Compare
CV Coach vs Multichoice installer payments vs Fashionly
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: CV Coach (2025 – present) → Fashionly (Apr 2026 – present)
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CV Coach ›
AI CV analysis, job matching and tailored applications
Multichoice installer payments ›
R5M+ paid to installers in the first six months
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
At a glance
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
- Role
- Integration engineer
- When
- Delivered
- Length
- —
- Status
- Delivered
- Type
- Platform
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
The problem
A Chrome extension captures job ads; a LangGraph workflow parses the CV and the ad, scores the match and drafts tailored cover letters and CV revisions.
Integrated the installer management system into Multichoice's payment process so installers are paid accurately and on time.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
What was built
A Chrome extension captures advertisements, stores them in PostgreSQL and queues analysis. The platform compares a CV with a job description, produces a structured match score and generates tailored cover letters and CV revisions.
—
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
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Payments integration
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- Processed over R5 million in installer payments within six months
- 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.