Most paid installs leak before activation.
Personalized onboarding, generated per user, rendered native. The first real question changes per ad source. Plug in AppsFlyer + your value props + your tiers.
See it in action
Real use cases, running live
All built with Wire RN. Same SDK, different verticals, different generated flows.
The full onboarding flow, generated per user and streamed to native components. One question at a time, picked and worded by the model, rendered with Wire RN.
The problem
Your attribution stack knows everything. Your onboarding ignores it.
You spent $8 on that install. The user from your Instagram wellness ad and the user from your Google performance ad both see the exact same first screen. Same questions. Same paywall. Wire RN changes that.
*~14% baseline from AppsFlyer Mobile App Trends 2025. The ~35% relative lift is a directional estimate from early pilot modeling, not a guaranteed result.
Built by
Malik Chohra. 9 yrs software, 7 in React Native mobile. App Lead at ScorePlay (Series A $20M; UEFA, Premier League, NBA, NHL, Paris 2024). On the Rx team at DocMorris (9M users; segment driving ~20% of group revenue, growing 33% YoY on Germany's eRezept rollout). Lead mobile at Mindshine (250k MAU; rating 4.3 → 4.9; acquired by Greator, Oct 2022). Also at CoachHub. Building Wire RN, AI Mobile Launcher, and writing Code Meet AI on AI-native mobile.
Architecture
How it works
Four layers, plain and named. Capture the signal, inject it, generate the flow, hand a plan to your paywall.
Capture
AppsFlyer / Branch / your provider hands you media source, campaign, ad creative, deep-link slug. Your app reads device locale, country, timezone, currency. All persisted locally before the user sees the first screen.
Inject
Those signals land in the system prompt as a USER CONTEXT block. The model sees: install came from TikTok ad fitness_creator_v2, country US, language en, organic false. Plus a media-source playbook telling it how each ad platform's users typically behave.
Generate
One question per turn. TextInputCard for name. SelectionCard for age. ChipSelectCard for goals. The model picks the right component, writes the question in the user's language, and shifts tone per the playbook. TikTok gets casual. Google gets intent-led. Organic stays neutral.
Plan + render
The moatA second structured LLM call turns the answers into a personalized plan plus 2-4 notification suggestions, Zod validated. Wire RN streams the JSON to native RN components via the A2UI protocol. Hermes-safe streaming. Your UI, not a template. Hands the plan + full context to your paywall.
Use cases
Built for B2C apps with paid acquisition
Mental Health Check-in
Daily check-in flow, generated in real-time. Questions adapt per session.
View demo Nutrition & LifestyleFood Recommendation
Preference-aware recommendation cards, streamed and rendered natively.
View demoAny B2C app with paid user acquisition and a subscription paywall can deploy personalized onboarding in under a sprint.
Pricing
Pilot pricing
We're testing this with 5 apps. If you're one of them, we build your integration together and you get pricing that won't exist post-launch.
Apply for a pilot spotSocial proof
Already running on
Morrow Self
Daily wellness check-in. LLM generates the question sequence based on user goals and past check-ins. Built on Wire RN.
More pilots in progress. Names shared post-NDA.
FAQ
Common questions, answered.
5 pilot spots. 4 weeks. Real data.
Book a 20-minute call. We'll scope your integration live.
Claim a pilot spot