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Fashionly vs CV Coach vs CaseNotes
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: Next.js, LangGraph
- Both kinds of work: AI, Web
- Order: CV Coach (2025 – present) → CaseNotes (Feb 2026 – present) → Fashionly (Apr 2026 – present)
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Fashionly ›
Your style, elevated: an AI stylist that starts with what you own
CV Coach ›
AI CV analysis, job matching and tailored applications
CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
At a glance
- Role
- Founder / Lead Engineer
- When
- Apr 2026 – present
- Length
- 7 mo so far
- Status
- MVP
- Type
- AI, Mobile, Web
- Role
- Founder / Lead Engineer
- When
- 2025 – present
- Length
- 1 yr 9 mo so far
- Status
- MVP
- Type
- AI, Web
- Role
- Founder / Lead Engineer
- When
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
The problem
Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
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.
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
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 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.
An integrated research, e-discovery and learning platform: hybrid semantic search across more than 10,000 judgments, a precedent citation graph, structured document extraction, AI-assisted discovery and stateful research workflows.
Stack
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- FastAPI
- Next.js
- Supabase
- LangGraph
- LangChain
- Celery
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
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
- LangGraph workflow with custom tools to parse, compare evidence and score the match
- Asynchronous analysis pipeline
- Five-stage Graph RAG pipeline in LangGraph
- Hybrid pgvector and Neo4j retrieval, and a TAR active-learning loop
- Human-in-the-loop tool-calling chat with PostgreSQL checkpoints
- MCP server for case search and drafting
Infrastructure choices
- Supabase with RLS
- MongoDB checkpoints
- SSE
—
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
Outcome
A working end-to-end MVP across web and Android: wardrobe management, catalogue discovery, conversational styling, saved looks and human-stylist booking.
—
An integrated research and review platform grounded in more than 10,000 judgments, with a structured learning bank of 1,624 rubric-scored questions. The architecture supports resumable jobs, traceable sources and human review.