Claude Reputation Management

False Claude summaries are an LLM hallucination problem: the model emits a confident, fluent statement that isn't grounded in accurate sources. The fix is not to argue with the model but to change the evidence it relies on. We audit every prompt-and-answer pair for your name, isolate the specific source material causing the hallucination, and replace it with authoritative, structured, indexable content. Because Claude re-ingests web content as models and corpora update, the corrected material propagates into the summary over time.

Claude is pretraining plus an optional web tool, so hallucinated defamation is addressed by correcting the web content the model was trained on and, where web tools are active, the live sources it retrieves. Subsequent model versions and web-enabled answers then reflect the corrected material rather than the defamatory content.

A common technical question is whether outdated corporate coverage can be pulled from Claude's training dataset. You cannot selectively delete a URL from a trained model's weights — but you can remove the live article so it stops being retrieved, re-cited, and re-ingested in future training corpora. We pursue lawful takedown, factual correction, deindexing, and suppression of the outdated corporate articles, then publish current, authoritative replacement content that Claude retrieves and cites instead. This is how outdated corporate articles are effectively removed from Claude's training data and retrieval index.

For technical buyers the honest answer is that Claude reputation remediation is retrieval-augmented correction, not model retraining. We do not — and cannot lawfully or practically — retrain Claude. Instead we fix three layers: the source content, the retrieval and ranking signals that decide what Claude cites, and the citation graph around your name. This is the only scalable, compliant lever for changing what Claude says about you.