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 Gemini has never heard of you; Perplexity's heavy citation behavior rewards different content than Claude'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 Gemini 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.