TeamAI

Turn expert teams into persistent, role-specific AI agents that coordinate the full SDLC — so startups ship faster, safer, and with production quality from day one.

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Inspiration

Many startups fail to ship or scale because they lack experienced personnel, repeatable processes, and engineering discipline. Projects stall or deliver fragile systems due to poor architecture, missing QA, insecure deployments, unclear responsibilities, and limited experience.

What it does

TeamAI provides role-specific AI agents (PM-AI, Backend-AI, Frontend-AI, QA-AI, DevOps-AI, Security-AI, Doc-AI, etc.) that pair with humans, automate structured handoffs, generate artifacts (CRs, PR skeletons, tests, infra manifests), and coordinate via an orchestrator to keep projects synchronized and auditable. Humans approve medium/high-risk actions and continuously improve their role-AIs, transforming team expertise into reusable AI knowledge.

How I built it

I built the backend with Django (DRF) and the frontend with React Vite, then packaged it as a VS Code extension to assist developers right inside their workflow. For speed and feasibility, I used a locally installed Ollama DeepSeek 6B model. The result is a work in progress prototype MVP, which lays the foundation for future expansion into a mobile app and a full web platform to keep all team members in sync.

Challenges we ran into

Using multiple AI models — DeepSeek, ChatGPT, Gemini, and Claude — I noticed a gap: each model had partial context and none saw the “full picture” of the development lifecycle. This often led to hallucinations and required heavy debugging. TeamAI aims to solve exactly this problem: ensuring all role-specific AIs stay in sync.

Accomplishments that we're proud of

In just two days, I:

  • Built a working backend MVP with authentication, role-based access control, and basic audit logs.
  • Created a frontend interface and integrated APIs.
  • Compiled everything into a VS Code extension using vsce.
  • Proved the concept that diverse AI APIs can collaborate to bootstrap real software delivery. ## What I learned
  • How to build and distribute VS Code extensions using AI-assisted guidance.
  • How to integrate locally hosted AI models into real developer workflows.
  • That building with AI is powerful but requires careful orchestration to avoid “hallucination chaos.” ## What's next for TeamAI
  • Expand beyond the extension into a full web app and mobile app.
  • Add streaming responses instead of polling for smoother dev workflows.
  • Strengthen the multi-agent orchestrator and broaden coverage across the SDLC.
  • Scale the MVP into a production-ready platform with the help of funding and resources.

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