USE CASE · CRO MANAGERS

Stop building "what to test next" decks.

You run CRO at a mid-market company. You know stats — you can spot an underpowered test before it starts, and you're tired of building decks to defend what to test next. Optimize Pilot respects that: every recommendation ships with a null hypothesis, a 95% CI, a required sample size, and a power analysis, and it won't queue a test that can't reach significance at your volume.

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◉ NULL HYPOTHESIS ON EVERY REC◉ 95% CI FRAMING◉ POWER ANALYSIS BUILT IN

WHO THIS IS FOR

You want an AI that speaks statistics, not vibes.

Most "AI-powered CRO" tools hand you a vibe. Optimize Pilot hands you a null hypothesis, expected lift with a 95% credible interval, a required sample size, and a power analysis — plus the reasoning trace, so you can audit the logic before you spend the traffic. This is CRO for people who know what a p-value actually tells them, and what it doesn't.

◆ WHAT YOU GET

Stats-native optimization.

FLIGHT PATH

It won't let you run an underpowered test.

Flight Path computes a continuous Traffic↔CRO score and suppresses experiments that can't reach significance at your current volume — the discipline you'd enforce by hand, enforced automatically. Pages that can't win yet route to traffic and keyword work until they're ready. Owner override is one slider away.

Continuous Traffic↔CRO score per page
Suppresses experiments that can't reach significance
Routes unwinnable pages to traffic and keyword work
Owner override is one slider away

PRODUCT VISUAL — PLACEHOLDER

RANKED QUEUE

A backlog that ranks itself by expected lift.

Navigator AI ranks every idea in the backlog by predicted lift × confidence × effort. The ranked queue surfaces the highest-expected-value test next, and re-orders as your site data shifts.

Ranked queue by predicted lift × confidence × effort
Expected lift with 95% credible interval
Required sample size + runtime estimate per idea
Re-ranks as data accrues

PRODUCT VISUAL — PLACEHOLDER

STATS ENGINE

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. Always-valid sequential testing (anytime-valid p-values) when early-stop math matters. No stats framework mystery.

Bayesian credible intervals (default)
Frequentist tests (opt-in)
Always-valid sequential testing — safe peeking
Guardrail metrics on every experiment

PRODUCT VISUAL — PLACEHOLDER

REASONING TRACE

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 calibrated from your own past experiments
Challenge mode: request diff explanation
Citations back to the underlying events + research

PRODUCT VISUAL — PLACEHOLDER

POST-EXP LEARNING

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.