$100.00
AI is revolutionizing mobile cross-platform migration by making it smarter, faster, and more cost-effective. Whether you’re expanding app reach or modernizing legacy software, intelligent migration powered by AI enables your development teams to innovate without starting from scratch.
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Pricing Table
| Features |
Small $9,500 / total Initial Business Idea Analysis and Documentation |
Medium $20,000 / total Solution Analysis Vision & Scope |
Large $35,000 /total Solution Analysis Vision & Scope (large-scale) |
|---|---|---|---|
|
Discovery Timeline |
up to 3 weeks |
from 4 to 7 weeks |
from 6 to 9 weeks |
|
Define Business goals and objectives of the expected product |
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Requirements Elisitation Sessions (stakeholders interviews, workshops) |
up to 5 workshops |
up to 7 workshops |
up to 9 workshops |
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Requirements Modeling and Documentation |
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Quality Attributes Workshop |
– |
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Business Requirements Specification |
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Competitor Analysis |
– |
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Product Succcess Metrics |
– |
– |
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Functional Decomposition |
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Key Business Processes & Sequence Diagrams |
– |
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Non-Functional requirements analysis and documentation |
– |
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Software Architecture Vision and High-level Design |
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Entity Relationship Diagram |
– |
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Technology Stack |
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Technical Risks Identification |
– |
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Architecture decisions report |
– |
– |
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Wireframes |
up to 3 core user flows |
up to 10 core user flows |
up to 15 core user flows |
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Product Information Architecture |
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Business Model Canvas |
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Customer Journey Map |
– |
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Value Proposition design Workshop |
– |
– |
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Delivery Time |
– |
– |
– |
Description
AI-driven mobile cross-platform migration refers to the use of artificial intelligence to automate and optimize the process of porting mobile applications between different operating systems (typically iOS and Android). Instead of rebuilding apps from scratch, AI tools can analyze, refactor, and translate codebases — reducing manual effort, time, and cost.
🔍 What is Cross-Platform Migration?
Cross-platform migration involves converting a mobile application developed for one operating system into a version that works on another platform. Traditionally, this requires rebuilding UI components, re-implementing business logic, and adapting platform-specific APIs. AI simplifies and accelerates this complex transformation.
🤖 How AI Enhances Migration
- Code Translation: Uses NLP and ML models to convert Swift to Kotlin or vice versa.
- UI Mapping: Automatically identifies and replicates user interface elements across platforms.
- API Mapping: Replaces platform-specific APIs with their equivalents on the target platform.
- Automated Testing: Generates test cases and scripts for regression and compatibility testing.
- Performance Optimization: AI evaluates and suggests improvements in responsiveness and efficiency.
🛠️ Tools and Technologies
- OpenAI Codex, GitHub Copilot – for intelligent code suggestions
- TensorFlow, PyTorch – for training custom ML models for migration
- Flutter & React Native – unified frameworks supporting both iOS and Android
- Jest, Appium – for automated mobile testing
- Azure AI, AWS CodeWhisperer – for intelligent development assistance
📱 Benefits of AI-Driven Migration
- ⚡ Faster development cycles with minimal manual coding
- 💸 Reduced development costs and resource usage
- 📲 Consistent user experience across platforms
- ✅ Higher accuracy in translation and logic mapping
- 🔄 Easier updates and maintenance post-migration
🔁 Migration Workflow
- Analyze existing codebase using AI models
- Extract business logic and UI definitions
- Translate code, components, and APIs to the target platform
- Generate UI and integrate app functionality
- Test across devices using AI-generated test cases
- Deploy and monitor with analytics feedback
📊 Real-World Use Cases
- 📦 E-commerce apps migrating from iOS to Android for broader market reach
- 🏥 Health apps replatforming for regulatory compliance across regions
- 🎮 Game developers seeking multi-platform launches
- 🧾 Fintech services migrating legacy apps into modern Flutter-based versions
⚠️ Challenges to Consider
- Differences in UI/UX patterns between platforms
- Handling platform-specific device capabilities
- AI model accuracy in code and component translation
- Security and compliance during automated migration
