This page connects implementation reality to commercial delivery: what got rejected, what was rebuilt, and how product framing, privacy claims, and monetization were made App Review compliant.
A companion-safety rejection pattern: emotional AI chat needs visible UGC controls, moderation policy, and explicit boundary routing before generation.
A Guideline 3.1.2(c) rejection pattern: subscription setup can be technically correct while paid value communication still fails App Review expectations.
A Guideline 2.3.1 metadata case: screenshot and promotional claims must match real model confidence and output boundaries, not idealized promises.
A Guideline 5.1.1 privacy pattern: reviewers block submissions when data-collection assumptions are unclear, even if local AI quality is already good.
A demo-access rejection pattern: if review credentials and seeded chat states are incomplete, safety and moderation paths are treated as unverified.
A Guideline 1.1 safety rejection pattern: dating-adjacent AI needs intent classification and constrained response modes before any free-form generation.