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OrderEase vs ParcelNow vs Fashionly
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: Next.js
- Both kinds of work: Web
- Order: OrderEase (2025 – 2026) → ParcelNow (2025 – 2026) → Fashionly (Apr 2026 – present)
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OrderEase ›
Branded online ordering for restaurants and retailers
ParcelNow ›
On-demand parcel delivery with real-time tracking
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
At a glance
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
The problem
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
What was built
—
—
Fashionly grounds recommendations in the user's wardrobe, builds outfits from existing items and recommends products only where a genuine wardrobe gap exists. Khanya, a LangGraph stylist with about 15 typed tools, handles wardrobe access, catalogue search, saved looks and human-stylist bookings.
Stack
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
- Khanya: a LangGraph tool-calling stylist with about 15 typed tools
- MongoDB-checkpointed conversation memory and SSE streaming of tool progress and grounded product cards
- Request-scoped Supabase clients with row-level security on every request
Infrastructure choices
—
- Docker-based development and deployment standards
- Supabase with RLS
- MongoDB checkpoints
- SSE
Outcome
—
—
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.