Flag it
Wrap any change in a typed flag — boolean, percentage, string, or JSON — with ordered targeting rules that decide who sees which variation. Everyone is bucketed deterministically, so a person always gets the same answer.
Bosca Experiments
One system for feature flags and A/B tests — typed flags with deterministic targeting, experiments that measure the difference, automated rollouts with guardrails, and results computed from your own analytics.
How it works
Wrap any change in a typed flag — boolean, percentage, string, or JSON — with ordered targeting rules that decide who sees which variation. Everyone is bucketed deterministically, so a person always gets the same answer.
Point an experiment at one of the flag's rules and it measures one variation against another. Assignment is sticky per person, and exclusion layers keep overlapping experiments from colliding.
Results come straight from your analytics with real statistics and a clear ship / don't-ship call. A rollout policy then ramps the winner step by step, halting the moment a guardrail regresses.
What makes it powerful
Everything you need to change software safely — typed flags, precise targeting, and gradual rollouts — plus the statistics to know whether a change actually worked.
Boolean, percentage, string, or JSON variations — a palette of typed values, each validated against the flag's type, with one marked default.
Rules evaluated top to bottom, with conditions on segments, principals, profile and device attributes, and even other flags.
A stable hash of the user plus a per-flag salt places everyone in the same variation every time. Regenerate the salt to reshuffle.
Each person — a signed-in principal or an anonymous install — keeps the same variation across sessions and devices.
Group experiments so a user enrolled in one is held out of the others, keeping concurrent tests from interfering.
Manual, scheduled, or data-adaptive ramps advance the winning variation step by step toward full traffic.
Guardrail goals block a ship — or halt a rollout in progress — the moment they regress past the threshold you set.
Frequentist or Bayesian analysis over your analytics warehouse, with lift, confidence, CUPED variance reduction, and sample-ratio checks.
One system
A flag owns the typed variations, the targeting rules, and the deterministic bucketing. An experiment is just a measurement lens over one of those rules — so what you test is exactly what you ship, with no drift between the two.
checkout-cta — variations control, treatmentmobile-users — 50 / 50 rolloutGo deeper
Typed flags with targeting rules and deterministic bucketing.
Measure one variation against another, with sticky assignment.
Ramp the winner automatically, with guardrails that halt on regression.
Real statistics from your analytics, with a ship / don't-ship call.
Flags to release safely, experiments to measure accurately, rollouts to ramp automatically, and statistics you can trust — all reading from the analytics you already collect. The docs cover flags, experiments, exclusion layers, rollouts, and results end to end.