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Fashionly vs Picking & Packing app vs ParcelNow
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: Picking & Packing app (2025 – 2026) → ParcelNow (2025 – 2026) → Fashionly (Apr 2026 – present)
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Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
ParcelNow ›
On-demand parcel delivery with real-time tracking
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
An Android app that drives picking and packing for warehouse and fulfilment operations.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
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
- Kotlin
- Android
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
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
- Led architecture and development
- Built in Kotlin for warehouse floor use
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
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- Docker-based development and deployment standards
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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