PaperGen.ai is a sophisticated full-stack AI content generation SaaS platform that empowers users to create high-quality written content through advanced AI workflows. The platform features a decoupled hybrid architecture combining Next.js 14 for the web tier, FastAPI with Prisma for API and data services, and AWS Lambda for asynchronous AI processing pipelines, all orchestrated through Infrastructure as Code using Terraform.
Key Features
Decoupled Hybrid Architecture: Designed a scalable three-tier architecture separating concerns between web presentation (Next.js), API/data services (FastAPI + Prisma), and asynchronous processing (AWS Lambda).
Multi-Stage AI Workflows: Orchestrated complex multi-model AI pipelines on AWS Lambda to generate content through distinct stages: draft → polish → humanize → reference generation.
Supabase BaaS Integration: Leveraged Supabase as a Backend-as-a-Service layer for user authentication, real-time data synchronization, and event streaming.
Infrastructure as Code: Implemented complete infrastructure provisioning and management using Terraform for reproducible, version-controlled deployments.
Fault-Tolerant Processing: Built resilient asynchronous pipelines with proper error handling, retry mechanisms, and concurrent processing for improved throughput.
Multi-Model AI Integration: Integrated multiple AI models to leverage different strengths for drafting, polishing, humanization, and reference generation.
The platform follows a clean separation of concerns:Web Tier (Next.js 14)
Server-side rendering for SEO and initial page loads
Client-side interactivity for rich user experiences
Optimized bundle size and code splitting
API route handlers for lightweight backend operations
API & Data Tier (FastAPI + Prisma)
High-performance RESTful API with automatic OpenAPI documentation
Type-safe database operations with Prisma ORM
Complex business logic and data validation
PostgreSQL for relational data with ACID compliance
Serverless Processing Tier (AWS Lambda)
Event-driven architecture for AI content generation
Concurrent execution for parallel processing
Auto-scaling based on demand
Cost-effective pay-per-use model
Multi-Stage AI Lifecycle
Implemented a sophisticated content generation pipeline with four distinct stages:
Draft Stage: Initial content generation using GPT-4 based on user prompts
Polish Stage: Refinement of structure, grammar, and coherence
Humanize Stage: Adding natural voice, reducing AI detection markers
Reference Stage: Automatic citation and source generation
Each stage is orchestrated as a separate Lambda function with proper error handling and retry logic, ensuring fault tolerance and reliability.
Supabase Integration
Utilized Supabase as a comprehensive BaaS solution:
Authentication: User management with email/password and OAuth providers
Real-time Database: Live updates for content generation progress
Row Level Security: Tenant isolation and data access policies
Event Streams: Real-time notifications for pipeline completion
Infrastructure as Code with Terraform
All infrastructure is provisioned and managed through Terraform:
AWS Lambda functions with proper IAM roles and policies
API Gateway configuration for RESTful endpoints
PostgreSQL RDS instances with automated backups
CloudWatch logging and monitoring
VPC networking and security groups
S3 buckets for static assets and generated content
Challenges and Learnings
Building a decoupled architecture required careful consideration of API contracts and data flow between tiers. One major challenge was handling long-running AI workflows while providing real-time feedback to users. This was solved by implementing a WebSocket connection through Supabase Realtime, allowing users to receive progress updates as each stage completes.Another challenge was optimizing Lambda cold starts for the AI pipelines. This was addressed by implementing Lambda warm-up strategies, connection pooling for database access, and lazy loading of ML models.Orchestrating multi-model workflows required implementing sophisticated error handling and retry mechanisms. If one stage fails, the system can retry with different parameters or fall back to alternative models, ensuring high success rates even when individual services experience issues.
Outcome
PaperGen.ai successfully launched as a production AI content platform with impressive performance metrics:
Fast content generation: Average complete workflow time of 45 seconds
High success rate: 98% successful content generation rate
Scalable architecture: Handles 1000+ concurrent content generation jobs
Cost-effective: 60% reduction in infrastructure costs vs. traditional server approach
Reliable processing: Fault-tolerant pipeline with automatic retries
The platform demonstrates advanced expertise in building complex, decoupled architectures, orchestrating AI workflows at scale, and leveraging modern serverless technologies for cost-effective, high-performance applications.The project showcases skills in:
