Moe's AI Agent Platform
Owned product / experiment. Built a complete AI agent platform from zero to deployed MVP in one weekend.
Key Results
Owned product / experiment. Built by GTA Labs to develop and test our own products and engineering methods. This is not a commissioned client engagement.
The Problem
Businesses want AI agents but face a brutal choice: expensive custom development or rigid no-code tools that don't fit their workflows. We needed to prove that a flexible, self-hostable agent platform could be built quickly and affordably.
The gap
- Custom agents: $50k+ and months of development
- No-code platforms: Limited customization, vendor lock-in
- Open source options: Require significant technical expertise
The Build
We built Moe's AI from scratch in a focused 48-hour sprint:
Technical stack
- Frontend: Next.js 15 with React 19, Tailwind CSS
- Backend: Supabase (Postgres + Auth + Realtime)
- AI: Multi-model support (GPT-5, Claude, Gemini)
- RAG: pgvector for knowledge base embeddings
Features shipped
- Agent builder: 4-step wizard for creating agents
- Multi-channel: Slack, Discord, WhatsApp, Email integrations
- Knowledge base: Upload docs, auto-chunk and embed
- Analytics: Conversation tracking, response quality metrics
- Human-in-loop: Approval workflows for sensitive actions
The Outcome
Deployed in 48 hours:
- 7 app screens: Dashboard, agents, knowledge, analytics, settings
- Full auth system: Sign up, sign in, password reset
- Marketing site: Landing page with waitlist
- Production-ready: Deployed on Vercel with Supabase backend
What this proves
A functional AI agent platform doesn't require months of development or massive budgets. With the right architecture decisions and focused execution, you can go from zero to deployed MVP in days.
Tech Stack
Next.js React 19 Supabase pgvector Tailwind CSS Vercel