Public Record Governance
Traditional reputation management watched search results. You could see page one, and if something damaging sat there, you knew. That feedback loop no longer covers the surface where opinions are now formed, because a model summarizing your brand to a prospective customer produces a private answer that leaves no trace you can audit.
Those answers are not always accurate, and they are rarely neutral. Models compress. Compression discards nuance, and what survives is whatever appeared most consistently across the sources the model absorbed — a four-year-old complaint thread, a confusion with a similarly named company, an outdated fact about your leadership or pricing.
You cannot argue with a model. You can change the material it draws from. That means finding the specific sources producing the distortion, correcting or displacing them, strengthening the authoritative record about who you are, and building enough corroborated positive signal that compression starts working in your favor.
The measurement discipline is the same as AEO, pointed at sentiment rather than citation: a fixed set of brand-directed prompts, run monthly across every engine, with what each one actually says recorded so change is visible.
What you actually receive.
Answer-engine sentiment baseline
Brand-directed prompt set run across five engines with verbatim responses captured and classified.
Source attribution
Identification of the specific pages and platforms producing negative or inaccurate model output.
Model-narrative correction
Corrective content, source-level intervention, and displacement of outdated material.
Entity disambiguation
Resolving confusion between your brand and similarly named organizations — a common and unnoticed source of damage.
Review velocity system
Structured review generation, monitoring and response across the platforms answer engines actually read.
Monthly sentiment report
What each engine now says about you, what changed, and what is being worked next.
Common questions.
Can you change what ChatGPT says about my business?
Not directly, and any agency claiming otherwise is misrepresenting how these systems work. What can be changed is the underlying material models retrieve and were trained on. By correcting or displacing the specific sources producing a distortion, strengthening the authoritative record, and building corroborated positive signal, model output changes over time — typically across three to six months.
How do I find out what AI says about my brand?
Build a fixed set of brand-directed prompts — questions about your company, its leadership, its pricing, its reliability, and comparisons against competitors — and run them across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews on a repeating schedule, capturing verbatim responses. Without a fixed prompt set and a recorded baseline, you cannot tell whether anything improved.
Is AI reputation management different from ORM?
Yes. Traditional online reputation management works on visible search results, which are auditable. AI reputation work addresses private synthesized answers that leave no public trace, so it requires deliberate monitoring, and it operates on source material and entity clarity rather than on ranking position.
Tell us what you are building.
If the question is about discovery, reputation, a new site, or a market move, send the context. AIGNCI will tell you whether an Audit, a build, or a more focused engagement is the right starting point.