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Joel Transport vs Fashionly vs WhatsApp platforms
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) → Joel Transport (Dec 2022 – May 2023) → Fashionly (Apr 2026 – present)
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Joel Transport ›
Online booking platform on AWS
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
WhatsApp platforms ›
Altur, Harambee and eButler on the WhatsApp Business API
At a glance
- Role
- Lead Engineer
- When
- Dec 2022 – May 2023
- Length
- 6 mo
- Status
- Live
- Type
- Web, Platform
- 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
The problem
Centralised the customer journey and operational data, reduced manual hand-offs and set up a documented AWS foundation.
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.
What was built
Led architecture and hands-on engineering for an online booking platform: Python APIs and the AWS deployment architecture, integrated with Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ.
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
- Python
- AWS
- Paystack
- Sage
- Zoho
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Python
- Django
- WhatsApp Business API
- Infobip
- Twilio
Highlights
- Python APIs and AWS deployment architecture
- Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ integrations
- 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
Infrastructure choices
- AWS
- Supabase with RLS
- MongoDB checkpoints
- SSE
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Outcome
Centralised the customer journey and key operational data, reduced manual hand-offs and established a documented AWS foundation for future development.
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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