Original Job Description
We're building a creator monetization platform powered by AI and automation. The product helps digital creators: Analyze and optimize their content performance, generate strategy and content ideas, automate messaging and conversion workflows, manage offers, payments, and analytics in one system.
Stack: Python (Django or FastAPI) OR Go, React/Next.js frontend, PostgreSQL, Redis, Docker + Kubernetes, OAuth/OIDC, LLM APIs (Anthropic/OpenAI), Stripe.
MVP Scope: Creator onboarding + authentication, social platform integrations (Instagram, YouTube), AI-powered audit/reporting system, content strategy generation, messaging/engagement workflows, offer + payments system (Stripe), analytics dashboard, internal AI assistant.
Timeline: MVP target ~6โ8 weeks. Potential for ongoing work after launch.
Proposal
Hi โ I build AI-powered SaaS platforms end-to-end with Django/FastAPI, Next.js, and cloud infrastructure, and I'm excited about the creator monetization space.
**How I'd approach the architecture:**
Given the scope, I'd use FastAPI for the backend (async-first, better for AI/LLM call orchestration than Django), Next.js for the frontend, PostgreSQL + Redis for the data layer, and Docker + Kubernetes for deployment.
For AI integration, I'd build a structured pipeline: content ingestion โ OpenAI/Anthropic API calls โ response parsing โ database writes. The key is building a clean abstraction layer so the AI calls are testable and swappable.
For Stripe integration, Stripe Checkout + webhooks for payment receipts and subscription management.
For the internal AI assistant โ human-in-the-loop workflow โ I'd model this as a task queue (Celery or similar) where the AI proposes actions and a human approves before anything irreversible happens.
**Data model approach:**
- Creators, content items, platform connections as core entities
- OAuth tokens stored encrypted for Instagram/YouTube integrations
- AI audit results stored as structured JSON for fast retrieval
- Analytics aggregated nightly via scheduled tasks, not computed on the fly
**One similar project I've built:**
An AI-powered content calendar SaaS where the system scraped social platforms, generated post recommendations via GPT-4, scheduled content, and tracked engagement โ all with a Next.js frontend, FastAPI backend, PostgreSQL, and deployed on Railway.
**Risks I see:**
1. Social platform OAuth scopes change frequently โ building a retry/re-auth flow is critical
2. AI response times vary; UI needs to handle async states gracefully
3. Stripe webhooks are finicky in dev โ need ngrok or similar for local testing
**Timeline estimate:**
Week 1โ2: Auth, core data model, creator onboarding flow
Week 3โ4: Social integrations, AI audit engine v1
Week 5โ6: Payments (Stripe), messaging workflows, analytics dashboard
Week 7โ8: Polish, testing, edge cases, deployment hardening