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Bridging Imagination and Reality

A Comprehensive Case Study on Transforming Hand-Drawn Sketches into Realistic Images 

Project Goal

To implement AI-powered solutions that transform sketches into realistic images, enhancing the designer’s experience by providing a preview of the product’s final appearance before launch. 

Industry

Fashion Industry

Location

New York City, USA

Tech Team

Project Manager  |  AI development Engineer  | Data Scientists (2)  | Sketch Draw Expert  | Prompt Engineer  | Full Stack Engineer (React/drf)  | User Experience (UX) Designer

Team Involved

  • Project manager 
  • Data Scientists
  • ML Engineers
  • Risk Assessment Specialists
  • Full Stack Engineers (React/Python)
  • Compliance Officers
  • UX Designer

Business Tasks the Client Wanted to Solve

Enhance Designer Experience

  • Before the launch of a project, designers can visualize the project in different colors and shades, getting a realistic preview of how the product will look. 

Visualize Product 

  • The image created by AI should resemble the real product. This will enhance the designer’s experience by allowing them to identify and improve areas before launching the product. 

Change Any Particular Thing in the Generated Image

  • Designers should be able to mark any location on the AI-generated image. Upon giving a prompt, only the specified area of the image should be updated, rather than the whole image. 

Multiple Images 

  • The solution should provide multiple images of the sketch given by the designer. The minimum number of images is 4, and the maximum can be up to 10. 
     

Scalability

  • The solution should be scalable, allowing multiple designers to work simultaneously without any degradation in performance or user experience. 

Business Tasks the Client Wanted to Solve:

1) Accelerate Loan Processing

  • Reduce the time taken to process and approve loan applications through automation of manual review processes
  • Enable real-time preliminary loan decisions

2) Enhance Risk Assessment

  • Implement more sophisticated risk assessment models using multiple data points
  • Improve the accuracy of default prediction
  • Reduce human bias in the loan approval process

3) Change Any Particular Thing in the Generated Image

  • Handle increasing application volumes without proportionally increasing staff
  • Maintain consistency in loan evaluations across all applications
  • Enable simultaneous processing of multiple applications.

4) Ensure Compliance

  • Maintain transparent decision-making processes
  • Provide clear audit trails for regulatory requirements

  • Implement fair lending practices

5 ) Improve Customer Experience

  • Reduce waiting times for loan decisions
  • Provide clear feedback on application status
  • Enable digital document submission and verification

What pitfalls did the client face? 

Lack of AI and ML Expertise The retailer had no in-house expertise in AI and machine learning, which is essential for developing a sophisticated image generation system. 
Maintaining designer sketch privacy and data security  Ensuring the confidentiality of designer sketch data, especially the sketches uploaded by the designer into the system.
Balancing Personalization with User Experience Creating a system that offers personalized advice without overwhelming the designer 
Image Has Realistic Feelings  When the image is generated by AI, it should feel like a real product designed based on the instructions given by the client. 

What pitfalls did the client face? 

1) Legacy System Integration

  • Existing systems were not designed for AI integration
  • Historical data was stored in various formats and locations
  • Manual processes were deeply embedded in operations

2) Data Quality and Standardization  

  • Inconsistent data formats across different sources
  • Missing or incomplete historical data
  • Lack of standardized documentation processes.

3) Regulatory Compliance

  • Need for explainable AI decisions
  • Ensuring fair lending practices
  • Meeting data privacy requirements

4) Staff Resistance

  • Concerns about job security
  • Reluctance to adopt new technologies
  • Learning curve for new systems

Our Suggestions

Requirement Analysis and Planning 

  • Conduct workshops to understand customer profiles and identify requirements for generating images. 
  • Define functionalities for the AI-generated images using sketches. 
  • Assess and plan resource allocation for development and implementation. 

System Design and Architecture 

  • Backend: Use Python and TensorFlow for AI model development. Leverage cloud services for scalability and data management.
  • Frontend: Develop a user-friendly web application using React to build a dynamic and interactive user interface.
  • Database: Integrated the MongoDB store data 

Development and Integration 

  • Use an iterative development approach with continuous client feedback. 
  • Train the AI model with diverse datasets to handle a wide range of designer inquiries. 
  • Ensure stringent data security measures. 

Deployment and Continuous Improvement 

  • Launch the application with embedded AI features. 
  • Gather user feedback and continuously refine the AI models for better accuracy and engagement. 
  • Provide ongoing support and updates to adapt to changing consumer trends and technology advancements.  

