Specify first.
Write the acceptance criteria before asking AI to generate the change.
AI-First
Turn AI speed into product progress.
We put AI-first mobile engineering to work on your actual product, alongside the team that owns it.
We build mobile products AI-first, using AI as a force multiplier for senior engineers, not a replacement for them.
That is the whole point. AI produces options at machine speed. Senior engineers decide what belongs in the product, prove that it works, and own the release.
The result is not more code. It is product you can prove.
AI-first is an engineering system, not a subscription to a coding tool. Without specifications, types, tests, review, and platform verification, AI just helps a team create uncertainty faster.
We use AI across product shaping, implementation, test creation, refactoring, documentation, and debugging. It can draft. It does not get the casting vote on architecture, security, privacy, or release quality.
The operating change
How your team starts working differently
On embedded build engagements, we introduce the method while delivering real product work. The change has to survive outside the chat window.
Keep context current.
Store architecture decisions and agent rules with the repository.
Review the seams.
Check generated changes against types, native boundaries, APIs, and runtime behaviour.
Make tests deterministic.
Use deterministic fixtures and real platform builds, not live model luck.
Keep gates visible.
Document how changes are reviewed, released, and rolled back.
Hand over the loop.
Record what the team owns and how the delivery system runs.
Choose the constraints first
One standard. Three serious build paths.
We do not force every mobile product into the framework we default to. Flutter is our preferred starting hypothesis. The product can overrule it.
Flutter starts in pole position.
Choose it when one controlled cross-platform UI, predictable rendering, and Dart feedback fit the product.
Why Flutter starts first →React Native can overtake it.
Choose it when strict TypeScript, React continuity, and controlled compatible OTA updates create the stronger case.
When React Native wins →Native is back.
Choose it when platform behaviour, hardware, accessibility, on-device AI, or polish is the product.
Why native is back →Need the whole argument in one place? Compare the constraints in our Flutter, React Native, and native guide.
The loop: specify, draft, reject, prove, ship
The assistant accelerates each step. It never gets to skip one.
1. Specify
Contracts, acceptance criteria, architecture notes, and repository rules tell the assistant what is true.
2. Draft
AI proposes code, tests, refactors, and debugging paths. Fast output is useful input, not automatic approval.
3. Reject
Types, linting, dependency policy, and senior review throw away confident nonsense before it spreads.
4. Prove
Deterministic fixtures, unit and integration tests, real platform builds, and device checks test the behaviour.
5. Ship
Release controls, observability, staged rollout, and rollback turn a passing change into an owned release.
The scaffolding that makes speed useful
AI is not the quality system. This is.
The repository is the memory
Specifications, decisions, conventions, and current interfaces live with the code. A long chat is not an architecture.
Types catch lies early
Dart, strict TypeScript, Swift, and Kotlin catch different generated-code mistakes. Runtime boundaries still need validation.
Dependencies face an interview
AI loves a convenient package. We check platform fit, maintenance, security, licences, and native compatibility first.
Live models stay out of CI
Recorded, hostile, deterministic model outputs test streaming, parsing, refusal, timeout, and malformed-response states.
Both platforms count
A green simulator is not a mobile strategy. Real iOS and Android builds, devices, profilers, and release paths remain mandatory.
Humans own the code
Senior engineers choose architecture, review consequential changes, and take responsibility for what reaches users.
Proof, with the footnote attached
In one internal prototype sprint, one senior engineer used AI-assisted development to rebuild the core of a customer self-care app for a mobile operator in four days. The working prototype used an existing backend environment for login and account tools, and included an AI assistant, voice, and multiple languages.
It was a prototype, not a production launch. That distinction matters. The sprint demonstrated how quickly an experienced engineer can test a product direction when AI sits inside a disciplined process. It measured direction-testing speed, not production delivery time or budget.
See the wider work and track record, or read why AI-assisted engineering is not vibe coding.
The product moves. The delivery system stays.
Your team keeps the codebase, decisions, tests, repository rules, and documented delivery practices. A defined handover explains how to run the loop. The repository is not a souvenir.