· 3 minutes · 5 questions · 150+ teams benchmarked ·

Where does your team rank on
AI evaluation quality?

Run the diagnostic and see where you sit, with benchmarks from real product teams.

No account needed. Results shown instantly

What the AI Eval Diagnostic measures

The diagnostic scores your team across four dimensions of AI evaluation maturity. These are the dimensions that separate teams shipping reliable AI from teams shipping and hoping.

Current evaluation practice

The shape of your team's quality assessment today — manual spot checks, informal test cases, or a repeatable process — and who owns it.

Decision discipline

What you rely on after a prompt or model change — gut feel, sample inspection, or measured comparison against benchmarks — and whether the answer is reproducible.

Model comparison rigor

Whether you've tested GPT-4, Claude, and Gemini head-to-head on your specific use case, or you're still running on the model you happened to start with.

Domain expert involvement

Where subject-matter experts sit in your evaluation loop, and how much of the quality bar is owned by engineers alone.

Why product teams take this diagnostic

Product teams building AI features face a quality problem most don't see clearly until it bites them in production. Outputs look fine in demos and silently fail in real user flows. Improvements ship without proof they helped. Stakeholders ask “is this getting better?” and the team has no good answer.

The 3-minute diagnostic gives you a structured read on where your evaluation process actually sits — early-stage, scaling, or fully operationalized — and benchmarks you against 214 other product teams shipping AI in 2026.

What you get in your report

  • A maturity score across all four dimensions
  • A side-by-side benchmark against teams at your stage
  • The specific gaps and the highest-leverage next step
  • A short Loom walkthrough of your results from the Lovelaice team

Who this is for

  • Product managers shipping AI features who need to defend quality decisions to leadership
  • AI/ML leads who want to formalize evaluation before scaling to more teams
  • Founders preparing for SOC2, EU AI Act, or other compliance reviews
  • Anyone considering new evaluation tooling who wants to know whether their current process holds up