Mobile apps that think
Cross-platform iOS and Android from a single codebase. On-device AI for privacy and performance. One senior engineer — no agency, no offshore team.
Your mobile project has no engineer
The agency-offshore trap
You hired an agency for your mobile app development. They subcontracted to an offshore team you'll never meet. Specs get misinterpreted, costs balloon, timelines slip. When the app ships buggy, the agency renegotiates — and the cost gets passed back to you. You paid for an expert. You got a chain of subcontractors.
AI bolted on, not built in
Your app has a chat widget that calls GPT. That's not AI-native — that's a feature tacked on after launch. Real mobile intelligence means on-device inference for privacy and speed, cloud AI for complex reasoning, and intelligence woven into the core UX. The same production-grade AI pipeline discipline that governs server-side workflows applies to mobile.
Two codebases, double the cost
Maintaining separate iOS and Android codebases means every feature is built twice, every bug is fixed twice, and your team needs specialists in two completely different ecosystems. Add AI features and the complexity doesn't double — it multiplies. A single cross-platform codebase cuts development cost by 30-40% and ships to both platforms from the same production-grade foundation.
From brief to both app stores
Discovery & architecture
I map your business requirements, user flows, and AI opportunities. We make the critical decision together: React Native for cross-platform efficiency, or native Swift/Kotlin where the platform genuinely demands it. Honest architectural judgment — not stack loyalty.
AI integration design
I design the AI layer — which models run on-device for privacy and speed, which run in the cloud for complex reasoning, and how they coordinate. ExecuTorch brings production-grade inference to the phone. Cloud AI handles the rest. The hybrid architecture is the standard.
Iterative build
Production development with weekly TestFlight and internal testing builds. You see a working app each week — not mockups. Cross-platform from day one: a single TypeScript codebase deploying to iOS and Android simultaneously. OTA updates via Expo bypass App Store review for JavaScript-only changes.
Store deployment
App Store and Play Store submission, review compliance, and launch. Monitoring, crash reporting, and OTA update infrastructure from day one. The app evolves with your business — new features deploy without waiting for store approval cycles.
The market has decided
Growing to $258 billion by 2034 at 29.3% CAGR, according to Fortune Business Insights. AI app downloads grew 92% year-over-year, with revenue up 180%. Worldwide AI spending hit $2.52 trillion in 2026. This isn't emerging technology — it's the new baseline for competitive mobile applications.
React Native at enterprise scale. Shopify shares 86% of code across iOS and Android with sub-500ms screen loads and 99.9%+ crash-free sessions. Discord cut median startup time in half with 60% fewer slow frames. Coinbase improved their buy/sell funnel by 80%. Microsoft runs Office for 600 million daily users on React Native.
Apple's on-device foundation model on iPhone 15 Pro — generating 30 tokens per second, outperforming Llama-3-8B and Mistral-7B despite being smaller. Meta's ExecuTorch brings the same production-grade inference to React Native — the engine behind Instagram and Facebook on-device AI. Data never leaves the phone. The same pipeline discipline that governs cloud AI now runs in your users' pockets.
The stack that ships
Common questions
When should I choose React Native over native development?
React Native delivers 86% code sharing (Shopify's production number), 30-40% lower cost, and simultaneous iOS + Android deployment from a single TypeScript codebase. For typical business apps — content, commerce, productivity, AI features — the performance gap with native is indistinguishable since the New Architecture shipped. Native Swift or Kotlin makes sense for Apple Watch complications, heavy on-device ML with no React Native bridge, or performance-critical paths requiring zero intermediary overhead. The decision is made during architecture based on your actual requirements — honest technical judgment, not stack loyalty.
How does on-device AI work in a mobile app?
Through a hybrid architecture. Lightweight models run directly on the phone via ExecuTorch (Meta's inference engine for React Native), CoreML (Apple), or LiteRT (Google) — handling privacy-sensitive tasks with sub-millisecond latency and zero cloud cost. Complex reasoning routes to cloud AI. Apple's Neural Engine delivers 30 tokens per second on iPhone 15 Pro. Data never leaves the device for sensitive operations. The same AI pipeline discipline used in server-side workflows — governance, quality gates, observability — applies directly to the mobile AI layer.
How much does a custom AI mobile app cost compared to an agency?
Direct engagement with a senior engineer eliminates agency markup and offshore delegation. No project managers relaying specs to a team you'll never meet. Agency MVPs typically run $50,000-$100,000. Direct engagement is significantly lower because there are no middlemen, no spec misinterpretation loops, and no renegotiation overhead. Cross-platform development with React Native cuts an additional 30-40% versus building separate native apps. You get production-grade software with transparent pricing and direct access to the person writing the code.
How long does it take to build a cross-platform AI mobile app?
MVP in 3-6 months, compared to 6-12 months for separate iOS and Android native builds. You see working builds weekly via TestFlight and internal testing — real functionality, not mockups. OTA updates through Expo's EAS Update deploy JavaScript-only changes directly to users, bypassing App Store review. A single codebase means one bug fix ships to both platforms simultaneously.
What about App Store review and EU AI Act compliance?
Both App Store and Play Store have AI-specific review guidelines, built into the development process from day one. On-device AI plays in our favour — Apple favours apps using its own ML frameworks. For the EU AI Act taking effect August 2026, on-device inference is a natural compliance path: data minimisation through local processing, purpose limitation through scoped models, and processing locality by design. The same architectural patterns that make mobile AI performant — on-device for sensitive tasks, cloud for complex reasoning — directly address regulatory requirements.
Ready for mobile that thinks?
Let's design the AI mobile experience your users actually need.