Extract Myntra Data
Fashion catalog, size availability, and review data from Myntra.
Tell AuraCrawl what you need from Myntra in plain language — "every kurta under ₹1,500 with its rating," "size availability across this brand's catalog," "all new reviews for these 800 styles." Fashion is where assortment matters more than headline price: one style exists in several colourways and a dozen sizes, each with its own stock state. We flatten that into a record per variant, so a gap analysis does not begin with a reshaping job.
What a response looks like
REQUEST
ask“Tell AuraCrawl what you need from Myntra in plain language — which entities, which fields, how often. That sentence is the spec.”
{
"product_id": "13780156",
"title": "Roadster Men Navy Blue Slim Fit Casual Shirt",
"brand": "Roadster",
"category": "Men’s Casual Shirts",
"selling_price_inr": 899,
"mrp_inr": 1799,
"discount_pct": 50,
"rating": 4.1,
"rating_count": 12483,
"sizes": [
{
"size": "M",
"in_stock": true
},
{
"size": "L",
"in_stock": true
},
{
"size": "XL",
"in_stock": false
}
],
"colourways": 4
}What you can extract
- Style names, brands, and category taxonomy per SKU
- Selling price versus MRP, plus discount percentage
- Size-level stock state, not just a product-level in-stock flag
- Colourway counts and variant grouping under one style
- Ratings, rating counts, and review text
- Assortment coverage across a brand or a price band
PIPELINE Aura Strike carries the anonymous session Myntra's catalog API requires before it will answer; Aura Build expands each style into one record per size, so stock gaps are queryable rather than buried in a variant blob.
Request Myntra data.
Describe the fields, the volume, and the cadence. We scope the extraction and deliver a sample before you commit.