RZR reaches iOS audiences through contextual signals, household IP targeting, and hashed audience matching — no IDFA or fingerprinting required. Models train on a proprietary data pipeline that maps non-attributed installs back to auction context, improving prediction accuracy over time without compromising user privacy.

RZR optimizes against the outcomes brands actually care about — D7 ROAS, LTV, CPA, purchase rate. Campaign goals are set at launch and bid decisions are aligned to hit them.

Campaign learning runs across three phases: Exploration, Optimization, and Scaling. Most campaigns show meaningful performance movement within the first two weeks as models accumulate signal across inventory sources and user segments. RZR's models also draw on cross-app behavioral patterns from the broader advertiser base, which means the Exploration phase moves faster, and campaigns reach productive optimization without relying solely on single-campaign history. Performance compounds from there.

RZR accesses the open in-app ecosystem, reaching users across thousands of apps at CPMs that scale beyond what walled garden inventory allows. Every auction is evaluated in real time and bid on only when the predicted return justifies the price.