Compare
Multichoice installer payments vs Ventaw vs Fashionly
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
Swipe sideways to compare ›
Multichoice installer payments ›
R5M+ paid to installers in the first six months
Ventaw ›
Secure, isolated environments for developer workloads
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
At a glance
- Role
- Integration engineer
- When
- Delivered
- Length
- —
- Status
- Delivered
- Type
- Platform
- Role
- Founder / Lead Engineer
- When
- In progress
- Length
- —
- Status
- In progress
- Type
- Platform, Web
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
The problem
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.
Environment provisioning, session management and resource monitoring, designed for security, multi-tenancy and concurrent workloads.
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
- Payments integration
- Next.js
- FastAPI
- Docker
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- Processed over R5 million in installer payments within six months
- Next.js management interface
- FastAPI orchestration APIs
- 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
Over R5 million in installer payments processed within six months.
—
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.