AI-Powered Fashion Discovery
Discover fashion that
understands you
A TikTok-style shopping feed powered by real-time deep learning. Every swipe, like, and second spent trains a model that learns your taste.
Acne Studios
Oversized Wool Coat
$890
Lemaire
Twisted Silk Blouse
$620
Toteme
Draped Knit Dress
$450
About Looksy
Fashion discovery, reimagined
Online fashion shopping is broken. Consumers face endless scrolling through generic product grids, irrelevant search results, and recommendation systems that haven't evolved past “customers also bought.” The result: decision fatigue, missed discovery, and a widening gap between what shoppers want and what they find.
Looksy solves this with AI-native product discovery. We replace the traditional product grid with a personalised, TikTok-style feed that learns from every interaction — every swipe, every pause, every like. Our real-time deep learning engine understands not just what you search for, but what you respond to visually and contextually.
The Problem
Fashion e-commerce relies on static category browsing and keyword search. Consumers spend more time searching than discovering. Conversion rates stagnate because product discovery hasn't kept pace with content discovery.
Our Solution
An AI-powered discovery feed that mirrors how people consume content — not how retailers organize inventory. Real-time personalisation powered by deep learning, multi-modal search combining text, visual, and semantic understanding.
Target Audience
Fashion-forward consumers aged 18-35 who shop across multiple retailers. Digital natives who expect TikTok-level personalisation from their shopping experience. Currently focused on the UK market with 27 integrated retailers.
Live Demo
Experience it yourself
Every swipe trains the model. Like products to see your recommendations improve in real-time.
How It Works
Intelligence in every swipe
01
Browse
Swipe through a personalized feed of products from 27+ UK fashion retailers. Each product is curated by our recommendation engine.
02
AI Learns
Every interaction — likes, dwell time, images viewed — feeds our real-time ML model. Six distinct signals train the recommendation engine.
03
Feed Adapts
Within seconds, your feed evolves. Real-time parameter sync means no waiting for overnight batch retraining. Your taste profile updates continuously.
Technology
The engine behind the feed
A production-grade ML system combining real-time deep learning, multi-modal search, and automated data pipelines.
Recommendation Engine
Learns in real-time
Our two-tower neural architecture separates user preference modeling from product understanding, enabling sub-100ms inference at scale.
Unlike traditional batch systems, our Monolith-based engine uses real-time parameter sync — user preferences propagate to the serving model in seconds, not hours.
Search Engine
Three search methods, one query
Search “red summer dress” and our system understands both the text meaning and the visual aesthetic.
Reciprocal Rank Fusion combines results from semantic text search (768-dim Marqo embeddings), visual search (512-dim CLIP embeddings), and keyword matching — ensuring no single method dominates.
Data Pipeline
From retailer to recommendation
Automated crawling and parsing of 27 UK fashion retailers. AI-generated CSS selectors via Gemini 2.0 ensure robust extraction across diverse site architectures.
Every product generates three embedding representations — CLIP for visual understanding, Marqo for semantic text, and learned embeddings from our two-tower model — all indexed in PostgreSQL with pgvector.
Architecture
Full-stack intelligence
From user interaction to model update in under a second. Every layer purpose-built for real-time personalization.
User · iOS / Web
Next.js + Capacitor
Recommendation API
FastAPI · Python
Hybrid Search
RRF Fusion Engine
PostgreSQL + pgvector
100K+ products · 3 embedding spaces
Kafka
Event Streaming
Monolith
Online Training
Firebase
Auth · Firestore
0K+
Products
0
Retailers
<100ms
Latency
0
Embedding Models
Real-time
Online Learning
ML Framework
Built on Monolith
Our recommendation engine is powered by Monolith — the open-source deep learning framework developed by ByteDance, battle-tested at TikTok scale for billions of users.
Read the paper →Collisionless Embedding Tables
Unique representations for every product and user. Cuckoo hashing guarantees O(1) lookups with zero hash collisions across millions of entities.
Real-Time Parameter Sync
Embedding updates propagate from training to serving in under 100ms — without reloading the model. Only changed parameters are synced via gRPC.
Two-Tower Architecture
Separate user and item towers enable independent scaling. User preferences are computed at request time; product embeddings are pre-computed and indexed via pgvector.
Team
Built by engineers
Zak Pyemont
Founder
Backend engineer at Spotify, building ETL and transcription pipelines at scale. Looksy combines deep experience in production data systems with a passion for applying real-time ML to consumer products.
LinkedInLooksy is a solo-founded startup. Every line of code — from the ML recommendation engine to the iOS app — is built by one engineer.