The index answers one question: when a real buyer asks an AI assistant what software to buy, which brands come back in the answer? Here is exactly how we measure it.
Each of the 39 categories has a fixed set of 8 prompts written the way real buyers ask: by company size, budget, use case, and "if you could only pick one." Prompts are brand-neutral. We never name a vendor in a question, because that would seed the answer. Every prompt is published verbatim on its category page.
We query the production APIs of the three most-used AI assistants:
gpt-5.3-chat-latest)claude-sonnet-4-6)gemini-3-flash-preview)Each model answers every prompt 3 times per monthly snapshot, because LLMs are non-deterministic and a single run can mislead. The July 2026 snapshot covers 2,808 answers in total.
A note on surface. An assistant's API and its consumer app are not the same measurement surface: the app layers web search, memory, and personalization on top of the model, so app answers can differ from the API baseline measured here. We measure the API deliberately, because it is stable, reproducible, and isolates what the model itself believes about a market. App-surface and AI-search-surface tracking are on the roadmap as their own labeled dimensions, never a silent substitute, and where the two disagree, that divergence will be reported rather than averaged away.
From every answer we extract the ordered list of products the model presents as recommendations, in order of first appearance. Products mentioned only as an integration, a comparison point, or a warning do not count. Brand names are normalized so "HubSpot CRM" and "HubSpot" are the same brand.
Our metric definitions align with how the AI-visibility industry measures brand presence in LLM answers. Mention rate, share of voice, and average position are the standard vocabulary used by dedicated tracking platforms like Profound, Peec AI, and Otterly and by benchmark research such as Conductor's AEO/GEO benchmarks report and Discovered Labs' AEO benchmark guide. Multi-sampling the same prompt is standard practice across these tools because single runs of a non-deterministic model mislead.
We add our own flavor on top of the standard metrics: a position-weighted Visibility Score that rewards being the first name out of the model's mouth, strictly brand-neutral prompts, and full publication of every prompt we ask. Most commercial trackers keep their query sets private; we think an index you can audit is an index you can cite.
The index publishes a fresh snapshot monthly, and every point on a trend line is a live measurement from one of those published snapshots. The index launched with the July 2026 snapshot, so trend lines and month-over-month deltas appear once a brand has two or more real months of data. We do not model, estimate, or backfill history: months we did not measure simply do not exist in the charts.
Every number on this site traces back to a real AI answer we collected ourselves. Answers come directly from the models' production APIs at measurement time; nothing is scraped from consumer apps, bought from panels, estimated from third-party data, or generated synthetically. The raw answers behind each monthly snapshot are archived verbatim, because an AI answer is a measurement of a moment: once the models move on, it can never be collected again.
No vendor pays to be included, excluded, or ranked. The index is free, carries no sponsorships, and no commercial relationship changes what the models said or how it is scored.
Everything here is free to use: quote it, chart it, republish it. All we ask is attribution with a link to thunderdome.io, and a note of the snapshot month (July 2026) since the rankings change monthly.