Less waiting on releases. More funnel shipped.
You spec the experiment in an afternoon. Then it waits for a sprint slot, a release, a review queue, and a data trickle — six weeks from idea to answer. AppDNA moves your paywall and onboarding out of the app binary and into a system you operate: change it, test it, roll it back, all server-side, all native.
AppDNA is the App Growth OS for subscription apps; for product managers it is the server-driven execution layer: paywalls, onboarding flows, messages, and surveys render natively (SwiftUI on iOS, Compose on Android — not webviews) and change without an App Store release, behind an experiments engine with staged rollouts, SRM detection, guardrail metrics, and instant rollback.
Five things standing between your spec and your data — and how they disappear.
The most valuable screen you can't change
Leo owns monetization. His annual-plan price test is spec'd, reviewed, and third in the sprint queue behind two feature commitments. It ships in release 8.3 — five weeks out — then waits for App Store review. If the test loses, removing it is release 8.4. His OKR says "increase trial→paid"; his actual job this quarter is scheduling.
One PM. One experiment. Two release cycles.
Leo builds the price test in the paywall composer Tuesday morning, sets trial→paid as the guarded metric, and approves it. Live at 10% of traffic by lunch. The engine scales it up as data comes in; if it hurts, the stop-loss pulls it without asking anyone to branch. Release 8.3 ships features. Leo ships experiments — this week, seven of them.
Same PM. Zero tickets. The funnel is his to operate.
What you actually get your hands on.
Not a strategy deck — an operating surface. Four pieces of machinery, each one replacing a queue you currently wait in.
The server-driven SDK — native rendering, zero-deploy changes.
One SDK for iOS, Android, Flutter, and React Native, installed in under an hour. It renders paywalls, onboarding, messages, and surveys as native views — SwiftUI and Compose — from server-side configuration. Growth surfaces stop shipping in the binary; your release train carries product, not price tests.
The flow editor — onboarding as configuration.
Edit onboarding flows visually: screens, order, copy, permissions timing. Publish variants, target them, measure activation per variant. The highest-leverage surface in a subscription app, finally editable without a build.
The paywall composer — 30 templates, 24 section types.
Compose paywalls from tested layouts, wire in products and trials, localize, and version everything. Every paywall you ship stays in the library with its results attached — your monetization surface gets a history, not a graveyard of hardcoded screens.
The experiments engine — rigor built in, not argued in.
Staged rollouts (10% → 50% → 100%), SRM detection, guardrail metrics with automatic stop-loss, winner selection by pre-set criteria, full audit trail. Full-lifecycle experiments across onboarding, paywalls, pricing, and messaging — one engine, one methodology, no more per-test statistics negotiations.
Why not just build this in-house?
Fair question — you have the engineers, and a config-driven paywall service is a fun design doc. Full control, exactly your way. Here's what the build actually costs.
| Build it in-house | AppDNA | |
|---|---|---|
| Time to first experiment | Months of platform work before the first test ships — config service, native renderers, targeting, stats engine, admin UI | SDK installed in under an hour; first experiment live within 14 days |
| The stats engine | SRM detection, guardrails, staged rollouts, stop-loss — each one a project your data scientist doesn't have time for | Built in, already argued about, already tested |
| Maintenance | Yours forever — every OS release, every StoreKit change, every "the paywall renderer broke on iPad" ticket | Ours. Your engineers go back to the product |
| Odds | Industry research: in-house AI builds succeed 22% of the time; buy/partner, 67% | The 67% path — plus what no internal build has: your category's benchmarks and 80+ encoded playbooks |
Build the thing that makes your app different. The experiment infrastructure is the same for everyone — that's why it should be bought.
For the engineer you'll forward this to
No SDK gets adopted on a marketing page, and we know the veto lives in Slack. The short version, verifiable in the docs in ten minutes:
- Integration is one initialization and a placement call. Public APIs only — no swizzling, no private frameworks, nothing App Review objects to. Remote configuration of native UI is standard, guideline-compliant practice.
import AppDNA
AppDNA.configure(apiKey: "appdna_live_…")
AppDNA.present(placement: .onboardingComplete)- native SwiftUI / Jetpack Compose. No webviews on the money path.
