Six decisions that shape the build
Native vs cross-platform
React Native and Flutter cover 90% of AI app use cases and cut time-to-market in half. Reach for native Swift/Kotlin only when you need heavy on-device ML or platform-specific SDKs.
Cloud vs on-device inference
Cloud models (GPT-4o, Claude, Gemini) win on quality; on-device (Core ML, Gemini Nano) wins on privacy, latency and offline. Most apps use both.
Streaming is the UX
Token streaming, optimistic UI and skeleton states are what make an AI app feel alive on a phone with flaky signal.
Onboarding sells the magic
The first 60 seconds decide retention. Show the AI value in the first screen — no signup wall before the wow moment.
Cost per active user
Model calls scale with DAU. Design tiers, throttles and caching from day one — otherwise your unit economics break at 10k users.
Ship a TestFlight in 6 weeks
A focused v1 with one hero AI flow beats a feature-complete v3 that never leaves staging.
A 10-week shipping cadence that works
- Week 1–2: Discovery, hero-flow prototype, model + eval plan.
- Week 3–5: Core app scaffolding, auth, one AI feature end-to-end with streaming.
- Week 6–7: Second feature, offline handling, telemetry, cost dashboards.
- Week 8: TestFlight / Play internal, closed beta with 20–50 users.
- Week 9–10: Fix based on real usage, submit to stores, prep launch.
Mistakes we see (and fix) every quarter
- Wrapping ChatGPT with a login screen and calling it a product — you need a differentiated flow, not another chat UI.
- No prompt versioning or evals — quality regresses silently between releases.
- Ignoring model cost per DAU until the first invoice hits and margins evaporate.
- Skipping offline states — mobile networks fail, and 'nothing happened' kills trust.
- Shipping a giant v1 that no real user has touched.
Have an AI app idea?
We ship AI mobile apps end-to-end — discovery to App Store.