Compare
CV Coach vs OrderEase 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: Next.js
- Both kinds of work: Web
- Order: CV Coach (2025 – present) → OrderEase (2025 – 2026) → Fashionly (Apr 2026 – present)
Swipe sideways to compare ›
CV Coach ›
AI CV analysis, job matching and tailored applications
OrderEase ›
Branded online ordering for restaurants and retailers
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
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Live
- Type
- Web, 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.
Direct ordering and order management: menus, orders, payments and fulfilment, with automated order intake and status updates that cut merchants' operational overhead.
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
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
Highlights
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- Fast, responsive ordering on Next.js
- FastAPI services for menus, orders, payments and fulfilment
- 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
—
—
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.