AI Fashion Discovery Platform
Intent-Based Search & Personalized Ranking — A mobile-first fashion discovery platform where shoppers search by style, occasion, or visual reference, powered by multimodal search and LLM-assisted intent understanding.
What we were solving for
Shoppers often know the style or occasion they want without knowing the right category, brand, or search term to find it.
How we built it
Natural language + visual search
Shoppers search by describing a look or referencing an image.
Multimodal vector search
Vector embeddings over the catalog on pgvector power intent-aware retrieval.
LLM-assisted intent understanding
LLM integrations interpret the broader style and occasion behind a search.
Personalized ranking
Results are ranked against individual style and preference signals.
Where it landed
Shipped as a live, product-facing discovery feature.
Lets shoppers search by intent, style, and visual reference.
Combines visual, text, and personalization signals in one search experience.
More Engineering Work
View All Case Studies →Lease Intelligence Platform
A lease intelligence platform where every client owns a fully isolated environment, extracting structured terms from lease PDFs, answering portfolio questions with cited sources, and flagging low-confidence results for review.
1.5M+ User Platform Infrastructure
Backend architecture and CRM development for a large-scale artist social platform, scaled from roughly 400K to 1.5M+ users with GraphQL, search, and AI-driven personalization.
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