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Fashionly vs WhatsApp platforms vs OrderEase
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) → OrderEase (2025 – 2026) → Fashionly (Apr 2026 – present)
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Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
OrderEase ›
Branded online ordering for restaurants and retailers
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Lead engineer, Mmogo Media
- When
- 2018 – 2022
- Length
- 4 yr
- Status
- Delivered
- Type
- Platform, AI
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, Platform
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
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.
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
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
- Python
- Django
- WhatsApp Business API
- Infobip
- Twilio
- Next.js
- FastAPI
- PostgreSQL
- Redis
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
- Conversational journeys on the WhatsApp Business API
- Payments via Flash, Paystack and MTN MoMo
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
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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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