What we suggested:

1. Initial Assessment and Planning

  • Conduct thorough analysis of existing loan approval processes
  • Identify key pain points and automation opportunities
  • Define success metrics and compliance requirements
  • Create implementation roadmap

2. System Design and Architecture

  • Backend: Use Python and TensorFlow for AI model development. Leverage cloud services for scalability and data management.
  • Frontend: React-based dashboard for loan officers
  • Database: MongoDB for flexible data storage
  • API Layer: FastAPI for high-performance backend services

3. Development and Integration 

  • Implement modular AI components for different aspects of loan processing
  • Create explainable AI features for transparency
  • Develop real-time monitoring and alerting systems
  • Build robust data validation and cleaning pipelines

4. Testing and Deployment

  • Conduct parallel testing with existing systems
  • Implement gradual rollout strategy
  • Provide comprehensive staff training
  • Establish feedback loops for continuous improvement

Technical Architecture

OpenAI GPT 

  • OpenAI GPT enhances user interaction with advanced natural language processing, understanding designer prompts, offering suggestions, and interacting with AI models to generate images. 

Backend Framework 

  • The backend, using Django and TensorFlow, develops AI models, manages data, and facilitates communication between frontend and backend with a PostgreSQL database for efficient storage and retrieval. 

Frontend Framework 

  • The front end, built with React, provides a user-friendly interface for uploading sketches, viewing AI-generated images, and marking specific areas for updates, with state management by Redux. 

Cloud Services 

  • Cloud services AWS ensure scalability, data storage, and security with compute instances, storage solutions, relational databases, and auto-scaling groups for optimal performance. 

Data Security and Privacy 

  • Data is encrypted at rest and in transit, with OAuth 2.0 for secure authentication, Cloud IAM for access control, and regular security audits to protect designer sketches and personal information. 

Continuous Learning Mechanism 

  • The system incorporates a continuous learning mechanism, gathering user feedback and refining AI models to improve accuracy and engagement, ensuring the system adapts to changing user needs and trends. 

Technical architecture:

1. AI/ML Stack

  • TensorFlow for deep learning models
  • scikit-learn for traditional ML algorithms
  • XGBoost for gradient boosting
  • SHAP for model explainability

2. Backend Framework

  • FastAPI for high-performance API development
  • Celery for task queue management
  • Redis for caching
  • MongoDB for document storage

3. Frontend Framework

  • T React with TypeScript
  • Redux for state management
  • Material-UI for component library
  • D3.js for data visualization

4. Cloud Services

  • AWS ECS for containerized applications
  • AWS Lambda for serverless functions
  • Amazon S3 for document storage
  • Amazon RDS for relational data

5. Security and Compliance

  • AWS KMS for encryption
  • OAuth 2.0 for authentication
  • Regular security audits
  • Automated compliance checking

6. Monitoring and Analytics

  • ELK Stack for log management
  • Prometheus for metrics
  • Grafana for dashboards
  • Custom analytics for model performance

Business Outcomes

Enhanced Designer Productivity 

  • Designers can now quickly visualize product concepts before actual production 
  • Reduced time spent on manual prototyping and physical mockups 
  • Ability to explore multiple design variations rapidly and efficiently 
  • Streamlined design iteration process with AI-powered image generation 

Accurate and Realistic Visualizations 

  • AI-generated images closely resemble real products 
  • Designers can preview product appearance in different colors and shades 
  • Improved decision-making through highly realistic image previews 
  • Ability to identify and improve design elements before product launch 

User-Friendly Design Interface 

  • Intuitive web application built with React 
  • Easy sketch upload and image generation process 
  • Capability to mark and update specific areas of generated images 
  • Supports generation of multiple images (4-10) from a single sketch 

Scalable Infrastructure 

  • Cloud-based solution using AWS 
  • Supports multiple designers working simultaneously 
  • Auto-scaling capabilities to handle increased usage 
  • Flexible and adaptable technological architecture 

Robust Data Security and Privacy 

  • Secure authentication using OAuth 2.0 
  • Cloud IAM for strict access control 
  • Regular security audits 
  • Protection of confidential designer sketches and personal information 

Adaptive and Continuous Improvement 

  • Built-in continuous learning mechanism 
  • AI models refined through user feedback 
  • System evolves to meet changing user needs and design trends 
  • Improved accuracy and engagement over time 

Business Outcomes:

1. Operational Efficiency

  • 40% reduction in loan processing time
  • 60% decrease in manual document review
  • 85% automation of routine tasks

2. Risk Management

  • 25% improvement in risk assessment accuracy
  • 30% reduction in default rates
  • Enhanced fraud detection capabilities

3. Customer Satisfaction

  • 50% faster loan decisions
  • 70% reduction in application errors
  • Improved transparency in decision-making

4. Scalability

  • 3x increase in application processing capacity
  • 45% reduction in operational costs
  • Improved resource utilization

5. Compliance and Reporting

  • 100% audit trail coverage
  • Automated compliance reporting
  • Reduced regulatory risks
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