Competitive AI Visibility Benchmarking

Competitive AI Visibility Benchmarking shows exactly how your brand performs against a named competitive set inside AI answers, and why. It runs a fixed prompt panel every quarter, ranks every player in the category by mention and recommendation rate, and attributes each movement to a specific change in the evidence base.

How the benchmark is built

We define a competitive set with you — usually the incumbents, the challengers you keep losing to, and any brand the models already name in your category, which is often not the list you expected. A fixed panel of buyer-stage prompts is run repeatedly across all six assistants and coded identically for every brand. The result is a like-for-like league table rather than a self-reported score.

Movement attribution

The value is in the why. When a competitor gains share we identify the source: a new analyst mention being cited, a documentation page that models find easy to quote, a knowledge-graph correction, a review-site record that changed. That turns the benchmark into an intelligence product — you can see which plays are working in your category before you commit budget to your own version of them.

Using benchmarks internally

Teams use the quarterly benchmark for planning and for internal alignment. It reframes GEO investment as a competitive position rather than an experiment, and it gives product marketing an evidence-based view of how the category is being described to buyers who never visit any vendor's website.

Ready to see where competitive ai visibility benchmarking would move the needle?

Start with a GEO Audit scoped to your category, prompts, and competitive set.