The new first impression
A growing share of research about people and companies now begins with an AI answer instead of a list of links. An investor asks ChatGPT for background on a founder. A patient asks Google's AI Mode which Orlando dermatologist has the best record. A general counsel asks Perplexity whether a vendor has been sued. The answer arrives as a confident paragraph, often with no visible sign of how old or reliable its sources are.
That format removes the context that used to protect people. On a results page, a searcher could see that a damaging article was nine years old and that the next five results described a successful career. In an AI summary, the old allegation may be the only fact mentioned.
How answer engines decide what to say
Each major engine forms its answers differently, and the correction method follows from the mechanism.
Entity correction
Every answer engine is trying to answer one prior question: which person or company does this query refer to? When that is unclear, errors follow. A Winter Park attorney may be merged with a namesake in another state. A Lake Mary company may inherit the lawsuits of a defunct business with a similar name.
Entity correction makes the subject unambiguous. It aligns names, titles, locations and dates across every authoritative profile. It adds structured data, such as Person and Organization schema with consistent identifiers and sameAs links, to owned sites. It works to establish or correct a Google Knowledge Panel where one is warranted. When the entity is clear, both search and AI systems can attach the right facts to the right subject.
Citation repair
Perplexity, ChatGPT search and Google AI Overviews all cite their sources. That makes the problem traceable. A proper audit records the answer each engine gives for the relevant prompts, lists every source cited, and then classifies each source. Some sources can be removed. Some can be updated by the publisher. Some must be outranked by stronger, accurate pages. The same removal-first discipline used in traditional search applies here.
LLM hallucination suppression
Hallucinations are statements that no source supports. They are most common for people with thin online profiles, because the model fills the gap with patterns borrowed from similar people. The remedy is not a takedown request, because there is no page to take down. The remedy is abundance. When clear, consistent and well-sourced information about the person exists on several authoritative pages, retrieval-based engines cite it, and future model versions learn it.
Where an engine repeats a damaging falsehood about an identifiable person, the provider's own reporting channels should also be used. OpenAI accepts privacy requests about personal data, and Google accepts feedback on specific AI Overviews. These channels are slow and inconsistent, so they supplement source correction rather than replace it.
Why legacy firms struggle with this
Traditional PR firms measure success in placements, and review platforms measure it in stars. Neither metric tells you what ChatGPT says when someone asks about you. AI answer correction requires prompt testing across engines, source tracing and entity engineering. In our 2026 review, it was the clearest point of separation between the first-ranked firm and the rest of the field.