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Fashionly vs Multichoice installer payments vs WDDNG
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: WDDNG (Jan 2026 – present) → Fashionly (Apr 2026 – present)
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Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
Multichoice installer payments ›
R5M+ paid to installers in the first six months
WDDNG ›
South Africa's wedding marketplace, with two apps on Google Play
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Integration engineer
- When
- Delivered
- Length
- —
- Status
- Delivered
- Type
- Platform
- Role
- Founder / Full-stack & Mobile Engineer
- When
- Jan 2026 – present
- Length
- 10 mo so far
- Status
- Live
- Type
- AI, Mobile, Web
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.
South African couples plan across fragmented directories, WhatsApp messages and opaque “price on request” listings, while many international tools don't support local multi-ceremony wedding journeys.
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.
—
A Next.js and Supabase marketplace with two Kotlin / Jetpack Compose Android apps on Google Play: one for couples, one for vendors. Thuli, the AI planner, is a LangGraph supervisor coordinating 16 specialist agents.
Stack
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Payments integration
- Next.js
- Supabase
- PostgreSQL
- Kotlin
- Jetpack Compose
- LangGraph
- Zod
- Paystack
- Azure
- LangSmith
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
- Processed over R5 million in installer payments within six months
- Two Kotlin / Jetpack Compose Android apps published on Google Play
- Thuli: a LangGraph supervisor coordinating 16 specialist agents through Zod-typed handoff tools
- Per-agent model selection, token metering and LangSmith tracing
- SSE streaming that renders vendor cards and progress during an answer
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
—
- Supabase with PostgreSQL RLS
Outcome
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
Over R5 million in installer payments processed within six months.
The marketplace runs across web and two production Android apps, giving couples structured planning tools and vendors better-qualified enquiries with date, budget and guest-count context.