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Joel Transport vs Picking & Packing app vs Fashionly
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: Joel Transport (Dec 2022 – May 2023) → Picking & Packing app (2025 – 2026) → Fashionly (Apr 2026 – present)
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Joel Transport ›
Online booking platform on AWS
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
At a glance
- Role
- Lead Engineer
- When
- Dec 2022 – May 2023
- Length
- 6 mo
- Status
- Live
- Type
- Web, Platform
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
The problem
Centralised the customer journey and operational data, reduced manual hand-offs and set up a documented AWS foundation.
An Android app that drives picking and packing for warehouse and fulfilment operations.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
What was built
Led architecture and hands-on engineering for an online booking platform: Python APIs and the AWS deployment architecture, integrated with Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ.
—
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
- Python
- AWS
- Paystack
- Sage
- Zoho
- Kotlin
- Android
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- Python APIs and AWS deployment architecture
- Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ integrations
- Led architecture and development
- Built in Kotlin for warehouse floor use
- 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
- AWS
—
- Supabase with RLS
- MongoDB checkpoints
- SSE
Outcome
Centralised the customer journey and key operational data, reduced manual hand-offs and established a documented AWS foundation for future development.
—
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.