HireFlutterDev FlutterStack is our AI-augmented Flutter delivery system: Claude Code, Cursor, and the GetWidget UI kit layered on senior engineers. Ships 40-60% faster than non-AI Flutter teams. Ship 2× faster · 40-60% less hours to MVP · same quality, lower total cost. FlutterStack is the operational default for every developer, not an experiment. This page is where we prove it.
The old model was wasting developer time and client money. Senior engineers were spending 60-70% of their hours on work that didn't require senior judgment. Here's how the math changed when we fixed that.
Traditional Flutter agency work is dominated by scaffolding: model classes from API schemas, REST clients, navigation routes, state management setup, test boilerplate, UI from Figma. Senior developers spent 60-70% of their time on work that didn't require senior judgment.
AI is exceptionally good at the 70%: pattern-matching, transformation, scaffolding from specs, refactoring. It's poor at the 30%: architecture decisions, hard debugging, novel features. Our workflow puts AI on the 70% and humans on the 30%. Same output, roughly half the hours.
GetWidget started in 2019 as an open-source Flutter UI kit. It's now used in 100,000+ Flutter apps. We've invested years building reusable Flutter primitives. That fabric is the second pillar of our AI workflow. Developers start UI work 20-30% closer to done on top of the AI velocity gain.
Internal data from our last 12 projects: 35-65% reduction in hours-to-MVP versus matched-scope estimates without AI workflow. Biggest gains in UI implementation, API client scaffolding, and test scaffolding. Smallest gains in architecture, still bounded by human thinking, and we say so.
Each pillar is a specific tool or asset. Individually useful. Combined, they compound, and that's where the 40-60% velocity gain comes from. No single tool does the whole job. The Flutter LLM stack we wire most often: Flutter Gemini for cost-sensitive workloads, Flutter ChatGPT (OpenAI) when clients already pay for it, and Claude for code-gen and agents. For a Flutter AI chatbot, that's usually Gemini Flash plus a streaming UI. For on-device Flutter machine learning we ship Flutter TensorFlow Lite via the official tflite_flutter plugin: image classification, OCR, gesture recognition, anything that needs to work offline or keep user data on the device.
Runs as a CLI alongside the developer's editor for agentic tasks spanning multiple files: "add a new screen, wire it into the router, add the corresponding state notifier and test." Strong on navigation refactors, state management migrations, and adding cross-cutting features (analytics, logging, error tracking) in one pass.
Handles inline work: "convert this StatefulWidget to StatelessWidget with Riverpod," "write a test for this notifier," "extract this widget into a reusable component." Faster than context-switching to a chat window. Our developers run Cursor as their primary IDE with Dart and Flutter extensions configured.
30+ pre-built Flutter components for buttons, cards, alerts, modals, navigation, forms, and loaders. Each component is theme-aware and accessibility-tested. Starting from GetWidget vs raw Flutter saves 80-120 hours per project on standard UI. Open-source. You can audit the code and contribute back.
The most valuable pillar and the most invisible to clients. 30+ vetted prompts for: Riverpod/BLoC/Provider state setup, go_router navigation, Firebase integration (Auth + Firestore + Cloud Functions), Stripe checkout, push notifications, App Store + Play Store submission scripts, and common refactor patterns. Each prompt is pre-debugged across 5-15 projects before entering the library.
Concrete walkthrough of a typical workday. Most of what's "different" is structural, not magical. AI is the default, not the exception. This is how a feature goes from ticket to merged PR faster than the old way.
Written standup in your Slack channel. What shipped yesterday, what's queued today, blockers. Developer picks the next ticket, reads the spec, drops it into Claude Code with codebase context: "Implement [ticket], following our existing patterns for state and routing."
Claude Code generates the scaffold: model classes, the screen, the state notifier, the routes. Developer reviews each change, modifies as needed, runs tests. By 11:30 the first cut is committed locally. This phase runs at roughly 2-3x the speed of hand-writing the same files.
Edge cases, error states, ambiguous design spec, race conditions, performance tradeoffs. This is where the developer earns their tier rate. AI is consulted ("how would Riverpod handle this race condition?") but doesn't drive. No AI-generated code replaces senior judgment on architecture decisions.
Cursor generates test scaffolds: unit tests for the notifier, widget tests for the screen, integration test for the flow. Developer reviews and adds the cases AI missed: edge inputs, error paths, boundary conditions. Test coverage is typically higher because scaffolds are cheap to generate.
