A/B Testing & Experimentation

01

Experimentation platform

Experiment registry with hypothesis tracking, pre-specified metric definitions, and minimum detectable effect calculations before any test runs. Assignment logic uses hashed user IDs with a configurable salt so the same user always sees the same variant and variant exposure is logged per impression.

02

Feature flagging

Flag evaluation at the edge with Redis-backed targeting rules. Roll out to 1% of users on a specific plan, measure for 7 days, then promote to 100% without a deploy. Kill switch fires in under 1 second across all active sessions.

03

Multi-armed bandit optimization

Thompson sampling or UCB1 allocation that shifts traffic toward winning variants as data accumulates. Faster than classical A/B testing when you have many variants and limited patience for weeks-long ramp periods.

04

Statistical analysis engine

Pre-registration of primary metric, guardrail metrics, and stopping rules before the test opens. Sequential testing with alpha spending so you can peek at results without inflating your false-positive rate. Automated significance report generated at experiment close.

Experiment Design + Metric Definition

We define the hypothesis, primary metric, guardrail metrics, and minimum detectable effect. Sample size and assignment logic are locked before any test runs.

Instrumentation + Randomization Engine

Event tracking and feature flag infrastructure deployed. Consistent user assignment, exposure logging, and holdout groups configured to eliminate carryover bias.

Analysis Engine + Reporting Handoff

Statistical analysis pipeline runs automatically at experiment close. Results dashboard and documented methodology transferred so your team can run future tests independently.