- the SDK falls back to the last known-good cached configuration. No network, no blank paywall.
- server-side, instant, audit-logged. Also available as a kill switch per surface.
- the SDK is built to stay out of your startup path and your binary-size review.
- Docs are public — quickstart, API reference, and the safety model, readable without a login. Read the docs → /docs
If your engineers can't verify all of this in ten minutes, that's our bug. Tell us.
Complete on its own. Compatible with everything you already run.
RevenueCat · Adapty · Firebase · AppsFlyer · Adjust · Amplitude · Mixpanel · Braze · OneSignal
Zero migration. AppDNA reads billing and events from your existing stack on day one — your instrumentation investment carries over, nothing gets rebuilt.
A note from the founder
See what your funnel would ship if it didn't have to wait.
Paste your App Store or Google Play link. The audit scores your onboarding, monetization, and the seven other modules, finds the biggest leak, and hands you a 90-day plan — including the experiments you'd run first. Free, and a decent internal doc even if you never install anything.
FAQ
Eight questions PMs and their engineers ask — usually with the docs already open.
01What's the blast radius when an experiment goes wrong?
Bounded by design. Staged rollout from 10% of traffic; KPI stop-loss with automatic withdrawal; one-click instant rollback; SRM detection on the experiments engine so a broken split doesn't masquerade as a result. Every state change is audit-logged. You get more rigor than most in-house experimentation setups, without building one.
02We already have feature flags and an A/B tool. Where does this fit?
Keep them — AppDNA doesn't demand a migration. What your flag system can't do is render new native paywalls and onboarding flows without a release, propose the experiments from full-funnel data, and carry results into a compounding memory. Run them side by side; consolidate later if the flag tool's renewal stops making sense.
03Will engineering veto the SDK?
Give them ten minutes with the docs — that's the test it's built to pass. Public APIs, installs in under an hour, App Review-safe, native rendering (SwiftUI / Jetpack Compose, not webviews), offline-safe cached configurations. After install, your growth surface stops generating release-cycle tickets entirely.
04Where does our event data go?
Into your isolated workspace and nowhere else — isolation enforced in code. It never trains a shared model, and no other tenant can see it. Benchmarks you see are anonymized aggregates; your data is never part of anyone else's view identifiably.
05Our funnel is complex — multiple entry points, web-to-app, several price tests running. Can it handle that?
That complexity is the reason it exists as one system: nine modules on one event pipeline, so cross-surface effects (onboarding change → paywall conversion) are visible instead of falling between tools. It's built for subscription funnels specifically — if yours is a pure-IAP game economy, we're honestly not the right fit.
06If we drop it in a year, what do we keep?
Your experiment history with hypotheses, targeting, and results; your strategies and playbooks; your Growth Memory — all exportable. Removing it is symmetric to adding it: uninstall the SDK, revoke tokens, take your data. No dark-pattern lock-in; the lock-in we aim for is that the weekly shipping rhythm is worth keeping.
07Will this get us flagged in App Review?
Server-driven configuration of native UI through public APIs is standard practice and consistent with store guidelines — no code injection, no private APIs, no behavior App Review objects to. What changes remotely is layout, content, pricing display, and targeting of surfaces your app already declares. The safety model is documented in full in the docs, written to be forwarded to whoever owns your release checklist.
08What does the SDK cost us in binary size and startup time?
It's engineered to stay out of both conversations: initialization is designed to be off the critical startup path, and rendering uses the platform's own UI frameworks rather than shipping one. Exact measured numbers live in the docs and we'd rather you check them than trust an adjective. (Measured benchmarks carried in the developer-credibility block above — not duplicated here.) Missing your question? Ask us directly — a human replies within one business day. Engineers welcome; bring the hard ones.
Missing your question? Ask us directly — a human replies within one business day. Engineers welcome; bring the hard ones.
See your biggest leak — free, in about 2 minutes.
Everything your app needs to grow. In one system.