We extract clothing catalogues, pricing signals, inventory status, and brand metadata from Pantaloons. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from pantaloons.com. All fields typed and schema-versioned.
"sku_id": "PT2134598", "title": "Men Navy Blue Slim Fit Chinos", "brand": "Peter England", "selling_price": 1299.0, "available_colours": "['Navy Blue', 'Khaki', 'Black']", "available_sizes": "['30', '32', '34', '36']", "fabric_details": "98% Cotton, 2% Elastane"
| # | sku_id | title | brand | category | sub_category | gender |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Offers objects from pantaloons.com. All fields typed and schema-versioned.
"sku_id": "PT2134598", "price_mrp": 1999.0, "selling_price": 1299.0, "discount_pct": 35, "greencard_price": 1199.0, "offer_text": "Buy 2 Get 1 Free", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku_id | price_mrp | selling_price | discount_pct | discount_abs | greencard_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Inventory & Availability objects from pantaloons.com. All fields typed and schema-versioned.
"sku_id": "PT2134598", "variant_id": "PT2134598-32-NAVY", "size": "32", "colour": "Navy Blue", "in_stock": true, "estimated_delivery_days": 4, "return_window_days": 15
| # | sku_id | variant_id | size | colour | in_stock | stock_level |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Specifications & Metadata objects from pantaloons.com. All fields typed and schema-versioned.
"sku_id": "PT2134598", "fit": "Slim Fit", "pattern": "Solid", "occasion": "Casual", "wash_care": "Machine Wash Cold", "country_of_origin": "India", "manufacturer_details": "Aditya Birla Fashion and Retail Ltd"
| # | sku_id | fit | pattern | occasion | sleeve_length | neck_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Navigation objects from pantaloons.com. All fields typed and schema-versioned.
"category_id": "CAT-MEN-CHINOS", "category_name": "Chinos", "breadcrumb": "Home > Men > Bottomwear > Chinos", "parent_category": "Bottomwear", "gender": "Men", "product_count": 412, "scraped_at": "2026-05-12T09:14:33Z"
| # | category_id | category_name | breadcrumb | parent_category | gender | url |
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Our Pantaloons scraper handles the complete retail taxonomy: pricing updates, variant mapping, stock levels, and detailed apparel metadata.
Title, descriptions, specifications, and deep category hierarchies mapped across all departments.
Map parent products to child SKUs, capturing complete size matrices and colour availability.
Capture MRP, selling price, percentage discounts, and specific promotional offer text.
Extract specific apparel attributes like material composition, fit type, and wash care instructions.
Isolate data by internal brands including Allen Solly, Peter England, and Pantaloons Junior.
Track in-stock flags per size and colour variant to monitor inventory depth and stockouts.
Capture specific deal text and multi-buy promotions displayed on the product page.
Extract CDN URLs for all product images, including variant-specific photography.
Run continuous pipelines at daily cadences with change-detection diffing to save compute.
Brief in. Clean data out.
Provide category URLs, brand names, or specific SKU lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for pantaloons.com.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or warehouse on agreed cadence.
Fashion retailers deploy strict rate limits. We handle the proxy orchestration and session management.
Retail sites monitor request velocity. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing.
Pantaloons loads size and colour availability dynamically. We run full Playwright browser sessions to trigger lazy-loads and hydrate variant matrices.
Retail DOM structures shift during sales events. Our selector strategy uses fallback chains so a layout change doesn't break the pipeline.
Apparel scraping requires mapping parent SKUs to multiple child variants. We structure this data relationally so you can query stock by specific size and colour.
For large catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing downstream processing load.
Retailers monitor pricing and discount strategies across Pantaloons private labels to remain competitive.
Merchandising teams analyse category depth and brand representation to identify gaps in their own catalogues.
Fashion analysts track popular styles, fabric compositions, and colour palettes across seasonal collections.
Brands track their representation, pricing, and stock availability within the Pantaloons marketplace.
Supply chain teams monitor stockouts at the size and colour level to gauge product velocity.
Machine learning teams use structured apparel metadata to train visual recommendation and search models.
"Pantaloons holds a massive, highly structured fashion catalogue, but extracting accurate variant matrices requires dedicated infrastructure."
Apparel scraping is notoriously complex due to nested size and colour variations. DataFlirt handles the JavaScript rendering, variant normalisation, and proxy management so your engineers can focus on retail analytics rather than pipeline maintenance.
Everything supported by our pantaloons.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and interaction flows for variant matrices.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request to bypass retail rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About pantaloons.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Pantaloons is generally permissible. DataFlirt targets only public, non-authenticated product and pricing data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour to bypass rate limits.
Yes. We map parent SKUs to all child variants, capturing pricing and stock availability for every size and colour combination.
Full catalogue refreshes at daily cadence complete within a 4-6 hour window. We can configure higher frequency runs for specific high-value categories.
Our smallest packages start at a defined category list with weekly delivery. Contact us with your use case for a scoped quote.
Yes. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process to validate schema fit.
Yes. We can inject specific target pincodes during the crawl to extract localised delivery timelines and stock availability.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across 150K SKUs, we scope, build, and operate the pipeline.
