Compare
Fashionly vs Picking & Packing app vs WhatsApp platforms
The problem, what was built, the stack and the outcome, next to each other.
Clear · Up to 3 at a time
In common
- Shared stack: none in common
- Order: WhatsApp platforms (2018 – 2022) → Picking & Packing app (2025 – 2026) → Fashionly (Apr 2026 – present)
Swipe sideways to compare ›
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
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
- Lead engineer, Mmogo Media
- When
- 2018 – 2022
- Length
- 4 yr
- Status
- Delivered
- Type
- Platform, AI
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.
A WhatsApp-based job platform (Altur), Harambee Youth Accelerator's move from a mobisite to WhatsApp, and eButler's WhatsApp ordering with payments through Flash.
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
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Kotlin
- Android
- Python
- Django
- WhatsApp Business API
- Infobip
- Twilio
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
- Conversational journeys on the WhatsApp Business API
- Payments via Flash, Paystack and MTN MoMo
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.
—
—