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CaseNotes vs Fashionly vs ParcelNow
The problem, what was built, the stack and the outcome, next to each other.
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In common
- Shared stack: Next.js
- Both kinds of work: Web
- Order: ParcelNow (2025 – 2026) → CaseNotes (Feb 2026 – present) → Fashionly (Apr 2026 – present)
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CaseNotes ›
South African legal research, e-discovery and learning, powered by AI
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
- Feb 2026 – present
- Length
- 9 mo so far
- Status
- Live
- Type
- AI, Web, Mobile
- 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
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
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
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.
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
- Next.js
- FastAPI
- PostgreSQL
- pgvector
- Neo4j
- Redis/ARQ
- Azure OpenAI
- LangChain
- LangGraph
- Next.js
- Kotlin
- Supabase
- LangGraph
- MongoDB
- SSE
- Next.js
- FastAPI
- PostgreSQL
- Redis
- Docker
Highlights
- 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
- 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
- pgvector + Neo4j
- Redis / ARQ workers
- LangGraph with PostgreSQL checkpoints
- Supabase with RLS
- MongoDB checkpoints
- SSE
- Docker-based development and deployment standards
Outcome
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.
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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