Multi-Model Monitoring

Each AI engine is its own ecosystem: different underlying models, different training data, different retrieval sources, different answer styles. ChatGPT may recommend you everywhere while Google AI Mode has never heard of you; Perplexity's heavy citation behavior rewards different content than ChatGPT's. A brand that only checks one engine is managing a fraction of its AI presence, and usually the fraction that least matches where its actual buyers ask questions.

Geonimo's daily pipeline sends the identical prompt to every engine and processes all answers the same way: same mention extraction, same scoring. That control is what makes the comparison meaningful: when ChatGPT mentions you in 70% of runs and Google AI Mode in 20%, you're looking at a genuine provider gap, not an artifact of different questions or methods. Provider views layer on trends, sentiment and citation differences per engine, and the deep-dive analysis explains how each platform builds its answers in your category.

Multi-model data turns "improve our AI visibility" into a sequenced plan. Match each engine's weight to your audience, then attack the biggest gap: if Perplexity is weak, that's usually a citation problem, get the right sources to mention you; if a model-driven engine is weak, it's typically an authority and entity problem with a longer arc. Provider-level trends then verify each fix where it was applied: far sharper feedback than watching a blended average drift.