Zero.to

From chaos to clarity: your AI workspace for going from idea to launch.

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Inspiration

In the fast-moving startup world, we noticed a growing number of "vibe coders" — builders who jump straight into shipping without structure, validation, or planning. While this creative energy is powerful, it often results in scattered execution, missed market opportunities, and burnout.

We wanted to create something that empowers both technical and non-technical users to move from idea to execution — without needing a team of consultants, planners, or marketers. That’s where Code Zeroto was born.

What We Built

Code Zeroto is a multimodal AI workspace that uses agent-based orchestration to guide users through the startup lifecycle:

  • Brainstorming and Idea Validation with LLM agents
  • Technical Planning via auto-generated SDLC and PRD documents
  • Outreach Automation with script, video, and email generation
  • Campaign Management with analytics, voice support, and follow-up tracking

Whether you’re a solo developer, a business major with an idea, or a founder with no AI knowledge — Code Zeroto is your co-pilot from zero to launch.

How We Built It

Alt text Frontend:

  • Next.js
  • Tailwind CSS
  • ShadCN for UI components and analytics graphs

Backend:

  • Nest.js
  • Supabase (auth + database)
  • Clerk for authentication (Google SSO)

Agentic Orchestration:

  • Letta Cloud for master orchestration
  • Fetch.ai for per-user agent execution (clarifier, SWOT, outreach, etc.)
  • Memory Blocks to track conversations and user sessions
  • Vapi for real-time voice interaction
  • RAG pipelines for grounding chatbot responses with company/user data

Models Used:

  • Groq for fast inference
  • Claude 4 for reasoning and code generation
  • Gemini API, LLaMA, and Veo 2 for video generation
  • LMNT + Vapi for voice synthesis

Supporting Tools:

  • Inspectmind: turns images into structured notes and reports
  • Resend API for email automation
  • RAG-over-uploaded FAQ PDFs for contextual chat

Challenges We Faced

  • Orchestrating multiple agents with memory while maintaining user context
  • Integrating voice with real-time LLM streaming responses
  • Creating a user-friendly experience that hides complexity behind helpful interactions
  • Designing a smooth flow between ideation, planning, and outreach
  • Balancing AI capabilities with user trust and education

What We Learned

  • How to build composable, multi-agent systems using Letta and Fetch.ai
  • Best practices for building voice-based agents with Vapi
  • Structuring user feedback and marketing campaigns using AI-generated content
  • Designing intuitive UX for users unfamiliar with startup planning tools
  • Creating scalable memory and RAG pipelines for agent personalization

What’s Next

  • Deeper Inspectmind integration for campaign and analytics reports
  • Support for additional startup types (non-profit, creator tools, SaaS, etc.)
  • Smarter onboarding with educational prompts and in-context help
  • Scaling the platform into a fully self-serve workspace for early-stage builders

Built With

  • clerk
  • fetch.ai
  • geimini
  • groq
  • letta
  • nest.js
  • next.js
  • shadcn
  • supabase
  • tailwind
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