Stop building "what to test next" decks.
You're the CRO manager at a mid-market company. You know stats. You know a confidence interval when you see one. You're tired of building PowerPoints to convince the team what to test next. Optimize Pilot respects your stats chops — every recommendation ships with a null hypothesis, a 95% CI, and a power analysis.
You want an AI that speaks statistics, not vibes.
Most "AI-powered CRO" tools spit out a vibe. Optimize Pilot spits out a null hypothesis, expected lift with a 90% credible interval, a required sample size, and a power analysis. The recommendation includes the reasoning trace so you can audit the logic before you burn the traffic. This is CRO for people who understand what a p-value actually tells you.
Stats-native optimization.
A backlog that ranks itself by expected lift.
Navigator AI ranks every idea in the backlog by predicted lift × confidence × effort. ICE / PIE / RICE all supported — pick your preferred framework or custom-weight the factors. Updates hourly as your site data shifts.
- ICE / PIE / RICE scoring (or custom weights)
- Expected lift with 90% credible interval
- Required sample size + runtime estimate per idea
- Hourly refresh as data accrues
Bayesian. Frequentist. Sequential. Your choice.
Default is Bayesian credible intervals with automated stopping rules. Frequentist tests available for teams that need them for reporting consistency. Sequential testing with Pocock and O'Brien-Fleming bounds when early-stop math matters. No stats framework mystery.
- Bayesian credible intervals (default)
- Frequentist tests (opt-in)
- Sequential testing with early-stop bounds
- Guardrail metrics on every experiment
Audit the logic before you ship.
Every Navigator recommendation includes the observed signal, the inferred cause, the proposed test, and the priors it drew from. You can disagree with it — "why does this outrank that?" — and get a diff explanation. No black box.
- Per-idea reasoning trace (observation → hypothesis → proposal)
- Priors drawn from 10,000+ past experiments
- Challenge mode: request diff explanation
- Citations back to the underlying events + research
Every test you run improves the next recommendation.
Positive or negative, every result feeds back into Navigator's priors. The next recommendation is calibrated against what actually happened on your site, not a generic corpus. This is why teams see recommendation quality improve over the first 90 days.
- Post-experiment recalibration
- Learning system personalizes to your site
- Feedback loop on accepted / rejected / edited recs
- Transparency panel showing how priors evolve
Better together.
Give your backlog a proper priority.
14-day trial. Connect your analytics. Get a stats-native ranked queue inside an hour.