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WhatsApp platforms 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: WhatsApp platforms (2018 – 2022) → Fashionly (Apr 2026 – present)
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WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
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 engineer, Mmogo Media
- When
- 2018 – 2022
- Length
- 4 yr
- Status
- Delivered
- Type
- Platform, AI
- 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
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.
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
- Python
- Django
- WhatsApp Business API
- Infobip
- Twilio
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Payments integration
Highlights
- Conversational journeys on the WhatsApp Business API
- Payments via Flash, Paystack and MTN MoMo
- 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.