Codegeist 2025: Williams Racing Edition
Enterprise Asset Management & AI Operations for Formula 1
In modern F1 operations, every second and every gram counts. Teams struggle with:
- Fragmented Data: Inventory in spreadsheets, telemetry in databases, logistics in emails.
- Opacity: "Where is the new front wing?" is a question that requires 3 phone calls.
- Reactive Maintenance: Replacing parts too early (waste) or too late (failure).
PitLane Ledger is a centralized operational OS built on Atlassian Forge. It combines rigorous asset tracking with the intelligence of the "Pit Boss" AI Agent to optimize the journey from factory to checkered flag.
Complete lifecycle tracking for every nut, bolt, and wing.
- QR Code Workflow: Pit crew can scan parts trackside for instant status and history.
- Digital Twin: Visual car configurator to filter inventory by zone (Aero, Chassis, Power Unit).
- Life Cycle: Tracks mileage, sessions, and wear % against FIA regulations.
A specialized Rovo Agent with deep context of the Williams Racing rulebook and inventory.
- Context-Aware: "Are we ready for FP1?" triggers a full audit of Car 1 and Car 2.
- Predictive: "Check the front wing wear" analyzes telemetry to predict failures.
- Operational: "Log damage on PIT-101" creates Jira tickets and order requests automatically.
Real-time "Go/No-Go" metrics for race engineers.
- Critical Parts Score: 0-100% readiness rating based on required spares.
- Visual Status: Red/Amber/Green indicators for all critical systems.
- Smart Alerts: "No spare Front Wing available for Car 2" (before it becomes a problem).
- CSV Import: Bulk onboard parts from factory manifests.
- Cost Cap: Real-time budget tracking against the $135M cost cap.
- Parc Fermé Mode: Locks configuration to prevent illegal changes post-qualifying.
- Platform: Atlassian Forge (Custom UI)
- Runtime: Node.js 22.x
- Frontend: React 18, Vite, Recharts, Lucide Icons
- AI: Atlassian Rovo (Agentic AI)
- Storage: Forge Storage API (Key-Value)
- Atlassian Site (Jira Cloud) with Admin access
- Forge CLI installed
- Docker (optional, for local testing)
# 1. Clone the repository
git clone https://github.com/your-repo/pitlane-ledger.git
cd pitlane-ledger
# 2. Install dependencies
npm install
# 3. Deploy to Development environment
forge deploy
# 4. Install on your Jira Cloud site
forge installThe app runs in two distinct modes for testing/demo purposes:
- DEMO Mode: Loads mock data (F1 2024 Inventory) for instant evaluation. Great for judges!
- PROD Mode: Connects to live Jira data and persistent storage for real operations.
For PROD Mode, upload your inventory using this CSV format:
| Column | Required | Description | Example |
|---|---|---|---|
Part Name |
✅ Yes | Component name | Front Wing Assembly |
Status |
✅ Yes | Current state | Trackside, In Transit |
Key |
No | Unique ID | PIT-101 (Auto-generated if empty) |
Assignment |
No | Allocation | Car 1, Car 2, Spares |
Life |
No | Health % (0-100) | 95 |
Life Remaining |
No | Est. Races | 5 (Auto-calculated if empty) |
(Downloadable template available in the app)
Scenario: FP3 Practice Session. Car 1 crashes.
- Detection: Telemetry alerts Pit Boss of high G-force impact.
- Analysis: Pit Boss advises: "Front Wing damage critical. 1 Spare available (85% life)."
- Action: Pit Crew scans the damaged part to mark
RETIRED. - Logistics: Manager approves the swap in dashboard. Inventory updates instantly.
- Result: Car 1 back on track for Qualifying in 12 minutes.
- Atlassian Marketplace Listing: Package for public distribution
- Multi-Team Support: Extend beyond Williams to any F1 constructor
- Jira Automation Rules: Auto-create tasks when parts approach FIA limits
- Bulk Operations: Update multiple parts simultaneously
- Live Telemetry Correlation: AWS F1 data feeds for real session data
- Confluence Race Briefings: Auto-generate pre-race documentation
- Slack/Teams Notifications: Push critical alerts to team channels
- Historical Analytics: Season-over-season comparison
- IoT Integration: BLE beacons on trackside crates
- AR Mechanic Assistant: Apple Vision Pro overlay for torque specs
- Predictive ML Models: Train on historical failure data
- Sustainability Dashboard: Carbon footprint for logistics decisions
Built by Amethyst for Williams Racing | Atlassian Codegeist 2025