Compare
Fashionly vs ParcelNow vs Joel Transport
The problem, what was built, the stack and the outcome, next to each other.
Clear · Up to 3 at a time
In common
- Shared stack: none in common
- Both kinds of work: Web
- Order: Joel Transport (Dec 2022 – May 2023) → ParcelNow (2025 – 2026) → Fashionly (Apr 2026 – present)
Swipe sideways to compare ›
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
ParcelNow ›
On-demand parcel delivery with real-time tracking
Joel Transport ›
Online booking platform on AWS
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
- Role
- Lead Engineer
- When
- Dec 2022 – May 2023
- Length
- 6 mo
- Status
- Live
- Type
- Web, Platform
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
Centralised the customer journey and operational data, reduced manual hand-offs and set up a documented AWS foundation.
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.
—
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.
Stack
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
- Python
- AWS
- Paystack
- Sage
- Zoho
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
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
- Python APIs and AWS deployment architecture
- Paystack, Sage Accounting, Zoho Bigin and Zoho SalesIQ integrations
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
- Docker-based development and deployment standards
- AWS
Outcome
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
—
Centralised the customer journey and key operational data, reduced manual hand-offs and established a documented AWS foundation for future development.