🚀 Introduction
Marketplace disputes are one of the biggest pain points in e-commerce platforms.
Most systems still rely heavily on manual workflows:
- Customers submit complaints
- Admins review screenshots and proofs
- Sellers respond manually
- Resolution takes days
I wanted to explore how AI and automation could improve this process.
So I built an AI-Powered Marketplace Dispute Engine using React, Flask, PostgreSQL, and AWS that helps automate and streamline dispute resolution workflows.
🧠 What the Project Does
The platform allows:
- Buyers to raise disputes
- Sellers to respond with evidence
- Admins to monitor dispute activity
- AI workflows to assist in dispute categorization and prioritization
The goal was to create a scalable backend system capable of handling structured dispute workflows efficiently.
⚙️ Tech Stack
Frontend
- React.js
- Tailwind CSS
Backend
- Flask
- REST APIs
- JWT Authentication
Database
- PostgreSQL
Cloud & Deployment
- AWS App Runner
- AWS RDS
- AWS Amplify
Other Tools
- Git & GitHub
- Postman
- AI-assisted workflow experimentation
🏗️ System Architecture
The workflow looks something like this:
- User creates a dispute request
- Backend validates and stores dispute data
- Seller receives response request
- AI workflow helps categorize dispute priority
- Admin dashboard tracks resolution status
- Resolution updates are synced in real-time
One of my major goals was keeping the architecture modular and scalable.
🔥 Key Features
✅ Secure Authentication
Implemented JWT-based authentication for secure access management.
✅ Role-Based Access
Different dashboards and permissions for:
- Buyers
- Sellers
- Admins
✅ Real-Time Notifications
Integrated real-time notification workflows to instantly update users about:
- Dispute status changes
- Seller responses
- Admin actions
- Resolution updates
This significantly improved user engagement and tracking efficiency.
✅ Analytics Dashboard
Built an analytics dashboard to monitor:
- Total disputes
- Resolution rates
- Pending cases
- Escalation metrics
- User activity insights
This helps admins better understand platform performance and dispute trends.
✅ AI-Based Fraud Detection
Implemented AI-assisted fraud detection mechanisms to identify:
- Suspicious dispute patterns
- Repeated fraudulent claims
- High-risk transactions
- Unusual user behavior
The system helps prioritize potentially risky disputes for faster review.
✅ Dispute Tracking
Users can monitor:
- Pending disputes
- Resolved cases
- Escalated disputes
✅ AI-Assisted Categorization
Experimented with AI workflows to:
- Identify dispute type
- Prioritize urgent cases
- Improve admin efficiency
✅ Cloud Deployment
Deployed the application using AWS services for scalability and reliability.
☁️ AWS Services Used
AWS Amplify
Used for frontend deployment and hosting.
AWS App Runner
Handled backend container deployment with simplified scaling.
AWS RDS
Managed PostgreSQL database hosting.
Working with cloud deployment taught me a lot about:
- Environment variables
- Deployment pipelines
- Backend connectivity
- Production debugging
🚧 Challenges I Faced
Like every real-world project, this one came with challenges.
Some major ones were:
- Handling authentication securely
- Managing API communication between frontend and backend
- Database relationship design
- Deployment configuration issues
- Backend service connection with PostgreSQL
- Debugging production errors on AWS
A lot of time went into debugging deployment and API issues rather than writing features 😅
📚 What I Learned
This project helped me gain practical experience in:
- Full-stack development
- Cloud deployment
- Backend architecture
- Database design
- API development
- Authentication systems
- AI-assisted workflows
More importantly, it taught me how real production systems require much more than just writing code.
🔮 Future Improvements
Some features I’d like to add next:
- AI-generated dispute summaries
- Payment gateway integration
- Multi-language support
🌐 Project Links
🚀 Live Demo
💻 GitHub Repository
📸 Project Screenshots
📊 Admin Dashboard
📈 Analytics Dashboard
✨ Platform Features
✨ Pricing Screenshot
💡 Final Thoughts
Building this project was a great learning experience because it combined:
- Full-stack engineering
- Cloud infrastructure
- Backend systems
- AI experimentation
I’m still improving the platform and exploring new ideas around AI-powered automation systems.
Would love to hear feedback or suggestions from the community 🚀
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