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Fashionly vs OrderEase vs Picking & Packing app
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: none in common
- Order: OrderEase (2025 – 2026) → Picking & Packing app (2025 – 2026) → Fashionly (Apr 2026 – present)
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Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
OrderEase ›
Branded online ordering for restaurants and retailers
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
An Android app that drives picking and packing for warehouse and fulfilment operations.
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.
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Stack
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Kotlin
- Android
Highlights
- 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
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- Led architecture and development
- Built in Kotlin for warehouse floor use
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
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Outcome
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
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