About the House
Fashion Intelligence uses machine learning and multi-source data aggregation to track, analyze, and forecast fashion trends at scale.
We process data from Google Trends, Reddit, YouTube, runway collections, and street style imagery to build a quantitative picture of what's happening in fashion — what's rising, what's fading, and what the data suggests comes next.
How It Works
Computer Vision
Our image analysis pipeline detects individual garments, classifies style, extracts color palettes, and matches outfits to runway collections — all from a single photo upload.
Trend Forecasting
We aggregate search volume, social signals, and runway data into composite trend scores with momentum tracking and time-series forecasting across 300+ fashion terms and brands.
Runway Analysis
8 major SS26/FW26 collections analyzed for color distribution, silhouette breakdown, and cross-referenced against real-time search data to measure runway-to-reality impact.
Prediction Tracking
We make public, falsifiable predictions about fashion trends — with confidence levels, target dates, and Brier score tracking for full accountability.
The Data
The Stack
The platform combines computer vision, time-series forecasting, and natural language processing across multiple data sources. The frontend is built with Next.js and React, backed by a serverless database and deployed globally on Vercel's edge network. Data pipelines aggregate signals from search engines, social platforms, video data, and runway photography to power the trend scoring and prediction systems.
“The goal is simple: apply real ML to fashion data, make public predictions, track accuracy transparently, and see if data can augment the creative intuition that makes fashion interesting.”