Compare

Fashionly vs CaseNotes 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: ParcelNow (2025 – 2026) → CaseNotes (Feb 2026 – present) → Fashionly (Apr 2026 – present)

Swipe sideways to compare ›

Fashionly ›

Your style, elevated: an AI stylist that starts with what you own

CaseNotes ›

South African legal research, e-discovery and learning, powered by AI

ParcelNow ›

On-demand parcel delivery with real-time tracking

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
Feb 2026 – present
Length
9 mo so far
Status
Live
Type
AI, Web, Mobile
Role
Technical lead, Smart Kitchen Co
When
2025 – 2026
Length
1 yr
Status
Delivered
Type
Web, Platform

The problem

Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.
South African legal information is scattered across portals with limited keyword search, while international e-discovery platforms are often priced beyond the local market.
Customer and operations dashboards with backend services for parcel lifecycles, tracking events and notifications, built for high-throughput transactional workflows.

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.
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
  • Next.js
  • FastAPI
  • PostgreSQL
  • pgvector
  • Neo4j
  • Redis/ARQ
  • Azure OpenAI
  • LangChain
  • LangGraph
  • Next.js
  • FastAPI
  • PostgreSQL
  • Redis
  • Docker

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
  • 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
  • Next.js dashboards over FastAPI services
  • Live delivery updates through logistics and messaging integrations

Infrastructure choices

  • Supabase with RLS
  • MongoDB checkpoints
  • SSE
  • pgvector + Neo4j
  • Redis / ARQ workers
  • LangGraph with PostgreSQL checkpoints
  • 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.
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.
—

See it