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Fashionly ›

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

At a glance

Role
Founder / Lead Engineer
When
Apr 2026 – present
Length
7 mo so far
Status
MVP
Type
AI, Mobile, Web

The problem

Fashion-commerce recommendations usually optimise for another purchase without understanding what the customer already owns.

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.

Stack

  • Next.js
  • Kotlin
  • Supabase
  • LangGraph
  • MongoDB
  • SSE

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

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.

See it