
Trace an inaccurate answer to the public record.
Capture the claim, compare the available evidence and identify where a correction can legitimately be made.
AI Reputation & Hallucination AuditReview framework
- Capture the answer
Record the prompt, platform, date and incorrect claim.
- Check the evidence
Compare cited sources, when shown, with verified facts.
- Trace the conflict
Identify outdated records, ambiguity or unsupported statements.
- Correct and recheck
Update authorized sources and monitor fresh answers.
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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Different answer systems produce conflicting descriptions
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An old affiliation or similarly named entity is blended into the current organization
Trace names and dated affiliations to distinguish an old but accurate statement from a current error.
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
Repeatable identity and fact prompts
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ChatGPT, Gemini, Perplexity and Copilot/Bing outputs
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Official website and structured data
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Directories, profiles and public databases
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Media, archived facts and third-party corroboration
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Entity confusion and citation patterns
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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Timestamped answer evidence set
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Claim-by-claim accuracy table
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Upstream source conflict map
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Correction ownership plan
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Priority implementation roadmap
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Repeat-test monitoring protocol
Process
Four stages. One accountable diagnostic path.
Each stage has a defined question, evidence threshold and next decision.
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Observe
Test the same factual questions across selected systems.
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Verify
Classify each claim against primary and independent evidence.
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Correct
Repair authorized source records in a defensible order.
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Retest
Monitor outputs without claiming control over them.
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.
AI Reputation & Hallucination Audit fit depends on the available evidence, access, service boundary and the client’s willingness to accept an honest diagnostic result.
Strong fit
- ✓ Organizations with verifiable correct facts
- ✓ People facing repeatable AI inaccuracies
- ✓ Teams able to update owned sources
- ✓ Projects that accept model outputs can vary
Not a fit
- × Deleting unfavorable but accurate information
- × Fabricating authority sources
- × A guaranteed change to AI answers
- × Claims of access to model training data or internals
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View case study →Why does ChatGPT have incorrect information about my company?
The answer may reflect conflicting public pages, stale sources, entity confusion, incomplete context or model error. The audit tests the claim and traces accessible evidence rather than assuming one cause.
Can Stonebridge edit ChatGPT’s internal knowledge?
No. Stonebridge has no control over OpenAI or another provider’s model. We improve accurate public sources, support appropriate feedback paths and monitor future outputs.
What counts as an AI hallucination?
For this audit, it is a factual claim presented without support or contradicted by reliable evidence. We separate fabrication from outdated, ambiguous or merely incomplete answers.
Will correcting my website fix every AI answer?
Not necessarily. An official site is important, but systems may use many sources and update on different schedules. Corrections are prioritized across the information ecosystem.
Do you remove negative information?
The service addresses inaccurate, outdated or conflated facts. Accurate criticism is not relabeled as a hallucination simply because it is unfavorable.
How is progress measured?
By repeating a controlled prompt set, comparing factual accuracy and source support, and recording changes over time.