AI-assisted car wash operations platform with shift management, fleet readiness tracking, washer workflow automation, and real-time operational reporting.
Live Demo: washq-platform-ps6mk36em-kostasuser01gr.vercel.app
WashQ is an operational platform designed for car wash businesses integrated into fleet or rental operations. It replaces manual coordination — spreadsheets, WhatsApp messages, verbal handoffs — with a structured digital workflow covering every stage of the vehicle preparation pipeline.
The platform is scoped around the operational reality of a small-to-medium fleet service team: vehicle intake, assignment to wash operators, status tracking, shift organization, and quality review before the vehicle returns to service.
Fleet and rental businesses struggle with vehicle turnaround visibility. When a vehicle comes in for cleaning, there is no reliable record of who is responsible, which step of the wash process it is in, whether it passed quality checks, or how long each operator's queue runs across a shift.
WashQ introduces a lightweight operational layer that gives supervisors and operators a shared view of the preparation pipeline.
| Feature | Description |
|---|---|
| Wash Queue | Real-time queue management per operator and shift |
| Fleet Readiness | Vehicle status tracking: intake to in-progress to review to ready |
| Shift Management | Operator assignment, shift start/end, and workload balancing |
| Workflow Automation | Step-by-step wash process with checklists and completion tracking |
| Reporting Dashboard | Daily throughput, operator performance, and time-to-ready metrics |
| Import Engine | Batch import of reservation or fleet data from external files |
| Design System | Consistent UI component library across all operational views |
washq-platform/
├── apps/ # Application modules
├── screens/ # Screen-level UI views
├── platform-ui.js # Core UI rendering engine
├── platform-data.js # Data model and state management
├── workflow.js # Wash workflow state machine
├── import-engine.js # File import and data normalization
├── import-data.js # Data transformation layer
├── import-validator.js # Input validation
├── import-normalizer.js # Schema normalization
├── import-audit.js # Audit trail for imports
├── wq-system.css # Design system styles
├── designsystem.js # Component library
├── vite.config.js # Build configuration
└── vercel.json # Vercel deployment config
The platform is built as a client-side SPA with no backend dependency. All operational state is managed locally or through imported data files, making it deployable as a static site with zero infrastructure cost.
- Frontend: JavaScript (ES2022 modules)
- Build: Vite 8
- Data: Client-side state, XLSX import via SheetJS
- Deployment: Vercel (static)
- Design: Custom operational-first design system
Import Engine — Handles batch import of reservation and fleet data. Includes validation, normalization, conflict detection, and audit logging.
Workflow State Machine — Tracks each vehicle through the wash pipeline: intake, assigned, in-progress, quality-check, ready. Transitions logged with timestamps and operator identity.
Platform Data Layer — Manages operational entities: vehicles, operators, shifts, wash assignments, and status events.
Reporting Dashboard — Aggregates shift summaries, operator metrics, and fleet readiness reports. Computed client-side from local state, no backend required.
This public deployment uses demo data only. All vehicle identifiers, operator names, reservation numbers, and operational records are randomly generated for demonstration purposes. No real customer data, vehicle records, employee information, or business data is included.
git clone https://github.com/kostasuser01gr/washq-platform.git
cd washq-platform
npm ci
npm run devRequirements: Node.js 20+
npm run build
vercel --prodThe vercel.json configures Vite as the framework and routes the root path to the main application entry.
- Supabase backend for persistent multi-user state
- Push notifications for shift assignments and queue updates
- Mobile-optimized operator view for on-floor use
- Integration with OpenClaw fleet management for unified vehicle lifecycle
- Analytics export in CSV/Excel format
- Role-based access: supervisor, operator, manager
Konstantinos Foskolakis Full-stack engineer — Heraklion, Crete, Greece github.com/kostasuser01gr
This project demonstrates operational system design — translating messy manual fleet workflows into structured digital processes. The modular front-end architecture maintains clean separation between data, workflow, UI, and import layers without framework dependency. The multi-step import pipeline with validation, normalization, conflict detection, and audit trail reflects the kind of data engineering work that operational platforms require in practice. The zero-backend static deployment keeps the public demo clean, cost-free, and privacy-safe.
Built as a portfolio-grade operational platform prototype, focused on workflow coordination problems that typically live inside spreadsheets and manual communication chains.