PR opened. AI code review runs first, flagging null safety issues, state management anti-patterns, missing error handlers. Developer addresses or justifies each flag. Then PR goes to a human Mid/Senior reviewer. No AI-generated code merges without a human approving it.
Internal project tracked with timestamped commits: a 6-screen Flutter MVP with auth, REST integration, Stripe payments, and push notifications. AI workflow path used Claude Code + Cursor + GetWidget UI kit + our prompt library. Client projects show similar patterns; specifics vary by codebase and scope.
| Task | Without AI workflow | With AI workflow | Reduction | |
|---|---|---|---|---|
| 6-screen MVP scaffolding | 40 hrs | 14 hrs | 65% faster | |
| REST API client (20 endpoints) | 16 hrs | 4 hrs | 75% faster | |
| UI implementation from Figma (15 screens) | 60 hrs | 22 hrs | 63% faster | |
| State management migration (Provider → Riverpod) | 24 hrs | 9 hrs | 62% faster | |
| Test scaffolding (50 tests) | 20 hrs | 10 hrs | 50% faster | |
| Hard performance optimization | 16 hrs | 14 hrs | 12% faster | |
| Novel architecture design | 12 hrs | 11 hrs | 8% faster | |
| TOTAL — full MVP delivery | 188 hrs | 84 hrs | 55% faster |
Measured on a real internal project. We can share the timestamped commit log on request during a discovery call.
We sell this workflow, so it's worth saying out loud where it doesn't fit. Three cases where the AI-augmented edge does not deliver the 40-60% velocity gain.
If your security policy forbids LLM API access to source code (defense, classified health, certain banking), the AI edge collapses. Self-hosted Claude/GPT alternatives exist but the workflow loses 30-40% of its velocity. Honest path: hire our standard tier and skip the AI-augmented premium.
Old Flutter apps on legacy Dart versions, bug triage queues, OS-version-bump work. AI helps marginally here; the bottleneck is reading old code, not writing new code. We staff these at standard rates, not the AI-augmented rate.
If the project is 70% custom widget polish and design micro-interactions, AI accelerates the boilerplate but not the design judgment. Velocity gain drops to 15-25% instead of 40-60%. We still recommend AI-augmented for the non-design parts; just calibrate expectations.
30+ pre-debugged prompts our developers reuse daily. Each has been refined across 5-15 real projects before entering the library. Here are three examples, the kind of structured input that drives the numbers in the table above.
Generate a Riverpod Notifier for [feature].
- State class with copyWith
- AsyncValue<State> on load
- Errors mapped to UI-friendly text
- Unit tests for load / success / error
- Use our existing project patterns
from lib/features/*/state/Generate Dart client from this OpenAPI:
[paste spec]
- One file per resource
- freezed models with json_serializable
- Dio with our interceptor stack
- Retry on 5xx, NOT on 4xx
- Match naming in lib/api/*/Write widget tests for [Screen]:
- Visual: golden test for default state
- Behavior: tap CTA fires intent
- Error: shows error banner on fail
- Loading: shows skeleton during fetch
- Use our test helpers in
test/_support/widget_test_helpers.dartThe full library covers Firebase, Stripe, App Store submission, push notifications, go_router patterns, Riverpod architecture, and more. The complete library is included at no extra cost at every tier.
The workflow (Claude Code, Cursor, GetWidget UI kit, and the prompt library) is included at every tier. You save through fewer hours billed, not a higher per-hour rate.
1-2 years Flutter. AI-augmented to punch above tier on routine work. AI lifts Junior productivity most. They produce Senior-quality scaffolding.
3-5 years. Independent on standard work, AI-accelerated on scaffolding and tests. Reviews Junior PRs.
5-8 years. AI handles grunt; Senior owns architecture, review, and the hard 30%. Default tier for most clients.
8+ years. Owns the AI workflow setup for your team. Defines prompts, review gates, and the velocity baseline.
All rates full-time dedicated (160 hrs/month). Part-time available at +20%. Monthly rolling contracts. 4-week minimum. 10% off at 3-month commit, 15% off at 6-month. See full rate breakdown →
The questions clients ask before committing. Covering what AI-augmented means, whether quality suffers, how it compares to Copilot, privacy and security, and how velocity gains translate to your specific project.
Questions about rates and contracts, dedicated team structure, or how the full engagement works? Each page has its own FAQ.