One Prompt Startup -X (OPS-X) - table 246B

OPS-X turns a single prompt into a deployable startup MVP in minutes. It coordinates AI agents for frontend, backend, manages GitHub workflows, reviews code, & builds your startup from idea to launch.

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Inspiration

We were inspired by how quickly AI can generate prototypes but how slowly real, collaborative products still come together. At every hackathon, we saw brilliant teams lose time switching between tools, rebuilding projects after every change, and managing communication across frontend, backend, and design contributors. We wanted to fix that.

OPS-X began with one simple question: What if a single prompt could launch not only an app but an entire AI-assisted startup team? That vision drove us to create a platform where one idea becomes a working MVP, where each stakeholder—frontend, backend, or founder—gets their own specialized AI agent, and where progress happens in real time.

What it does

OPS-X transforms a single natural language prompt into a deployable web application and collaborative workspace. The system:

  • Generates a complete MVP using V0, producing frontend, backend, and configuration files.
  • Creates a GitHub repository automatically, setting up branches and PRs for each stakeholder.
  • Assigns role-specific AI agents: V0 for frontend, Claude for backend logic, and Gemini for documentation and branch naming.
  • Integrates CodeRabbit for automated pull-request reviews and Chroma DB for semantic code search.
  • Supports real-time refinement without re-generation, meaning users can update only what needs to change.
  • Tracks every iteration and produces a pitch deck and audio summary for investors.

OPS-X is designed to feel like having a live, intelligent engineering team working beside you.

How we built it

We built OPS-X with a modular architecture centered around AI routing and reproducible collaboration.

  • Frontend: Next.js with the V0 SDK handles one-prompt app creation, live streaming of file generation (via Server-Sent Events), and a workspace for stakeholders to refine outputs.
  • Backend: FastAPI with Model Context Protocol (MCP) manages state, routes requests to the right AI agent, integrates with GitHub for repo management, and coordinates automated CodeRabbit reviews.
  • Databases: PostgreSQL stores users, projects, and branches; Chroma DB holds vector embeddings of all code for semantic search.
  • Automation: GitHub Actions merge low-severity PRs automatically after AI review, maintaining version control while speeding iteration.

This pipeline connects generation, collaboration, and deployment in one continuous flow—from prompt to production.

Challenges we ran into

  • Maintaining Context: Coordinating multiple AI models with different scopes often led to context loss. We solved this by summarizing and embedding state into Chroma after each interaction.
  • Merge Conflicts: Simultaneous refinements caused overlapping changes. We added schema validation and CodeRabbit’s automated review layer to prevent breaking merges.
  • API and Token Limits: V0 and model APIs had rate limits during streaming; we implemented caching and throttling to sustain performance.
  • Time Pressure: Achieving an end-to-end working demo with frontend, backend, database, and AI orchestration within 36 hours required careful prioritization and parallel work.

Accomplishments that we're proud of

  • Building a functioning multi-agent orchestration system that actually collaborates, not just generates code.
  • Completing an end-to-end workflow: from one prompt to deployed code, GitHub integration, auto-reviews, and semantic search.
  • Integrating CodeRabbit successfully into the GitHub pipeline with automatic review, scoring, and merging.
  • Creating a scalable MCP backend that can later plug into other agent frameworks.
  • Demonstrating how teams can co-create software with AI in real time, not asynchronously.

What we learned

We learned that the hardest part of multi-AI systems is not the models themselves but synchronizing intelligence—deciding when each agent should act, what information to share, and how to maintain shared understanding over time.

We gained practical skills in:

  • Multi-model coordination using V0, Claude, and Gemini.
  • Embedding and semantic search with Chroma for contextual persistence.
  • GitHub API automation for branches, commits, and PRs.
  • Designing resilient asynchronous workflows with FastAPI and Server-Sent Events.

We also realized that small details—like when to summarize chat logs or how to define branch names—have a large effect on system coherence.

What's next for One Prompt Startup-X (OPS-X)

Our next steps include:

  1. Adding multiplayer chat integration using Janitor AI to let multiple stakeholders ideate with the system in real time.
  2. Improving context management through adaptive summarization and long-term vector memory.
  3. Expanding MCP tools to support automated conflict resolution, code linting, and deployment hooks.
  4. Integrating Postman Flows for visual orchestration and replay of build pipelines.
  5. Open-sourcing the framework so others can build AI-collaborative development platforms on top of it.

Our long-term vision is to make OPS-X the standard layer for agent-driven collaboration—a place where anyone can go from concept to company in one prompt.

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