What happens during peak demand?

The scheduling engine balances driver capacity, service-centre load and delivery radius in real time. When weekday mornings surge, it extends pickup windows and shows an honest turnaround time rather than promising a slot it cannot hold.

What services are available?

Wash & Fold, Wash & Iron, Ironing Only, Dry Clean, Premium Laundry and Steam Iron, available per order or on weekly and bi-weekly subscriptions with real pause and cancel controls.

Ecommerce

Green Wash Co.

Premium on-demand laundry with a sub-minute ordering flow, subscription plans and tight arrival estimates.

Country: India
Platform: Web, Android

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About the client

Green Wash Co.

Green Wash Co. is a premium laundry and dry-cleaning service. We designed the brand experience and the technology together so the product felt as considered as the service.

Client background: A premium laundry and dry-cleaning operation launching with an app-first model.
Business model: Per-order and subscription laundry with pickup and delivery.
Target audience: Urban households and professionals who value turnaround reliability over price.

Research & discovery

What we learned before building

Discovery ran before a line of production code. These are the findings that changed the brief.

User Research
Customers paying a premium for laundry were not buying cleaning, they were buying not having to think about it. Every question the app asked — fabric type, preferences, instructions — moved the product away from that promise, so the ordering flow was cut back until it fit in under a minute.
Competitor Analysis
Rival laundry apps competed on price and showed it everywhere. For a premium brand that framing is actively harmful, so the interface leads with turnaround certainty and finish quality instead of discounts.
Market Analysis
Demand concentrates sharply on weekday mornings. Any scheduling model that ignored that would quietly miss pickup windows during the busiest hours and break the on-time promise the brand rests on.
Journey Mapping
The trust moment is not the order, it is the pickup. Customers hand over clothes to a stranger and hear nothing. Tightening arrival estimates to a two-minute window was the single change that most affected how the service felt.

Our solution

The decisions we made

Each one traceable to a constraint above, not to a preference.

A deliberately minimal ordering flow — order in under 60 seconds, pick a window, forget about it.

A scheduling engine balancing driver capacity, service-centre load and delivery radius in real time.

Driver tracking accurate to a two-minute arrival window using GPS polling plus map-matching.

Weekly and bi-weekly subscriptions with genuine pause and cancel controls.

Key features

What Green Wash Co. does

Everything shipped and in production, not a roadmap.

Wash & fold
Standard laundry by weight.
Dry clean
Garment-specific cleaning.
Steam iron
Press-only service.
Scheduled pickup
Choose a window; the engine balances load.
Live driver tracking
Arrival estimates within a two-minute window.
Subscriptions
Weekly and bi-weekly plans with pause and cancel.

Technology stack

What it runs on

The choices behind the build, grouped by layer.

Frontend: React Native
Backend: Node.js
Database: MongoDB
Cloud: Firebase
Third Party APIs: Google Maps API
Razorpay

Results

What it does today

To place an order: <60s
Arrival estimate precision: 2 min

Source: project write-up.

Screens

Inside the product

FAQ

Common questions

Arrival estimates are accurate to a two-minute window, built on GPS polling plus map-matching that corrects for drift in dense urban areas — comparable to what tier-one food delivery apps offer.

More work