Stop Guessing. Start Shipping Winners.
Bayesian traffic allocation that kills losing variants before you finish your morning standup. No manual analysis. No spreadsheet exports. Just the winner, automatically.
conversion rate
conversion rate
Your current workflow, disassembled.
Every row is a conversation you've already had in a retro.
Manual test configuration
Define traffic splits by hand. Set up tracking pixels. Wait for engineering to deploy. Repeat for every test.
// traffic_split = 0.5 // manual_deploy = true // start_date = "pending"
One-line SDK, instant deploy
Drop in the SDK. Define variants in YAML or UI. Optimize handles traffic allocation, tracking, and rollout automatically.
optimize.experiment("checkout_cta",
variants=["blue","green"],
auto_allocate=True)Waiting weeks for p < 0.05
Fixed 50/50 splits mean you waste half your traffic on the loser — every day, until the test ends. Or ends inconclusive.
// p_value = 0.073 // status = "not significant" // extend_test = true
Bayesian convergence, not p-values
Our engine continuously updates posterior probabilities. Traffic shifts toward the winner in real time. No arbitrary significance thresholds.
posterior_B = 0.94 regret_minimized = True winner = "variant_b"
Export CSV. Open Excel. Pivot.
Test ends. Download raw data. Build your own analysis. Present findings 5 days later. By then, the moment has passed.
// export_format = "csv" // rows = 2,847,392 // analyst_queue = 4 tickets
Auto-called in hours, not days
Optimize declares a winner the moment statistical confidence is reached — then automatically shifts 100% of traffic. No human needed.
experiment.status = "CONCLUDED" winner_deployed = True time_to_decision = "3.2h"
A/B only. No adaptation.
Traditional tools lock you into fixed splits. Miss the signal. Lose revenue. Start over with a new test next sprint.
// algorithm = "frequentist" // adaptation = false // variants_max = 2
Contextual bandit automation
Epsilon-greedy, UCB, and Thompson sampling — select your bandit strategy. Contextual signals from user data make every allocation smarter.
bandit = ContextualBandit( strategy="thompson", context=user_features)
How We Compare
Column by column. No marketing copy. Just what each platform does or doesn't do.
Data sourced from public documentation and BuiltWith usage reports · Feb 2026
People who dream in confidence intervals
We run 20+ tests a week across our checkout flow. Optimize killed three losers before our Tuesday standup — we didn't even have to look at a dashboard.
The Bayesian engine is the real deal. We got statistical confidence in 4 hours on a paywall test that would have taken Optimizely two weeks. The ROI paid for a year of the platform in one experiment.
I check p-values before coffee. Optimize made that habit irrelevant — the platform calls the winner before I'm done with the first cup. That's the only endorsement I need.
Your next winner is already in the data. Ship it.
Join growth leads at SaaS companies, media publishers, and e-commerce teams who've replaced spreadsheet analysis with a Bayesian engine that never sleeps.