AI Feature Integration for Mobile Apps — Flutter, iOS & Android
Adding AI to an existing mobile app requires three decisions: where the model runs, how state is managed, and how latency is handled in the UX. I help product teams make those decisions and ship them to production.
What this service covers
- NLP integration and conversational interfaces inside existing apps
- LLM API integration — OpenAI, Claude, Gemini — wired into production state management
- On-device ML for latency-sensitive or offline-first features
- AI-driven UX features: smart search, recommendations, summarisation
- Platform channel bridging for native AI SDKs in Flutter apps
Who it's for
- Product teams with an existing Flutter or native app who want to add AI without a rewrite
- CTOs and engineering leads evaluating AI modernisation for a production codebase
How I work
- Discovery — understand the existing architecture, constraints, and what "AI feature" actually needs to mean for your users
- Architecture proposal — where the model runs, how it talks to your state layer, what changes and what doesn't
- Implementation — built inside your existing codebase and conventions, not bolted on
- Production support — monitoring, latency tuning, and iteration after launch
Proof
AI Chatbot Integration — Large-Scale Retail App
Conversational AI chatbot shipped to production across iOS and Android.
Read the case study →Frequently asked questions
How long does AI integration in a mobile app take?+
A focused feature — a chat assistant, smart search, or a summarisation flow — typically takes 3 to 8 weeks from discovery to production, depending on how much of the existing architecture needs to change to support it.
Can you add AI features to an existing Flutter app without rewriting it?+
Yes. Most AI features can be added as a new module that talks to your existing state management (BLoC, Provider, Riverpod) through a clean interface, without touching unrelated parts of the app.
What's the difference between on-device AI and API-based AI in mobile apps?+
On-device AI runs inference locally — faster, works offline, no per-request cost, but limited to smaller models. API-based AI calls a hosted LLM — more capable, but adds network latency and per-request cost. Most production apps end up using a mix of both.
How do you handle AI response latency in mobile UX?+
Streaming responses token-by-token, optimistic UI states, and clear loading patterns that set expectations. The goal is to make a 2-4 second response feel intentional rather than like the app is frozen.