Compare
Fashionly vs OrderEase
The problem, what was built, the stack and the outcome, next to each other.
In common
- Shared stack: Next.js
- Both kinds of work: Web
- Order: OrderEase (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
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
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.
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
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
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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