# occasion-based-shopping

Published articles for occasion-based-shopping.

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## From Products to Inspiration: Inside the Engine of Occasion-based outfit visualiser

DevFeed: [From Products to Inspiration: Inside the Engine of Occasion-based outfit visualiser](<https://devfeed.tech/articles/from-products-to-inspiration-inside-the-engine-of-occasion-based-outfit-visualiser-20137.md>)

Original publisher: [Read original article](<https://medium.com/myntra-engineering/from-products-to-inspiration-inside-the-engine-of-occasion-based-outfit-visualiser-a09f494d43ae?source=rss----7484818e9f88---4>)

Author: Ankit Kumar

Published: 2026-04-23T18:23:51Z

Content type: article

Language: en

Sources: [Myntra](<https://devfeed.tech/sources/myntra.md>)

Topics: [Data Science](<https://devfeed.tech/topics/data-science.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [JSON](<https://devfeed.tech/topics/json.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [drapes](<https://devfeed.tech/tags/drapes.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [json](<https://devfeed.tech/tags/json.md>), [occasion-based-shopping](<https://devfeed.tech/tags/occasion-based-shopping.md>), [outfit-ideas](<https://devfeed.tech/tags/outfit-ideas.md>), [product](<https://devfeed.tech/tags/product.md>), [shopping](<https://devfeed.tech/tags/shopping.md>)

### AI overview

This article describes how Myntra built its Looks occasion-based outfit visualiser. The feature combines fashion intelligence, data science, computer vision, generative AI, and JSON-based outfit rules to turn individual product images into coordinated outfit recommendations and visualisations. The supplied text details its style taxonomy and curation of over a million styles, but the article is truncated before the visualisation implementation is fully explained.

### Source excerpt

Ankit Kumar | Oct 2025 - 6 min read The "Why": Moving Beyond the Grid Picture this: A white background. A shirt. Fabric details. Fit specs. A price tag. For decades, this has been the status quo of online shopping. It is clinical, clear, and -- let's be honest -- completely detached from real life. In this model, the customer does all the heavy lifting. "Where would I wear this?" they wonder. "Does this go with those beige chinos I bought last year?" They close their eyes. They imagine. They guess. Sometimes they buy; often, they bounce. Traditional Product Detail Page (PDP) recommendations tried to help by suggesting jeans to pair with shirts. But the truth is, they remained a list of ingredients, not a prepared meal. At Myntra, we decided to change that. We set out to build Looks, a feature designed to transport a static product into a lived experience -- a Friday night in Bangalore, a high-intensity gym in Gurgaon, or a quiet art gallery in Mumbai. This is the story of how we orchestrated Data Science, Computer Vision, and Generative AI to build a personal stylist that scales to millions. Phase 1: The Brain -- Orchestrating the Look Before we could visualize an outfit, we had to understand fashion. Not just as data points, but as a language. This required Fashion Intelligence: a system that knows what works, what doesn't, and why. Our Data Science team undertook a massive curation effort, analyzing over a million styles. They didn't just tag clothes; they mapped them to the "cascading tree of style." For every Primary Style (e.g., a Polo shirt), the engine identifies four critical layers: The Occasion: (Weekend Outing, Office Smart-Casual) Secondary Style: (The bottom wear) Tertiary Style: (Footwear) Tertiary (others) : (Accessories like watches or sunglasses) The Recipe in the Code The logic is powered by a JSON structure that acts as the "AI Stylist's" brain: JSON "29936239": [ { "Weekend outing": [ [ 29936239, // Primary: The Polo T-Shirt 33551732, // Secondary: B