AI & LLM Reputation Management

AI reputation management is simply the practice of changing how generative AI models like ChatGPT, Perplexity, Google, Gemini, and Claude describe you or your business. It takes traditional reputation work and blends it with generative engine optimisation, structured data setups, Wikipedia notability tracking, and publishing clear content across the exact sources these LLM models cite.

Generative engine optimisation is the process of building and tweaking your online content so AI tools easily find it, cite it, and quote it. While old-school SEO focuses entirely on winning those standard blue links on a search page, GEO targets the blended text summaries that AI tools write for users. They overlap a bit, but GEO cares much more about different signals like citation weight, structured data, fresh sources, and plain factual statements.

You can, but you have to do it indirectly. There is no delete button for an AI tool since they build answers from their training data and live web lookups. To change what they say about you, you have to change the source pages they read. This means pushing down old articles, ranking accurate text, fixing Wikipedia mistakes, and aligning your background data. Over a few weeks, the AI summaries will follow those changes.

AI tools actually update their summaries much faster than Google shifts its standard search results. For platforms like Perplexity that search the live web, you can see changes within days of a source update. For ChatGPT, it usually takes two to four weeks, while Google's AI Overviews take longer because they stick close to normal search rankings. Generally, turning your whole AI footprint around takes about 8 to 14 weeks.

No, and no legitimate provider can. Training data is completely fixed at the point a model is trained. Instead, what we influence is what the engine finds and cites when a user types a query, as well as what is available for the company to scrape in their next major training cycle. Wikipedia is unusually high-leverage here because it is so heavily weighted in almost every language model training dataset.

It overlaps a lot, but they are not identical. Traditional ORM focuses on Google's blue-link results and works entirely on visibility and search rankings. On the other hand, AI reputation work targets the blended paragraph that an AI engine writes about you, which depends on completely different signals like schema markup, source authority, citation patterns, factual structuring, and Wikipedia data. Most clients honestly need both.