Compare
WhatsApp platforms vs OrderEase 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: WhatsApp platforms (2018 – 2022) → OrderEase (2025 – 2026) → Fashionly (Apr 2026 – present)
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WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
OrderEase ›
Branded online ordering for restaurants and retailers
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
At a glance
- 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
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
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.
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
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
- FastAPI
- PostgreSQL
- Redis
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- 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
- 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
—
—
- 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.