Cloud9 Stratos — Automated Scouting Report Generator

An AI-powered system that automatically analyzes GRID esports data to generate concise, coach-ready scouting reports with opponent tendencies, player threats, and counter-strategies.

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Inspiration

Professional esports scouting is still largely manual. Analysts and coaches often review match VODs, spreadsheets, and fragmented statistics under tight time constraints. With official GRID esports data now available, we asked a simple question:

What if a coach could generate a complete scouting report in seconds instead of hours?

Cloud9 Stratos was inspired by real League of Legends and VALORANT coaching workflows and Cloud9’s emphasis on preparation, precision, and competitive excellence.

What it does

Cloud9 Stratos is an AI-powered automated scouting report generator. Given an upcoming opponent, the system automatically:

  • Pulls recent match data from the official GRID Esports Data API
  • Analyzes team playstyle, tendencies, and player impact
  • Identifies key threats and exploitable weaknesses
  • Generates a concise, coach-ready scouting report
  • Exports a professional PDF dossier for team briefings

The output is designed to be immediately actionable for professional coaches and analysts.

How we built it

Cloud9 Stratos is built as a production-style, API-first platform:

  • FastAPI powers a high-performance backend for scouting, matchup analysis, and report generation
  • LangChain orchestrates multi-step AI reasoning over structured GRID data
  • Azure OpenAI (GPT-4o) performs strategic pattern recognition and insight generation
  • A lightweight HTML/JavaScript frontend communicates with the FastAPI engine
  • A PDF generation pipeline produces Cloud9-inspired scouting reports

Throughout development, JetBrains IDEs and the AI Coding Agent Junie were used as an engineering copilot—accelerating API design, refining data pipelines, and validating complex LangChain workflows. All system architecture and final logic decisions were authored by the developer.

Challenges we ran into

  • Complex GRID data structures required robust parsing and normalization across teams and tournaments
  • Preventing AI hallucinations when match data was limited required validation and fallback logic
  • Balancing insight depth with clarity, ensuring reports remained concise and coach-friendly

Accomplishments that we're proud of

  • Successfully built a fully automated scouting workflow from opponent selection to PDF output
  • Delivered concise, professional scouting reports using only official esports data
  • Designed a scalable, API-first system suitable for real-world coaching environments
  • Effectively integrated LangChain for deterministic, structured AI reasoning

What we learned

  • High-quality, structured esports data is essential for reliable AI insights
  • LangChain is highly effective for enforcing consistent and safe AI outputs
  • JetBrains Junie significantly improves development velocity when used as a collaborative coding assistant
  • Coaches value clarity and actionability over raw statistical volume

What's next for Cloud9 Stratos — Automated Scouting Report Generator?

  • Expand support for additional tournaments and esports titles
  • Add deeper player-vs-player matchup analytics
  • Introduce draft and composition-aware scouting insights
  • Enable secure deployment for team-specific internal coaching tools

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