📈 Grow

Per app, the AI writes a budget strategy — how to split a cold-traffic test across platforms and ad-set angles — then pushes it to an AI Brain that carries the product's full context: its research, its funnel numbers, its creative history. From there it's live Meta management inside the platform: rules that act on conditions, structured experiments, audience management, and a full account audit trail.

Attribution is per-app through one pixel: every event carries the app's identity, so ten products share infrastructure without sharing credit. Purchases report through the server-side API with real order values — the ad platform optimizes on money, not proxies.

Creative briefs generate from the idea's research — objective, audience psychographics, key message, tone, do/don't lists, brand colors — so the creative starts from what the market actually said. From the brief: AI image and video generation with editable prompts, or assignment to a human designer.

Designers get their own portal — a walled garden showing only their assigned briefs. They see the full brief, upload rounds, get feedback, resubmit; you approve; approved work flows into the campaign pipeline. Assignment pings them by bell and email with a direct link. They never see the rest of the operation.

Flows run until the user completes the core loop or opts out — a general sequence covers every app, and any app can take over with its own. Every touch can carry A/B variants with per-variant sent / delivered / came-back-and-finished stats, so subject-line opinions die quickly.

Underneath: auto-built audience segments per app and funnel stage (stalled after signup, completed but never bought), custom segment building, one-off blasts to any segment, deliverability tracking with hard-failure auto-suppression — and revenue attribution that ties purchases back to the emails that preceded them.

First-party instrumentation on every app: tap heatmaps on a real density grid, element-level tap counts, scroll-depth maps, and full session replays — bot-filtered, verified against real accounts. No third-party snippet, no sampling fog.

The difference is what happens next: agents digest the heat daily and write findings with evidence and a hypothesis — then push the improvement prompt straight into that app's builder chat. On a schedule, if you want: fresh report, then the fix starts building. The optimization loop runs while you sleep.