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CV Coach vs Fashionly vs Picking & Packing app
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) → Picking & Packing app (2025 – 2026) → Fashionly (Apr 2026 – present)
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CV Coach ›
AI CV analysis, job matching and tailored applications
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
Picking & Packing app ›
A Kotlin warehouse app for fulfilment teams
At a glance
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Mobile
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.
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
An Android app that drives picking and packing for warehouse and fulfilment operations.
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.
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Stack
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Kotlin
- Android
Highlights
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- 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
- Led architecture and development
- Built in Kotlin for warehouse floor use
Infrastructure choices
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- Supabase with RLS
- MongoDB checkpoints
- SSE
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Outcome
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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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