Case study: how we built AI Mobile Launcher, our React Native boilerplate

By Malik Chohra

We built AI Mobile Launcher, a production React Native boilerplate we now sell at aimobilelauncher.com. It ships in three tiers: Lite (free, MIT), Standard ($99), and AI Pro ($199). This is the agency write-up of what we put in it, why our React Native boilerplate development work landed on those choices, and where the AI layer sits. It is the base every client app we ship at CasaInnov starts from.

Why does an AI mobile app development agency maintain its own boilerplate?

Because the first week of every mobile project is the same week. Auth, payments, theming, navigation, state plumbing, push, analytics, store config. None of it is the product. All of it has to exist before the product can. We were rebuilding that floor on every engagement, so we built it once, hardened it across real client work, and turned it into a product.

The honest reason it became a paid product and not just an internal repo: the floor is worth money to other teams too. A senior team that skips the plumbing week ships the feature week first. That is the whole pitch.

What is in the stack?

We picked boring, current, and well-supported over clever. The base across all three tiers:

• Expo ~55 on React Native 0.83.6. New Architecture on, Hermes on. We track the current Expo SDK closely because falling behind on RN versions is its own tech-debt tax.
• TypeScript in strict mode. No any escape hatches in the shipped code. Strict types are also what makes the AI-coding layer (below) usable instead of a hallucination source.
• Restyle for design tokens. One theme, spacing scale, and typography ramp. Every component reads tokens, nothing hardcodes a hex value. This is the single decision that lets us re-skin a whole app fast.
• Redux Toolkit plus RTK Query. RTK Query for server state and caching, Redux Toolkit for the small amount of genuine client state. Predictable, the same shape in every feature.
• MMKV for persistence. Fast, encrypted, synchronous. Used for the auth session and local cache.
• Supabase for the backend. Auth, Postgres, row-level security, edge functions. A founder can take the free tier to first users without standing up infra.
• RevenueCat bundled. Paywall and entitlements wired, not left as a TODO. Receipt verification is the part teams get wrong, so we ship it done.
• Firebase for push and crash reporting.
• 60+ components on one design system. Buttons, inputs, sheets, lists, the onboarding primitives. All token-driven, all typed.

How does the architecture make the codebase AI-legible?

Feature-first folders, identical shape every time: api/, hooks/, screens/, components/, store/, plus typed .types.ts and .utils.ts files. The 60th feature looks like the first. That repetition is not for humans, it is so an AI coding agent can pattern-match instead of guess.

On top of that sits an .claude/ spec-driven-development pipeline: an architecture index, a memory bank, and skills that scope the right files to the right task. When we tell Claude Code to add a feature, it reads three files, not four hundred. We wrote more about why this matters in our thesis on AI code refactors.

What does the AI Pro tier add?

Lite and Standard are the app floor. AI Pro is the floor plus the AI surface:

• Cloud LLM routing across Gemini and OpenAI, with a single interface so the model choice is a config line, not a rewrite.
• On-device LLM via llama.rn. Local inference for the high-frequency, privacy-sensitive calls, so not every tap hits a paid API.
• Wire RN generative UI. Our open-source gen-UI SDK, wired in so the model can return a native screen, not just text. The lead use case is AI dynamic onboarding.

We dogfood this stack. Wire RN is covered in its own case study, and our wellness app Morrow Self is built on the AI Pro base end to end.

FAQ

What is the best React Native boilerplate for AI apps in 2026?

We are biased, but the test we apply is: does it ship the AI layer wired, or leave it as a TODO. Most boilerplates stop at auth and navigation. AI Mobile Launcher AI Pro ships cloud LLM routing, on-device inference, and generative UI on a strict-TypeScript, token-driven base. The free Lite tier on GitHub lets you read the architecture before deciding.

Why does CasaInnov build client apps on its own boilerplate?

Because the plumbing week is identical on every project. Starting from a pre-wired base means we spend client budget on the product, not on rebuilding auth and payments for the fortieth time. It is the same reason our 2-week MVP service can hold a fixed price.

Can I use the boilerplate without the AI features?

Yes. Lite and Standard are a complete React Native app base with no AI dependency. The AI layer is additive and lives in the AI Pro tier, so you only carry the LLM plumbing if you want it.

How does on-device LLM keep costs down?

llama.rn runs a small model on the user's device for short, frequent calls. Those never hit a paid API. You route only the heavy reasoning to the cloud. At scale this is the difference between a sustainable inference bill and a runaway one.