- Audit your current mobile stack and delivery bottlenecks
- Identify the highest-leverage AI feature worth shipping first
- Define KMP / Compose Multiplatform fit vs native escape hatches
- Deliver a concrete implementation plan or a thin vertical slice
Ship AI features in mobile products without doubling your team.
I help startups and product teams build practical AI-powered mobile experiences using Kotlin Multiplatform, Compose Multiplatform, Android, and iOS — without forcing a rewrite or sacrificing native quality where it matters.
Start narrow. Ship fast. Expand only when the product earns it.
The model is simple: begin with a clear technical wedge, validate real product value, then scale the implementation without accumulating platform debt.
- Hands-on implementation, architecture, and technical product guidance
- Ship AI workflows like voice, summaries, chat, search, copilots
- Reduce Android / iOS duplication without forcing a rewrite
- Unblock teams that need speed, not architecture theater
- Shared domain and data layer strategy for Android + iOS
- Proof-of-concept module to validate migration risk early
- Pragmatic plan for what to share and what to keep native
- Built for teams already feeling the cost of duplicated effort
A strong point of view beats a generic service menu.
Most teams either bolt on a fake AI feature nobody retains, or overbuild mobile architecture before proving value. The better path is narrower: choose the right workflow, share code where it creates leverage, and keep native control where quality matters.
- You already have a product, users, or a serious internal roadmap.
- Mobile matters strategically, not as a side project.
- You need execution and judgment, not a six-month discovery phase.
- You are open to pragmatic technical tradeoffs.
- You want the cheapest implementation possible.
- You want AI for optics, not because it improves the product.
- You need a large agency to throw bodies at vague scope.
- You want slide decks instead of shipped work.
$ identify_real_user_problem $ choose_narrow_ai_workflow $ define_shared_vs_native_boundary $ ship_vertical_slice $ expand_only_if_usage_is_real status: focused, pragmatic, production-minded