Claim AI
mindshare
More mindshare, more agent sessions. We measure where you stand across Claude, GPT-5.5, Codex, and Cursor.
One free sample report per company while we're onboarding design partners. Delivery in a few business days.
AGENT-LAYER MINDSHARE n=144 cells YOUR BRAND avg 68.8% Sonnet ███████████████████ 75% Haiku ██████████████████ 72% GPT-5.5 ███████████████ 60% Codex █████████████████ 67% COMPETITOR A avg 47.9% Sonnet █████████████ 50% Haiku ████████████ 48% GPT-5.5 ████████████ 46% Codex ████████████ 47% COMPETITOR B avg 44.4% Sonnet ████████████ 47% Haiku ███████████ 44% GPT-5.5 ██████████ 41% Codex ███████████ 45% COMPETITOR C avg 40.3% Sonnet ███████████ 42% Haiku ██████████ 41% GPT-5.5 ██████████ 38% Codex ██████████ 40% Wilson 95% CI ±4.2pp · re-measured each major model release
What's in the report
~10 pages. Designed to read in 15 minutes and give your growth engineer something concrete to ship this week.
Foundation channels
Reddit, X, GitHub, arXiv, Google Trends, Hacker News. Traditional channels measured so we can quantify how much discovery has migrated to the agent layer.
Agent layer
Install-decision queries run through Claude, GPT-5.5, Codex. Per-query mention share with Wilson 95% confidence intervals.
Velocity
Post-cutoff content rate vs pre-cutoff, by channel. A leading indicator of next-cycle AI mindshare, not just a snapshot of today.
Conversion ratios
Percentage points of AI mindshare each Reddit post or X engagement produces. Quantifies the moat or the gap.
Per-rival capability map
What every named competitor claims publicly, what they ship, and how they currently win or lose at the agent layer.
Engineering-actionable fix list
6-10 prioritized changes with target metrics and effort estimates. Concrete, not 'publish more content.'
Continuous measurement
Re-measured on every major model release (Claude, GPT, Codex). Mindshare moves when models change; one-shot reports go stale within months.
The interesting findings live between the two layers
Most reports confirm what teams already suspect. The valuable ones surface contradictions between channels: a brand winning every traditional buyer signal but losing the agent layer (fixable with content density), or losing every traditional channel but winning the agent layer (rare, but it identifies a structural moat).
For one design partner, traditional CI showed the competitor with 4.2x the Google search volume and 4.3x the X engagement. The agent layer inverted the picture: 3.2x advantage to the smaller brand. That kind of finding sits below every CI dashboard in production.