Compare
CV Coach vs Fashionly vs ParcelNow
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) → ParcelNow (2025 – 2026) → Fashionly (Apr 2026 – present)
Swipe sideways to compare ›
CV Coach ›
AI CV analysis, job matching and tailored applications
Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
ParcelNow ›
On-demand parcel delivery with real-time tracking
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
- Technical lead, Smart Kitchen Co
- When
- 2025 – 2026
- Length
- 1 yr
- Status
- Delivered
- Type
- Web, Platform
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.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.
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
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
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
- Next.js dashboards over FastAPI services
- Live delivery updates through logistics and messaging integrations
Infrastructure choices
—
- Supabase with RLS
- MongoDB checkpoints
- SSE
- Docker-based development and deployment standards
Outcome
—
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
—