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Picking & Packing app vs Fashionly vs Multichoice installer payments

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) → Fashionly (Apr 2026 – present)

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Picking & Packing app ›

A Kotlin warehouse app for fulfilment teams

Fashionly ›

Your style, elevated: an AI stylist that starts with what you own

Multichoice installer payments ›

R5M+ paid to installers in the first six months

At a glance

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
Role
Integration engineer
When
Delivered
Length
—
Status
Delivered
Type
Platform

The problem

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.
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.

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

  • Kotlin
  • Android
  • Next.js
  • Kotlin
  • Supabase
  • LangGraph
  • MongoDB
  • SSE
  • Payments integration

Highlights

  • 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
  • Processed over R5 million in installer payments within six months

Infrastructure choices

—
  • 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.
Over R5 million in installer payments processed within six months.