How to run a correction propagation audit
A practical guide to checking whether a corrected fact has reached publishers, citations, knowledge sources, AI answers and summaries.
AI reputation management
AI reputation management covers how companies, executives and brands are described, ranked and interpreted by AI systems. This section examines AI answers, summaries, citations, reviews, search results and public records as new environments where reputation is formed and contested.
What is reputational data integrity?
When prices, policies, ownership records or credentials disagree across platforms, companies leave machines and customers to determine which version deserves to be trusted.
Publisher deals decide which corporate history AI sees
Archive access can determine which past investigations and corrections are easiest for AI systems to retrieve.
A customer-facing AI agent can create the crisis itself
A bad refund promise or eligibility decision can become a public corporate commitment before any employee has approved it.
Covert Reddit marketing carries a reputation cost
Brands chasing AI visibility through disguised community activity risk leaving a searchable record of manipulation.
Google is making reputation search less uniform
Preferred Sources can give different users different publisher mixes around the same developing company story.
Paywalls are distorting how negative coverage travels
Subscribers see evidence and caveats that search and AI often leave behind.
Executive sign-off is a weak defense for AI-written content
Companies may need to show who actually examined AI-written public-interest text before publication, not simply who approved it.
Reddit AI search raises the cost of old brand complaints
Its AI search can pull years-old threads into present-day answers, extending the usable life of consumer criticism.
How to build a corporate source-of-truth register
A practical guide to governing public corporate facts across pages, systems and AI-generated communications.