Sprint.AI

Sprint.AI is your AI-native workspace for product and engineering teams — replacing Confluence, Jira, and GitHub workflows with real-time documentation, task automation, and PR creation.

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🚀 Sprint.AI — AI-native Sprint Planning & Docs

🌟 About the Project

Sprint.AI was born out of a frustration with fragmented product development workflows.

Modern product and engineering teams juggle disconnected tools:

  • Confluence for documentation.
  • Jira for task planning.
  • GitHub for code and PRs.

This patchwork results in lost context, outdated specs, and hours spent in sync meetings.
We asked: What if planning, documentation, and execution all lived in one AI-native flow?

Sprint.AI is our answer — a disruptive platform that unifies:

  • Living, AI-synced documentation
  • Autonomous ticketing and PR generation
  • Developer-aware sprint planning

💡 What Inspired Us

  • Observing product managers struggle to scope work without developer input
  • Watching engineers burn time manually writing docs or setup tasks
  • Working in async-first environments where coordination was always the bottleneck
  • Seeing the power of LLMs + code understanding, but not applied natively in sprints

🛠️ How We Built It

  • Frontend: Next.js with Tailwind CSS for a clean, fast UI
  • AI Engine: Powered by the Groq API for code summarization, spec parsing, and PR generation
  • Repo Integration: GitHub API to analyze structure and commit history
  • Database: Cloud first Supabase for task and profile storage
  • Overlay Interface: Custom keybinding Ctrl + . summons an AI command palette to interact with specs in real-time
  • Sprint Engine: Tracks velocity, scopes effort, and dynamically updates boards

🧠 What We Learned

  • Building intuitive UX for AI tools requires designing around trust and transparency.
  • Developers don’t just want automation — they want context. That’s why we layered explanations into PR drafts and task estimations.
  • Async teams value tools that don't interrupt them — so we made the AI overlay feel ambient and lightweight.

⚠️ Challenges We Faced

  • Parsing and summarizing complex codebases reliably with LLMs
  • Creating meaningful developer profiles from noisy Git commit data
  • Balancing automation with human control — letting users confirm or override AI actions
  • Designing an interface that feels invisible until it’s needed (the overlay UX)

🧭 What's Next

  • Integrating Slack to capture product conversations as feature ideas
  • Exporting sprint insights and velocity reports
  • Improve LLM performance, finalize the leftover features, expand on more new features.

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