What is Five Blocks’ AI reputation methodology?

Our AI reputation methodology is the same Track / Analyze / Impact framework we built for the Google era, applied to the AI layer. We diagnose what the engines are saying with data, identify the sources actually shaping that picture, then fix it at those sources rather than at the answer people see.

The three phases, applied to AI

1. Track, Using AIQ, we capture what each of the eight engines AIQ currently tracks: ChatGPT, Gemini, Copilot, Perplexity, Claude, Grok, Google AI Overviews, and Google AI Mode, is actually saying about the client and named peers, the sources each engine is citing, the sentiment and themes, and the trajectory over time.
2. Analyze, We identify the leverage points: which sources are driving which framings, where the engines diverge from one another, where peer comparisons reveal opportunity, and which interventions will most efficiently move the picture.
3. Impact; We do the hands-on work at the sources themselves: Wikipedia, Wikidata, owned content, authoritative third-party coverage, and structured entity signals, because those are the sources the engines actually read.

Why we work at the source, not the prompt

You can’t fix what AI says by prompting the engine or telling it it’s wrong, it doesn’t remember what you tell it, and it rebuilds every answer fresh from the sources it trusts. So the only durable way to change an answer is to change what those sources say.