Quick Facts

• Client: Individual Founder
• Industry: Self-help / Health & Wellness
• Vision: To help people manifest and achieve their dreams using AI technology, making the process accessible and effortless.
• Platform: Flutter (iOS & Android), Node.js + AWS Lambda Backend
• AI Stack: ChatGPT Function Calling, Runware AI, Advanced Prompt Engineering
• Infrastructure: AWS (Cognito, Aurora SQL, S3, SES, SNS, SQS, Lambda)
• Timeline: 4–5 months from kickoff to launch
• Post-launch: 1,000+ installations

About the Client

• Client: Individual Founder
• Industry: Self-help / Health & Wellness
• Vision: To help people manifest and achieve their dreams using AI technology, making the process accessible and effortless.

The Challenge

Before this platform, manifestation felt overwhelming and time-consuming for most people. Users had to:

• Spend significant time researching manifestation techniques
• Manually create vision boards and affirmations
• Navigate complex processes that discouraged them from pursuing their goals

This complexity caused many people to avoid or abandon their dreams altogether.

Market Gap

Few practical, AI-integrated applications existed in the manifestation space that could simplify this process.

Constraints

• Tight budget: Required cost-effective solutions
• Tight timeline: Needed rapid delivery to market
• Technology: Had to balance AI capabilities with affordability

Methodology

• Agile/Sprint-based development with frequent client communication
• Regular calls to ensure alignment with vision

Phases

• Design Phase: Created user experience and interface designs
• Solution Architecture: Built scalable technical foundation
• Development: Implemented AI integration, mobile app, and backend
• Testing & Launch: Quality assurance and deployment

Key Decisions & Trade-offs

• AI Model Selection: Initially planned to use Grok and Luma for manifest generation
• Cost Optimization: Shifted to Runware AI for image generation to stay within budget
• Performance: Moved to a faster model for manifestation generation to improve user experience
• Trade-off: Prioritized affordability and speed without compromising quality

Solution

Mobile Application (Flutter)

• Cross-platform iOS and Android support
• Smooth, native-like user experience
• Real-time AI-powered manifestation generation

AI Integration

• Advanced prompt engineering for personalized manifestation content
• Runware AI for cost-effective image generation
• Optimized faster models for quick manifest creation (2-4 second generation time)
• Context-aware AI responses tailored to user goals

Backend Infrastructure (Node.js + Express)

• RESTful API architecture
• Strapi CMS for content management
• AWS SQS queues for asynchronous processing
• Scalable microservices design

Database & Storage

• Aurora SQL for relational data management
• Secure cloud storage for user-generated content

AWS Services

• AWS Cognito for authentication and user management
• AWS SNS for push notifications
• AWS SES for email delivery
• SQS queues for background job processing

Deployment & DevOps

• AWS cloud infrastructure for reliability
• CI/CD pipeline for continuous updates
• Automated testing and monitoring

Impact

Delivery Timeline

• 4-5 months from kickoff to launch

Performance Metrics

• 1,000+ installations post-launch
• 2-4 seconds average AI generation time
• Stable, scalable infrastructure supporting growing user base

Business Impact

• Successfully launched within budget constraints
• Created an accessible, affordable solution for manifestation
• Reduced manifestation process from hours to seconds

Client Satisfaction

• Client was highly satisfied with the delivered application and its performance.

Technical Highlights

• Cost-Efficient AI: Reduced AI costs by 60%+ through strategic model selection
• Fast Generation: Optimized pipeline delivers results in 2-4 seconds
• Scalable Architecture: Built to handle 10x user growth without major refactoring
• Cross-Platform: Single codebase serving both iOS and Android

Future Vision

• The platform is architected for future growth and enhancements:
• Infrastructure supports easy integration of new AI models
• Scalable backend ready for increased user load
• Modular design allows rapid feature additions
• Cloud infrastructure positioned for global expansion