AI Reputation Management

Large language models are trained on vast datasets of web content, including news articles, Wikipedia, social media, and other publicly available material. When asked about an individual, they synthesise this training data to generate a response. They also increasingly use retrieval systems - searching the web in real time for current information. The accuracy of what they say is therefore directly tied to the quality, volume, and recency of positive, accurate content about you online.

Both platforms have processes for reporting inaccurate or harmful content, though these are limited in their effectiveness for individual reputation cases. The more reliable approach is to address the underlying source content - creating accurate, authoritative material that the AI systems will prioritise, and suppressing or removing inaccurate sources they currently draw upon. We pursue platform correction routes where available but focus primarily on source-level management.

Yes - AI reputation management as a distinct discipline has emerged in the last two to three years as AI-generated responses have become mainstream. It builds on established ORM techniques but requires specific knowledge of how AI systems source and process information. Pavesen has been developing and refining our AI reputation management approach since the technology entered mainstream use.

We establish a baseline by systematically testing AI platform responses about you at the start of an engagement. Progress is measured by regular re-testing to assess how responses change as our work takes effect. Full accuracy across all major platforms is the objective, though timeline varies significantly depending on the volume and quality of existing content.

AI reputation management operates on a longer timeline than traditional search engine work. The reason is structural: AI systems update their knowledge bases less frequently than search engines index content. Creating and placing authoritative source content is only the first step - that content must then be indexed, weighted, and incorporated into the models' outputs.

In practice, measurable improvement in AI-generated summaries typically emerges within three to six months of a structured programme. The most significant changes often occur when new content reaches sufficient authority to displace the sources AI systems were previously drawing from. Ongoing monitoring is essential to track progress and identify the specific sources driving any remaining inaccuracies.

We monitor and manage reputation across all major AI systems that are actively used for research and due diligence. This includes ChatGPT and GPT-4 based products, Perplexity AI, Google AI Overviews (formerly SGE), Microsoft Copilot, and Claude. We also monitor emerging AI search tools as they gain adoption.

The approach to each platform varies because they draw from different sources, update at different frequencies, and weight different types of content. A comprehensive AI reputation programme addresses each of these environments with strategies specific to how each platform generates its outputs.

Yes - and this is among the most urgent situations we address. When an AI system is generating demonstrably false information about an individual - not simply incomplete or outdated information, but factually incorrect statements - the immediate priority is identifying the source content driving those outputs.

AI systems do not invent information; they synthesise it from sources they have indexed. Identifying and addressing the specific content driving false outputs is the most direct route to correction. In parallel, creating high-quality authoritative content that contradicts the false narrative provides AI systems with better source material to draw from as they update their knowledge.