DOMLAV — Fullstack On-Demand Laundry Service App (Next.js + Node.js + MongoDB)

Overview

DOMLAV is a fullstack on-demand laundry and dry cleaning service application I developed as a senior fullstack developer for a Moroccan startup. The platform connects customers needing laundry services with professional cleaners and delivery drivers, handling everything from order placement and scheduling to real-time tracking and payment processing.

The Problem

Traditional laundry services in Morocco are fragmented and inconvenient:

• Customers waste time dropping off and picking up laundry
• Quality inconsistency — No way to verify cleaner reputation before trying
• No transparency — Unclear pricing, timing, and handling processes
• Cash-only payments — Inconvenient and insecure
• No tracking — Customers don't know when laundry will be ready

My Role

As the lead senior fullstack developer, I was responsible for:

• Complete system architecture and database design
• Frontend development with Next.js and Pillar UI
• Backend API development with Node.js and Express
• MongoDB database modeling and optimization
• Firebase integration for authentication and real-time features
• Real-time order tracking system implementation
• Payment gateway integration
• Deployment and DevOps configuration

Technical Architecture

User Journeys

Customer Flow

1. Register/Login — Phone number with OTP verification
2. Select Service — Wash & fold, dry cleaning, ironing, specialty items
3. Schedule Pickup — Date, time window, and address selection
4. Prepare Laundry — Bag items, attach provided QR code label
5. Driver Pickup — Real-time tracking of driver arrival
6. Processing — Track cleaning status (received → washing → drying → ironing → ready)
7. Delivery — Scheduled drop-off with real-time driver tracking
8. Rate & Pay — Review service quality, automatic payment processing

Driver Flow

1. Accept Order — View available pickups in assigned zone
2. Navigate to Pickup — GPS directions to customer address
3. Scan QR Code — Verify correct order, update status
4. Deliver to Facility — Drop off at cleaning partner location
5. Pickup Clean Laundry — Collect completed orders
6. Deliver to Customer — Final drop-off, confirm delivery

Cleaner/Partner Flow

1. Receive Orders — View incoming laundry batches
2. Update Status — Mark items as washing, drying, ironing, ready
3. Quality Check — Photo documentation before packaging
4. Hand to Driver — Confirm transfer with QR scan

Key Features

Customer Features

• Service Selection — Multiple service types with transparent pricing
• Smart Scheduling — Pickup and delivery time slots with availability
• Real-Time Tracking — Live driver location and ETA on map
• Order History — Past orders with reorder functionality
• Subscription Plans — Weekly/monthly laundry packages with discounts
• Referral Program — Earn credits by inviting friends

Driver Features

• Route Optimization — Efficient multi-stop pickup and delivery routes
• Earnings Dashboard — Daily/weekly earnings with breakdown
• Rating System — Customer feedback on delivery service
• Offline Support — Queue updates when connectivity is poor

Admin Features

• Order Management — Full overview of all active and completed orders
• Driver Dispatch — Assign orders, monitor routes, handle exceptions
• Cleaner Partners — Manage partner network, quality standards, payments
• Analytics — Revenue, customer retention, peak times, popular services
• Customer Support — Issue tracking, refunds, dispute resolution

Real-Time Features

• Live Driver Tracking — Customers see driver on map approaching
• Push Notifications — Status updates, promotions, delivery alerts
• In-App Chat — Customer support and driver communication
• Live Order Status — Real-time updates as laundry progresses

Challenges & Solutions

Challenge 1: Real-Time Order Tracking

Customers expect Uber-like tracking for laundry. I implemented:

• Firebase Realtime Database for live location updates
• Geofencing to trigger status changes automatically
• Optimized location polling — Battery-efficient tracking for drivers
• Fallback SMS — Status updates via text when app is closed

Challenge 2: Handling Different Service Types

Laundry, dry cleaning, and ironing have different workflows. I designed:

• Flexible order schema in MongoDB for variable service pipelines
• Status machine — Each service type has its own state transitions
• Price calculation engine — Weight, item count, fabric type, urgency

Challenge 3: Driver-Cleaner Coordination

Multiple stakeholders need seamless handoffs. I built:

• QR code system — Each order bag has unique QR for scanning
• Photo verification — Drivers photograph bags at pickup/delivery
• Digital signatures — Customer confirmation on delivery
• Dispute resolution — Photo evidence for damage/loss claims

Challenge 4: Payment Trust

Moroccan customers are cautious about online payments. I addressed:

• Cash on delivery option — For customers preferring physical payment
• Transparent pricing — Full cost breakdown before confirmation
• Secure Stripe integration — PCI-compliant payment processing
• Receipt automation — Digital receipts sent immediately after payment

Results & Impact

• Active service across multiple Moroccan cities
• Customer satisfaction — 4.7/5 average rating
• Driver network — 50+ active delivery partners
• Cleaner partners — 15+ verified cleaning facilities
• Order volume — 500+ orders per month and growing
• Reduc src: ed no-shows — Real-time tracking decreased missed pickups by 80% alt: DOMLAV — fullstack laundry service web application built with Next.js, Node.js, Express, and MongoDB by developer Hamza Miloud Amar

Testimonial

"Hamza built us a complete logistics platform that handles the complexity of coordinating customers, drivers, and cleaners. The real-time tracking and automated workflows have transformed our operations." — DOMLAV Founder

Lessons Learned

Building DOMLAV taught me that on-demand service apps are logistics platforms first, consumer apps second. The customer-facing UI is important, but the operational dashboard, driver tools, and real-time coordination systems are what make or break the business.

Future Plans

• AI-powered scheduling — Predict demand and optimize driver routes
• Fabric recognition — Photo-based item identification and pricing
• Eco-friendly options — Green cleaning partner network
• B2B expansion — Hotel and restaurant laundry services

---

Need a fullstack on-demand service platform? Contact me to discuss your project.