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Joel Transport vs Fashionly vs Ventaw
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
- Both kinds of work: Web
- Order: 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
Ventaw ›
Secure, isolated environments for developer workloads
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
- Founder / Lead Engineer
- When
- In progress
- Length
- —
- Status
- In progress
- Type
- Platform, Web
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.
Environment provisioning, session management and resource monitoring, designed for security, multi-tenancy and concurrent workloads.
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
- Next.js
- FastAPI
- Docker
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
- Next.js management interface
- FastAPI orchestration APIs
Infrastructure choices
- AWS
- Supabase with RLS
- MongoDB checkpoints
- SSE
—
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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