DhiWise: Figma-to-Code AI Tool - Technical Intelligence Summary

Core Functionality

Tool Type: Figma design to Flutter/React code converter with AI assistance
Success Rate: 80% UI conversion accuracy for standard patterns
Primary Use Case: MVP development and UI boilerplate generation

Critical Technical Limitations

State Management Lock-in

• Flutter projects force GetX architecture - non-negotiable
• Alternative state management (Bloc, Provider, Riverpod) requires complete refactoring
• Every screen gets GetX controller, navigation uses Get.to(), dependency injection via Get.put()
• Impact: Teams using different Flutter patterns face significant rework

Code Quality Issues

• Authentication handling is fundamentally broken
  • Generic error handling: catch (e) { throw Exception('Something went wrong'); }
  • No proper HTTP status codes (401, 403) - just generic exceptions
  • Basic token storage via GetStorage, minimal refresh token logic
  • Auth interceptor shows "Something went wrong" instead of redirecting to login
• API error handling is production-unusable
  • All API calls use same generic error message
  • No retry logic or request deduplication in Dio interceptors
  • No specific error types for different failure scenarios

Performance Bottlenecks

• Bundle size bloat: Apps start 12-15MB, can reach 45MB due to unnecessary dependencies
• No performance optimizations included:
  • No lazy loading implementation
  • No image caching
  • No background task handling
  • No code splitting for large applications
• Minimal loading states: Basic spinners only, no skeleton screens or progress indicators
• Basic accessibility: Semantic labels exist but no screen reader optimization, focus management, or keyboard navigation

Technical Specifications

Supported Frameworks

Framework	Output Quality	Key Issues
Flutter	80% success rate	Forced GetX architecture, platform-specific issues
React	Functional components with hooks	CSS modules only (no styled-components/Tailwind)
React Native	Standard mobile patterns	Limited to basic UI conversion
Next.js	Modern patterns	Opinionated component structure

Platform-Specific Failures

• iOS: Missing Info.plist entries for permissions (camera, location, notifications)
• Android: Runtime permissions setup included but iOS requires manual fixes
• Responsive Design: Fixed breakpoints (768px, 1024px) don't match custom design systems

Resource Requirements

Time Investment

• Initial generation: 20 minutes (up to 1 hour during server issues)
• Getting functional UI: 2 hours to 2 days (depends on Figma file organization)
• Production-ready code: Same time as traditional development for business logic
• Debugging generated code: Significantly longer due to unfamiliar patterns

Skill Requirements

• Must learn DhiWise's file organization and naming conventions
• GetX proficiency required for Flutter projects
• Understanding of generated architecture patterns for maintenance

Pricing Structure

• Free tier: Unlimited app creation, no code export (effectively useless)
• Pro tier: $149/year for 30 screens, code export, team collaboration
• Enterprise: 1000+ screens with custom AI training

Critical Warnings

Production Readiness Reality

• "Production-ready" is marketing fiction
• Generated code compiles but lacks:
  • Proper error handling
  • Loading states
  • Accessibility features
  • Performance optimizations
  • Security best practices

Breaking Points

• Figma file quality directly impacts output quality
  • Emojis in layer names break the system
  • Poor grouping and inconsistent naming produce garbage output
  • Clean design structure is essential for usable results

Debugging Nightmare Scenarios

• Generated code debugging is exponentially harder
• Following unfamiliar patterns slows troubleshooting
• Generic error messages provide no debugging context
• No stack traces for common failures

When Tool Actually Provides Value

Optimal Use Cases

1. MVP development where UI fidelity > architecture quality
2. Prototyping complex interfaces before proper implementation
3. CRUD applications with standard patterns (forms, lists, detail views)
4. Learning Flutter from web development background (learn proper patterns afterward)

Cost-Benefit Analysis

• Saves time on: UI boilerplate, responsive CSS writing, component scaffolding
• Costs time on: Architecture refactoring, debugging generated code, learning DhiWise patterns
• Break-even point: Projects with substantial UI work and tolerance for technical debt

Integration Reality

Backend Integration Limitations

• Supabase: Basic auth flows only (email/password), manual implementation required for social auth
• Stripe: Starter payment forms, no subscription handling/webhooks/error scenarios
• Firebase: Basic setup, no real-time subscriptions or RLS policies

Development Workflow Impact

• GitHub sync: Creates new branches with "DhiWise" prefixes every time
• Merge conflicts: Manual management required
• CI/CD: Generated code works with existing pipelines but may need modifications

Competitive Analysis

Tool	Annual Cost	Primary Strength	Critical Weakness	Best Use Case
DhiWise	$149	Figma-to-code conversion (80% accuracy)	GetX lock-in, poor error handling	Rapid prototyping with UI priority
Retool	$120	Admin panels, internal tools	Identical-looking dashboards, poor performance at scale	Internal tools acceptance of 2015 aesthetics
Firebase	Variable (expensive at scale)	Real-time features, working auth	Vendor lock-in, cost escalation	Real-time apps with Google dependency acceptance
FlutterFlow	Similar to DhiWise	Better state management options, intuitive UI builder	Less AI assistance	Visual Flutter development without GetX

Decision Criteria

Choose DhiWise When:

• UI boilerplate reduction is primary goal
• GetX architecture is acceptable
• MVP timeline is critical
• Team can handle generated code maintenance

Avoid DhiWise When:

• Production-quality architecture is required
• Custom state management patterns are mandated
• Complex business logic is primary development effort
• Team lacks capacity for generated code debugging

Technical Migration Considerations

Generated Code Modification Strategy

1. Understand DhiWise patterns first - file organization, naming conventions
2. Plan architecture refactoring if GetX incompatible with team standards
3. Implement proper error handling as immediate priority
4. Add performance optimizations before production deployment
5. Enhance accessibility for compliance requirements

Risk Mitigation

• Plan for 2-3x debugging time for generated code issues
• Budget additional development time for production hardening
• Establish code review processes for generated output quality
• Maintain fallback plan for manual implementation if generation fails